Continuum

by ShyftLabs

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.

[](https://www.python.org/downloads/) [](LICENSE) [](https://github.com/shyftlabs/continuum/releases)

[](https://github.com/shyftlabs/continuum/actions/workflows/ci.yml) [](https://docs.continuum.shyftlabs.io/) [](CONTRIBUTING.md) [](CODEOFCONDUCT.md)

📖 Documentation · [⚡ Quick start](#-quick-start) · [⚙️ Configuration](#️-configuring-continuum) · [🧩 Components](#-components) · [🧪 Examples](#-examples) · [🤝 Contributing](CONTRIBUTING.md)

---

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.

✨ Features

• 🤖 Agentic core & orchestration — a strongly-typed agent primitive with full lifecycle hooks, schema-validated structured outputs, and nine composable multi-agent patterns (sequential, parallel, loop, routing, planning, reflection, debate, scatter, supervised).
• 🔀 Smart Inference — cost-aware inference routing that classifies every request by complexity and dispatches it to the cheapest capable model, with seamless cross-provider failover and zero lock-in.
• 🧠 Stateful memory — persistent semantic long-term recall plus low-latency working memory, with multi-tenant isolation scopes and built-in PII redaction for privacy-by-default agents.
• 🔌 Open tool calling — plug into any standards-based tool ecosystem (Model Context Protocol) across multiple transports, with fine-grained capability scoping, context capture/injection, and rich generative-UI artifacts.
• 🔁 Durable execution — long-running, crash- and restart-safe agent workflows with human-in-the-loop approval gates and exactly-once guarantees.
• 🔭 Full observability — first-class distributed tracing, token/latency/error telemetry, and one-line function instrumentation for complete run transparency.
• 🌐 Model-agnostic — target frontier or open-weight models through a single model string; swap providers without touching agent code.
• 🤝 Multi-agent handoffs — context-preserving agent-to-agent delegation with history summarization, cycle detection, and depth control.
• 📡 Real-time streaming — token-, tool-, handoff-, and memory-level events streamed the moment they happen.
• ✅ Built-in evaluation — turn live production traces into golden datasets and regression-test agent quality with standard LLM-evaluation metrics.

🚀 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

| Command | Services started | Use it when | |---|---|---| | continuum up (minimal) | Redis + Qdrant (2 containers) | Day-to-day development — a stateful agent with memory, nothing heavy. | | continuum up standard | minimal + Langfuse stack (8) | You want tracing/observability in the Langfuse UI. | | continuum up full | everything (13), incl. Temporal + Milvus | Durable workflows (Temporal) or the Milvus vector store. |

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.

⚙️ Configuring Continuum

Continuum is configured through environment variables (copy [.env.template](.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

| Variable | Description | Example | |---|---|---| | OPENAI_API_KEY / ANTHROPIC_API_KEY / GEMINI_API_KEY | Provider API keys — set the one(s) you use | sk-… | | DEFAULT_LLM_MODEL | Default model (provider/model, or bare name for OpenAI) | gemini/gemini-2.5-flash | | FALLBACK_LLM_MODEL | Model used if the default fails | gpt-4o-mini | | LLM_ENABLE_FALLBACK | Automatically fall back on provider errors | true | | SMART_LAYER_ENABLED | Enable cost-aware tier routing (Smart Inference) | true |

Memory (long-term) & embeddings

| Variable | Description | Example | |---|---|---| | MEMORY_ENABLED | Enable mem0-backed long-term memory | true | | VECTOR_STORE_PROVIDER | Vector store backend | qdrant / milvus | | EMBEDDER_PROVIDER / EMBEDDER_MODEL | Embedding provider & model | openai / text-embedding-3-small | | MEMORY_ISOLATION | Scope of memory isolation | user / agent / run / shared |

Sessions (short-term)

| Variable | Description | Example | |---|---|---| | SESSION_ENABLED | Enable Redis-backed conversation sessions | true | | SESSION_REDIS_HOST / SESSION_REDIS_PORT | Redis connection | localhost / 6380 | | SESSION_TTL_SECONDS | Session lifetime | 172800 |

Observability (Langfuse)

| Variable | Description | Example | |---|---|---| | LANGFUSE_ENABLED | Enable tracing | true | | LANGFUSE_PUBLIC_KEY / LANGFUSE_SECRET_KEY | Langfuse credentials | pk-… / sk-… | | LANGFUSE_HOST | Langfuse endpoint | http://localhost:3000 |

Temporal (optional, durable workflows)

| Variable | Description | Example | |---|---|---| | TEMPORAL_ENABLED | Enable durable workflow orchestration | false | | TEMPORAL_HOST | Temporal frontend | localhost:7233 |

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

🧩 Components

| Component | What it does | |---|---| | Agents | BaseAgent + AgentRunner — config, hooks, structured outputs, ReAct | | Workflows | Nine multi-agent patterns for chaining, branching, looping, and self-improvement | | Smart Inference | Request classifier + cost-aware model routing with fallback | | Memory | mem0 + Qdrant/Milvus (long-term) · Redis (sessions) · multi-tenant scopes | | Tools / MCP | MCP servers over Stdio/SSE/StreamableHTTP, tool filtering, widget artifacts | | Temporal | Durable, restart-safe workflows with human-in-the-loop gates | | Observability | Langfuse traces, metrics, @observe decorators | | Evaluation | Golden datasets + DeepEval / RAGAS metrics |

📚 Documentation

Full documentation lives at docs.continuum.shyftlabs.io — guides for building & running agents, Smart Inference, memory, tools/MCP, workflows, handoffs, streaming, evaluation, and the research behind it.

Markdown sources are also in [docs/](docs/) if you prefer reading on GitHub — e.g. [agent.md](docs/agent.md), [memory.md](docs/memory.md), [tools.md](docs/tools.md), and the integration [GUIDE.md](docs/GUIDE.md).

🧪 Examples

Runnable demos live under [playground/](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).

🤝 Contributing

Contributions are welcome! Please read [CONTRIBUTING.md](CONTRIBUTING.md) for the branch model, Conventional Commits, DCO sign-off, and local setup. By participating you agree to our [Code of Conduct](CODEOFCONDUCT.md).

• 🐛 Bugs & features — use the [issue templates](.github/ISSUE_TEMPLATE)
• 💬 Questions & ideas — GitHub Discussions
• 🔒 Security — report privately via [SECURITY.md](SECURITY.md), never a public issue

📄 License

Licensed under the [Apache License, Version 2.0](LICENSE). Copyright © 2025–2026 ShyftLabs Inc.

For commercial / enterprise inquiries — SLAs, indemnification, hosted offerings, custom features — contact [continuum@shyftlabs.io](mailto:continuum@shyftlabs.io).

Built with ❤️ by ShyftLabs · continuum@shyftlabs.io

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: shyftlabs
• Source: shyftlabs/continuum
• License: Apache-2.0
• Homepage: https://docs.continuum.shyftlabs.io/

Install and usage instructions live in the source repository linked above.