Updates on Burr (OS) – a full-stack AI agent framework

What it does

For a quick overview of Apache Burr, watch this walkthrough or read our blog post . The following video is a longer demo of building a simple chatbot application with Apache Burr using this notebook : For more details about how Apache Burr works, what problems it solves, and how to use it, read on! You’ll find this documentation separated into three sections. If you’re looking to build a mental model/read more, go to concepts . If you’re looking for a specific piece of information, go to reference . Report any bugs, issues, or feature requests via GitHub Issues or GitHub Discussions . "After evaluating several other obfuscating LLM frame-works, their elegant yet comprehensive state…from burr.dagworks.io

In the maker’s words, at launch

Hey HN, In the months since we initially released Burr (https://news.ycombinator.com/item?id=39917364), we have been hard at work. We wanted to share some of the most exciting changes we’ve made to build Burr out as a full-stack development framework for AI agents. In case you don’t recall, Burr is an…

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  Burr – A framework for building and debugging GenAI apps faster2024 · github.com · ▲94

  Hey HN, we're developing Burr (github.com/dagworks-inc/burr), an open-source python framework that makes it easier to build and debug GenAI applications. Burr is a lightweight library that can integrate with your favorite tools and comes with a debugging UI. If you prefer a video introduction, you can watch me build a chatbot here: https://www.youtube.com/watch?v=rEZ4oDN0GdU. Common friction points we’ve seen with GenAI applications include logically modeling application flow, debugging and recreating error cases, and curating data for testing/evaluation (see…

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  GraphBit2025 · graphbit.ai · ▲437

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  Git for AI AgentsMay 2026 · github.com · ▲129

  hi guys. been working on something i think is fundamentally missing in today's workflow with ai agents. vcs. i find myself struggling with questions that agents can't answer like "why did you do it?", "when did u delete this folder? why?", etc. or trying to /rewind (after a /compact...) or basically `bisect` to find when and why something was done by the agent in the current / previous session. just like git did for code, i think we are the same core capabilities with ai agents so... i developed an open source solution for that (currently supporting claude code) would love to…

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  Axe – A 12MB binary that replaces your AI frameworkMar 2026 · github.com · ▲227

  I built Axe because I got tired of every AI tool trying to be a chatbot. Most frameworks want a long-lived session with a massive context window doing everything at once. That's expensive, slow, and fragile. Good software is small, focused, and composable... AI agents should be too. Axe treats LLM agents like Unix programs. Each agent is a TOML config with a focused job. Such as code reviewer, log analyzer, commit message writer. You can run them from the CLI, pipe data in, get results out. You can use pipes to chain them together. Or trigger from cron, git hooks, CI. What Axe is: - 12MB…

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  Progress AI ObservabilityAug 2026 · telerik.com · ▲168

  Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.

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