连续体

The agent runtime for builders who ship.

Build, run, and deploy reliable AI agents at enterprise scale — multi-LLM routing, persistent memory, MCP-native tools, durable workflows, and full observability, out of the box.

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📖 Documentation · ⚡ Quick start · ⚙️ Configuration · 🧩 Components · 🧪 Examples · 🤝 Contributing

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Continuum is a production-grade Python framework for building, orchestrating, and shipping autonomous AI agents at enterprise scale. It unifies a clean, typed agent core with cost-aware multi-model inference, stateful long- and short-term memory, open standards-based tool calling, durable execution, and end-to-end observability — all behind one small, composable, type-safe API.

🚀 Quick start

Requirements: Python 3.13+ and Docker (for Redis · Milvus/Qdrant · Langfuse).

git clone https://github.com/shyftlabs/continuum.git
cd continuum

python3.13 -m venv .venv && source .venv/bin/activate
pip install -e .

cp .env.template .env        # add your provider key(s) — see Configuration below
docker compose up -d         # Redis · Milvus/Qdrant · Langfuse

Your first agent:

import asyncio
from orchestrator.agent import BaseAgent, AgentRunner

async def main():
    agent = BaseAgent(
        name="hello-agent",
        instructions="You are a friendly assistant.",
        model="gpt-4o-mini",
    )
    runner = AgentRunner()
    response = await runner.run(agent, "Hi!")
    print(response.content)

asyncio.run(main())

AgentRunner.run() returns an AgentResponse with content, structured_output, usage, tool_calls, run_artifacts, latency_ms, and the full handoff chain. See the docs for streaming, tools/MCP, memory, handoffs, and workflows.

🧪 Examples

Runnable demos live under playground/:

• gateway-local-shop — an MCP server + agent + chat UI for a pet-shop assistant (end-to-end: server → agent → UI).
• gateway-multi-agent-shop — a multi-agent workflow variant with routing and handoffs.
• frontend/ — the demo web UIs (assortment, commerce-chat).

⚙️ Configuring Continuum

Continuum is configured through environment variables (copy .env.template → .env). Set keys only for the providers and components you use — everything else has sensible defaults. The most common settings:

LLM providers & routing

Memory (long-term) & embeddings

Sessions (short-term)

Observability (Langfuse)

Temporal (optional, durable workflows)

Optional extras: pip install -e ".[temporal]" for Temporal, ".[eval]" for evaluation, ".[embeddings]" for local embeddings. See .env.template for the complete, annotated reference.

🧩 Components