Migrating from LangChain

This guide walks through migrating an existing raw LangChain agent onto SwarmD using the swarmd-langchain package. By the end your agent’s main.py contains only domain logic — the tools, the prompt, and the model choice — and swarmd-langchain owns every platform concern. If you have not read LangChain on SwarmD yet, do that first. It explains what each of create_runtime, create_llm_agent, and serve actually does. This guide focuses on the migration journey — what to delete, what to replace, and how to roll it out — rather than re-explaining the helpers. Each step shows the before (raw LangChain) and the after (SwarmD) side by side. The shape mirrors Migrating from Google ADK on purpose — the helper trio is the same.

What you are migrating away from

A typical standalone LangChain agent — one file owning everything: the LangChain tool functions, hand-rolled HTTP tools that POST to other agents directly, the LLM, and (if it serves over A2A at all) a custom Starlette adapter.

main.py

import os

import httpx
from dotenv import load_dotenv
from langchain.agents import create_agent
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI

load_dotenv()


@tool
def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"The weather in {city} is sunny"


# Every remote agent is a hand-rolled HTTP tool — URL baked into source
@tool
def call_time_agent(prompt: str) -> str:
    """Ask the time agent for time information."""
    r = httpx.post(
        "https://time-agent.internal:8080/messages",
        json={"prompt": prompt},
        timeout=60,
    )
    return r.text


@tool
def call_calendar_agent(prompt: str) -> str:
    """Ask the calendar agent to manage events."""
    r = httpx.post(
        "https://calendar-agent.internal:8080/messages",
        json={"prompt": prompt},
        timeout=60,
    )
    return r.text


agent = create_agent(
    model=ChatOpenAI(model="gpt-4o", api_key=os.environ["OPENAI_API_KEY"]),
    tools=[get_weather, call_time_agent, call_calendar_agent],
    system_prompt="You are a helpful weather agent.",
)

# ...plus whatever Starlette / FastAPI you wrote to expose this over HTTP

Pain points that compound the moment this leaves your laptop:

Sub-agent URLs and request shapes are baked into source. Each call_*_agent tool reinvents request/response, error handling, and retries.
No A2A protocol. Your “tools” call arbitrary endpoints — the downstream might be A2A, REST, or something else, and your code has to know.
No polling for long-running tasks. If the downstream returns a working task, your tool either blocks forever or returns a useless intermediate payload.
No identity between agents.
No central audit, policy, or HITL — the relay isn’t in the path.
MCP tools aren’t there at all.

The goal of the migration is to keep the tool function (get_weather) and the agent’s identity (system prompt, model) exactly as they are while deleting every call_*_agent tool and the bespoke Starlette / FastAPI wrapper.

What you are migrating to

main.py

from dotenv import load_dotenv
load_dotenv()

from langchain_core.tools import tool

from swarmd_langchain import create_llm_agent, create_runtime, serve


@tool
def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"The weather in {city} is sunny"


runtime = create_runtime()
agent = create_llm_agent(
    runtime,
    name="weather_agent",
    description="A weather agent that collaborates with other agents",
    instruction="You are a helpful weather agent.",
    tools=[get_weather],
)

if __name__ == "__main__":
    serve(agent, runtime)

That’s the whole file. For what each helper does under the hood, see LangChain on SwarmD.

Migration steps

Step 1 — Register the agent

In the dashboard (or via POST /registry/v1/agents), register weather_agent. Capture the three secrets shown once in the response: agentId, clientSecret, and webhookSecret. Store them with your deployment configuration. After this step: the platform knows your agent exists. You have the OAuth2 credentials it will use for outbound calls.

Step 2 — Install the SDK

pip install swarmd-langchain

You can keep your existing LangChain pins — swarmd-langchain declares compatible ranges, not exact versions. After this step: from swarmd_langchain import create_runtime, create_llm_agent, serve resolves.

Step 3 — Add platform credentials to `.env`

Append to your existing .env:

.env

SWARMD_AGENT_ID=00000000-0000-0000-0000-000000000000
SWARMD_CLIENT_SECRET=...
SWARMD_WEBHOOK_SECRET=...
SWARMD_BASE_URL=https://api.swarmd.ai
SWARMD_TOKEN_URL=https://auth.swarmd.ai/realms/swarmd/protocol/openid-connect/token

After this step: with these vars set, create_runtime() will attach the agent to SwarmD. With them unset, the agent still runs locally as a plain LangChain agent against direct OpenAI — useful for local dev without a SwarmD account.

Step 4 — Replace bootstrap with `create_runtime()`

# Before — load .env, then everything below is hand-wired
load_dotenv()

# After
load_dotenv()
from swarmd_langchain import create_runtime
runtime = create_runtime()

After this step: the runtime owns auth, token caching, registry calls, MCP discovery, and correlation-ID propagation. You hold one reference to runtime and pass it into the next two helpers.

Step 5 — Delete the hand-rolled HTTP tools

# Before — each remote agent is its own @tool with hardcoded URL
@tool
def call_time_agent(prompt: str) -> str:
    ...
@tool
def call_calendar_agent(prompt: str) -> str:
    ...

tools = [get_weather, call_time_agent, call_calendar_agent]

# After — keep local tools only; remote agents arrive via discovery
tools = [get_weather]

You don’t import httpx, you don’t write retry logic, and you don’t type the downstream URL anywhere in source. After this step: half the surface area of main.py disappears. This is the highest-leverage change in the migration.

Step 6 — Replace `create_agent(...)` with `create_llm_agent(runtime, ...)`

# Before
agent = create_agent(
    model=ChatOpenAI(model="gpt-4o", api_key=os.environ["OPENAI_API_KEY"]),
    tools=[get_weather, call_time_agent, call_calendar_agent],   # mixed local + remote
    system_prompt="You are a helpful weather agent.",
)

# After
agent = create_llm_agent(
    runtime,
    name="weather_agent",
    description="A weather agent that collaborates with other agents",
    instruction="You are a helpful weather agent.",
    tools=[get_weather],   # local tools only — sub-agents + MCP discovered
)

Your system prompt becomes instruction, and the agent gets a stable name and description — these show up on its A2A card, in audit logs, and in the dashboard. For exactly what create_llm_agent does internally, see the helper reference. After this step: the returned CompiledStateGraph is a regular LangGraph with local tools + remote sub-agent tools + MCP tools already wired in. You can still call agent.ainvoke({"messages": [...]}) against it exactly as before.

Step 7 — Replace your A2A wrapper with `serve(agent, runtime)`

If you previously hand-rolled a Starlette / FastAPI server to expose the LangChain agent over HTTP, delete it. If you weren’t exposing the agent over HTTP at all (you were calling agent.ainvoke(...) from another process), serve is what makes the migration’s value land — it gives your agent the same A2A surface every SwarmD agent has, so the relay can route to it like any other.

# Before — bespoke FastAPI / Starlette wrapper, or no wrapper at all

# After
serve(agent, runtime)

For exactly what serve mounts (A2A executor, agent card, /admin, correlation middleware, task store, refresh-in-place), see the helper reference. After this step: the agent process exposes the A2A protocol on HOST:PORT, plus the /admin surface SwarmD uses to push refresh events. Configuration only — no code change. In the dashboard subscribe weather_agent to the sub-agents and MCP servers it needs (the same ones you used to hardcode in Step 5). The dashboard’s POST /registry/v1/agents/{id}/subscriptions endpoint works too. After this step: the platform fires a SUBSCRIPTION_CHANGED webhook. Your running agent verifies the HMAC, rebuilds the LangGraph from the recipe stashed by create_llm_agent, and the new PollingA2aTool (or MCP tool) appears in the next LLM call’s tool list. No restart.

Step 9 — Verify and roll out

In order, before flipping production traffic:

Boot the agent locally with SWARMD_* vars set. Look for the banner with the agent name, then Found N subscribed agents and Loaded N MCP tool(s) from M server(s) in the logs. Check credentials and base URL if either is missing.
curl /.well-known/agent-card.json — the response should match the agent’s name, description, and at least the synthetic skill.
Send a message that exercises a sub-agent via POST / (JSON-RPC message/send). Confirm the LLM picks the right PollingA2aTool, that the call shows up in the audit log under a single correlation ID, and that the response makes its way back even if the downstream went through a working state first.
Send a message that exercises an MCP tool. Confirm the same.
Rotate a subscription in the dashboard. Watch /admin/webhook fire and Loaded N+/-1 MCP tool(s) reappear in logs. No restart.
Roll out. The migration is purely additive on the agent process — the same binary runs unconfigured (standalone) or configured (SwarmD-attached) depending on env, so you can canary it.

Summary

What changed.

main.py shrinks to domain logic. No more bespoke HTTP tools, no more hand-rolled FastAPI / Starlette server.
Outbound calls go through the relay. You get audit, policy, HITL, and rate limits for free.
Sub-agents and MCP tools are discovered from subscriptions. Each one is a standard LangChain BaseTool.
Long-running sub-agents stop blocking your LLM — PollingA2aTool drives tasks/get until terminal.
/admin/webhook keeps the agent’s tool catalogue in sync without restarts.

What didn’t.

Your @tool-decorated functions.
Your agent.ainvoke({"messages": [...]}) call shape from anything that already calls the agent locally.
Your existing LangChain integrations on the graph (chat history, memory, structured output) — they live on the CompiledStateGraph returned by create_llm_agent.
Local-only behaviour: with SWARMD_* unset the agent runs as a standalone LangChain agent against direct OpenAI.

For the Google ADK equivalent of this guide, see Migrating from Google ADK.

TypeScript

Python

Migrating from LangChain

Migrating from LangChain

What you are migrating away from

What you are migrating to

Migration steps

Step 1 — Register the agent

Step 2 — Install the SDK

Step 3 — Add platform credentials to `.env`

Step 4 — Replace bootstrap with `create_runtime()`

Step 5 — Delete the hand-rolled HTTP tools

Step 6 — Replace `create_agent(...)` with `create_llm_agent(runtime, ...)`

Step 7 — Replace your A2A wrapper with `serve(agent, runtime)`

Step 9 — Verify and roll out

Summary

​Migrating from LangChain

​What you are migrating away from

​What you are migrating to

​Migration steps

​Step 1 — Register the agent

​Step 2 — Install the SDK

​Step 3 — Add platform credentials to .env

​Step 4 — Replace bootstrap with create_runtime()

​Step 5 — Delete the hand-rolled HTTP tools

​Step 6 — Replace create_agent(...) with create_llm_agent(runtime, ...)

​Step 7 — Replace your A2A wrapper with serve(agent, runtime)

​Step 8 — Subscribe to downstream agents and MCP servers

​Step 9 — Verify and roll out

​Summary

Migrating from LangChain

What you are migrating away from

What you are migrating to

Migration steps

Step 1 — Register the agent

Step 2 — Install the SDK

Step 3 — Add platform credentials to `.env`

Step 4 — Replace bootstrap with `create_runtime()`

Step 5 — Delete the hand-rolled HTTP tools

Step 6 — Replace `create_agent(...)` with `create_llm_agent(runtime, ...)`

Step 7 — Replace your A2A wrapper with `serve(agent, runtime)`

Step 8 — Subscribe to downstream agents and MCP servers

Step 9 — Verify and roll out

Summary