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AI · June 5, 2026

Context Graph Agent

Local code graphs for AI coding agents

Context Graph Agent launched on June 5, 2026 with 3 votes, #6,658 of 22,647 launches that month and more than 50% of that year's launches.

What it does

CGA gives AI coding agents focused repository evidence instead of broad prompt dumps. It indexes files, symbols, calls, imports, dependencies, and lightweight data flow into a local graph, then exposes MCP-compatible tools for retrieval before generation. The admin UI, 3D graph viewer, CGA-Relay, schedules, backup, and benchmark workflow help teams test local-first context quality.

CGA, Context Graph Agent, helps AI coding agents use less context, lower hallucination pressure, and find repository evidence faster.

Context Graph Agent helps AI coding agents use about 90% fewer prompt tokens, lower hallucination pressure, and find the right files, symbols, and dependencies faster. The admin graph view shows repository relationships as traversable evidence instead of dumping broad source into a prompt. Context Graph Agent retrieves the right evidence before generation, so AI coding agents query repository relationships, dependencies, files, and symbols instead of relying on keyword search alone. Retrieves the relevant code, symbols, dependencies, and recent project facts before an agent starts writing. Lets models query calls, imports, definitions, ownership, and dependency paths, not only keyword…from nascousa.github.io

Does a similar job

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  • Context GatewayMar 2026 · github.com · ▲220

    We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…

  • Cq – Stack Overflow for AI coding agentsMar 2026 · blog.mozilla.ai · ▲225

    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…

  • ContextPoolApr 2026 · contextpool.io · ▲180

    Persistent memory for AI coding agents

  • 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…

  • ContextbergMay 2026 · contextberg.com · ▲102

    Turn your work into AI agent memory, served over MCP

  • ContextsBaseSep 2026 · contextsbase.com · ▲75

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