langgraph-a2a 0.1.5

LangGraph A2A server framework with GenericAgentExecutor and pluggable agents

LangGraph A2A Server Framework

What is this?

langgraph-a2a is a Python package that hosts LangGraph agents behind an Agent-to-Agent (A2A) HTTP server. It gives you a ready-made server, protocol handling, and a pluggable executor so you can focus on building graphs—not wiring JSON-RPC, streaming, or agent discovery yourself.

Objective

Goal	How this framework helps
Expose a LangGraph agent over the network	A2A-compatible HTTP API (agent card, JSON-RPC, streaming)
Plug in your own agent logic	You ship a separate Python project; the framework loads it via entry points
Run one agent per process	CLI: langgraph-a2a --agent <name>
Support conversation memory	Maps each request’s A2A contextId to LangGraph thread_id when you compile with a checkpointer
Optional startup/shutdown	Per-agent lifespan hooks (e.g. DB pools, compile graph after secrets load)

The framework does not include your business agents. It provides the server and executor only.

Package

PyPI name	langgraph-a2a
Import	langgraph_a2a
CLI	langgraph-a2a
License	MIT

Install:

pip install langgraph-a2a

What you get:

• GenericAgentExecutor — runs LangGraph graphs via ainvoke / astream with A2A message format
• langgraph-a2a CLI — list and run registered agents
• Registry — discovers agents from [project.entry-points."langgraph_a2a.agents"] in your pyproject.toml
• Helpers — app config, secrets, agent card, default in-memory checkpointer utilities

Requirements

Requirement	Version / note
Python	>=3.11, <3.14
Your agent project	Separate installable package depending on langgraph-a2a
LangGraph	Declared in your project (e.g. langgraph>=0.3.0)
Per-agent config file	local_app_config.json (host, port, agent card)
Per-agent secrets (optional)	local_secrets.json
Checkpointer (optional)	Required only if you need conversation memory; choose in-memory or a LangGraph backend (e.g. PostgreSQL) in your agent code

Client requirement for multi-turn chat: send the same A2A contextId on each message in a conversation. The framework passes it as LangGraph configurable.thread_id; you do not set thread_id in graph nodes when using GenericAgentExecutor.

---

Quickstart

Build a separate agent project from scratch, run it with langgraph-a2a, and test with curl.

You will do two parts:

Part	Checkpointer	Memory survives server restart?
A	Default in-memory (MemorySaver)	No
B	PostgreSQL + lifespan	Yes

Both parts use the same graph.py. Part B adds lifespan.py and changes agent.py.

---

Step 1 — Create the project folder

mkdir -p ~/my-agent-app/my_agent_app
cd ~/my-agent-app
touch my_agent_app/__init__.py

Your work happens in ~/my-agent-app, not inside the langgraph-a2a source repo.

---

Step 2 — Folder structure

After Part A (in-memory):

my-agent-app/
├── pyproject.toml
└── my_agent_app/
    ├── __init__.py
    ├── graph.py
    ├── agent.py
    └── local_app_config.json

After Part B (PostgreSQL), add:

    ├── lifespan.py
    └── local_secrets.json

File	Purpose
pyproject.toml	Dependencies + agent entry point
graph.py	LangGraph nodes and state
agent.py	Registers agent with the framework
lifespan.py	(Part B) Opens Postgres, compiles graph with AsyncPostgresSaver
local_app_config.json	Host, port, agent card (required)
local_secrets.json	(Part B) DATABASE_URL — do not commit to git

---

Step 3 — Shared graph.py

Create my_agent_app/graph.py (used in both parts):

from typing import TypedDict

from langgraph.graph import END, START, StateGraph


class State(TypedDict, total=False):
    user_input: str
    output: str
    turn_count: int


def greet(state: State) -> dict:
    text = state.get("user_input") or ""
    count = (state.get("turn_count") or 0) + 1
    return {
        "output": f"Hello! You said: {text} (turn {count})",
        "turn_count": count,
    }


def build_graph():
    builder = StateGraph(State)
    builder.add_node("greet", greet)
    builder.add_edge(START, "greet")
    builder.add_edge("greet", END)
    return builder

• Framework sends user_input.
• Your node returns output (reply text).
• turn_count helps verify memory: same contextId → turn 2, 3, …

---

Part A — Default checkpointer (in-memory)

Step A1 — pyproject.toml

[project]
name = "my-agent-app"
version = "0.1.0"
requires-python = ">=3.11, <3.14"
dependencies = [
    "langgraph-a2a",
]

[project.entry-points."langgraph_a2a.agents"]
my_agent = "my_agent_app.agent:Agent"

Step A2 — agent.py

from pathlib import Path
from typing import Any

from langgraph_a2a.base_utils import compile_with_default_checkpointer
from langgraph_a2a.executor import GenericAgentExecutor

from .graph import build_graph


class Agent:
    name = "my_agent"

    @staticmethod
    def register() -> dict[str, Any]:
        agent_dir = Path(__file__).parent
        graph = compile_with_default_checkpointer(build_graph())
        return {
            "name": Agent.name,
            "executor": GenericAgentExecutor(agent_impl=graph, enable_streaming=True),
            "local_config_path": agent_dir / "local_app_config.json",
        }

Part A does not use local_secrets.json. Omitting local_secrets_path is fine — the server sets app.state.secrets to {}.

compile_with_default_checkpointer attaches MemorySaver. You do not pass thread_id; the server sets it from each request’s contextId.

Step A3 — local_app_config.json

{
  "host": "127.0.0.1",
  "port": 9000,
  "protocol": "http",
  "logging": { "level": "INFO" },
  "agent_card": {
    "name": "my-agent",
    "description": "Quickstart agent (in-memory memory)",
    "version": "0.1.0",
    "skills": [
      {
        "id": "chat",
        "name": "Chat",
        "description": "Stateful chat",
        "tags": ["quickstart"],
        "examples": ["Hello"]
      }
    ],
    "capabilities": {
      "streaming": true,
      "push_notifications": false
    }
  }
}

Step A4 — Install and run (Part A)

cd ~/my-agent-app
pip install -e .

langgraph-a2a --list-agents
langgraph-a2a --agent my_agent

Leave the server running. Open a second terminal for curl.

Step A5 — Test with curl (Part A)

Message 1 — use contextId demo-memory-001:

curl -sS -N -X POST http://127.0.0.1:9000/ \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": "1",
    "method": "message/stream",
    "params": {
      "message": {
        "kind": "message",
        "messageId": "msg-1",
        "contextId": "demo-memory-001",
        "role": "user",
        "parts": [{"kind": "text", "text": "Hi"}]
      }
    }
  }'

Expect turn 1 in the streamed JSON.

Message 2 — same contextId, new messageId:

curl -sS -N -X POST http://127.0.0.1:9000/ \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": "2",
    "method": "message/stream",
    "params": {
      "message": {
        "kind": "message",
        "messageId": "msg-2",
        "contextId": "demo-memory-001",
        "role": "user",
        "parts": [{"kind": "text", "text": "Again"}]
      }
    }
  }'

Expect turn 2.

If you restart the server and send again with the same contextId, you will get turn 1 again (in-memory only).

---

Part B — PostgreSQL checkpointer + lifespan

Use Part B when conversation state must survive server restarts.

Step B0 — PostgreSQL (Docker example)

docker run -d --name langgraph-a2a-pg \
  -e POSTGRES_PASSWORD=postgres \
  -e POSTGRES_DB=myagent \
  -p 5432:5432 \
  postgres:16

URL:

postgresql://postgres:postgres@localhost:5432/myagent

Step B1 — Update pyproject.toml

Add Postgres packages:

dependencies = [
    "langgraph-a2a",
    "langgraph>=0.3.0",
    "langgraph-checkpoint-postgres",
    "psycopg[pool]>=3.1",
]

Re-install:

pip install -e .

Step B2 — local_secrets.json

Create my_agent_app/local_secrets.json:

{
  "DATABASE_URL": "postgresql://postgres:postgres@localhost:5432/myagent"
}

Add local_secrets.json to .gitignore.

Step B3 — lifespan.py

Create my_agent_app/lifespan.py:

from typing import Any

from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
from psycopg_pool import AsyncConnectionPool

from langgraph_a2a.base_utils import compile_with_default_checkpointer

from .graph import build_graph


async def startup(app: Any) -> None:
    url = (app.state.secrets or {}).get("DATABASE_URL")
    if not url:
        raise ValueError("DATABASE_URL missing in local_secrets.json")

    pool = AsyncConnectionPool(conninfo=url, max_size=10, open=False)
    await pool.open()
    app.state.db_pool = pool

    checkpointer = AsyncPostgresSaver(pool)
    await checkpointer.setup()

    app.state.executor.agent = compile_with_default_checkpointer(
        build_graph(),
        checkpointer=checkpointer,
    )


async def shutdown(app: Any) -> None:
    pool = getattr(app.state, "db_pool", None)
    if pool is not None:
        await pool.close()

Lifespan sync with app.state:

1. Framework loads local_app_config.json → app.state.app_config
2. Framework loads local_secrets.json → app.state.secrets
3. Framework creates app.state.executor and calls bind_app(app)
4. startup(app) runs — pool + Postgres checkpointer + executor.agent = compiled graph
5. Requests run; each contextId → thread_id → Postgres checkpoint row
6. shutdown(app) — closes the pool

Step B4 — Replace agent.py (Part B)

from pathlib import Path
from types import SimpleNamespace
from typing import Any

from langgraph_a2a.executor import GenericAgentExecutor

from . import lifespan as agent_lifespan


class Agent:
    name = "my_agent"

    @staticmethod
    def register() -> dict[str, Any]:
        agent_dir = Path(__file__).parent
        return {
            "name": Agent.name,
            "executor": GenericAgentExecutor(agent_impl=None, enable_streaming=True),
            "local_config_path": agent_dir / "local_app_config.json",
            "local_secrets_path": agent_dir / "local_secrets.json",
            "lifespan": SimpleNamespace(
                startup=agent_lifespan.startup,
                shutdown=agent_lifespan.shutdown,
            ),
        }

agent_impl=None at import time is intentional; lifespan.startup sets app.state.executor.agent after Postgres is ready.

Step B5 — Run (Part B)

langgraph-a2a --agent my_agent

First run creates checkpoint tables via await checkpointer.setup().

Step B6 — Test with curl (Part B)

Use the same curl commands as Part A (Steps A5), e.g. contextId demo-memory-001.

1. First message → turn 1
2. Second message (same contextId) → turn 2
3. Stop and restart langgraph-a2a, same contextId again → turn 3 (Postgres kept state)

New contextId → starts at turn 1 again.

Quick reference — memory rules

	Part A (in-memory)	Part B (Postgres + lifespan)
Compile	In agent.py at register	In lifespan.startup
thread_id	From client contextId (automatic)	Same
Survives restart	No	Yes
local_secrets_path in register()	Omit	Required (DATABASE_URL in local_secrets.json)

Look for artifact-update and parts in the SSE stream for the agent reply text.