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Products that do what Updates on Burr (OS) – a full-stack AI agent framework does
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 open-source python library that makes it easier to build and debug GenAI applications & agents by representing them as graphs of simple python objects/functions. Burr only abstracts away system-level concerns (state persistence, debugging, observability), and does not…
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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…
2024 · github.com
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Hello everyone. I've been working on this experimental editor called Huzzah. I've been working almost exclusively with coding agents since January of this year, and over the past few months I began to feel utterly exhausted by them. They're great, but I'm finding it more and more tedious to write full sentences for every change I want. Not only that, but it seems there's a complexity limit for codebases - beyond a certain point the agent begins confusing itself. I'd like to go back to writing code, but I don't want to go all the way back to fully manual coding. So I've come up with this…
17d ago · danielvaughn.dev
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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…
May 2026 · github.com
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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…
Mar 2026 · github.com
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Hello HN! The day has finally come to stop adding features and start sharing what I've been building the last 5-6 months. It's a bit of CrewAI, OpenDevon, LangFuse/Cloud all in one, providing devs who prefer TypeScript an integrated framework thats provides a lot out of the box to start experimenting and building agents with. It started after peeking at the LangChain docs a few times and never liking the example code. I began experimenting with automating a simple Jira request from the engineering team to add an index to one of our Google Spanner databases (for context I'm the…
2024 · github.com
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Hello hackernews! I'm excited to share a new open source python library I just released for creating AI agent-integrated systems. The name is `agency`. It differs from other agent libraries, most importantly in that it's intended to address a distinct part of the overall problem, that of agent integration. It is not an agent toolchain like LangChain and others. `agency` is a framework intended for safely integrating agents with computing systems and humans in a way that all parties can easily understand and communicate with each other. I've spent a lot of time on the readme which contains a…
2023 · github.com
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I've been building computer-use tools for a while, and I quietly launched this about a month ago (122 Stars on GH). I figured it was worth sharing here. Over the last few months, a lot of computer-use agents have come out: Codex, Claude Code, CUA, and others. Most of them seem to work roughly like this: 1. Take a screenshot 2. Have the model predict pixel coordinates 3. Click x,y 4. Take another screenshot 5. Repeat That works, but it's slow, expensive in tokens, and fragile. If the UI shifts a few pixels, things break. And the model still doesn't know what any element actually is. But the…
May 2026 · github.com
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Hey HN! Wanted to show our open source agent harness called Gambit. If you’re not familiar, agent harnesses are sort of like an operating system for an agent... they handle tool calling, planning, context window management, and don’t require as much developer orchestration. Normally you might see an agent orchestration framework pipeline like: compute -> compute -> compute -> LLM -> compute -> compute -> LLM we invert this so with an agent harness, it’s more like: LLM -> LLM -> LLM -> compute -> LLM -> LLM -> compute -> LLM Essentially you describe each agent in either a self contained…
Jan 2026 · github.com
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Hello! Introducing geniusrise, an agent framework and component ecosystem for building AI agent networks that are as flexible as your team. landing page: https://geniusrise.ai (fancy but useless) docs: https://docs.geniusrise.ai (please check this out) github: https://github.com/geniusrise (for dear devs) ## Thought process Since the ChatGPT disruption, I've been pondering on what the tooling layer is going to look like for building LLM-interfacing agents. Saw a plethora of tools coming out as we witness here every week. I'd broadly categorize them into the…
2023 · github.com
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Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…
Jul 2026 · oodle.ai
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Open-source AI agent runtime — build Agents in plain English
Jul 2026 · syntheticbrew.ai
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Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…
Jul 2026 · shikigami.dev
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We were both genuinely impressed by Claude Code after it helped each of us fix nasty CI problems overnight. Doing those fixes manually would have taken days. After that experience, we each found ourselves struggling through Ctrl+Tab through multiple Claude Code windows in our terminals. While we enjoyed having agents working for us in parallel, context switching and cycling through each terminal tab was a real pain. So we thought: Can we design a TUI dashboard that manages a large swarm of agents in one place? Even better, can agents manage agents hierarchically, like how companies work?…
May 2026 · omar.tech
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At Metabase, we built an AI agent called Repro-Bot that reads our GitHub issues and attempts to reproduce reported bugs automatically. It started as a hackathon project and is now part of our daily workflow, so we wrote about it and open-sourced the code as an example for others. How have similar tools been working for you? What has worked well and what has not?
Apr 2026 · metabase.com
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The goal was to bring down the cost at the context eng. level. We do it with Layout Memoization. Instead of dumping HTML into the context window, we have built a continual learning browser harness (read only for now). We have built an early prototype for you to try out, where you can: 1. Spins up a browser instance 2. Extract any structured or tabular data from anywhere on the open-web 3. And you can do all this at the cost of a vector search Would love to hear your thoughts on this. Thanks for taking the time to read it.
8d ago · makralabs.org
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If agent's tools are exposed as functions/objects in a Python REPL (as opposed to JSON schemas) they perform better, I linked the explainer article we wrote, but if you want to jump straight in check out the docs! https://docs.symbolica.ai/
Dec 2025 · symbolica.ai
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We built meta-agent: an open-source library that automatically and continuously improves agent harnesses from production traces. Point it at an existing agent, a stream of unlabeled production traces, and a small labeled holdout set. An LLM judge scores unlabeled production traces as they stream. A proposer reads failed traces and writes one targeted harness update at a time, such as changes to prompts, hooks, tools, or subagents. The update is kept only if it improves holdout accuracy. On tau-bench v3 airline, meta-agent improved holdout accuracy from 67% to 87%. We open-sourced meta-agent.…
Apr 2026 · github.com
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Hi everyone! My team and I just open-sourced a bunch of cool agent dev tools: Invariant Explorer to visually inspect and understand AI traces and a testing framework, building on pytest.
2024 · github.com
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I have been trying to create AI retool where tooling is done via AI, to create full stack apps like internal portals, ERP apps. Which led me to an architecture where we give ai pre build component, tools and let is just do the binding, content generation work to create full stack apps. With this approach in a single prompt AI is able to generate final config jsons using chained/looped agentic llm flow and we render a full stack app with the configs at the end. I have open sourced the whole project whole code, app builder, agentic architecture, backend for you to use. Github:…
2025 · oneshotcodegen.com
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We just open-sourced Computer, a Computer-Use Interface (CUI) framework that enables AI agents to interact with isolated macOS and Linux sandboxes, with near-native performance on Apple Silicon. Computer provides a PyAutoGUI-compatible interface that can be plugged into any AI agent system (OpenAI Agents SDK , Langchain, CrewAI, AutoGen, etc.). Why Computer? As CUA AI agents become more capable, they need secure environments to operate in. Computer solves this with: • Isolation: Run agents in sandboxes completely separate from your host system. • Reliability: Create reproducible environments…
2025 · github.com
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