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API Reference

This document provides a complete reference for the DeerFlow backend APIs.

Overview

DeerFlow backend exposes two sets of APIs:

LangGraph-compatible API - Agent interactions, threads, and streaming (/api/langgraph/*)
Gateway API - Models, MCP, skills, uploads, and artifacts (/api/*)

All APIs are accessed through the Nginx reverse proxy at port 2026.

For agent conversations, clients can either pre-create a thread (POST /api/langgraph/threads) or start immediately with the stateless stream endpoint (POST /api/langgraph/runs/stream). The latter auto-creates a thread and returns thread_id and run_id in the response Content-Location header.

LangGraph-compatible API

Base URL: /api/langgraph

The public LangGraph-compatible API follows LangGraph SDK conventions. In the unified nginx deployment, Gateway owns /api/langgraph/* and translates those paths to its native /api/* run, thread, and streaming routers.

Threads

Create Thread

POST /api/langgraph/threads
Content-Type: application/json

Request Body:

{
  "metadata": {}
}

Response:

{
  "thread_id": "abc123",
  "created_at": "2024-01-15T10:30:00Z",
  "metadata": {}
}

Get Thread State

GET /api/langgraph/threads/{thread_id}/state

Response:

{
  "values": {
    "messages": [...],
    "sandbox": {...},
    "artifacts": [...],
    "thread_data": {...},
    "title": "Conversation Title"
  },
  "next": [],
  "config": {...}
}

Runs

Create Run

Execute the agent with input.

POST /api/langgraph/threads/{thread_id}/runs
Content-Type: application/json

Request Body:

{
  "input": {
    "messages": [
      {
        "role": "user",
        "content": "Hello, can you help me?"
      }
    ]
  },
  "config": {
    "recursion_limit": 100,
    "configurable": {
      "model_name": "gpt-4",
      "thinking_enabled": false,
      "is_plan_mode": false
    }
  },
  "stream_mode": ["values", "messages-tuple", "custom"]
}

Stream Mode Compatibility:

Use: values, messages-tuple, custom, updates, debug, tasks, checkpoints
Unsupported modes, including messages, events, and tools, return 422 before a run is created. DeerFlow never substitutes values for an unsupported mode.

Run Option Compatibility:

Supported concurrency strategies: reject, rollback, and interrupt
Compatibility default: if_not_exists="create"; this matches DeerFlow's current behavior
Artifact delivery is enforced automatically when a run creates or modifies regular files under /mnt/user-data/outputs. present_files must present at least one path produced by the current run (or a directory containing it), and the terminal receipt must be persisted; presenting only an unrelated file does not satisfy delivery. Runs without changed outputs retain ordinary conversational behavior. artifact_delivery is not a client-settable run option.
Unsupported options return 422: webhook, stream_resumable=true, after_seconds, feedback_keys, any non-null on_completion value (including the SDK values "complete" and "continue"), if_not_exists="reject", and multitask_strategy="enqueue"
stream_resumable=false is accepted: it is the LangGraph SDK's default and requests the non-resumable stream DeerFlow already serves
Undeclared SDK options, including checkpoint_during and durability, also return 422 instead of being silently discarded

When outputs changed during the run, run.delivery events retain the Slice 1 facts (presented, paths, and by_tool) and add produced_paths, presented_paths, matched_paths, plus an explicit verdict: verification, stage (presented, mismatched, or not_started), and satisfied. Receipts for runs without changed outputs keep their existing shape.

Recursion Limit:

config.recursion_limit caps the number of graph steps LangGraph will execute in a single run. The unified Gateway path defaults to 100 in build_run_config (see backend/app/gateway/services.py), which is a safer starting point for plan-mode or subagent-heavy runs. Clients can still set recursion_limit explicitly in the request body; increase it if you run deeply nested subagent graphs. For safety, the Gateway clamps any client-supplied value to a configurable server ceiling (max_recursion_limit in config.yaml, default 1000) so a single run cannot execute unbounded graph steps (runaway LLM cost / DoS); invalid or non-positive values fall back to the 100 default.

Configurable Options:

model_name (string): Override the default model
thinking_enabled (boolean): Enable extended thinking for supported models
is_plan_mode (boolean): Enable TodoList middleware for task tracking

Response: Server-Sent Events (SSE) stream

event: values
data: {"messages": [...], "title": "..."}

event: messages
data: {"content": "Hello! I'd be happy to help.", "role": "assistant"}

event: end
data: {}

Get Run History

GET /api/langgraph/threads/{thread_id}/runs

Response:

{
  "runs": [
    {
      "run_id": "run123",
      "status": "success",
      "created_at": "2024-01-15T10:30:00Z"
    }
  ]
}

Stream Run

Stream responses in real-time.

POST /api/langgraph/threads/{thread_id}/runs/stream
Content-Type: application/json

Same request body as Create Run. Returns SSE stream.

Stateless Stream Run

Start a conversation without creating a thread first. Gateway auto-creates a thread when config.configurable.thread_id is omitted, and returns both identifiers in the response Content-Location header.

POST /api/langgraph/runs/stream
Content-Type: application/json
Accept: text/event-stream

Through Nginx, /api/langgraph/runs/stream is rewritten to the native Gateway path POST /api/runs/stream.

Request Body: Same as Create Run. Omit thread_id to start a new conversation; include it to continue an existing one:

{
  "input": {
    "messages": [
      {
        "role": "user",
        "content": "Hello, can you help me?"
      }
    ]
  },
  "config": {
    "recursion_limit": 100,
    "configurable": {
      "model_name": "gpt-4",
      "thinking_enabled": false,
      "is_plan_mode": false
    }
  },
  "stream_mode": ["values", "messages-tuple", "custom"]
}

Response: Server-Sent Events (SSE) stream with a Content-Location header:

Content-Location: /api/threads/{thread_id}/runs/{run_id}

Clients should parse thread_id and run_id from this header (the path ends with /runs/{run_id}). Persist thread_id and send it back on the next turn via config.configurable.thread_id to keep conversation history.

Continuing a conversation:

{
  "input": {
    "messages": [
      {
        "role": "user",
        "content": "What did I just ask?"
      }
    ]
  },
  "config": {
    "configurable": {
      "thread_id": "abc123",
      "model_name": "gpt-4"
    }
  },
  "stream_mode": ["values", "messages-tuple", "custom"]
}

Gateway API

Base URL: /api

Models

List Models

Get all available LLM models from configuration.

GET /api/models

Response:

{
  "models": [
    {
      "name": "gpt-4",
      "display_name": "GPT-4",
      "supports_thinking": false,
      "supports_vision": true
    },
    {
      "name": "claude-3-opus",
      "display_name": "Claude 3 Opus",
      "supports_thinking": false,
      "supports_vision": true
    },
    {
      "name": "deepseek-v3",
      "display_name": "DeepSeek V3",
      "supports_thinking": true,
      "supports_vision": false
    }
  ]
}

Get Model Details

GET /api/models/{model_name}

Response:

{
  "name": "gpt-4",
  "display_name": "GPT-4",
  "model": "gpt-4",
  "max_tokens": 4096,
  "supports_thinking": false,
  "supports_vision": true
}

MCP Configuration

Get MCP Config

Get current MCP server configurations.

GET /api/mcp/config

Requires an authenticated admin session. Sensitive env/header/OAuth secret values are masked in the response.

Response:

{
  "mcp_servers": {
    "github": {
      "enabled": true,
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "***"
      },
      "description": "GitHub operations"
    }
  }
}

Update MCP Config

Update MCP server configurations.

PUT /api/mcp/config
Content-Type: application/json

Requires an authenticated admin session. API-managed stdio MCP servers may only use allowed executable names for command (default: npx, uvx). Set DEER_FLOW_MCP_STDIO_COMMAND_ALLOWLIST to a comma-separated list when a deployment needs additional trusted launchers.

Request Body:

{
  "mcp_servers": {
    "github": {
      "enabled": true,
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "$GITHUB_TOKEN"
      },
      "description": "GitHub operations"
    }
  }
}

Response:

{
  "mcp_servers": {
    "github": {
      "enabled": true,
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "***"
      },
      "description": "GitHub operations"
    }
  }
}

Update One MCP Server State

Enable or disable one configured MCP server without replacing the full extensions configuration.

PATCH /api/mcp/config
Content-Type: application/json

Requires an authenticated admin session. Enabling a stdio server validates that server's command against the same allowlist used by the full PUT endpoint. Disabling a server does not require its command to be allowlisted, and invalid commands on other servers do not block the update. The endpoint preserves secrets, environment-variable placeholders, skills, custom server fields, and other top-level extensions config. SSE/HTTP targets may use either DeerFlow's type field or the MCP-spec transport field.

Request Body:

{
  "server_name": "semantic-scholar",
  "enabled": false
}

The response is the full masked MCP configuration, matching GET and PUT. An unknown server_name returns 404; attempting to enable a server with a disallowed stdio command returns 400.

Reset MCP Tools Cache

Clear cached MCP tools and persistent MCP sessions process-wide. This affects all threads and users in the current Gateway process. Tools are loaded again from configured MCP servers on the next agent run or tool lookup.

POST /api/mcp/cache/reset

Requires an authenticated admin session.

Response:

{
  "success": true,
  "message": "MCP tools cache reset. Tools will reload on next use."
}

Skills

List Skills

Get all available skills.

GET /api/skills

Response:

{
  "skills": [
    {
      "name": "pdf-processing",
      "display_name": "PDF Processing",
      "description": "Handle PDF documents efficiently",
      "enabled": true,
      "license": "MIT",
      "path": "public/pdf-processing"
    },
    {
      "name": "frontend-design",
      "display_name": "Frontend Design",
      "description": "Design and build frontend interfaces",
      "enabled": false,
      "license": "MIT",
      "path": "public/frontend-design"
    }
  ]
}

Get Skill Details

GET /api/skills/{skill_name}

Response:

{
  "name": "pdf-processing",
  "display_name": "PDF Processing",
  "description": "Handle PDF documents efficiently",
  "enabled": true,
  "license": "MIT",
  "path": "public/pdf-processing",
  "allowed_tools": ["read_file", "write_file", "bash"],
  "content": "# PDF Processing\n\nInstructions for the agent..."
}

Enable Skill

POST /api/skills/{skill_name}/enable

Response:

{
  "success": true,
  "message": "Skill 'pdf-processing' enabled"
}

Disable Skill

POST /api/skills/{skill_name}/disable

Response:

{
  "success": true,
  "message": "Skill 'pdf-processing' disabled"
}

Install Skill

Install a skill from a .skill file.

POST /api/skills/install
Content-Type: multipart/form-data

Request Body:

file: The .skill file to install

Response:

{
  "success": true,
  "message": "Skill 'my-skill' installed successfully",
  "skill": {
    "name": "my-skill",
    "display_name": "My Skill",
    "path": "custom/my-skill"
  }
}

Reload Skills

Invalidate the skill prompt caches for every user in the current Gateway process. Subsequent runs rescan the configured public, custom, and legacy skill directories; runs that have already started keep their existing skill snapshot.

POST /api/skills/reload

The request has no body and requires an authenticated administrator. For a cookie-authenticated request, send the CSRF cookie value in the matching header:

curl -X POST http://localhost:2026/api/skills/reload \
  -b cookies.txt \
  -H "X-CSRF-Token: <csrf_token-cookie-value>"

Response:

{
  "success": true,
  "scope": "process",
  "message": "Skill caches invalidated; subsequent runs in this Gateway process will rescan the latest skills."
}

success confirms cache invalidation, not that every file on disk was valid: malformed skills retain the existing parser behavior of being skipped and logged. The endpoint returns 401 for unauthenticated callers, 403 for non-admin users, and a generic 500 if the invalidation mechanism itself fails or the process-local background scan does not finish within the cache refresh timeout. A loader-level failure, such as an unavailable mounted root, does not publish an empty catalog: the last successfully loaded process cache remains available. A timed-out scan continues in its daemon worker and can still populate the process cache when it finishes.

The scope is deliberately process-local. Each Uvicorn worker or Kubernetes Pod must be called directly; repeated requests through a load-balanced Service do not guarantee that every instance is reached. External MinIO/NFS/CSI writes bypass the validation, SkillScan, and history used by the install/edit APIs, so the mounted directory must be writable only by trusted operators.

File Uploads

Upload Files

Upload one or more files to a thread.

POST /api/threads/{thread_id}/uploads
Content-Type: multipart/form-data

Request Body:

files: One or more files to upload

Response:

{
  "success": true,
  "files": [
    {
      "filename": "document.pdf",
      "size": 1234567,
      "path": ".deer-flow/threads/abc123/user-data/uploads/document.pdf",
      "virtual_path": "/mnt/user-data/uploads/document.pdf",
      "artifact_url": "/api/threads/abc123/artifacts/mnt/user-data/uploads/document.pdf",
      "markdown_file": "document.pdf.md",
      "markdown_path": ".deer-flow/threads/abc123/user-data/.upload-conversions/document.pdf.md",
      "markdown_virtual_path": "/mnt/user-data/.upload-conversions/document.pdf.md",
      "markdown_artifact_url": "/api/threads/abc123/artifacts/mnt/user-data/.upload-conversions/document.pdf.md"
    }
  ],
  "message": "Successfully uploaded 1 file(s)"
}

Supported Document Formats (auto-converted to Markdown):

PDF (.pdf)
PowerPoint (.ppt, .pptx)
Excel (.xls, .xlsx)
Word (.doc, .docx)

All upload entry points publish complete payloads without replacing an existing name. Concurrent collisions are returned as document.pdf, document_1.pdf, document_2.pdf, and so on. A published filename remains leased through conversion, permission adjustment, sandbox synchronization, and response construction; deletion of that exact filename waits for the active lifecycle, while other filenames remain independent. Basenames matching the internal .upload-*.part staging pattern are rejected.

Generated Markdown is stored outside the primary namespace and is not returned by the list endpoint. Normal conversion names are <actual-primary-filename>.md; if that component would exceed 255 UTF-8 bytes, the response contains a deterministic UTF-8-safe prefix plus the full SHA-256 digest and .md. Clients must consume the returned markdown_* fields rather than derive the path. Local and AIO sandboxes mount .upload-conversions read-only. Deleting document.pdf also deletes only its exact generated conversion; an independent uploads/document.md is preserved.

List Uploaded Files

GET /api/threads/{thread_id}/uploads/list

Response:

{
  "files": [
    {
      "filename": "document.pdf",
      "size": 1234567,
      "path": ".deer-flow/threads/abc123/user-data/uploads/document.pdf",
      "virtual_path": "/mnt/user-data/uploads/document.pdf",
      "artifact_url": "/api/threads/abc123/artifacts/mnt/user-data/uploads/document.pdf",
      "extension": ".pdf",
      "modified": 1705997600.0
    }
  ],
  "count": 1
}

Delete File

DELETE /api/threads/{thread_id}/uploads/{filename}

Response:

{
  "success": true,
  "message": "Deleted document.pdf"
}

If an upload, conversion, or sandbox synchronization still owns this exact filename, the delete waits for that lifecycle to finish before removing the primary and its generated conversion. Work on unrelated filenames is not serialized.

Thread Cleanup

Remove DeerFlow-managed local thread files under .deer-flow/threads/{thread_id} after the LangGraph thread itself has been deleted.

DELETE /api/threads/{thread_id}

Response:

{
  "success": true,
  "message": "Deleted local thread data for abc123"
}

Error behavior:

422 for invalid thread IDs
500 returns a generic {"detail": "Failed to delete local thread data."} response while full exception details stay in server logs

Artifacts

Get Artifact

Download or view an artifact generated by the agent.

GET /api/threads/{thread_id}/artifacts/{path}

Path Examples:

/api/threads/abc123/artifacts/mnt/user-data/outputs/result.txt
/api/threads/abc123/artifacts/mnt/user-data/uploads/document.pdf

Query Parameters:

download (boolean): If true, force download with Content-Disposition header

Response: File content with appropriate Content-Type

Error Responses

All APIs return errors in a consistent format:

{
  "detail": "Error message describing what went wrong"
}

HTTP Status Codes:

400 - Bad Request: Invalid input
404 - Not Found: Resource not found
422 - Validation Error: Request validation failed
500 - Internal Server Error: Server-side error

Authentication

DeerFlow supports four HTTP identity sources. They share the same thread/run isolation rules but differ in whether a row is created in users and how external identities are mapped. See AUTH_DESIGN.md for the full design.

Model	Entry	`users` table	Isolation key
Browser session	`access_token` cookie after login/register	Yes	`users.id`
OIDC / SSO	OAuth callback → cookie	Yes	`users.id` (see SSO.md)
IM channel binding	Connect code + `channel_connections`	Bound to registered user	`channel_connections.owner_user_id`
Internal Auth	`X-DeerFlow-Internal-Token` + `X-DeerFlow-Owner-User-Id`	No	Owner string on `threads_meta.user_id`

IM channel binding and Internal Auth are both platform-trust integrations: DeerFlow trusts the channel/platform to authenticate end users. IM bindings persist the mapping in channel_connections / channel_conversations and require a DeerFlow users row. Internal Auth lets a platform call the Gateway API directly with a deployment-shared token and a per-request owner header—no users row, but thread/run/checkpoint isolation works the same way.

Browser session (default)

DeerFlow enforces authentication for all non-public HTTP routes. Public routes are limited to health/docs metadata and these public auth endpoints:

POST /api/v1/auth/initialize creates the first admin account when no admin exists.
POST /api/v1/auth/login/local logs in with email/password and sets an HttpOnly access_token cookie.
POST /api/v1/auth/register creates a regular user account and sets the session cookie.
POST /api/v1/auth/logout clears the session cookie.
GET /api/v1/auth/setup-status reports whether the first admin still needs to be created.

The authenticated auth endpoints are:

GET /api/v1/auth/me returns the current user.
POST /api/v1/auth/change-password changes password, optionally changes email during setup, increments token_version, and reissues the cookie.

Protected state-changing requests also require the CSRF double-submit token: send the csrf_token cookie value as the X-CSRF-Token header. Login/register/initialize/logout are bootstrap auth endpoints: they are exempt from the double-submit token but still reject hostile browser Origin headers.

User isolation is enforced from the authenticated user context:

Thread metadata is scoped by threads_meta.user_id; search/read/write/delete APIs only expose the current user's threads.
Thread files live under {base_dir}/users/{user_id}/threads/{thread_id}/user-data/ and are exposed inside the sandbox as /mnt/user-data/.
Memory and custom agents are stored under {base_dir}/users/{user_id}/....

Note: MCP outbound connections can still use OAuth for configured HTTP/SSE MCP servers; that is separate from DeerFlow API authentication.

Internal Auth (platform HTTP integration)

For server-to-server integrations (e.g. a Feishu or WeCom/Enterprise WeChat bot backend), configure:

export DEER_FLOW_INTERNAL_AUTH_TOKEN="<long-random-secret>"

Header	Required	Description
`X-DeerFlow-Internal-Token`	Yes	Must match `DEER_FLOW_INTERNAL_AUTH_TOKEN`; missing/invalid → `401`
`X-DeerFlow-Owner-User-Id`	Yes for per-user isolation	Platform user id (e.g. `feishu_ou_alice`, `wecom_user_bob`); omit → `default` bucket

Does not use browser cookies or CSRF tokens. Does not insert into users; sets threads_meta.user_id / runs.user_id from the owner header. DeerFlow validates only the platform token—not whether the owner id represents a real end user; user validity is entirely the platform's responsibility. See AUTH_DESIGN.md — Internal Auth for trust boundaries, persistence, and security notes.

Use the standard Gateway thread/run endpoints (POST /api/threads, POST /api/threads/{thread_id}/runs/stream, etc.) with the headers above on every request.

Rate Limiting

No rate limiting is implemented by default. For production deployments, configure rate limiting in Nginx:

limit_req_zone $binary_remote_addr zone=api:10m rate=10r/s;

location /api/ {
    limit_req zone=api burst=20 nodelay;
    proxy_pass http://backend;
}

Streaming Support

Gateway's LangGraph-compatible API streams run events with Server-Sent Events (SSE).

Thread-scoped streaming (thread must exist):

POST /api/langgraph/threads/{thread_id}/runs/stream
Accept: text/event-stream

Stateless streaming (no pre-created thread; Gateway auto-creates one):

POST /api/langgraph/runs/stream
Accept: text/event-stream

Both endpoints return Content-Location: /api/threads/{thread_id}/runs/{run_id}. The DeerFlow web UI and LangGraph SDK clients rely on this header to discover the assigned thread_id and run_id on the first message of a new chat.

SSE replay retention and gaps

Clients may reconnect to a run stream with Last-Event-ID. Replay history is bounded by stream_bridge.queue_maxsize (default 256) and, for Redis, by the rolling stream_ttl_seconds. A retained cursor resumes after that event with no additional control frame.

When a syntactically valid cursor is older than the retained watermark, the server sends exactly one gap event before any retained data and closes that subscription without an end event:

event: gap
data: {"code":"stream_replay_gap","run_id":"run-123","requested_event_id":"1718000000000-1","earliest_available_event_id":"1718000000100-42","latest_available_event_id":"1718000000200-84","recovery":"reload_durable_state"}

The frame deliberately has no SSE id:. Consumers must reload durable thread state and persisted run events/messages, then may reconnect from latest_available_event_id to follow newer live events. A gap does not cancel the active run. The same signal applies when a no-cursor subscriber has already established an empty-stream wait but the first Redis wake-up falls behind before delivery; in that case requested_event_id is null. Malformed cursor handling is backend-specific and is not the same as a valid cursor that was evicted.

SDK Usage

Python (LangGraph SDK)

from langgraph_sdk import get_client

client = get_client(url="http://localhost:2026/api/langgraph")
run_meta: dict[str, str] = {}


def on_run_created(meta) -> None:
    # langgraph-sdk 0.3.x parses Content-Location only when this callback is set.
    if meta.thread_id:
        run_meta["thread_id"] = meta.thread_id
    run_meta["run_id"] = meta.run_id


# Option A: stateless stream — no thread pre-creation
# Gateway auto-creates a thread and returns thread_id/run_id in Content-Location.
async for event in client.runs.stream(
    None,
    "lead_agent",
    input={"messages": [{"role": "user", "content": "Hello"}]},
    config={"configurable": {"model_name": "gpt-4"}},
    stream_mode=["values", "messages-tuple", "custom"],
    on_run_created=on_run_created,
):
    print(event)

thread_id = run_meta["thread_id"]  # persist before the next turn

# Option A (continued): same thread on the next turn
async for event in client.runs.stream(
    None,
    "lead_agent",
    input={"messages": [{"role": "user", "content": "What did I just ask?"}]},
    config={"configurable": {"thread_id": thread_id, "model_name": "gpt-4"}},
    stream_mode=["values", "messages-tuple", "custom"],
    on_run_created=on_run_created,
):
    print(event)

# Option B: thread-scoped stream — create thread first, then stream
thread = await client.threads.create()
async for event in client.runs.stream(
    thread["thread_id"],
    "lead_agent",
    input={"messages": [{"role": "user", "content": "Hello"}]},
    config={"configurable": {"model_name": "gpt-4"}},
    stream_mode=["values", "messages-tuple", "custom"],
    on_run_created=on_run_created,
):
    print(event)

JavaScript/TypeScript

// Using fetch for Gateway API
const response = await fetch('/api/models');
const data = await response.json();
console.log(data.models);

function parseRunLocation(contentLocation: string | null) {
  if (!contentLocation) return null;
  const match = /\/threads\/([^/]+)\/runs\/([^/]+)/.exec(contentLocation);
  if (!match) return null;
  return { threadId: match[1], runId: match[2] };
}

// Option A: stateless stream — no thread pre-creation
let threadId: string | undefined;
const firstResponse = await fetch("/api/langgraph/runs/stream", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    Accept: "text/event-stream",
  },
  body: JSON.stringify({
    input: { messages: [{ role: "user", content: "Hello" }] },
    stream_mode: ["values", "messages-tuple", "custom"],
  }),
});

const created = parseRunLocation(firstResponse.headers.get("Content-Location"));
threadId = created?.threadId;
console.log("thread_id:", created?.threadId, "run_id:", created?.runId);

// Option B: continue the same thread on the next turn
const followUpResponse = await fetch("/api/langgraph/runs/stream", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    Accept: "text/event-stream",
  },
  body: JSON.stringify({
    input: { messages: [{ role: "user", content: "What did I just ask?" }] },
    config: { configurable: { thread_id: threadId } },
    stream_mode: ["values", "messages-tuple", "custom"],
  }),
});

// Option C: thread-scoped stream when you already have a thread_id
const streamResponse = await fetch(`/api/langgraph/threads/${threadId}/runs/stream`, {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    Accept: "text/event-stream",
  },
  body: JSON.stringify({
    input: { messages: [{ role: "user", content: "Hello" }] },
    stream_mode: ["values", "messages-tuple", "custom"],
  }),
});

const reader = streamResponse.body?.getReader();
// Decode and parse SSE frames from reader in your client code.

cURL Examples

# List models
curl http://localhost:2026/api/models

# Get MCP config
curl http://localhost:2026/api/mcp/config

# Upload file
curl -X POST http://localhost:2026/api/threads/abc123/uploads \
  -F "files=@document.pdf"

# Enable skill
curl -X POST http://localhost:2026/api/skills/pdf-processing/enable

# Stateless stream — no thread pre-creation
curl -s -D - -N -X POST http://localhost:2026/api/langgraph/runs/stream \
  -H "Content-Type: application/json" \
  -H "Accept: text/event-stream" \
  -d '{
    "input": {"messages": [{"role": "user", "content": "Hello"}]},
    "config": {
      "recursion_limit": 100,
      "configurable": {"model_name": "gpt-4"}
    },
    "stream_mode": ["values", "messages-tuple", "custom"]
  }'
# Read Content-Location: /api/threads/{thread_id}/runs/{run_id} from the headers.

# Continue the same thread on the next turn
curl -s -N -X POST http://localhost:2026/api/langgraph/runs/stream \
  -H "Content-Type: application/json" \
  -H "Accept: text/event-stream" \
  -d '{
    "input": {"messages": [{"role": "user", "content": "What did I just ask?"}]},
    "config": {
      "configurable": {"thread_id": "abc123", "model_name": "gpt-4"}
    },
    "stream_mode": ["values", "messages-tuple", "custom"]
  }'

# Thread-scoped flow — create thread first, then stream
curl -X POST http://localhost:2026/api/langgraph/threads \
  -H "Content-Type: application/json" \
  -d '{}'

curl -X POST http://localhost:2026/api/langgraph/threads/abc123/runs/stream \
  -H "Content-Type: application/json" \
  -H "Accept: text/event-stream" \
  -d '{
    "input": {"messages": [{"role": "user", "content": "Hello"}]},
    "config": {
      "recursion_limit": 100,
      "configurable": {"model_name": "gpt-4"}
    },
    "stream_mode": ["values", "messages-tuple", "custom"]
  }'

The unified Gateway path defaults config.recursion_limit to 100 for plan-mode and subagent-heavy runs. Clients may still set config.recursion_limit explicitly — see the Create Run section for details.

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API Reference

Overview

LangGraph-compatible API

Threads

Create Thread

Get Thread State

Runs

Create Run

Get Run History

Stream Run

Stateless Stream Run

Gateway API

Models

List Models

Get Model Details

MCP Configuration

Get MCP Config

Update MCP Config

Update One MCP Server State

Reset MCP Tools Cache

Skills

List Skills

Get Skill Details

Enable Skill

Disable Skill

Install Skill

Reload Skills

File Uploads

Upload Files

List Uploaded Files

Delete File

Thread Cleanup

Artifacts

Get Artifact

Error Responses

Authentication

Browser session (default)

Internal Auth (platform HTTP integration)

Rate Limiting

Streaming Support

SSE replay retention and gaps

SDK Usage

Python (LangGraph SDK)

JavaScript/TypeScript

cURL Examples

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