--- title: "Update Node" method: PATCH path: "/workflows/v1/nodes/{node_id}" tags: ["Nodes"] --- # Update Node `PATCH /workflows/v1/nodes/{node_id}` Endpoint to update a node by its ID. Optionally verify the node belongs to a specific version number before updating. ## Path parameters - `node_id` string, required ## Query parameters - `version_number` integer, nullable — Verify node belongs to this version number before updating (0 includes legacy nodes) ## Request body - object ## Response `200` Successful Response - NodesResponse — Response model for a single node. Contains a NodesModel object. - `node` NodesModel, required — Stores a node configuration present in the workflow system. - `team_id` string, nullable — ID of the team that owns this edge - `created_by` string, nullable — ID of the user who created this edge - `updated_by` string, nullable — ID of the user who last updated this edge - `created_at` string, date-time - `updated_at` string, date-time - `_id` string - `node_config` union — Node Configuration - WorkerLLMNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `main_response_config` object, nullable — Main response configuration. Contains either a LLM system prompt or exact static messages - `prompt` string, nullable — System prompt for the LLM - `attachable_llm_config_id` string, nullable — ID of the named LLM Configuration to use. If provided, overrides inline configuration. - `llms_config` union — LLM or a group of LLMs to be used in this node - AzureOpenAILLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gpt-5-chat' | 'gpt-5-mini' | 'gpt-5-nano' | 'gpt-4.1-nano' | 'gpt-4.1-mini' | 'gpt-4.1' - `type` 'azure_openai_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `endpoint` string, nullable — Endpoint URL for Azure OpenAI API requests. Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided. - `api_version` string, nullable — Azure OpenAI API version. Falls back to `OPENAI_API_VERSION` env var if not provided. - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'low', 'medium', 'high'. - OpenAILLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gpt-5.4' | 'gpt-5.4-mini' | 'gpt-5.4-nano' | 'gpt-5.2' | 'gpt-5.1' | 'gpt-5-nano' | 'gpt-5-mini' | 'gpt-5' | 'gpt-5.4-pro' | 'gpt-5.2-pro' | 'gpt-5.2-chat-latest' | 'gpt-5.3-chat-latest' | 'gpt-4.1-nano' | 'gpt-4.1-mini' | 'gpt-4.1' | 'gpt-4' | 'gpt-4o-mini' | 'gpt-4o' | 'gpt-3.5-turbo' - `type` 'openai_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `base_url` string, nullable — Base URL path for OpenAI API requests, leave blank if not using a proxy or service emulator. - `organization` string, nullable — Organization ID for OpenAI API requests, Automatically inferred from env var `OPENAI_ORG_ID` if not provided. - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'minimal', 'low', 'medium', 'high'. - GoogleLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gemini-flash-latest' | 'gemini-flash-lite-latest' | 'gemini-3.6-flash' | 'gemini-3.1-pro-preview' | 'gemini-3.5-flash' | 'gemini-3.5-flash-lite' | 'gemini-3.1-flash-lite' | 'gemini-3-flash-preview' | 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' - `type` 'google_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `thinking_budget` integer, nullable — Indicates the thinking budget in tokens. By default, it is set to 0. - AnthropicLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'claude-opus-5' | 'claude-sonnet-5' | 'claude-fable-5' | 'claude-opus-4-8' | 'claude-opus-4-7' | 'claude-opus-4-6' | 'claude-opus-4-5-20251101' | 'claude-opus-4-1-20250805' | 'claude-sonnet-4-6' | 'claude-sonnet-4-5-20250929' | 'claude-haiku-4-5' | 'claude-haiku-4-5-20251001' - `type` 'anthropic_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `thinking_budget` integer, nullable — Token budget for Claude's extended thinking feature. Set to 0 to disable. When enabled, Claude will show its reasoning process before providing a final answer. - BedrockLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — Amazon Bedrock long-term API key (bearer token). If set, it is used for authentication and the access-key fields below are ignored. Leave empty to authenticate with an access-key pair or the default AWS credential chain (IAM role). - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'zai.glm-5' | 'zai.glm-4.7' | 'zai.glm-4.7-flash' | 'moonshotai.kimi-k2.5' | 'moonshot.kimi-k2-thinking' | 'qwen.qwen3-vl-235b-a22b' | 'qwen.qwen3-32b-v1:0' - `type` 'bedrock_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `region` string, nullable — AWS region the Bedrock model is invoked in. Defaults to us-east-1 if not provided. - `aws_access_key_id` string, nullable — AWS access key ID for Bedrock. Leave empty to use the platform default (`BEDROCK_ACCESS_KEY` env var, then the AWS credential chain). - `aws_secret_access_key` string, nullable — AWS secret access key for Bedrock. Leave empty to use the platform default (`BEDROCK_SECRET_KEY` env var, then the AWS credential chain). - CustomLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` string, nullable — Name of the model served behind the custom LLM gateway (free-form string, e.g. 'gpt-4.1-mini', 'llama-3-70b', 'mistral-large'). - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'minimal', 'low', 'medium', 'high'. - `type` 'custom_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `base_url` string, nullable — Base URL of the custom OpenAI-compatible LLM gateway (e.g. 'http://api.interactly.ai/workflows/llm-proxy'). - `default_headers` object, nullable — Optional HTTP headers to include in every request to the gateway (e.g. routing keys, tenant IDs). - `rewrite_base_url` boolean — When True, uses an httpx event hook to rewrite every request URL to the exact base_url. Use this for gateways that don't accept the /chat/completions suffix the SDK appends. - `verify_ssl` boolean — Whether to verify SSL certificates. Set to False for gateways with self-signed certificates. - `response_unwrap_key` string, nullable — When set, the gateway response JSON is expected to wrap the standard OpenAI response inside this key (e.g. 'result'). The runtime will extract the nested object before passing it to the OpenAI SDK. Leave empty for gateways that already return standard format. - `okta_auth` OktaAuthConfig — Configuration for Okta-based bearer-token authentication. - … - `use_responses_api` boolean — Whether to use the GPT 5.x+ style Responses API, instead of Chat Completions API for this Custom LLM. See https://developers.openai.com/api/reference/resources/responses - WorkflowDefaultLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `type` 'global_default_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - NoLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `type` 'no_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - LLMGroupConfig - `type` 'llm_group' — Discriminator field which must always be 'llm_group' - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `logical_id` string, nullable — Unique identifier for the LLM Group - `llms` union[] — List of LLM configurations, in preferred order of use. - … - `operation_mode` 'parallel_select_one' | 'sequential_with_proactive' - `min_patience_time_ms` integer, nullable — Grace period in milliseconds until which we will unconditionally wait, unless all LLMs have responded. - `max_patience_time_ms` integer, nullable — Threshold milliseconds after which we will stop waiting. If no LLM has responded by this time, we will return None. - LLMGroupWithBackchannelConfig - `type` 'llm_group_with_backchannel' — Discriminator field which must always be 'llm_group_with_backchannel' - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `id` string, nullable — Unique identifier for the LLM Group - `main_llm_config` LLMGroupConfig - … - `non_backchannel_response_prefix` string, nullable — If back channel response is being sent, only the LLM output following this prefix will follow the backchannel response - `backchannel_llm_config` LLMGroupConfig - … - `backchannel_static_responses` string[] — List of static responses from which one will be selected as a backchannel response. - `backchannel_static_responses_selection_mode` 'random' | 'sequence' - `backchannel_min_patience_time_ms` integer, nullable — Grace period in milliseconds until which we will unconditionally wait for the main LLM to respond, unless it has already responded. - `tools_config` ToolsConfig - `tools` union[] — List of tool configurations - union - … - `self_loop` boolean — Whether this node will execute again if not transitioned to another node - `wait_for_user_message` boolean — Whether the node should wait for a user message before processing - `max_consecutive_tool_calls` integer — Maximum number of consecutive tool calls allowed in a single node execution - `default_error_message` string, nullable — Default error message to be returned if the LLM invocation fails - `use_mcp_tools` boolean — Whether this node should use tools discovered from workflow-level MCP server connections - `ignore_default_prompt_prefix` boolean — If true, the workflow's default_prompt_prefix will not be prepended to this node's prompt. - `ignore_default_prompt_suffix` boolean — If true, the workflow's default_prompt_suffix will not be appended to this node's prompt. - `ignore_content_received_during_llm_tool_call_specification` boolean — If true, any free-text content this node's LLM returns in the same response as one or more tool calls is ignored: not emitted via AssistantResponseEvent, not added to chat history, and not added to the node's structured output. Implies the tool-level flag for every tool call this node makes. - `type` 'worker_llm' — Type of the node. Must be 'worker_llm' - `structured_output_schema` object, nullable — Schema for the structured output of the worker node. Example: { "name": "SearchQuery", "description": "A search query with justification", "input_schema": { "title": "AnswerWithJustification", "type": "object", "properties": { "search_query": { "title": "Search Query", "type": "string", "description": "The field where search query is stored" }, "justification": { "title": "Justification", "type": "string", "description": "The field where justification string is stored" } }, "required": ["search_query", "justification"] } } - `backchannel_response_config` PromptConfig - `prompt` string, nullable — System prompt for the LLM - SayLLMNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `main_response_config` object, nullable — Main response configuration. Contains either a LLM system prompt or exact static messages - `prompt` string, nullable — System prompt for the LLM - `attachable_llm_config_id` string, nullable — ID of the named LLM Configuration to use. If provided, overrides inline configuration. - `llms_config` union — LLM or a group of LLMs to be used in this node - AzureOpenAILLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gpt-5-chat' | 'gpt-5-mini' | 'gpt-5-nano' | 'gpt-4.1-nano' | 'gpt-4.1-mini' | 'gpt-4.1' - `type` 'azure_openai_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `endpoint` string, nullable — Endpoint URL for Azure OpenAI API requests. Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided. - `api_version` string, nullable — Azure OpenAI API version. Falls back to `OPENAI_API_VERSION` env var if not provided. - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'low', 'medium', 'high'. - OpenAILLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gpt-5.4' | 'gpt-5.4-mini' | 'gpt-5.4-nano' | 'gpt-5.2' | 'gpt-5.1' | 'gpt-5-nano' | 'gpt-5-mini' | 'gpt-5' | 'gpt-5.4-pro' | 'gpt-5.2-pro' | 'gpt-5.2-chat-latest' | 'gpt-5.3-chat-latest' | 'gpt-4.1-nano' | 'gpt-4.1-mini' | 'gpt-4.1' | 'gpt-4' | 'gpt-4o-mini' | 'gpt-4o' | 'gpt-3.5-turbo' - `type` 'openai_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `base_url` string, nullable — Base URL path for OpenAI API requests, leave blank if not using a proxy or service emulator. - `organization` string, nullable — Organization ID for OpenAI API requests, Automatically inferred from env var `OPENAI_ORG_ID` if not provided. - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'minimal', 'low', 'medium', 'high'. - GoogleLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gemini-flash-latest' | 'gemini-flash-lite-latest' | 'gemini-3.6-flash' | 'gemini-3.1-pro-preview' | 'gemini-3.5-flash' | 'gemini-3.5-flash-lite' | 'gemini-3.1-flash-lite' | 'gemini-3-flash-preview' | 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' - `type` 'google_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `thinking_budget` integer, nullable — Indicates the thinking budget in tokens. By default, it is set to 0. - AnthropicLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'claude-opus-5' | 'claude-sonnet-5' | 'claude-fable-5' | 'claude-opus-4-8' | 'claude-opus-4-7' | 'claude-opus-4-6' | 'claude-opus-4-5-20251101' | 'claude-opus-4-1-20250805' | 'claude-sonnet-4-6' | 'claude-sonnet-4-5-20250929' | 'claude-haiku-4-5' | 'claude-haiku-4-5-20251001' - `type` 'anthropic_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `thinking_budget` integer, nullable — Token budget for Claude's extended thinking feature. Set to 0 to disable. When enabled, Claude will show its reasoning process before providing a final answer. - BedrockLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — Amazon Bedrock long-term API key (bearer token). If set, it is used for authentication and the access-key fields below are ignored. Leave empty to authenticate with an access-key pair or the default AWS credential chain (IAM role). - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'zai.glm-5' | 'zai.glm-4.7' | 'zai.glm-4.7-flash' | 'moonshotai.kimi-k2.5' | 'moonshot.kimi-k2-thinking' | 'qwen.qwen3-vl-235b-a22b' | 'qwen.qwen3-32b-v1:0' - `type` 'bedrock_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `region` string, nullable — AWS region the Bedrock model is invoked in. Defaults to us-east-1 if not provided. - `aws_access_key_id` string, nullable — AWS access key ID for Bedrock. Leave empty to use the platform default (`BEDROCK_ACCESS_KEY` env var, then the AWS credential chain). - `aws_secret_access_key` string, nullable — AWS secret access key for Bedrock. Leave empty to use the platform default (`BEDROCK_SECRET_KEY` env var, then the AWS credential chain). - CustomLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` string, nullable — Name of the model served behind the custom LLM gateway (free-form string, e.g. 'gpt-4.1-mini', 'llama-3-70b', 'mistral-large'). - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'minimal', 'low', 'medium', 'high'. - `type` 'custom_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `base_url` string, nullable — Base URL of the custom OpenAI-compatible LLM gateway (e.g. 'http://api.interactly.ai/workflows/llm-proxy'). - `default_headers` object, nullable — Optional HTTP headers to include in every request to the gateway (e.g. routing keys, tenant IDs). - `rewrite_base_url` boolean — When True, uses an httpx event hook to rewrite every request URL to the exact base_url. Use this for gateways that don't accept the /chat/completions suffix the SDK appends. - `verify_ssl` boolean — Whether to verify SSL certificates. Set to False for gateways with self-signed certificates. - `response_unwrap_key` string, nullable — When set, the gateway response JSON is expected to wrap the standard OpenAI response inside this key (e.g. 'result'). The runtime will extract the nested object before passing it to the OpenAI SDK. Leave empty for gateways that already return standard format. - `okta_auth` OktaAuthConfig — Configuration for Okta-based bearer-token authentication. - … - `use_responses_api` boolean — Whether to use the GPT 5.x+ style Responses API, instead of Chat Completions API for this Custom LLM. See https://developers.openai.com/api/reference/resources/responses - WorkflowDefaultLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `type` 'global_default_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - NoLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `type` 'no_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - LLMGroupConfig - `type` 'llm_group' — Discriminator field which must always be 'llm_group' - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `logical_id` string, nullable — Unique identifier for the LLM Group - `llms` union[] — List of LLM configurations, in preferred order of use. - … - `operation_mode` 'parallel_select_one' | 'sequential_with_proactive' - `min_patience_time_ms` integer, nullable — Grace period in milliseconds until which we will unconditionally wait, unless all LLMs have responded. - `max_patience_time_ms` integer, nullable — Threshold milliseconds after which we will stop waiting. If no LLM has responded by this time, we will return None. - LLMGroupWithBackchannelConfig - `type` 'llm_group_with_backchannel' — Discriminator field which must always be 'llm_group_with_backchannel' - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `id` string, nullable — Unique identifier for the LLM Group - `main_llm_config` LLMGroupConfig - … - `non_backchannel_response_prefix` string, nullable — If back channel response is being sent, only the LLM output following this prefix will follow the backchannel response - `backchannel_llm_config` LLMGroupConfig - … - `backchannel_static_responses` string[] — List of static responses from which one will be selected as a backchannel response. - `backchannel_static_responses_selection_mode` 'random' | 'sequence' - `backchannel_min_patience_time_ms` integer, nullable — Grace period in milliseconds until which we will unconditionally wait for the main LLM to respond, unless it has already responded. - `tools_config` ToolsConfig - `tools` union[] — List of tool configurations - union - … - `self_loop` boolean — Whether this node will execute again if not transitioned to another node - `wait_for_user_message` boolean — Whether the node should wait for a user message before processing - `max_consecutive_tool_calls` integer — Maximum number of consecutive tool calls allowed in a single node execution - `default_error_message` string, nullable — Default error message to be returned if the LLM invocation fails - `use_mcp_tools` boolean — Whether this node should use tools discovered from workflow-level MCP server connections - `ignore_default_prompt_prefix` boolean — If true, the workflow's default_prompt_prefix will not be prepended to this node's prompt. - `ignore_default_prompt_suffix` boolean — If true, the workflow's default_prompt_suffix will not be appended to this node's prompt. - `ignore_content_received_during_llm_tool_call_specification` boolean — If true, any free-text content this node's LLM returns in the same response as one or more tool calls is ignored: not emitted via AssistantResponseEvent, not added to chat history, and not added to the node's structured output. Implies the tool-level flag for every tool call this node makes. - `type` 'say_llm' — Type of the node. Must be 'say_llm' - `structured_output_schema` object, nullable — Schema for the structured output requested from the say node after it produces a response. Example: { "name": "SearchQuery", "description": "A search query with justification", "input_schema": { "title": "AnswerWithJustification", "type": "object", "properties": { "search_query": { "title": "Search Query", "type": "string", "description": "The field where search query is stored" }, "justification": { "title": "Justification", "type": "string", "description": "The field where justification string is stored" } }, "required": ["search_query", "justification"] } } - `backchannel_response_config` PromptConfig - `prompt` string, nullable — System prompt for the LLM - SayStaticMessageNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `type` 'say_static' — Type of the node. Must be 'say_static' - `static_messages_config` object, nullable — Configuration for static messages. Contains a list of pre-configured messages - `static_messages` string[] — List of pre-configured messages from which one will be emitted - `static_messages_selection_mode` 'random' | 'sequence' - SuperNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `type` 'super_node' — Type of the node. Must be 'super_node' - `super_workflow_id` string, nullable — The ID of the workflow encapsulated by this super node - `super_workflow_version_number` integer, nullable — Specific version of the encapsulated workflow. If None, the active version is used at execution time. - `field_values` object — Values provided by the calling workflow for each of the super node's declared input fields. Keyed by SuperNodeInputField.name. - `override_llm_config` boolean — When true, LLM-based nodes inside this super node use the LLM configuration specified here (attachable_llm_config_id or llms_config) instead of their own authored config. When false (the default), interior nodes keep their authored LLM configuration. - `override_only_workflow_default_nodes` boolean — Scopes the LLM override. When false (the default), every interior LLM node is overridden. When true, only interior LLM nodes that are themselves set to 'Workflow Default LLM' (i.e. had no explicit config of their own) are overridden; nodes with an explicit inline/named config are left untouched. Only relevant when override_llm_config is true. - `attachable_llm_config_id` string, nullable — ID of the named LLM Configuration to apply to the targeted interior LLM nodes. If provided, overrides the inline llms_config. Only relevant when override_llm_config is true. - `llms_config` union — Inline LLM configuration applied to the targeted interior LLM nodes. Use 'Workflow Default LLM' to fall back to the base workflow's workflow-level config. Only relevant when override_llm_config is true. - AzureOpenAILLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gpt-5-chat' | 'gpt-5-mini' | 'gpt-5-nano' | 'gpt-4.1-nano' | 'gpt-4.1-mini' | 'gpt-4.1' - `type` 'azure_openai_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `endpoint` string, nullable — Endpoint URL for Azure OpenAI API requests. Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided. - `api_version` string, nullable — Azure OpenAI API version. Falls back to `OPENAI_API_VERSION` env var if not provided. - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'low', 'medium', 'high'. - OpenAILLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gpt-5.4' | 'gpt-5.4-mini' | 'gpt-5.4-nano' | 'gpt-5.2' | 'gpt-5.1' | 'gpt-5-nano' | 'gpt-5-mini' | 'gpt-5' | 'gpt-5.4-pro' | 'gpt-5.2-pro' | 'gpt-5.2-chat-latest' | 'gpt-5.3-chat-latest' | 'gpt-4.1-nano' | 'gpt-4.1-mini' | 'gpt-4.1' | 'gpt-4' | 'gpt-4o-mini' | 'gpt-4o' | 'gpt-3.5-turbo' - `type` 'openai_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `base_url` string, nullable — Base URL path for OpenAI API requests, leave blank if not using a proxy or service emulator. - `organization` string, nullable — Organization ID for OpenAI API requests, Automatically inferred from env var `OPENAI_ORG_ID` if not provided. - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'minimal', 'low', 'medium', 'high'. - GoogleLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gemini-flash-latest' | 'gemini-flash-lite-latest' | 'gemini-3.6-flash' | 'gemini-3.1-pro-preview' | 'gemini-3.5-flash' | 'gemini-3.5-flash-lite' | 'gemini-3.1-flash-lite' | 'gemini-3-flash-preview' | 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' - `type` 'google_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `thinking_budget` integer, nullable — Indicates the thinking budget in tokens. By default, it is set to 0. - AnthropicLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'claude-opus-5' | 'claude-sonnet-5' | 'claude-fable-5' | 'claude-opus-4-8' | 'claude-opus-4-7' | 'claude-opus-4-6' | 'claude-opus-4-5-20251101' | 'claude-opus-4-1-20250805' | 'claude-sonnet-4-6' | 'claude-sonnet-4-5-20250929' | 'claude-haiku-4-5' | 'claude-haiku-4-5-20251001' - `type` 'anthropic_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `thinking_budget` integer, nullable — Token budget for Claude's extended thinking feature. Set to 0 to disable. When enabled, Claude will show its reasoning process before providing a final answer. - BedrockLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — Amazon Bedrock long-term API key (bearer token). If set, it is used for authentication and the access-key fields below are ignored. Leave empty to authenticate with an access-key pair or the default AWS credential chain (IAM role). - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'zai.glm-5' | 'zai.glm-4.7' | 'zai.glm-4.7-flash' | 'moonshotai.kimi-k2.5' | 'moonshot.kimi-k2-thinking' | 'qwen.qwen3-vl-235b-a22b' | 'qwen.qwen3-32b-v1:0' - `type` 'bedrock_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `region` string, nullable — AWS region the Bedrock model is invoked in. Defaults to us-east-1 if not provided. - `aws_access_key_id` string, nullable — AWS access key ID for Bedrock. Leave empty to use the platform default (`BEDROCK_ACCESS_KEY` env var, then the AWS credential chain). - `aws_secret_access_key` string, nullable — AWS secret access key for Bedrock. Leave empty to use the platform default (`BEDROCK_SECRET_KEY` env var, then the AWS credential chain). - CustomLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` string, nullable — Name of the model served behind the custom LLM gateway (free-form string, e.g. 'gpt-4.1-mini', 'llama-3-70b', 'mistral-large'). - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'minimal', 'low', 'medium', 'high'. - `type` 'custom_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `base_url` string, nullable — Base URL of the custom OpenAI-compatible LLM gateway (e.g. 'http://api.interactly.ai/workflows/llm-proxy'). - `default_headers` object, nullable — Optional HTTP headers to include in every request to the gateway (e.g. routing keys, tenant IDs). - `rewrite_base_url` boolean — When True, uses an httpx event hook to rewrite every request URL to the exact base_url. Use this for gateways that don't accept the /chat/completions suffix the SDK appends. - `verify_ssl` boolean — Whether to verify SSL certificates. Set to False for gateways with self-signed certificates. - `response_unwrap_key` string, nullable — When set, the gateway response JSON is expected to wrap the standard OpenAI response inside this key (e.g. 'result'). The runtime will extract the nested object before passing it to the OpenAI SDK. Leave empty for gateways that already return standard format. - `okta_auth` OktaAuthConfig — Configuration for Okta-based bearer-token authentication. - … - `use_responses_api` boolean — Whether to use the GPT 5.x+ style Responses API, instead of Chat Completions API for this Custom LLM. See https://developers.openai.com/api/reference/resources/responses - WorkflowDefaultLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `type` 'global_default_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - NoLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `type` 'no_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - LLMGroupConfig - `type` 'llm_group' — Discriminator field which must always be 'llm_group' - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `logical_id` string, nullable — Unique identifier for the LLM Group - `llms` union[] — List of LLM configurations, in preferred order of use. - … - `operation_mode` 'parallel_select_one' | 'sequential_with_proactive' - `min_patience_time_ms` integer, nullable — Grace period in milliseconds until which we will unconditionally wait, unless all LLMs have responded. - `max_patience_time_ms` integer, nullable — Threshold milliseconds after which we will stop waiting. If no LLM has responded by this time, we will return None. - LLMGroupWithBackchannelConfig - `type` 'llm_group_with_backchannel' — Discriminator field which must always be 'llm_group_with_backchannel' - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `id` string, nullable — Unique identifier for the LLM Group - `main_llm_config` LLMGroupConfig - … - `non_backchannel_response_prefix` string, nullable — If back channel response is being sent, only the LLM output following this prefix will follow the backchannel response - `backchannel_llm_config` LLMGroupConfig - … - `backchannel_static_responses` string[] — List of static responses from which one will be selected as a backchannel response. - `backchannel_static_responses_selection_mode` 'random' | 'sequence' - `backchannel_min_patience_time_ms` integer, nullable — Grace period in milliseconds until which we will unconditionally wait for the main LLM to respond, unless it has already responded. - `reverse_edge_scope` 'all_interior_llm_nodes' | 'terminal_nodes' — Controls which inlined sub-workflow nodes receive the super node placeholder's ``global_node_config.reverse_conditional_edge`` when the super node is expanded. The reverse edge is surfaced to the model as a per-turn LLM tool, so in BOTH modes only LLM-capable nodes are targeted — a static / HTTP / EndConversation node could never fire it. - `super_node_interface` object, nullable — The interface definition of a super node. Describes what configuration fields callers must provide when instantiating this super node, and how those field values map into the encapsulated sub-workflow's nodes, edges, and dynamic variables. - `input_fields` SuperNodeInputField[] — List of input fields exposed by this super node to calling workflows - `name` string, required — Unique key for this field within the super node interface. Callers set field_values[name] = value. - `description` string, nullable — Human-readable description of what this field controls - `required` boolean — Whether this field must be provided by callers. If False and not provided, default_value is used. - `default_value` unknown - `json_schema` object, nullable — JSON Schema for this field, used by the UI to render and validate the config form - `mappings` SuperNodeFieldMapping[] — Where and how to inject the provided value into the sub-workflow (node/edge config fields or dynamic variables) - … - `value_type` 'string' | 'integer' | 'number' | 'boolean' | 'object' | 'array' | 'enum' | 'any' — Declared shape of a super node input field's leaf value. Used at parent-workflow publish time to validate ``SuperNodeConfig.field_values`` against the interface declared by the sub-workflow. - `enum_values` unknown[], nullable — Required when value_type='enum'. Caller's value must be in this list. - … - `min_value` number, nullable — Inclusive lower bound for integer/number value types. - `max_value` number, nullable — Inclusive upper bound for integer/number value types. - `min_length` integer, nullable — Inclusive lower length for string/array value types. - `max_length` integer, nullable — Inclusive upper length for string/array value types. - `encapsulated_workflow_config` object, nullable — Snapshot of the fully-hydrated sub-workflow config at the time this super node was embedded. Used by the expander at runtime instead of a DB fetch. Not user-editable. - `workflow_config` WorkflowConfigOutput - `logical_id` string, nullable — Unique identifier for the workflow - `name` string, nullable — Name of the workflow - `description` string, nullable — Description of the workflow - `category` string, nullable — Category of the workflow (e.g. 'User Created', 'System Examples', 'System Internal') - `attachable_llm_config_id` string, nullable — ID of the named LLM Configuration to use. If provided, overrides inline configuration. - `llms_config` union — Workflow level configuration for LLM based nodes used in the workflow. This will be used if no specific LLM configuration is provided for a node - … - `main_response_config` PromptConfig - … - `backchannel_response_config` PromptConfig - … - `default_prompt_prefix` string, nullable — If present, this string will be added as prefix to every node's system prompt - `default_prompt_suffix` string, nullable — If present, this string will be added as suffix to every node's system prompt - `ignore_content_received_during_llm_tool_call_specification` boolean — If true, every LLM node in this workflow behaves as if ignore_content_received_during_llm_tool_call_specification is true, and every tool behaves as if ignore_content_received_during_llm_tool_call_specification is true: free-text content the LLM returns in the same response as one or more tool calls is ignored and treated as empty. - `tools_config` ToolsConfig - … - `mcp_servers` MCPServerConfig[], nullable — List of MCP server connections. Tools discovered from these servers are available to nodes with use_mcp_tools enabled. - … - `global_condition_evaluation_method` 'tool_call' | 'independent_llm_evaluations' - `nodes` string[] — List of node configurations in the workflow - `edges` string[] — List of edge configurations in the workflow - `evaluation_config` EvaluationConfigOutput - … - `guardrail_strikes_config` GuardrailStrikesConfig - … - `miscellaneous` object — Miscellaneous config data that can be used by the workflow - `access_config` object, nullable — Access control configuration for the workflow - … - `node_configs` union[] — List of node configurations for runtime - union - … - `edge_configs` union[] — List of edge configurations for runtime - union - … - `dynamic_variables` object — Dynamic variables that can be used across nodes and edges - `runtime_variables` object — Runtime variables that can be used across nodes and edges - WorkflowRunFetchNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `type` 'workflow_run_fetch' — Type of the node. Must be 'workflow_run_fetch' - `workflow_run_id` string, nullable — ID of the workflow run to fetch - `result_runtime_variable_name` string, nullable — Name of the runtime variable to store the result in - WorkflowRunEvalLLMNodeConfigOutput — Configuration for nodes that evaluate workflow runs. Evaluator nodes are used to assess, summarize and score outputs from workflow runs. - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `main_response_config` object, nullable — Main response configuration. Contains either a LLM system prompt or exact static messages - `prompt` string, nullable — System prompt for the LLM - `attachable_llm_config_id` string, nullable — ID of the named LLM Configuration to use. If provided, overrides inline configuration. - `llms_config` union — LLM or a group of LLMs to be used in this node - AzureOpenAILLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gpt-5-chat' | 'gpt-5-mini' | 'gpt-5-nano' | 'gpt-4.1-nano' | 'gpt-4.1-mini' | 'gpt-4.1' - `type` 'azure_openai_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `endpoint` string, nullable — Endpoint URL for Azure OpenAI API requests. Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided. - `api_version` string, nullable — Azure OpenAI API version. Falls back to `OPENAI_API_VERSION` env var if not provided. - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'low', 'medium', 'high'. - OpenAILLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gpt-5.4' | 'gpt-5.4-mini' | 'gpt-5.4-nano' | 'gpt-5.2' | 'gpt-5.1' | 'gpt-5-nano' | 'gpt-5-mini' | 'gpt-5' | 'gpt-5.4-pro' | 'gpt-5.2-pro' | 'gpt-5.2-chat-latest' | 'gpt-5.3-chat-latest' | 'gpt-4.1-nano' | 'gpt-4.1-mini' | 'gpt-4.1' | 'gpt-4' | 'gpt-4o-mini' | 'gpt-4o' | 'gpt-3.5-turbo' - `type` 'openai_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `base_url` string, nullable — Base URL path for OpenAI API requests, leave blank if not using a proxy or service emulator. - `organization` string, nullable — Organization ID for OpenAI API requests, Automatically inferred from env var `OPENAI_ORG_ID` if not provided. - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'minimal', 'low', 'medium', 'high'. - GoogleLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gemini-flash-latest' | 'gemini-flash-lite-latest' | 'gemini-3.6-flash' | 'gemini-3.1-pro-preview' | 'gemini-3.5-flash' | 'gemini-3.5-flash-lite' | 'gemini-3.1-flash-lite' | 'gemini-3-flash-preview' | 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' - `type` 'google_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `thinking_budget` integer, nullable — Indicates the thinking budget in tokens. By default, it is set to 0. - AnthropicLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'claude-opus-5' | 'claude-sonnet-5' | 'claude-fable-5' | 'claude-opus-4-8' | 'claude-opus-4-7' | 'claude-opus-4-6' | 'claude-opus-4-5-20251101' | 'claude-opus-4-1-20250805' | 'claude-sonnet-4-6' | 'claude-sonnet-4-5-20250929' | 'claude-haiku-4-5' | 'claude-haiku-4-5-20251001' - `type` 'anthropic_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `thinking_budget` integer, nullable — Token budget for Claude's extended thinking feature. Set to 0 to disable. When enabled, Claude will show its reasoning process before providing a final answer. - BedrockLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — Amazon Bedrock long-term API key (bearer token). If set, it is used for authentication and the access-key fields below are ignored. Leave empty to authenticate with an access-key pair or the default AWS credential chain (IAM role). - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'zai.glm-5' | 'zai.glm-4.7' | 'zai.glm-4.7-flash' | 'moonshotai.kimi-k2.5' | 'moonshot.kimi-k2-thinking' | 'qwen.qwen3-vl-235b-a22b' | 'qwen.qwen3-32b-v1:0' - `type` 'bedrock_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `region` string, nullable — AWS region the Bedrock model is invoked in. Defaults to us-east-1 if not provided. - `aws_access_key_id` string, nullable — AWS access key ID for Bedrock. Leave empty to use the platform default (`BEDROCK_ACCESS_KEY` env var, then the AWS credential chain). - `aws_secret_access_key` string, nullable — AWS secret access key for Bedrock. Leave empty to use the platform default (`BEDROCK_SECRET_KEY` env var, then the AWS credential chain). - CustomLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` string, nullable — Name of the model served behind the custom LLM gateway (free-form string, e.g. 'gpt-4.1-mini', 'llama-3-70b', 'mistral-large'). - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'minimal', 'low', 'medium', 'high'. - `type` 'custom_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `base_url` string, nullable — Base URL of the custom OpenAI-compatible LLM gateway (e.g. 'http://api.interactly.ai/workflows/llm-proxy'). - `default_headers` object, nullable — Optional HTTP headers to include in every request to the gateway (e.g. routing keys, tenant IDs). - `rewrite_base_url` boolean — When True, uses an httpx event hook to rewrite every request URL to the exact base_url. Use this for gateways that don't accept the /chat/completions suffix the SDK appends. - `verify_ssl` boolean — Whether to verify SSL certificates. Set to False for gateways with self-signed certificates. - `response_unwrap_key` string, nullable — When set, the gateway response JSON is expected to wrap the standard OpenAI response inside this key (e.g. 'result'). The runtime will extract the nested object before passing it to the OpenAI SDK. Leave empty for gateways that already return standard format. - `okta_auth` OktaAuthConfig — Configuration for Okta-based bearer-token authentication. - … - `use_responses_api` boolean — Whether to use the GPT 5.x+ style Responses API, instead of Chat Completions API for this Custom LLM. See https://developers.openai.com/api/reference/resources/responses - WorkflowDefaultLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `type` 'global_default_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - NoLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `type` 'no_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - LLMGroupConfig - `type` 'llm_group' — Discriminator field which must always be 'llm_group' - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `logical_id` string, nullable — Unique identifier for the LLM Group - `llms` union[] — List of LLM configurations, in preferred order of use. - … - `operation_mode` 'parallel_select_one' | 'sequential_with_proactive' - `min_patience_time_ms` integer, nullable — Grace period in milliseconds until which we will unconditionally wait, unless all LLMs have responded. - `max_patience_time_ms` integer, nullable — Threshold milliseconds after which we will stop waiting. If no LLM has responded by this time, we will return None. - LLMGroupWithBackchannelConfig - `type` 'llm_group_with_backchannel' — Discriminator field which must always be 'llm_group_with_backchannel' - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `id` string, nullable — Unique identifier for the LLM Group - `main_llm_config` LLMGroupConfig - … - `non_backchannel_response_prefix` string, nullable — If back channel response is being sent, only the LLM output following this prefix will follow the backchannel response - `backchannel_llm_config` LLMGroupConfig - … - `backchannel_static_responses` string[] — List of static responses from which one will be selected as a backchannel response. - `backchannel_static_responses_selection_mode` 'random' | 'sequence' - `backchannel_min_patience_time_ms` integer, nullable — Grace period in milliseconds until which we will unconditionally wait for the main LLM to respond, unless it has already responded. - `tools_config` ToolsConfig - `tools` union[] — List of tool configurations - union - … - `self_loop` boolean — Evaluator nodes should not have self-loops, so this option is disabled - `wait_for_user_message` boolean — Evaluator nodes should not wait for user messages to proceed, so this option is disabled - `max_consecutive_tool_calls` integer — Maximum number of consecutive tool calls allowed in a single node execution - `default_error_message` string, nullable — Evaluator nodes should not have default error messages, so this option is disabled - `use_mcp_tools` boolean — Whether this node should use tools discovered from workflow-level MCP server connections - `ignore_default_prompt_prefix` boolean — If true, the workflow's default_prompt_prefix will not be prepended to this node's prompt. - `ignore_default_prompt_suffix` boolean — If true, the workflow's default_prompt_suffix will not be appended to this node's prompt. - `ignore_content_received_during_llm_tool_call_specification` boolean — If true, any free-text content this node's LLM returns in the same response as one or more tool calls is ignored: not emitted via AssistantResponseEvent, not added to chat history, and not added to the node's structured output. Implies the tool-level flag for every tool call this node makes. - `type` 'workflow_run_evaluator' — Type of the node. Must be 'workflow_run_evaluator' - `structured_output_schema` object, nullable — Schema for the structured evaluation output. If empty or None, no structured output is expected. - `backchannel_response_config` PromptConfig - `prompt` string, nullable — System prompt for the LLM - `input_runtime_variable_name` string, nullable — Runtime variable name from which to read the input to be evaluated. It is expected to contain the 'WorkflowRun' object to be evaluated. - `output_runtime_variable_name` string, nullable — Runtime variable name to store the structured evaluation output - `is_turn_by_turn_evaluator` boolean — Indicates if this evaluator is intended to evaluate each turn in a multi-turn workflow run - ToolNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `type` 'tool_node' — Type of the node. Must be 'tool_node' - `tool_config` union — Configuration for the tool to be executed - InlinePythonToolConfig — Configuration for an inline Python tool. The "code" field should contain a self-contained executable Python function. Example value of "code" string could be: def add( a: float, b: float, ) -> float: return float(a + b) - `logical_id` string, nullable — Unique identifier for the tool - `tool_id` string, nullable — Reference to the tool already created in the Workflow System. If not provided, the tool config is assumed to be provided inline here. - `name` string, nullable — Name for the tool - `description` string, nullable — Human friendly description for the tool (not used by AI) - `category` string, nullable — Category for the tool. E.g math, ehr, etc - `side_effect` string, nullable — Side-effect classification used by the test-tool feature to decide whether the tool can be safely executed directly, or must be dry-run / confirmation-gated first. One of 'none', 'reads', 'writes', 'sends', 'unknown'. When not set, it is inferred from the tool type. - `signature` string, nullable — Docstring or signature for the tool used by AI. If provided and there is a default signature already, it will override the default signature. - `args_schema` object, nullable — Schema for the arguments that the tool accepts. This should be a JSON schema dictionary. Information provided here will override any default arguments schema. - `static_messages_config` StaticMessagesConfig - … - `result_runtime_variable_name` string, nullable — Name of the runtime variable to store the result from this tool call - `ignore_content_received_during_llm_tool_call_specification` boolean — If true, any free-text content the LLM returns in the same response as a call to this tool is ignored: not emitted via AssistantResponseEvent, not added to chat history, and not added to the node's structured output. When a response contains multiple tool calls, the text is ignored only if every tool call targets a tool for which this flag is set. - `type` 'inline_python' — Type of the tool. Must be 'inline_python' - `code` string, nullable — Python code to be executed by the tool. It should define a function with proper signature and descriptions for its parameters. - InbuiltFunctionToolConfig — Configuration for an inbuilt function tool. This tool type refers to functions that are already registered in the tools registry. The "tool_id" field must match the ID (decorated with "@tool_id") of a registered tool. - `logical_id` string, nullable — Unique identifier for the tool - `tool_id` string, nullable — Reference to the tool already created in the Workflow System. If not provided, the tool config is assumed to be provided inline here. - `name` string, nullable — Name for the tool - `description` string, nullable — Human friendly description for the tool (not used by AI) - `category` string, nullable — Category for the tool. E.g math, ehr, etc - `side_effect` string, nullable — Side-effect classification used by the test-tool feature to decide whether the tool can be safely executed directly, or must be dry-run / confirmation-gated first. One of 'none', 'reads', 'writes', 'sends', 'unknown'. When not set, it is inferred from the tool type. - `signature` string, nullable — Docstring or signature for the tool used by AI. If provided and there is a default signature already, it will override the default signature. - `args_schema` object, nullable — Schema for the arguments that the tool accepts. This should be a JSON schema dictionary. Information provided here will override any default arguments schema. - `static_messages_config` StaticMessagesConfig - … - `result_runtime_variable_name` string, nullable — Name of the runtime variable to store the result from this tool call - `ignore_content_received_during_llm_tool_call_specification` boolean — If true, any free-text content the LLM returns in the same response as a call to this tool is ignored: not emitted via AssistantResponseEvent, not added to chat history, and not added to the node's structured output. When a response contains multiple tool calls, the text is ignored only if every tool call targets a tool for which this flag is set. - `type` 'inbuilt_function' — Type of the tool. Must be 'inbuilt_function' - `configurable_key` string, nullable — Stable key identifying which configurable inbuilt tool this is (e.g. 'call_forward'). None for plain static inbuilt references. - `extra_config` object, nullable — Tool-specific user configuration values. Schema is defined per configurable key. - ExternalAPIToolConfig — Configuration for an external API tool. This tool type represents an external API endpoint that the LLM can call. - `logical_id` string, nullable — Unique identifier for the tool - `tool_id` string, nullable — Reference to the tool already created in the Workflow System. If not provided, the tool config is assumed to be provided inline here. - `name` string, nullable — Name for the tool - `description` string, nullable — Human friendly description for the tool (not used by AI) - `category` string, nullable — Category for the tool. E.g math, ehr, etc - `side_effect` string, nullable — Side-effect classification used by the test-tool feature to decide whether the tool can be safely executed directly, or must be dry-run / confirmation-gated first. One of 'none', 'reads', 'writes', 'sends', 'unknown'. When not set, it is inferred from the tool type. - `signature` string, nullable — Docstring or signature for the tool used by AI. If provided and there is a default signature already, it will override the default signature. - `args_schema` object, nullable — Schema for the arguments that the tool accepts. This should be a JSON schema dictionary. Information provided here will override any default arguments schema. - `static_messages_config` StaticMessagesConfig - … - `result_runtime_variable_name` string, nullable — Name of the runtime variable to store the result from this tool call - `ignore_content_received_during_llm_tool_call_specification` boolean — If true, any free-text content the LLM returns in the same response as a call to this tool is ignored: not emitted via AssistantResponseEvent, not added to chat history, and not added to the node's structured output. When a response contains multiple tool calls, the text is ignored only if every tool call targets a tool for which this flag is set. - `type` 'external_api' — Type of the tool. Must be 'external_api' - `api_endpoint` string, nullable — The endpoint URL of the external API - `api_method` 'GET' | 'POST' | 'PUT' | 'DELETE' — Enumeration of HTTP methods for API calls. - `api_headers` object — HTTP headers to include with the API request (e.g., authorization, content-type) - `api_body` union — The request body payload for the API call. Usually applicable for POST/PUT methods. - … - KnowledgeBaseToolConfig — Configuration for a knowledge base tool. This tool type represents a knowledge base endpoint that the LLM can call. - `logical_id` string, nullable — Unique identifier for the tool - `tool_id` string, nullable — Reference to the tool already created in the Workflow System. If not provided, the tool config is assumed to be provided inline here. - `name` string, nullable — Name for the tool - `description` string, nullable — Human friendly description for the tool (not used by AI) - `category` string, nullable — Category for the tool. E.g math, ehr, etc - `side_effect` string, nullable — Side-effect classification used by the test-tool feature to decide whether the tool can be safely executed directly, or must be dry-run / confirmation-gated first. One of 'none', 'reads', 'writes', 'sends', 'unknown'. When not set, it is inferred from the tool type. - `signature` string, nullable — Docstring or signature for the tool used by AI. If provided and there is a default signature already, it will override the default signature. - `args_schema` object, nullable — Schema for the arguments that the tool accepts. This should be a JSON schema dictionary. Information provided here will override any default arguments schema. - `static_messages_config` StaticMessagesConfig - … - `result_runtime_variable_name` string, nullable — Name of the runtime variable to store the result from this tool call - `ignore_content_received_during_llm_tool_call_specification` boolean — If true, any free-text content the LLM returns in the same response as a call to this tool is ignored: not emitted via AssistantResponseEvent, not added to chat history, and not added to the node's structured output. When a response contains multiple tool calls, the text is ignored only if every tool call targets a tool for which this flag is set. - `type` 'knowledge_base' — Type of the tool. Must be 'knowledge_base' - `target_knowledge_base_ids` string[] — The IDs of the knowledge bases to query - `tool_arguments` object, nullable — Arguments to pass to the tool. Can contain dynamic/runtime variables as strings or inside nested structures. - `result_runtime_variable_name` string, nullable — Name of the runtime variable to store the result of the tool execution - StartConversationNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `type` 'start_conversation' — Type of the node. Must be 'start_conversation' - `is_voice_conversation` boolean — Whether this conversation is a voice-based conversation - `is_outbound_call` boolean — Whether this conversation is an outbound call - `bearer_token` string, nullable — The bearer token used for authenticating with the outbound phone call service - `assistant_number` string, nullable — The phone number assigned to the assistant to make the call from (in E.164 format) - `assistant_id` string, nullable — The ID of the assistant to use for the conversation - `user_number` string, nullable — The phone number of the user to call (in E.164 format) - `conversation_id` string, nullable — The ID of the conversation to poll. If provided, an outbound call will not be initiated, and the system will instead poll for an existing conversation with this ID. - `polling_interval_seconds` integer, nullable — If polling for an existing conversation, the interval (in seconds) at which to poll for updates - EndConversationNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `type` 'end_conversation' — Type of the node. Must be 'end_conversation' - `process_transcript_prompt` PromptConfig - `prompt` string, nullable — System prompt for the LLM - `process_transcript_llms_config` union — LLM or a group of LLMs to be used for processing the conversation transcript - AzureOpenAILLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gpt-5-chat' | 'gpt-5-mini' | 'gpt-5-nano' | 'gpt-4.1-nano' | 'gpt-4.1-mini' | 'gpt-4.1' - `type` 'azure_openai_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `endpoint` string, nullable — Endpoint URL for Azure OpenAI API requests. Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided. - `api_version` string, nullable — Azure OpenAI API version. Falls back to `OPENAI_API_VERSION` env var if not provided. - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'low', 'medium', 'high'. - OpenAILLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gpt-5.4' | 'gpt-5.4-mini' | 'gpt-5.4-nano' | 'gpt-5.2' | 'gpt-5.1' | 'gpt-5-nano' | 'gpt-5-mini' | 'gpt-5' | 'gpt-5.4-pro' | 'gpt-5.2-pro' | 'gpt-5.2-chat-latest' | 'gpt-5.3-chat-latest' | 'gpt-4.1-nano' | 'gpt-4.1-mini' | 'gpt-4.1' | 'gpt-4' | 'gpt-4o-mini' | 'gpt-4o' | 'gpt-3.5-turbo' - `type` 'openai_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `base_url` string, nullable — Base URL path for OpenAI API requests, leave blank if not using a proxy or service emulator. - `organization` string, nullable — Organization ID for OpenAI API requests, Automatically inferred from env var `OPENAI_ORG_ID` if not provided. - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'minimal', 'low', 'medium', 'high'. - GoogleLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'gemini-flash-latest' | 'gemini-flash-lite-latest' | 'gemini-3.6-flash' | 'gemini-3.1-pro-preview' | 'gemini-3.5-flash' | 'gemini-3.5-flash-lite' | 'gemini-3.1-flash-lite' | 'gemini-3-flash-preview' | 'gemini-2.5-pro' | 'gemini-2.5-flash' | 'gemini-2.5-flash-lite' - `type` 'google_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `thinking_budget` integer, nullable — Indicates the thinking budget in tokens. By default, it is set to 0. - AnthropicLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'claude-opus-5' | 'claude-sonnet-5' | 'claude-fable-5' | 'claude-opus-4-8' | 'claude-opus-4-7' | 'claude-opus-4-6' | 'claude-opus-4-5-20251101' | 'claude-opus-4-1-20250805' | 'claude-sonnet-4-6' | 'claude-sonnet-4-5-20250929' | 'claude-haiku-4-5' | 'claude-haiku-4-5-20251001' - `type` 'anthropic_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `thinking_budget` integer, nullable — Token budget for Claude's extended thinking feature. Set to 0 to disable. When enabled, Claude will show its reasoning process before providing a final answer. - BedrockLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — Amazon Bedrock long-term API key (bearer token). If set, it is used for authentication and the access-key fields below are ignored. Leave empty to authenticate with an access-key pair or the default AWS credential chain (IAM role). - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` 'zai.glm-5' | 'zai.glm-4.7' | 'zai.glm-4.7-flash' | 'moonshotai.kimi-k2.5' | 'moonshot.kimi-k2-thinking' | 'qwen.qwen3-vl-235b-a22b' | 'qwen.qwen3-32b-v1:0' - `type` 'bedrock_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `region` string, nullable — AWS region the Bedrock model is invoked in. Defaults to us-east-1 if not provided. - `aws_access_key_id` string, nullable — AWS access key ID for Bedrock. Leave empty to use the platform default (`BEDROCK_ACCESS_KEY` env var, then the AWS credential chain). - `aws_secret_access_key` string, nullable — AWS secret access key for Bedrock. Leave empty to use the platform default (`BEDROCK_SECRET_KEY` env var, then the AWS credential chain). - CustomLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `model` string, nullable — Name of the model served behind the custom LLM gateway (free-form string, e.g. 'gpt-4.1-mini', 'llama-3-70b', 'mistral-large'). - `reasoning_effort` string, nullable — Reasoning effort level for GPT-5-* models. Options are 'minimal', 'low', 'medium', 'high'. - `type` 'custom_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - `base_url` string, nullable — Base URL of the custom OpenAI-compatible LLM gateway (e.g. 'http://api.interactly.ai/workflows/llm-proxy'). - `default_headers` object, nullable — Optional HTTP headers to include in every request to the gateway (e.g. routing keys, tenant IDs). - `rewrite_base_url` boolean — When True, uses an httpx event hook to rewrite every request URL to the exact base_url. Use this for gateways that don't accept the /chat/completions suffix the SDK appends. - `verify_ssl` boolean — Whether to verify SSL certificates. Set to False for gateways with self-signed certificates. - `response_unwrap_key` string, nullable — When set, the gateway response JSON is expected to wrap the standard OpenAI response inside this key (e.g. 'result'). The runtime will extract the nested object before passing it to the OpenAI SDK. Leave empty for gateways that already return standard format. - `okta_auth` OktaAuthConfig — Configuration for Okta-based bearer-token authentication. - … - `use_responses_api` boolean — Whether to use the GPT 5.x+ style Responses API, instead of Chat Completions API for this Custom LLM. See https://developers.openai.com/api/reference/resources/responses - WorkflowDefaultLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `type` 'global_default_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - NoLLMConfig - `logical_id` string, nullable — Unique identifier for the LLM configuration - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `provider` 'default_provider' | 'azure_openai' | 'openai' | 'google' | 'anthropic' | 'bedrock' | 'custom', nullable — The selected Large Language Model provider from the available options. - `streaming` boolean — Whether to enable streaming for the LLM response. - `max_retries` integer, nullable — Maximum number of retries for the LLM request in case of failure. - `max_parse_retries` integer, nullable — Maximum number of times to re-invoke the LLM when a successful response cannot be parsed into the expected structured (JSON) output. Distinct from `max_retries`, which controls transport-level retries (network errors, rate limits, 5xx) inside the LLM client. - `max_tokens` integer, nullable — Maximum number of tokens to generate in the response. - `temperature` number, nullable — Controls the randomness of the model's output. Lower values make the output more deterministic. - `request_timeout_ms` integer, nullable — Timeout for the request in milliseconds. - `seed` integer, nullable — Seed for random number generation to ensure reproducibility. - `model_kwargs` object — Additional keyword arguments to pass to the LLM provider's API. - `api_key` string, nullable — API KEY to access the LLM provider endpoint - `do_not_split_sentences` boolean — Whether to avoid splitting sentences in the LLM response. - `truncated_max_recent_messages` integer, nullable — Maximum number of recent messages to keep when truncating the conversation history. If not set, no truncation is applied. If set, the recent conversation history will be truncated to contain only the specified number of messages when invoking the LLM for next response. If set to 0, only the system prompt is used. - `type` 'no_llm' — Differentiator field that helps in identifying this particular type of config when serializing and deserializing - LLMGroupConfig - `type` 'llm_group' — Discriminator field which must always be 'llm_group' - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `logical_id` string, nullable — Unique identifier for the LLM Group - `llms` union[] — List of LLM configurations, in preferred order of use. - … - `operation_mode` 'parallel_select_one' | 'sequential_with_proactive' - `min_patience_time_ms` integer, nullable — Grace period in milliseconds until which we will unconditionally wait, unless all LLMs have responded. - `max_patience_time_ms` integer, nullable — Threshold milliseconds after which we will stop waiting. If no LLM has responded by this time, we will return None. - LLMGroupWithBackchannelConfig - `type` 'llm_group_with_backchannel' — Discriminator field which must always be 'llm_group_with_backchannel' - `named_llm_config_id` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its ID. - `named_llm_config_name` string, nullable — If this configuration was resolved from a reusable named LLM configuration, this holds its name. - `id` string, nullable — Unique identifier for the LLM Group - `main_llm_config` LLMGroupConfig - … - `non_backchannel_response_prefix` string, nullable — If back channel response is being sent, only the LLM output following this prefix will follow the backchannel response - `backchannel_llm_config` LLMGroupConfig - … - `backchannel_static_responses` string[] — List of static responses from which one will be selected as a backchannel response. - `backchannel_static_responses_selection_mode` 'random' | 'sequence' - `backchannel_min_patience_time_ms` integer, nullable — Grace period in milliseconds until which we will unconditionally wait for the main LLM to respond, unless it has already responded. - SendSMSNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `type` 'send_sms' — Type of the node. Must be 'send_sms' - `destination_phone_number` string, nullable — Destination phone number in E.164 format. Example of E.164 format: +1234567890 - `message` string, nullable — SMS message to be sent. Can include dynamic and runtime variable placeholders for dynamic content. - GoogleDocsNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `type` 'google_docs' — Type of the node. Must be 'google_docs' - `credentials` union — Credentials for Google Workspace. If not provided, the default credentials will be used. - GoogleWorkspaceOAuth2Credentials — Configuration for Google Workspace OAuth2 credentials. - `type` 'google_workspace_oauth2' — Type of the credentials. Must be 'google_workspace_oauth2' - `oauth_redirect_uri` string, nullable — OAuth redirect URI for Google Workspace. If not provided, the default value at os.environ['GOOGLE_OAUTH_REDIRECT_URI'] will be used. - `client_id` string, nullable — Client ID for Google Workspace. If not provided, the default value at os.environ['GOOGLE_CLIENT_ID'] will be used. - `client_secret` string, nullable — Client Secret for Google Workspace. If not provided, the default value at os.environ['GOOGLE_CLIENT_SECRET'] will be used. - GoogleWorkspaceServiceAccountCredentials — Configuration for Google Workspace service account credentials. - `type` 'google_workspace_service_account' — Type of the credentials. Must be 'google_workspace_service_account' - `service_account_email` string, nullable — Service account email for Google Workspace. If not provided, the default value at os.environ['GOOGLE_SERVICE_ACCOUNT_EMAIL'] will be used. - `private_key_id` string, nullable — Private key ID for Google Workspace. If not provided, the default value at os.environ['GOOGLE_SERVICE_ACCOUNT_PRIVATE_KEY_ID'] will be used. - `private_key` string, nullable — Private key for Google Workspace. If not provided, the default value at os.environ['GOOGLE_SERVICE_ACCOUNT_PRIVATE_KEY'] will be used. - `impersonation_email` string, nullable — Email to impersonate for Google Workspace. If not provided, the default value at os.environ['GOOGLE_SERVICE_ACCOUNT_IMPERSONATION_EMAIL'] will be used. - GoogleWorkspaceAPIKeyCredentials — Configuration for Google Workspace API Key credentials. - `type` 'google_workspace_api_key' — Type of the credentials. Must be 'google_workspace_api_key' - `api_key` string, nullable — API Key for Google Workspace, used to access public APIs. If not provided, the default value at os.environ['GOOGLE_API_KEY'] will be used. - `operate_document` union — Configuration for operating on a Google Docs document (create, get, or update). - GoogleDocsCreateDocumentConfig — Configuration for creating a Google Docs document. - `type` 'google_docs_create_document' — Type of the node. Must be 'google_docs_create_document' - `title` string, nullable — Title of the Google Docs document to be created. - `drive_name_or_id` string, nullable — Name or ID of the Google Drive where the document will be created. If not provided, the document will be created in the user's default drive. - `folder_name_or_id` string, nullable — Name or ID of the folder where the document will be created. If not provided, the document will be created in the root of the drive. - GoogleDocsGetDocumentConfig — Configuration for getting a Google Docs document. - `type` 'google_docs_get_document' — Type of the node. Must be 'google_docs_get_document' - `document_id_or_url` string, nullable — ID or URL of the Google Docs document to be retrieved. - `return_full_model` boolean — Whether to return the full document model or just the plain text content. - GoogleDocsUpdateDocumentConfig — Configuration for updating a Google Docs document. - `type` 'google_docs_update_document' — Type of the node. Must be 'google_docs_update_document' - `document_id_or_url` string, nullable — ID or URL of the Google Docs document to be updated. - `updates` GoogleDocsUpdateWithTextConfig — Configuration for updating a Google Docs document with text. - … - HttpRequestNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `type` 'http_request' — Type of the node. Must be 'http_request' - `url` string, nullable — The URL to send the HTTP request to - `method` 'GET' | 'POST' | 'PUT' | 'PATCH' | 'DELETE' | 'HEAD' | 'OPTIONS' - `headers` object, nullable — Optional HTTP headers as a dictionary - `query_parameters` string, nullable — Query parameters as a dictionary - `body_parameters` string, nullable — Body parameters as a dictionary (for POST, PUT, PATCH) - `body_content_type` 'application/json' | 'application/x-www-form-urlencoded' | 'multipart/form-data' | 'text/plain' - `timeout` integer, nullable — Timeout for the HTTP request in seconds - `response_format` 'json' | 'text' | 'binary' - `result_runtime_variable_name` string, nullable — Name of the runtime variable to store the result - AthenaPatientsSearchNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `source_id` string, nullable — Source ID of the node - `department_id` integer, nullable — Department ID of the node - `api_key` string, nullable — API key for the node - `timezone` string, nullable — Timezone in IANA format ex: 'America/New_York' - `reminders` boolean, nullable — Whether to send reminders for the node - `slotfill_outreach` boolean, nullable — Whether to enable slot fill outreach for the node - `working_hours` object, nullable — Working hours for the node - `monday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `tuesday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `wednesday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `thursday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `friday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `saturday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `sunday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `use_cases` UseCase[] — Use cases for the node - `type` 'athena_patients_search' — Type of the node. Must be 'athena_patients_search' - `phone_number` string, nullable — Phone number of the patient to create - `full_name` string, nullable — Full name of the patient to retrieve - `birthdate` string, nullable — Birthdate of the patient to retrieve - AthenaPatientsCreateNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `source_id` string, nullable — Source ID of the node - `department_id` integer, nullable — Department ID of the node - `api_key` string, nullable — API key for the node - `timezone` string, nullable — Timezone in IANA format ex: 'America/New_York' - `reminders` boolean, nullable — Whether to send reminders for the node - `slotfill_outreach` boolean, nullable — Whether to enable slot fill outreach for the node - `working_hours` object, nullable — Working hours for the node - `monday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `tuesday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `wednesday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `thursday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `friday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `saturday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `sunday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `use_cases` UseCase[] — Use cases for the node - `type` 'athena_patients_create' — Type of the node. Must be 'athena_patients_create' - `first_name` string, nullable — First name of the patient to create - `last_name` string, nullable — Last name of the patient to create - `phone_number` string, nullable — Phone number of the patient to create - `birthdate` string, nullable — Birthdate of the patient to retrieve - `gender` '' | 'Female' | 'Male' | 'Non-binary gender identity' | 'Additional gender category or other' | 'Choose not to disclose' | 'Genderqueer (neither exclusively male nor female)' | 'Unknown' - `email` string, nullable — Email of the patient to create - `notes` string, nullable — Notes about the patient to create - `mobile_number` string, nullable — Mobile number of the patient to create - AthenaPatientsUpdateNodeConfigOutput - `logical_id` string, nullable — Unique identifier for the node - `name` string, nullable — Name of the node - `description` string, nullable — Description of the node - `workflow_id` string, nullable — The DB Object ID of the workflow this entity belongs to - `version_number` integer, nullable — Version number of the workflow this entity belongs to. 0 is the initial version (default). - `disabled` boolean — If true, this entity will be disabled and will not execute its function. Useful for testing workflows without actually executing this specific entity - `miscellaneous` object — Miscellaneous config data that can be used by the entity - `primary_category` string, nullable — Primary category of the node - `secondary_category` string, nullable — Secondary category of the node - `is_start` boolean — Whether this node is the starting node of the workflow - `is_guardrail_node` boolean — Whether this node is a guardrail node. When the workflow-level `miscellaneous.no_hopping_between_guardrail_nodes` setting is enabled (the default), the runtime blocks conditional transitions from this guardrail node to any other guardrail node — including regular freeform/expression conditional edges and synthesized global-node edges. Self-edges and self-loops on the same guardrail node remain allowed. - `global_node_config` object, nullable — Configuration for when this node is a global node - `is_global` boolean — Whether this node is a global node available as a conditional pathway to all nodes in the workflow - `condition` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `global_condition_edge_evaluation_method` 'workflow_default' | 'tool_call' | 'independent_llm_evaluations' - `reverse_conditional_edge` ConditionConfig - `condition_freeform` string, nullable — Condition (expressed in natural language) that determines whether this path is taken - `condition_expression` string, nullable — Condition (expressed in a structured equation with variables) that determines whether this path is taken - `args_schema` object, nullable — JSON Schema describing the arguments that will be passed when condition_freeform is evaluated - `static_messages_config` StaticMessagesConfig - … - `dynamic_messages_config` DynamicMessagesConfig - … - `self_loop_config` object, nullable — Bounded self-loop / retry policy for a node. When ``enabled``, the node re-executes its functionality repeatedly until ONE of the following happens, whichever comes first: 1. an outgoing conditional edge matches (the loop's exit), or 2. ``max_retries`` re-executions have been performed, or 3. the ``expiry_time`` wall-clock budget for the whole loop elapses. Outgoing conditional edges are re-evaluated after every execution to decide whether to exit (take a matching edge) or run again. When the loop stops without a matching exit edge, the runtime publishes a ``self_loop_outcome`` runtime variable (``"max_retries"`` | ``"expiry_time"``) that authors can branch on with a dedicated "exhausted"/"timeout" conditional edge. This is distinct from the LLM-node ``self_loop: bool`` flag, which governs loop-back while waiting for the next user message and is unaffected by this config. - `enabled` boolean — Re-execute this node until a conditional edge matches, max_retries is reached, or expiry_time elapses (whichever comes first). - `max_retries` integer, nullable — Maximum number of re-executions after the first attempt (total attempts = max_retries + 1). Leave empty for no bound by count. - `expiry_time` integer, nullable — Overall wall-clock budget in seconds for the whole self-loop. An execution still running when this elapses is terminated. Leave empty for no time bound. Note: a CPU-bound inline-Python body with no await points cannot be interrupted mid-execution; expiry reliably bounds I/O-bound work. - `time_between_retries` integer — Seconds to wait between the end of one execution and the start of the next. - `source_id` string, nullable — Source ID of the node - `department_id` integer, nullable — Department ID of the node - `api_key` string, nullable — API key for the node - `timezone` string, nullable — Timezone in IANA format ex: 'America/New_York' - `reminders` boolean, nullable — Whether to send reminders for the node - `slotfill_outreach` boolean, nullable — Whether to enable slot fill outreach for the node - `working_hours` object, nullable — Working hours for the node - `monday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `tuesday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `wednesday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - `thursday` StartEndTimings, required - `start` string, required — Start time in HH:MM format - `end` string, required — End time in HH:MM format - … truncated; see the full OpenAPI document linked below ## Other responses - `422` — Validation Error --- [API](https://skmtc.net/interactly/apis/interactly-api-3.md) · [All operations](https://skmtc.net/interactly/apis/interactly-api-3/llms.txt) · [OpenAPI document](https://skmtc-service-staging.skmtc.workers.dev/v1/apis/interactly/interactly-api-3/versions/9f4e7dc7e094/schema)