Poirot is a deep research agent kernel with a middleware-first architecture, five-layer long-term memory, multi-agent orchestration, sandbox isolation, and a three-layer skill self-evolution system, backed by 2400+ tests.

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License

MIT

Last updated

2026-07-28

Why it is worth attention

It treats agent internals—memory, skill injection, tool routing, and context governance—as pluggable middleware, not embedded logic, enabling independent testing and modular evolution.

Who it is for

  • Developers building custom research agents or multi-agent systems
  • AI researchers interested in cognitive‑science‑inspired memory architectures
  • Engineers needing sandboxed, multi‑LLM fallback, and observable agent pipelines
  • System architects evaluating modular agent frameworks for production

Use cases

  • Conducting long‑running, multi‑step research with persistent memory across sessions
  • Delegating sub‑tasks (e.g., coding) to specialized agents sharing a sandbox
  • Building skill sets that self‑evolve via LLM‑driven mutation and quality gating
  • Enforcing sandboxed file isolation and path whitelisting for agent tool execution

Strengths

  • Decoupled middleware design (21 middleware) cross-cutting every lifecycle hook
  • Five‑layer memory with lazy Ebbinghaus decay, pure BM25 retrieval, and non‑blocking auto‑consolidation
  • Real context‑window resolution through `FallbackChatModel` for accurate token budgeting
  • Over 2400 tests guarding every module, ensuring independent verifiability

Considerations

  • Requires Python 3.12+ and a configured DeepSeek API key as the ultimate fallback
  • Production sandbox isolation depends on Docker, adding infrastructure overhead
  • Configuration surface is large (environment variables, MCP servers, skill settings) which may overwhelm simple use cases

README quick start

Quick Start

Description

Poirot is a deep research agent kernel built for those who care about how agents are architected.

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