deer-flow/skills/public/claude-to-deerflow/SKILL.md

name, description

name

description

claude-to-deerflow

Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to DeerFlow threads, or delegate complex research tasks to DeerFlow. Also use when the user mentions deerflow, deer flow, or wants to run a deep research task that DeerFlow can handle.

DeerFlow Skill

Communicate with a running DeerFlow instance via its HTTP API. DeerFlow is an AI agent platform built on LangGraph that orchestrates sub-agents for research, code execution, web browsing, and more.

Architecture

DeerFlow exposes two API surfaces behind an Nginx reverse proxy:

Service	Direct Port	Via Proxy	Purpose
Gateway API	8001	$DEERFLOW_GATEWAY_URL	REST endpoints (models, skills, memory, uploads)
LangGraph API	2024	$DEERFLOW_LANGGRAPH_URL	Agent threads, runs, streaming

Environment Variables

All URLs are configurable via environment variables. Read these env vars before making any request.

Variable	Default	Description
DEERFLOW_URL	http://localhost:2026	Unified proxy base URL
DEERFLOW_GATEWAY_URL	${DEERFLOW_URL}	Gateway API base (models, skills, memory, uploads)
DEERFLOW_LANGGRAPH_URL	${DEERFLOW_URL}/api/langgraph	LangGraph API base (threads, runs)

When making curl calls, always resolve the URL like this:

# Resolve base URLs from env (do this FIRST before any API call)
DEERFLOW_URL="${DEERFLOW_URL:-http://localhost:2026}"
DEERFLOW_GATEWAY_URL="${DEERFLOW_GATEWAY_URL:-$DEERFLOW_URL}"
DEERFLOW_LANGGRAPH_URL="${DEERFLOW_LANGGRAPH_URL:-$DEERFLOW_URL/api/langgraph}"

Available Operations

1. Health Check

Verify DeerFlow is running:

curl -s "$DEERFLOW_GATEWAY_URL/health"

2. Send a Message (Streaming)

This is the primary operation. It creates a thread and streams the agent's response.

Step 1: Create a thread

curl -s -X POST "$DEERFLOW_LANGGRAPH_URL/threads" \
  -H "Content-Type: application/json" \
  -d '{}'

Response: {"thread_id": "<uuid>", ...}

Step 2: Stream a run

curl -s -N -X POST "$DEERFLOW_LANGGRAPH_URL/threads/<thread_id>/runs/stream" \
  -H "Content-Type: application/json" \
  -d '{
    "assistant_id": "lead_agent",
    "input": {
      "messages": [
        {
          "type": "human",
          "content": [{"type": "text", "text": "YOUR MESSAGE HERE"}]
        }
      ]
    },
    "stream_mode": ["values", "messages-tuple"],
    "stream_subgraphs": true,
    "config": {
      "recursion_limit": 1000
    },
    "context": {
      "thinking_enabled": true,
      "is_plan_mode": true,
      "subagent_enabled": true,
      "thread_id": "<thread_id>"
    }
  }'

The response is an SSE stream. Each event has the format:

event: <event_type>
data: <json_data>

Key event types:

• metadata — run metadata including run_id
• values — full state snapshot with messages array
• messages-tuple — incremental message updates (AI text chunks, tool calls, tool results)
• end — stream is complete

Context modes (set via context):

• Flash mode: thinking_enabled: false, is_plan_mode: false, subagent_enabled: false
• Standard mode: thinking_enabled: true, is_plan_mode: false, subagent_enabled: false
• Pro mode: thinking_enabled: true, is_plan_mode: true, subagent_enabled: false
• Ultra mode: thinking_enabled: true, is_plan_mode: true, subagent_enabled: true

3. Continue a Conversation

To send follow-up messages, reuse the same thread_id from step 2 and POST another run with the new message.

4. List Models

curl -s "$DEERFLOW_GATEWAY_URL/api/models"

Returns: {"models": [{"name": "...", "provider": "...", ...}, ...]}

5. List Skills

curl -s "$DEERFLOW_GATEWAY_URL/api/skills"

Returns: {"skills": [{"name": "...", "enabled": true, ...}, ...]}

6. Enable/Disable a Skill

curl -s -X PUT "$DEERFLOW_GATEWAY_URL/api/skills/<skill_name>" \
  -H "Content-Type: application/json" \
  -d '{"enabled": true}'

7. List Agents

curl -s "$DEERFLOW_GATEWAY_URL/api/agents"

Returns: {"agents": [{"name": "...", ...}, ...]}

8. Get Memory

curl -s "$DEERFLOW_GATEWAY_URL/api/memory"

Returns user context, facts, and conversation history summaries.

9. Upload Files to a Thread

curl -s -X POST "$DEERFLOW_GATEWAY_URL/api/threads/<thread_id>/uploads" \
  -F "files=@/path/to/file.pdf"

Supports PDF, PPTX, XLSX, DOCX — automatically converts to Markdown.

10. List Uploaded Files

curl -s "$DEERFLOW_GATEWAY_URL/api/threads/<thread_id>/uploads/list"

11. Get Thread History

curl -s "$DEERFLOW_LANGGRAPH_URL/threads/<thread_id>/history"

12. List Threads

curl -s -X POST "$DEERFLOW_LANGGRAPH_URL/threads/search" \
  -H "Content-Type: application/json" \
  -d '{"limit": 20, "sort_by": "updated_at", "sort_order": "desc"}'

Usage Script

For sending messages and collecting the full response, use the helper script:

bash /path/to/skills/claude-to-deerflow/scripts/chat.sh "Your question here"

See scripts/chat.sh for the implementation. The script:

1. Checks health
2. Creates a thread
3. Streams the run and collects the final AI response
4. Prints the result

Parsing SSE Output

The stream returns SSE events. To extract the final AI response from a values event:

• Look for the last event: values block
• Parse its data JSON
• The messages array contains all messages; the last one with type: "ai" is the response
• The content field of that message is the AI's text reply

Error Handling

• If health check fails, DeerFlow is not running. Inform the user they need to start it.
• If the stream returns an error event, extract and display the error message.
• Common issues: port not open, services still starting up, config errors.

Tips

• For quick questions, use flash mode (fastest, no planning).
• For research tasks, use pro or ultra mode (enables planning and sub-agents).
• You can upload files first, then reference them in your message.
• Thread IDs persist — you can return to a conversation later.