export LANGSMITH_API_KEY="<LANGSMITH_API_KEY>"
export DEEPAGENTS_BASE_URL="https://api.smith.langchain.com/v1/deepagents"

X-Api-Key: <LANGSMITH_API_KEY>

1. Register MCP server(s) →  POST /v1/deepagents/mcp-servers
2. Create the agent       →  POST /v1/deepagents/agents
3. Create a thread        →  POST /v1/deepagents/threads
4. Stream a run           →  POST /v1/deepagents/threads/{thread_id}/runs/stream
5. Inspect in LangSmith   →  Traces UI

{
  "tools": [
    {
      "name": "tavily-search",
      "mcp_server_url": "https://mcp.tavily.com/mcp/",
      "mcp_server_name": "tavily",
      "display_name": "tavily-search"
    }
  ],
  "interrupt_config": {
    "https://mcp.tavily.com/mcp/::tavily-search::tavily": false
  }
}

import os, httpx

BASE_URL = os.environ["DEEPAGENTS_BASE_URL"]
HEADERS = {"X-Api-Key": os.environ["LANGSMITH_API_KEY"]}

response = httpx.post(
    f"{BASE_URL}/mcp-servers",
    headers=HEADERS,
    json={
        "name": "tavily",
        "url": "https://mcp.tavily.com/mcp/",
        "headers": [
            {"key": "Authorization", "value": "Bearer tvly-..."},
        ],
    },
)
response.raise_for_status()
mcp_server_id = response.json()["id"]

response = httpx.post(
    f"{BASE_URL}/agents",
    headers=HEADERS,
    json={
        "name": "research-assistant",
        "description": "Research assistant that can search the web and summarize sources.",
        "runtime": {"model": {"model_id": "anthropic:claude-sonnet-4-6"}},
        "instructions": (
            "You are a careful research assistant. Search for sources, "
            "keep notes, and return concise answers with citations."
        ),
        "tools": {
            "tools": [
                {
                    "name": "tavily-search",
                    "mcp_server_url": "https://mcp.tavily.com/mcp/",
                    "mcp_server_name": "tavily",
                    "display_name": "tavily-search",
                },
            ],
            "interrupt_config": {
                "https://mcp.tavily.com/mcp/::tavily-search::tavily": False,
            },
        },
    },
)
response.raise_for_status()
agent_id = response.json()["id"]

response = httpx.post(
    f"{BASE_URL}/threads",
    headers=HEADERS,
    json={
        "agent_id": agent_id,
        "options": {
            "test_run": False,
            "skip_memory_write_protection": False,
        },
    },
)
response.raise_for_status()
thread_id = response.json()["id"]

payload = {
    "agent_id": agent_id,
    "messages": [
        {
            "role": "user",
            "content": "Research recent approaches to agent memory and summarize the main tradeoffs.",
        }
    ],
    "stream_mode": ["values", "updates", "messages-tuple"],
    "stream_subgraphs": True,
    "user_timezone": "America/Los_Angeles",
}

with httpx.stream(
    "POST",
    f"{BASE_URL}/threads/{thread_id}/runs/stream",
    headers={**HEADERS, "Accept": "text/event-stream"},
    json=payload,
    timeout=None,
) as response:
    response.raise_for_status()
    for line in response.iter_lines():
        if line:
            print(line)

const BASE_URL = process.env.DEEPAGENTS_BASE_URL;
const HEADERS = {
  "X-Api-Key": process.env.LANGSMITH_API_KEY,
  "Content-Type": "application/json",
};

const response = await fetch(`${BASE_URL}/agents`, {
  method: "POST",
  headers: HEADERS,
  body: JSON.stringify({ /* same payload */ }),
});
if (!response.ok) throw new Error(await response.text());
const { id: agentId } = await response.json();