aroha 1.1.0

Aroha Protocol — Build and register agents, MCPs, and skills on the Aroha Hub. Supports FastAPI, LangChain, CrewAI, Google ADK, OpenAI SDK.

aroha — Aroha Protocol Python SDK

Build agents that register on the Aroha Hub and are callable by any client through the standard /v1/run protocol.

pip install aroha

Quick start

from aroha.agent import serve

@serve(
    name="My Agent",
    system_prompt="You are a helpful assistant.",
    provider="anthropic",
    model="claude-sonnet-4-6",
    memory="session",
)
async def my_agent(message: str, session_id: str, history: list, context: dict) -> str:
    import anthropic
    client = anthropic.Anthropic()
    resp = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        system="You are a helpful assistant.",
        messages=[{"role": "user", "content": message}],
    )
    return resp.content[0].text

if __name__ == "__main__":
    my_agent.start(port=8000)

This starts a FastAPI server with:

• POST /v1/run — the Aroha Protocol invocation endpoint
• GET /.well-known/aroha-agent.json — agent manifest
• GET /health — liveness check
• GET /docs — Swagger UI

Register on the Aroha Hub

import os
result = my_agent.register(
    endpoint_url="https://my-agent.fly.dev",
    api_key=os.environ["AROHA_API_KEY"],
)
print(result["didHash"])  # chat URL: https://aroha-labs.com/chat/<didHash>

Framework bridges

LangChain / LangGraph

pip install aroha[langchain]

from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
from aroha.bridges.langchain_v1 import from_chain

llm = ChatAnthropic(model="claude-sonnet-4-6")
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("human", "{message}"),
])
chain = prompt | llm

agent = from_chain(chain, name="LangChain Agent")
agent.start(port=8000)

CrewAI

pip install aroha[crewai]

from crewai import Agent, Task, Crew
from aroha.bridges.crewai_v1 import from_crew

researcher = Agent(
    role="Researcher",
    goal="Research the given topic thoroughly",
    backstory="Expert researcher",
    llm="anthropic/claude-sonnet-4-6",
)

def make_crew(message: str) -> Crew:
    task = Task(description=message, agent=researcher, expected_output="Research findings")
    return Crew(agents=[researcher], tasks=[task])

agent = from_crew(make_crew, name="Research Agent")
agent.start(port=8000)

Google ADK

pip install aroha[google-adk]

from google.adk.agents import LlmAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from aroha.bridges.google_adk import from_runner

adk_agent = LlmAgent(
    name="assistant",
    model="gemini-2.0-flash",
    instruction="You are a helpful assistant.",
)
session_service = InMemorySessionService()
runner = Runner(agent=adk_agent, app_name="my-app", session_service=session_service)

agent = from_runner(runner, name="Gemini Agent", memory="session")
agent.start(port=8000)

OpenAI SDK

pip install aroha[openai]

from openai import OpenAI
from aroha.bridges.openai_v1 import from_openai_client

client = OpenAI()
agent = from_openai_client(
    client,
    model="gpt-4o",
    system_prompt="You are a helpful assistant.",
    name="GPT Agent",
    memory="session",
)
agent.start(port=8000)

Hub client

from aroha.hub import register_agent, resolve_agent, call_agent, list_agents

# Resolve by didHash
agent = resolve_agent("abc123def")

# Call an agent by didHash (resolves endpoint automatically)
reply = call_agent("abc123def", "What is the weather in London?")
print(reply["message"])

# List all agents
agents = list_agents(limit=20)

Advanced: A2A envelope transport

The SDK also includes the full Aroha cryptographic envelope protocol for zero-trust agent-to-agent messaging:

from aroha import generate_did, ArohaServer, ArohaClient, build_envelope

/v1/run protocol

Any compliant agent must implement:

POST /v1/run
Content-Type: application/json

{
  "message":   "user message",
  "sessionId": "uuid (optional, echoed back)",
  "history":   [{"role": "user"|"assistant", "content": "..."}],
  "context":   {}
}

Response:

{
  "message":   "agent reply",
  "sessionId": "uuid",
  "artifacts": [],
  "usage":     {}
}

The @serve decorator handles all of this automatically.