Apache Burr, an incubating project under the Apache Software Foundation, offers a pure Python framework for building decision‑making AI agents, from simple chatbots to multi‑agent workflows. The library eliminates DSLs and YAML, letting developers define actions and transitions with plain functions and decorators. Its clean API aims to lower the barrier for reliable, testable AI application development. It also supports seamless deployment.

Burr bundles observability tools, a UI that streams state changes, and built‑in persistence that can write to disk, databases, or custom backends. Developers can pause execution for human input, enabling approval workflows, and can run actions in parallel, fan‑out/fan‑in, or compose sub‑applications as DAGs. Unit testing and replay features let teams validate state transitions and rebuild confidence in production agents. This reduces debugging time significantly.

Early adopters such as Peanut Robotics, Wattto.ai, and Provectus report smoother integration with existing stacks and no vendor lock‑in. By exposing simple decorators yet supporting advanced branching, Burr positions itself as a unifying layer for AI‑centric services without wrapping external frameworks. The project’s open‑source model invites community contributions, aiming to become the go‑to toolkit for reliable AI agent construction.