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Claude To Deerflow

  • 3.3k installs
  • 78k repo stars
  • Updated July 27, 2026
  • bytedance/deer-flow

claude-to-deerflow is an agent skill for interacting with DeerFlow research platform via Gateway and LangGraph HTTP APIs.

About

Claude to DeerFlow is an integration skill for calling a running DeerFlow AI agent platform over HTTP. DeerFlow sits behind an Nginx proxy with a Gateway API for models, skills, memory, and uploads and a LangGraph-compatible API for threads and streaming runs. Environment variables DEERFLOW_URL, DEERFLOW_GATEWAY_URL, and DEERFLOW_LANGGRAPH_URL must be resolved before any curl call, defaulting to localhost port 2026. Primary flow creates a thread, then POSTs a streaming run with assistant_id lead_agent, human messages, stream_mode values and messages-tuple, and context flags for thinking, plan mode, and subagents. Modes range from flash without planning to ultra with subagents enabled. Additional operations list models, skills, agents, memory, upload PDF or Office files converted to markdown, fetch thread history, and search threads. A chat.sh helper checks health, streams SSE events, and prints the final AI message. Errors surface when health fails or the instance is not running.

  • Gateway and LangGraph APIs cover health, threads, streaming runs, models, skills, and memory.
  • Streaming run supports flash, standard, pro, and ultra context modes via thinking and subagent flags.
  • File upload converts PDF, PPTX, XLSX, and DOCX to markdown on thread uploads endpoint.
  • chat.sh helper creates thread, streams SSE, and extracts final AI response text.
  • Environment URLs default to localhost:2026 but are fully configurable before requests.

Claude To Deerflow by the numbers

  • 3,258 all-time installs (skills.sh)
  • +96 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Ranked #227 of 16,659 AI & Agent Building skills by installs in the Skillselion catalog
  • Security screen: HIGH risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
At a glance

claude-to-deerflow capabilities & compatibility

Capabilities
health check and thread creation on langgraph ap · sse streaming runs with configurable thinking an · gateway endpoints for models, skills, agents, an · thread file upload with office and pdf to markdo · chat.sh helper for end to end message and respon
Use cases
research · orchestration · web search
Runs
Local or remote
From the docs

What claude-to-deerflow says it does

DeerFlow is an AI agent platform built on LangGraph that orchestrates sub-agents for research
SKILL.md
npx skills add https://github.com/bytedance/deer-flow --skill claude-to-deerflow

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Listed on Skillselion
Installs3.3k
repo stars78k
Security audit2 / 3 scanners passed
Last updatedJuly 27, 2026
Repositorybytedance/deer-flow

How do I delegate deep research or analysis tasks from Claude to a running DeerFlow agent over HTTP?

Send streaming research messages to DeerFlow via LangGraph threads, list models and skills, manage memory, and upload files over HTTP API.

Who is it for?

Developers with a local or hosted DeerFlow instance who want curl or script-based agent delegation from Claude.

Skip if: Skip when DeerFlow is not running or the user needs standalone research without an external agent server.

When should I use this skill?

User mentions deerflow, wants deep research delegated, or needs thread streaming, models, skills, or memory APIs.

What you get

Streaming AI responses from DeerFlow threads with optional file uploads, skill toggles, and conversation history retrieval.

  • Streaming LangGraph responses
  • Thread IDs for follow-up turns

By the numbers

  • Supports 4 DeerFlow modes: flash, standard, pro, and ultra
  • Defaults DEERFLOW_URL to http://localhost:2026

Files

SKILL.mdMarkdownGitHub ↗

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:

ServiceDirect PortVia ProxyPurpose
Gateway API8001$DEERFLOW_GATEWAY_URLREST endpoints and embedded agent runtime
LangGraph-compatible API8001$DEERFLOW_LANGGRAPH_URLAgent threads, runs, streaming

Environment Variables

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

VariableDefaultDescription
DEERFLOW_URLhttp://localhost:2026Unified proxy base URL
DEERFLOW_GATEWAY_URL${DEERFLOW_URL}Gateway API base (models, skills, memory, uploads)
DEERFLOW_LANGGRAPH_URL${DEERFLOW_URL}/api/langgraphLangGraph 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.

Related skills

How it compares

Pick claude-to-deerflow when DeerFlow is already running locally and you need threaded LangGraph streaming from Claude rather than a generic HTTP client skill.

FAQ

What URL env vars must be set?

DEERFLOW_URL defaults to http://localhost:2026 with DEERFLOW_GATEWAY_URL and DEERFLOW_LANGGRAPH_URL derived unless overridden.

Which mode enables sub-agents?

Ultra mode sets thinking_enabled, is_plan_mode, and subagent_enabled all true; flash disables all three.

What file types can upload to a thread?

PDF, PPTX, XLSX, and DOCX upload via multipart form and convert automatically to markdown.

Is Claude To Deerflow safe to install?

skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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