openagentmesh 0.2.0

✨ Highlights

• Decoupled multi-agent system. Run agents and tools however you want. Have them talk to each other as if they were running in the same process.
• Bring your own agent. Works with any agentic framework (LangChain, CrewAI, PydanticAI, etc.).
• Agents as functions. Just wrap your agents in a handler function and the @mesh.agent decorator. Done.
• Batteries included. Agent catalog with runtime discovery, sync/async requests, pub/sub, shared KV and Object store, load balancing. All from a single infrastructure dependency.
• Protocol-first. The protocol is the product. Python SDK and CLI facilitate use. Any NATS client in any language can participate by following the subject and envelope format.
• No-effort scaling. Code remains the same. Just add instances.

📦 Installation

As a dependency (projects using OAM agents):

uv add openagentmesh
# or
pip install openagentmesh

As a CLI tool (using oam commands directly):

uv tool install openagentmesh

For development:

git clone https://github.com/openagentmesh/openagentmesh.git
cd openagentmesh
uv sync

🎬 See it in action

oam demo

🚀 Quickstart

1. Start a local mesh:

oam mesh up

2. Register an agent (agent.py):

from openagentmesh import AgentMesh, AgentSpec

mesh = AgentMesh()

@mesh.agent(AgentSpec(name="echo", description="Echoes a message back."))
async def echo(req: str) -> str: 
    return f"Echo: {req}"

mesh.run()

python agent.py

3. Discover and call it from the terminal:

oam mesh catalog               # list registered agents
oam agent contract echo        # view the full contract
oam agent call echo '"hello"'  # invoke it

No hardcoded addresses. The CLI discovers echo from the mesh, reads its contract, and calls it.

🔗 How OAM relates to MCP and A2A

OAM is not a replacement for MCP or A2A. It complements both.

MCP is the standard for connecting LLMs to tools. OAM adds value where MCP alone hits friction: context bloat from loading too many tool schemas, patterns beyond request/reply (pub/sub, async callbacks, shared context), and automatic cross-team discovery.

A2A is Google's protocol for cross-organization agent federation over HTTP. OAM contracts are an A2A-compatible superset: A2A fields at top level, OAM extensions under x-agentmesh. Use OAM internally, project to A2A at the boundary where internal meets external.

Concern	MCP	OAM	A2A
Scope	Third party toolsets	Agent-to-agent/tool (internal)	Agent-to-agent (cross-org)
Transport	stdio / SSE	NATS	HTTP
Discovery	Manual config	Automatic	Agent Card directory
Load balancing	N/A	NATS queue groups	External
Interaction	Streaming	Request/reply, streaming, async callback, pub/sub, reactive watcher	Request/reply, streaming

📖 How to use

Any async function can be an agent: LLM chains, deterministic tools, data transformers, event publishers. The @mesh.agent decorator inspects the function signature to infer capabilities automatically.

Five handler patterns, determined by the function shape:

Pattern	Signature	What it does
Responder	async def f(req: TypeIn) -> TypeOut (returns)	Takes input, returns output
Streamer	async def f(req: In) -> TypeOut (yields)	Takes input, streams data back
Trigger	async def f() -> TypeOut (returns)	No input, returns output on call
Publisher	async def f() -> TypeOut (yields)	Emits events continuously
Watcher	async def f()	No input/output. Background task (KV watch, polling)

No capability flags to set. The handler shape is the source of truth.

📡 Invocation patterns

Four ways to interact with agents:

# Synchronous request/reply
result = await mesh.call("summarizer", {"text": doc})

# Streaming
async for chunk in mesh.stream("summarizer", {"text": doc}):
    print(chunk["delta"], end="")

# Async callback (non-blocking)
await mesh.send("summarizer", payload, on_reply=callback)

# Pub/sub events
async for event in mesh.subscribe(agent="price-feed"):
    print(event["price"])

🔍 Discovery

Two-tier discovery designed for efficient agent selection, including by LLMs:

# Tier 1: lightweight catalog (~20-30 tokens per agent)
catalog = await mesh.catalog() # Get summary of all agents on the mesh
catalog = await mesh.catalog(channel="finance.risk") # Filter the catalog

# Tier 2: full contract with JSON Schemas (only for the agent you need)
contract = await mesh.contract("summarizer")
contract.input_schema   # JSON Schema dict
contract.description    # LLM-consumable description

Catalog entries are compact enough for direct LLM consumption. The LLM picks from the catalog, then only the selected agent's full schema is fetched. Channels and tags narrow the candidate set further.

💾 Shared state

Agents can share context through KV Store and binary artifacts through Workspace, and watch for changes in both.

# KV: structured data with watch and compare-and-swap
await mesh.kv.put("config/threshold", "0.85")
value = await mesh.kv.get("config/threshold")

async for value in mesh.kv.watch("pipeline.*.status"): 
    print(f"Status changed: {value}")

# Workspace: binary artifacts (files, images, embeddings)
await mesh.workspace.put("docs/report.pdf", pdf_bytes)
data = await mesh.workspace.get("docs/report.pdf")

🤝 Participation patterns

Two ways to participate: register agents, or just connect and call.

Registered processes use @mesh.agent to declare agents. They appear in the catalog, have contracts, and participate in liveness tracking.

Unregistered processes connect, discover, call, and disconnect. Scripts, notebooks, CLI tools can use this pattern:

async with mesh:
    catalog = await mesh.catalog()
    result = await mesh.call("summarizer", {"text": "..."})

⚙️ Technology

OAM uses NATS as its single infrastructure dependency. One embedded binary provides everything, no need for additional servers/services:

NATS capability	OAM use
Pub/sub	Event fan-out, callbacks, streaming
Request/reply	mesh.call()
Queue groups	Automatic load balancing across agent instances
KV store	Contract registry, agent catalog, shared context
Object store	Shared workspace for artifacts

No Consul for discovery. No Redis for state. No RabbitMQ or Kafka for messaging. One connection, all primitives.

Pydantic v2 generates JSON Schemas from type hints for every agent contract. Runtime validation at the mesh boundary; malformed requests are rejected before reaching your handler.

Protocol-first. The protocol is the product, the Python SDK and CLI are the first implementation. Any NATS client in any language (Javascript, Go, Rust, etc.) can participate by following the subject naming and envelope format. Dedicated SDKs will come in the future.

Scaling

Deploy multiple instances of the same agent. NATS queue groups distribute requests automatically:

# Same code, any number of instances. No config changes.
@mesh.agent(AgentSpec(name="summarizer", channel="nlp", description="..."))
async def summarize(req: SummarizeInput) -> SummarizeOutput:
    ...

Local and production use the same architecture. The only thing that changes is the connection string:

mesh = AgentMesh()                                # local (oam mesh up)
mesh = AgentMesh("nats://mesh.company.com:4222")  # production

💻 CLI

The oam CLI is installed with the SDK:

Command	Description
oam demo	Launch interactive demo with sample agents
oam mesh up	Start local dev server (NATS + JetStream + KV)
oam mesh down	Stop local server
oam mesh connect <url>	Point at a remote mesh
oam mesh listen <subject>	Subscribe to live traffic
oam mesh catalog	List registered agents
oam agent contract <name>	View an agent's contract
oam agent call <name> <json>	Invoke an agent
oam agent stream <name> <json>	Stream from an agent
oam agent subscribe <name>	Subscribe to publisher events

See the full documentation for more details.

🤲 Contributing

Contributions are welcome. Open an issue for bugs or new features before submitting a pull request.

For bug fixes, include a failing test that demonstrates the issue. For new features, start with the use case: describe what you're trying to build and why the current API doesn't cover it.

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License

Distributed under the MIT License. See LICENSE for details.