Apache Burr Tutorial: Get Started in 5 Minutes [2026]

What it does:

Define applications as explicit state machines with actions as Python functions and transitions as logical conditions. The built-in UI visualizes execution flow in real-time, showing state changes, transition triggers, and execution paths. This approach transforms opaque agent behavior into debuggable, testable workflows that teams can understand and maintain.

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What it does:

Works seamlessly with any LLM provider (OpenAI, Anthropic, local models), vector database (Pinecone, Weaviate, Chroma), or Python library. Actions are standard functions with no framework lock-in, enabling teams to adopt Burr incrementally without abandoning existing toolchains or requiring architectural rewrites.

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What it does:

Every installation includes a sophisticated web interface for monitoring application execution. View state transitions, action timing, execution graphs, and replay past runs. The UI comes pre-loaded with demo data and supports real-time collaboration for team debugging sessions.

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What it does:

Pluggable persistence backends support in-memory, filesystem, and database storage. Applications can pause, resume, and recover from failures while maintaining exact state. Enables human-in-the-loop workflows and long-running processes with automatic state serialization.

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What it does:

Deploy applications as web services with built-in FastAPI support. The framework handles concurrent execution, state isolation, and API endpoint generation automatically. Includes health checks, metrics collection, and deployment-ready configurations for cloud environments.

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Benefits from ASF's proven governance model ensuring community-driven development, vendor neutrality, and long-term sustainability. The incubation status provides enterprise confidence while maintaining open-source accessibility and avoiding vendor lock-in concerns.

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What it does:

Deep introspection capabilities allow examination of state at any point in execution. Debug complex workflows by stepping through state changes, analyzing decision trees, and identifying bottlenecks. Essential for production AI systems requiring explainable decision-making processes.

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What it does:

Seamlessly manages state containing text, images, audio, structured data, and custom objects. Actions can process rich inputs and maintain complex state relationships while preserving full visibility through the telemetry interface. Critical for modern AI applications combining multiple data types.

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