burr
Apache Burr is a Python framework for building stateful AI applications (chatbots, agents, simulations) using a state-machine model. It provides built-in monitoring, tracing, and persistence capabilities, plus a web UI for real-time inspection and debugging.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | apache/burr |
| Owner | apache |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 2.5k |
| Forks | 169 |
| Open issues | 118 |
| Latest release | v0.42.0-incubating (2026-05-10) |
| Last updated | 2026-06-28 |
| Source | https://github.com/apache/burr |
What burr is
Burr models application logic as a directed graph of actions with explicit state transitions, reads/writes declarations, and pluggable persisters. It includes a telemetry UI, integrates with LLM frameworks, and works with non-LLM workflows; released under Apache 2.0 and currently in incubating status.
Get the burr source
Clone the repository and explore it locally.
git clone https://github.com/apache/burr.gitcd burr# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- State machine model requires upfront design clarity; actions must declare reads/writes to enable proper dependency tracking and persistence.
- UI telemetry is optional but recommended; understand how persister backends (in-memory, database, cloud) integrate with your infrastructure.
- LLM integrations are framework-agnostic but require manual setup; examples show OpenAI, but you configure prompting, API calls, and error handling yourself.
- Python-only; no Go, Node, or JVM support—ensure team expertise and deployment environment support Python runtimes.
- Incubating status means API surface may shift; pin versions carefully and monitor release notes for breaking changes.
When to avoid it — and what to weigh
- Asynchronous event-driven requirements — If your system requires event queues, pub/sub, or reactive event streams (e.g., real-time data pipelines), temporal orchestrators or event frameworks are better suited.
- Low-latency synchronous APIs — Burr's execution model and UI tracking may add overhead; if sub-millisecond latency is critical, consider lightweight libraries or purpose-built systems.
- Incubating stability concerns — Project is marked incubating; breaking changes are possible. If you need production-hardened, stable APIs, defer until graduation or use a mature alternative.
- Simple stateless request-response logic — For trivial CRUD or REST endpoints without state management complexity, Burr's graph abstraction introduces unnecessary overhead.
License & commercial use
Apache License 2.0 (Apache-2.0): permissive open-source license allowing commercial use, modification, and distribution. Requires preservation of license text, copyright notices, and NOTICE file; provides liability and warranty disclaimers.
Apache 2.0 permits commercial use, including in proprietary products. No copyleft requirement. However, projects under ASF incubation may have governance or support implications—verify your organization's open-source policy and consider support arrangements separately.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
No explicit security audit or threat model provided in available data. Considerations: (1) state persister backends must be secured (credentials, encryption at rest/transit); (2) UI telemetry server should not expose sensitive state without authentication; (3) LLM API keys and prompts should not leak into logs; (4) incubating projects may not have undergone formal security review—assess before handling sensitive data.
Alternatives to consider
LangGraph
Also models state machines with graph-based workflows for LLM agents; has official LangChain integration; but lacks built-in open-source UI and is tightly coupled to LangChain ecosystem.
Temporal
Mature, distributed workflow orchestration with durability guarantees; supports asynchronous, event-driven patterns; but does not explicitly model state machines and has higher operational complexity.
Apache Hamilton
DAG-based data transformation and orchestration framework from same ecosystem; works with non-LLM workflows; but is acyclic and lacks built-in tracing UI, hence Burr was created to add state-machine semantics.
Build on burr with DEV.co software developers
Contact our team to evaluate Burr for your use case, design your state machine architecture, or integrate it with your existing infrastructure and observability stack.
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burr FAQ
Can I use Burr in production today?
Do I have to use an LLM with Burr?
How do I persist state between application restarts?
Is there a managed hosting option?
Custom software development services
Need help beyond evaluating burr? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and ai frameworks integrations — and maintain them long-term.
Ready to build stateful AI applications?
Contact our team to evaluate Burr for your use case, design your state machine architecture, or integrate it with your existing infrastructure and observability stack.