claude-to-deerflow

Files (3)

• SKILL.md
• chat.sh · scripts/
• status.sh · scripts/

SKILL.md (first lines)

# 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        | `$DEERFLOW_GATEWAY_URL`          | REST endpoints and embedded agent runtime |
| LangGraph-compatible API | 8001 | `$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:

```bash
# 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:

```bash
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**

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

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

**Step 2: Stream a run**

```bash
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

```bash
curl -s "$DEERFLOW_GATEWAY_URL/api/models"
```

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

### 5. List Skills

From Zephyrsz/deer-flow, MIT.