JeevesAgent

Production-ready async agent harness. Multi-tenant by default, typed outputs, retries on transient errors, model-agnostic, MCP-native.

import asyncio
from pydantic import BaseModel
from jeevesagent import Agent

class WeatherReport(BaseModel):
    city: str
    temp_c: float
    conditions: str

async def main():
    agent = Agent("Be precise.", model="gpt-4.1-mini")

    r = await agent.run("Hi, my name is Alice.", user_id="alice")
    print(r.output)

    r = await agent.run(
        "Weather in Tokyo: sunny, 22°C, light wind. Extract.",
        user_id="alice",
        session_id="conv_42",
        output_schema=WeatherReport,
    )
    report: WeatherReport = r.parsed   # typed, validated
    print(f"{report.city}: {report.temp_c}°C — {report.conditions}")

asyncio.run(main())

Why pick this

  • user_id is a first-class typed primitive. One Agent + one Memory partitions automatically across N tenants. No more “forgot to namespace” data leaks.

  • output_schema= accepts any Pydantic model. Framework augments the system prompt, parses, validates, retries with feedback on failure. Typed outputs by default.

  • Network model adapters auto-wrapped with a typed error taxonomy

    • retry policy. Rate limits, 5xx, network blips don’t blow up your run. Resilient by default.

  • session_id is a real conversation handle. Reuse it and prior turns rehydrate as real chat history. No reducer protocol.

  • Twelve agent-loop architectures shipped behind one Agent constructor. One kwarg flips the iteration pattern.

Get started

Migrating from

Reference

Indices