ClawHub

ChatDev 2.0 Multi-Agent Team

Data & APIs

Invoke ChatDev ability units (workflows) via local API (port 6400). Use when the user needs specialized agent workflows like data visualization (from CSV), or when they mention "ChatDev", "ability units", or "workflows" at http://127.0.0.1:6400.

Install

openclaw skills install chatdev

ChatDev Ability Units

Ability units represent capability units. You can browse what's available, inspect required args, run them, or upload new ones. Use these endpoints when you need to invoke a predefined ability unit (e.g., paper review, data visualization) via the local API.

Run ability units via the local API and return final_message from the JSON response.

API endpoints

  1. Browse ability units
  1. Get ability unit raw content
  • Method: GET
  • URL: http://127.0.0.1:6400/api/workflows//get
  • Purpose: fetch the raw YAML content for an ability unit file.
  • Example: 
    curl --noproxy 127.0.0.1 -v \
  http://127.0.0.1:6400/api/workflows/test.yaml/get
  1. Get ability unit args
  • Method: GET
  • URL: http://127.0.0.1:6400/api/workflows/<ability_unit>.yaml/args
  • Purpose: fetch input parameter schema for an ability unit.
  • Example: 
    curl --noproxy 127.0.0.1 -v -X GET \
  http://127.0.0.1:6400/api/workflows/test.yaml/args
  1. Run ability unit

  • Method: POST

  • URL: http://127.0.0.1:6400/api/workflow/run

  • Content-Type: application/json

  • SSE support: set Accept: text/event-stream to stream events (started, log, completed, error). Omit the header to get the normal JSON response.

  • Output: parse JSON and return final_message. If missing, report failure and include any error fields. After you get output_dir, move it to your working directory.

  • Available ability units:

    1. Paper Review
    • yaml_file: yaml_instance/paper_review.yaml
    • task_prompt: arXiv URL/ID or paper title.
    • Purpose: fetch content and deliver technical + writing reviews.
    • Example: 
      curl --noproxy 127.0.0.1 -v -X POST http://127.0.0.1:6400/api/workflow/run \
  -H "Content-Type: application/json" \
  -H "Accept: text/event-stream" \
  -d '{
    "yaml_file": "yaml_instance/paper_review.yaml",
    "task_prompt": "https://arxiv.org/abs/1706.03762"
  }'
    1. Data Visualization
    • yaml_file: yaml_instance/data_visualization_basic.yaml
    • task_prompt: dataset description + analysis goals.
    • attachments: absolute paths to CSV files.
    • Purpose: profile, clean, plan 4-6 charts, iterate visualizations.
    • Example: 
      curl --noproxy 127.0.0.1 -v -X POST http://127.0.0.1:6400/api/workflow/run \
  -H "Content-Type: application/json" \
  -d '{
    "yaml_file": "yaml_instance/data_visualization_basic.yaml",
    "task_prompt": "please analyze and visualize the data",
     "attachments": ["/Users/yufan/Projects/bugfix/ChatDev/sample_sales.csv"]
  }'
  1. Upload ability unit
  • Method: POST
  • URL: http://127.0.0.1:6400/api/workflows/upload/content
  • Content-Type: application/json
  • Format notes: filename should end with .yaml; content must be a valid YAML string that includes version, vars, and a graph with id, start, nodes, and edges (as in the example).
  • Tip: if uploads keep failing with format errors, call the "get ability unit raw content" endpoint to inspect an existing ability unit's structure and copy its format.
  • Example: 
    curl --noproxy 127.0.0.1 -v -X POST \
  http://127.0.0.1:6400/api/workflows/upload/content \
  -H "Content-Type: application/json" \
  -d @- <<'EOF'
{
  "filename": "test.yaml",
  "content": "version: 0.4.0\nvars: {}\ngraph:\n  id: paper_review\n  description: Three agents collaboratively review academic papers from different perspectives. Input can be an arxiv URL or paper title.\n  is_majority_voting: false\n  start:\n    - Paper Fetcher\n  nodes:\n    - id: Paper Fetcher\n      type: agent\n      config:\n        name: gpt-4o\n        provider: openai\n        role: |\n          You are a paper content fetcher.\n        base_url: ${BASE_URL}\n        api_key: ${API_KEY}\n      description: Fetches paper content from arxiv\n      context_window: 0\n  edges: []"
}
EOF
  1. Update ability unit
  • Method: PUT
  • URL: http://127.0.0.1:6400/api/workflows//update
  • Content-Type: application/json
  • Format notes: same payload as upload (filename + YAML content string).
  • Example: 
    curl --noproxy 127.0.0.1 -v -X PUT \
  http://127.0.0.1:6400/api/workflows/test.yaml/update \
  -H "Content-Type: application/json" \
  -d @- <<'EOF'
{
  "filename": "test.yaml",
  "content": "version: 0.4.0\nvars: {}\ngraph:\n  id: paper_review\n  description: Update example.\n  is_majority_voting: false\n  start:\n    - Paper Fetcher\n  nodes:\n    - id: Paper Fetcher\n      type: agent\n      config:\n        name: gpt-4o\n        provider: openai\n        role: |\n          You are a paper content fetcher.\n        base_url: ${BASE_URL}\n        api_key: ${API_KEY}\n      description: Fetches paper content from arxiv\n      context_window: 0\n  edges: []"
}
EOF
  1. Rename ability unit
  • Method: POST
  • URL: http://127.0.0.1:6400/api/workflows//rename
  • Content-Type: application/json
  • Body: { "new_filename": "new_name.yaml" }
  • Example: 
    curl --noproxy 127.0.0.1 -v -X POST \
  http://127.0.0.1:6400/api/workflows/test.yaml/rename \
  -H "Content-Type: application/json" \
  -d '{"new_filename":"renamed.yaml"}'
  1. Copy ability unit
  • Method: POST
  • URL: http://127.0.0.1:6400/api/workflows//copy
  • Content-Type: application/json
  • Body: { "new_filename": "copy.yaml" }
  • Example: 
    curl --noproxy 127.0.0.1 -v -X POST \
  http://127.0.0.1:6400/api/workflows/test.yaml/copy \
  -H "Content-Type: application/json" \
  -d '{"new_filename":"test-copy.yaml"}'
  1. Delete ability unit
  1. List local tools (function_calling)

Tools hot updates

Local function tools are managed via the same backend (port 6400).

Endpoints:

  • List local tools: GET http://127.0.0.1:6400/api/tools/local
  • Create/overwrite local tool file: POST http://127.0.0.1:6400/api/tools/local

Create example:

curl --noproxy 127.0.0.1 -X POST http://127.0.0.1:6400/api/tools/local \
  -H "Content-Type: application/json" \
  -d '{
    "filename": "add.py",
    "content": "def add(a: int, b: int) -> dict:\n    return {\"result\": a + b}\n",
    "overwrite": true
  }'

YAML example (local function_calling):

tooling:
  - type: function
    config:
      tools:
        - name: add

Common scenarios and tool suggestions

  • If the agent needs to search the web, use web_search.
  • If the agent needs to fetch a URL's content, use read_webpage_content.
  • If the agent needs to inspect local files, use describe_available_files and list_directory.
  • If the agent needs to read a file snippet, use read_text_file_snippet or read_file_segment.
  • If the agent needs to write output files, use save_file.

Advanced Workflow Templates

Below are advanced YAML templates you can copy and adapt. They cover debate loops, subgraph reuse, conditional edges, and selective payload routing.

1) Two-Agent Debate Loop with Judge Stop Criteria

version: 0.4.0
vars: {}
graph:
  id: debate_loop
  description: Two agents debate until the judge outputs "Verdict: STOP".
  is_majority_voting: false
  start:
    - Topic
  nodes:
    - id: Topic
      type: passthrough
      config: {}
    - id: Pro
      type: agent
      config:
        provider: openai
        base_url: ${BASE_URL}
        api_key: ${API_KEY}
        name: gpt-4o
        role: |
          You are the PRO debater. Respond with arguments and counterpoints.
    - id: Con
      type: agent
      config:
        provider: openai
        base_url: ${BASE_URL}
        api_key: ${API_KEY}
        name: gpt-4o
        role: |
          You are the CON debater. Respond with arguments and counterpoints.
    - id: Judge
      type: agent
      config:
        provider: openai
        base_url: ${BASE_URL}
        api_key: ${API_KEY}
        name: gpt-4o
        role: |
          Evaluate the debate. Output:
          Score: <0-1>
          Notes: <brief>
          Verdict: CONTINUE|STOP
    - id: Final
      type: agent
      config:
        provider: openai
        base_url: ${BASE_URL}
        api_key: ${API_KEY}
        name: gpt-4o
        role: |
          Produce the final summary of the debate and recommendation.
  edges:
    - from: Topic
      to: Pro
      keep_message: true
    - from: Topic
      to: Con
      keep_message: true
    - from: Pro
      to: Judge
    - from: Con
      to: Judge
    - from: Judge
      to: Pro
      condition: need_reflection_loop
    - from: Judge
      to: Con
      condition: need_reflection_loop
    - from: Judge
      to: Final
      condition: should_stop_loop

2) Subgraph Reuse + Selective Payload Passing

version: 0.4.0
vars: {}
graph:
  id: parent_with_subgraph
  description: Uses a subgraph and only forwards the "Summary" section.
  start:
    - Request
  nodes:
    - id: Request
      type: passthrough
      config: {}
    - id: ResearchSubgraph
      type: subgraph
      config:
        type: file
        config:
          path: "subgraphs/deep_research_executor_sub.yaml"
    - id: Writer
      type: agent
      config:
        provider: openai
        base_url: ${BASE_URL}
        api_key: ${API_KEY}
        name: gpt-4o
        role: |
          Write the final report using only the provided Summary.
  edges:
    - from: Request
      to: ResearchSubgraph
      keep_message: true
    - from: ResearchSubgraph
      to: Writer
      process:
        type: regex_extract
        config:
          pattern: "Summary:\\s*(.*)"
          group: 1
          multiline: true
          dotall: true
          on_no_match: pass

3) Conditional Routing + Minimal Payload

version: 0.4.0
vars: {}
graph:
  id: router_with_conditions
  description: Route to different nodes based on tags; forward only CONTENT block.
  start:
    - Router
  nodes:
    - id: Router
      type: agent
      config:
        provider: openai
        base_url: ${BASE_URL}
        api_key: ${API_KEY}
        name: gpt-4o
        role: |
          Decide the route. Output exactly:
          ROUTE: QA|REPORT
          CONTENT: <payload to forward>
    - id: QA
      type: agent
      config:
        provider: openai
        base_url: ${BASE_URL}
        api_key: ${API_KEY}
        name: gpt-4o
        role: |
          Answer the question concisely.
    - id: REPORT
      type: agent
      config:
        provider: openai
        base_url: ${BASE_URL}
        api_key: ${API_KEY}
        name: gpt-4o
        role: |
          Write a structured report.
  edges:
    - from: Router
      to: QA
      condition:
        type: keyword
        config:
          any: ["ROUTE: QA"]
      process:
        type: regex_extract
        config:
          pattern: "CONTENT:\\s*(.*)"
          group: 1
          dotall: true
          on_no_match: drop
    - from: Router
      to: REPORT
      condition:
        type: keyword
        config:
          any: ["ROUTE: REPORT"]
      process:
        type: regex_extract
        config:
          pattern: "CONTENT:\\s*(.*)"
          group: 1
          dotall: true
          on_no_match: drop

Tips

  • Any workflow YAML can be used as a subgraph to compose more complex tasks, including existing ability units/workflows. Example: 
    - id: Research Subgraph
  type: subgraph
  config:
    type: file
    config:
      path: "yaml_instance/deep_research_v1.yaml"
  • Ensure that all generated artifacts are output to your workspace file paths whenever possible
  • Ensure that all agents’ roles are concise, sophisticated, and equipped with sufficient tools
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