Babelagent

The Babel that lets AI agents understand each other. One shared tongue for agents that were never meant to talk.

Every agent framework has its own idea of what an agent is. LangChain has Runnables with .invoke, CrewAI has Crews with .kickoff, AutoGen has agents with .generate_reply, an LLM has messages.create, a microservice has an HTTP route. None of them agree on a shape, so connecting any two means writing glue.

Babelagent is the neutral layer in between. You bring your own agents, whatever they are (a callable, an HTTP/OpenAPI endpoint, an MCP tool, a framework agent, or an LLM), and it wraps each one in a single shared interface so they can exchange messages and collaborate on a task, agent-to-agent (A2A). The value is not any single adapter. It is that once something is adapted, it works with everything else you have adapted.

The headline is adapt(): hand Babelagent almost anything and it builds the connector on the fly.

import asyncio
from babelagent import Graph

async def main():
    graph = (
        Graph()
        .node("clean",   str.strip)
        .node("shout",   str.upper)
        .node("exclaim", lambda s: s + "!")
    )
    result = await graph.run("  hello  ")
    print(result.output)   # "HELLO!"

asyncio.run(main())

Let different agents work in parallel and hand their results to each other, with barrier policies on the join:

g = Graph()
g.node("src",     lambda text: text)
g.node("summary", adapt(langchain_agent), after=["src"])   # a LangChain agent
g.node("labels",  adapt("https://api.example.com/classify"), after=["src"])  # an HTTP service
g.join("merge", after=["summary", "labels"], agent=combine, barrier="all")
result = await g.run(document)

Concepts

Concept What it is
Graph Builds the network of agents (linear chaining and a DAG API)
CompiledGraph The validated, runnable graph; await graph.run(payload)
Node One participant: an agent plus an optional quality check/gate
Agent The uniform async run(message, ctx) -> message interface
adapt() Turns any brought object into an Agent, inferring the adapter
Message / Result The envelope agents exchange / the final output + run trace
Barrier Fan-in join policy: all · k_of_n · optional

Install

pip install babelagent                 # light base (callables + HTTP)
pip install "babelagent[mcp]"          # MCP tools
pip install "babelagent[cloud]"        # Anthropic / OpenAI
pip install "babelagent[ollama]"       # local models
pip install "babelagent[frameworks]"   # LangChain / CrewAI / AutoGen
pip install "babelagent[serve]"        # REST service
pip install "babelagent[all]"

Try it (no API key)

babelagent demo      # a broken agent (gated FAIL) then a fixed one (PASS)
babelagent doctor    # which adapter families are available
babelagent inspect   # print a graph's topology as JSON

Status

Alpha, in active development. Local-only for now. MIT licensed.

— amitpatole

Key dates

PyPI data
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  • Released:
Newer release available (0.1.3)

1 maintainer

PyPI data
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Avatar for amitpatole from gravatar.com amitpatole

Credits

Author: Amit Patole

License

MIT License (MIT)

Requires

Python >=3.11

Provides Extra

agentsensory all cloud dev frameworks mcp ollama serve

Tags

a2a adapters agents ai dag interoperability multi-agent orchestration

Classifiers

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