continuum

🚀 Quick start

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

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

continuum up                 # start local infra (Redis + Qdrant); writes ./.env
echo "OPENAI_API_KEY=sk-…" >> .env   # add your provider key(s) — see Configuration below

continuum up ships with the package — it locates the bundled Docker stack and starts it for you, so there's no compose file to find or copy. It defaults to the minimal profile (Redis + Qdrant); pick a bigger one with continuum up standard / continuum up full.

Contributors working from a clone: git clone https://github.com/shyftlabs/continuum.git && cd continuum, then python3.13 -m venv .venv && source .venv/bin/activate, pip install -e ".[dev]", cp .env.template .env, and continuum up.

Infrastructure profiles

Each profile also writes a managed block to ./.env (VECTOR_STORE_PROVIDER, LANGFUSE_ENABLED, …) so the SDK only talks to services that are actually running. Other commands: continuum down [-v], continuum status, continuum logs [service] [-f], continuum config-path.

Port conflicts? Every published host port is overridable via .env (defaults shown): SESSION_REDIS_PORT=6380, QDRANT_PORT=6333, QDRANT_GRPC_PORT=6334, MILVUS_PORT=19530, LANGFUSE_WEB_PORT=3000, LANGFUSE_WORKER_PORT=3030, LANGFUSE_POSTGRES_PORT=5433, LANGFUSE_REDIS_PORT=6382, CLICKHOUSE_HTTP_PORT=8123, CLICKHOUSE_NATIVE_PORT=9000, MINIO_API_PORT=9090, MINIO_CONSOLE_PORT=9091, TEMPORAL_PORT=7233, TEMPORAL_UI_PORT=8233, TEMPORAL_POSTGRES_PORT=5434. For the stores the SDK connects to (QDRANT_PORT, MILVUS_PORT, SESSION_REDIS_PORT), the same variable drives both the container and the client, so they stay in sync.

Your first agent:

import asyncio
from continuum.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.

📄 License

Licensed under the Apache License, Version 2.0. Copyright © 2025–2026 ShyftLabs Inc.

For commercial / enterprise inquiries — SLAs, indemnification, hosted offerings, custom features — contact continuum@shyftlabs.io.