> ## Documentation Index
> Fetch the complete documentation index at: https://api-docs.datagol.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Get Conversation Messages

> Retrieves a chronologically ordered list of user messages, AI responses, and deep system agent routing execution data for a specific conversation thread.



## OpenAPI

````yaml /openapi/agents-openapi.yaml get /conversations/{conversationId}/messages
openapi: 3.1.0
info:
  title: DataGOL Custom Agents API
  description: >-
    Complete API for creating, configuring, sharing, and interacting with Custom
    AI Agents on the DataGOL platform. Covers agent CRUD, permissions,
    conversations, and streaming.
  version: 1.0.0
  contact:
    name: DataGOL Support
    url: https://datagol.ai
servers:
  - url: https://be.datagol.ai
    description: Backend API (Production) — agent management, conversations, permissions
  - url: https://ai.datagol.ai
    description: AI Core (Production) — streaming, chat completion, conversation naming
security:
  - ServiceAccountAuth: []
  - BearerAuth: []
tags:
  - name: Custom Agents
    description: Create, read, and manage Custom AI Agents.
  - name: Permissions
    description: View and manage who has access to an agent.
  - name: Conversations
    description: Open and list conversation sessions backed by an agent.
  - name: Messaging
    description: Send messages and stream AI responses.
  - name: Reference Data
    description: >-
      Lookup APIs for workspaces, connectors, and team members used when
      building agent configs.
paths:
  /conversations/{conversationId}/messages:
    get:
      tags:
        - Messaging
      summary: Get Conversation Messages
      description: >-
        Retrieves a chronologically ordered list of user messages, AI responses,
        and deep system agent routing execution data for a specific conversation
        thread.
      operationId: getConversationMessages
      parameters:
        - name: conversationId
          in: path
          required: true
          description: The unique UUID identifying the conversation thread.
          schema:
            type: string
            format: uuid
            example: 6718e680-194b-47fb-b8f6-029b35565b99
      responses:
        '200':
          description: >-
            A JSON array containing the message history list along with agent
            analytics metadata.
          content:
            application/json:
              schema:
                type: array
                items:
                  $ref: '#/components/schemas/ConversationMessage'
              example:
                - id: 7902
                  conversationId: c920e1f5-2ac7-4f17-aed7-b260e464c022
                  userId: 206
                  userQuery: Can you do a RFM analysis on this data?
                  modelResponse:
                    - code: null
                      data: >-
                        Here’s the plan and step-by-step pseudo-code for the RFM
                        analysis, followed by a complete, executable Python
                        script...


                        ```python

                        import pandas as pd

                        import numpy as np...
                      type: TEXT
                      state: null
                      message: null
                      metadata: null
                  uiMetadata: null
                  messageMetadata:
                    chainId: '1762280699'
                    agentType: NewPythonAgent
                    workbookId: null
                    executionData:
                      - content: |-
                          <table class="dataframe dataframe">
                            <thead>
                              <tr style="text-align: right;">
                                <th>user_id</th>
                                <th>email</th>
                                <th>full_name</th>...
                        response_type: dataframe
                      - content:
                          agent: Expert Selector
                          model: o4-mini
                        response_type: tool_start
                      - content:
                          input: 'Reasoning Effort: medium'
                          action: Thinking
                        response_type: tool_call
                      - content: |-
                          requires_dataset: true
                          expert: "Data Analyst"
                          confidence: 9
                        response_type: processing
                  isPinned: false
                  timestamp: '2025-11-04T18:24:59.695784Z'
                  feedback: null
components:
  schemas:
    ConversationMessage:
      type: object
      properties:
        id:
          type: integer
          description: Internal unique message identifier.
          example: 7902
        conversationId:
          type: string
          format: uuid
          description: Back-reference to the parent conversation UUID.
          example: c920e1f5-2ac7-4f17-aed7-b260e464c022
        userId:
          type: integer
          description: The structural ID of the user authoring the text.
          example: 206
        userQuery:
          type: string
          description: The exact prompt raw input supplied by the user.
          example: Can you do some analysis on this data?
        modelResponse:
          type: array
          description: Content block payload parts yielded by the processing model.
          items:
            $ref: '#/components/schemas/ModelResponseBlock'
        uiMetadata:
          type: object
          nullable: true
          description: Client UI specific flags or tracking configurations.
        messageMetadata:
          $ref: '#/components/schemas/MessageMetadata'
        isPinned:
          type: boolean
          description: Flags whether this thread item was pinned in the interface.
          example: false
        timestamp:
          type: string
          format: date-time
          description: Message generation window log timestamp.
          example: '2025-11-04T18:24:59.695784Z'
        feedback:
          type: object
          nullable: true
          description: User satisfaction feedback payload data.
    ModelResponseBlock:
      type: object
      properties:
        code:
          type: string
          nullable: true
          description: Isolated code segment if format type requires extraction.
        data:
          type: string
          description: Main textual response or markdown block data string.
          example: Here's the plan and step-by-step pseudo-code for the RFM analysis...
        type:
          type: string
          description: Format payload classification tag.
          example: TEXT
        state:
          type: string
          nullable: true
        message:
          type: string
          nullable: true
        metadata:
          type: object
          nullable: true
    MessageMetadata:
      type: object
      properties:
        chainId:
          type: string
          description: Unique orchestration trace track ID.
          example: '1762280699'
        agentType:
          type: string
          description: The core executing processor class tag name.
          example: NewPythonAgent
        workbookId:
          type: string
          nullable: true
        executionData:
          type: array
          description: Step traces recording diagnostic multi-agent tool execution steps.
          items:
            $ref: '#/components/schemas/ExecutionTraceItem'
    ExecutionTraceItem:
      type: object
      properties:
        content:
          oneOf:
            - type: string
              description: Text data, raw code string, or HTML dataframe representations.
            - type: object
              description: Structured structural step metadata maps.
          example:
            agent: Expert Selector
            model: o4-mini
        response_type:
          type: string
          description: Internal node process event tags classification.
          example: tool_start
  securitySchemes:
    ServiceAccountAuth:
      type: apiKey
      in: header
      name: x-auth-token
      description: >
        For server-to-server or automated access, use a DataGOL Service Account
        key.
                Pass it via the `x-auth-token` header instead of `Authorization`:
                `x-auth-token: <service-account-key>`
    BearerAuth:
      type: http
      scheme: bearer
      bearerFormat: JWT
      description: >
        JWT token issued by the DataGOL Identity Provider. Embed `Authorization:
        Bearer <token>` in every request. Tokens contain `companyId`, `userId`,
        `roles`, and `permissions` claims.

````