Alternatives
Products that do what Context Graph Agent does
Local code graphs for AI coding agents
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Hi all, I'm Peter at Staff Engineer and Mozilla.ai and I want to share our idea for a standard for shared agent learning, conceptually it seemed to fit easily in my mental model as a Stack Overflow for agents. The project is trying to see if we can get agents (any agent, any model) to propose 'knowledge units' (KUs) as a standard schema based on gotchas it runs into during use, and proactively query for existing KUs in order to get insights which it can verify and confirm if they prove useful. It's currently very much a PoC with a more lofty proposal in the repo, we're trying to iterate from…
Mar 2026 · blog.mozilla.ai
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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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Attach reference projects for AI coding tools
Apr 2026 · marketplace.visualstudio.com
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This project (Agents Observe) started as an exploration into building automation harnesses around claude code. I needed a way to see exactly what teams of agents were doing in realtime and to filter and search their output. A few interesting learnings from building and using this: - Claude code hooks are blocking - performance degrades rapidly if you have a lot of plugins that use hooks - Hooks provide a lot more useful info than OTEL data - Claude's jsonl files provide the full picture - Lifecycle management of MCP processes started by plugins is a bit kludgy at best The biggest takeaway is…
Apr 2026 · github.com
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Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Feb 2026 · github.com
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Hey HN! We’ve been building an MCP server to help AI-assisted web app developers by using browser agents to test whether changes made by an AI inside an editor actually work. We've been testing it on scenarios like verifying new flows in a UI, or checking that sending a chat request triggers a response. The idea is to let your coding agent both code and evaluate if what it did was correct. Here’s a short demo with Cursor: https://www.youtube.com/watch?v=_AoQK-bwR0w When building apps, we found the hardest part of AI-assisted coding isn’t the coding—it’s tedious point-and-click…
2025 · github.com
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Context Graphs for your AI Agents, MCPs and LLMs.
Mar 2026 · akto.io
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Aug 2026 · github.com
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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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Jul 2026 · github.com
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Hi HN, I'm Tony. I built Grov (https://grov.dev/) because I hit a wall with current AI coding assistants: they are "single-player." The moment I kill a terminal pane or close a chat session, the high-level reasoning and architectural decisions generated during that session are lost. If a teammate touches that same code an hour later, their agent has to re-derive everything from scratch or read many documentation files for basically any feature implemented or bug fixed. I wanted to stop writing a lot of docs for everything just to give context to my agents or have to re-explain…
Jan 2026 · github.com
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