Launching today

Context Graph Agent

Launching today

Local code graphs for AI coding agents

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Local code graphs for AI coding agents

1 follower

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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.

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Launch tags:Open Source•Developer Tools•Artificial Intelligence

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Brand Context APIBrand context for AI agents. 50M+ brands, one API call.

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Hi Product Hunt, I built CGA because AI coding agents often fail before generation: they do not get the right code evidence. CGA indexes a repo into a local graph of files, symbols, calls, imports, dependencies, and lightweight data flow, then exposes MCP-compatible tools so agents can retrieve focused context before editing. The first benchmark covers 102 real-code cases: 90.44% average prompt-token reduction and 13.34% average HPS reduction, with the nuance documented publicly. I would love feedback on MCP retrieval, the graph viewer, install flow, and benchmark design.

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6h ago

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6h ago

What should CGA measure next for AI coding context?

CGA (Context Graph Agent) is launching today, and I would love technical feedback from people building or using AI coding agents.

The core idea is simple: instead of giving an agent broad file dumps or noisy search results, CGA indexes a repository into a local graph of files, symbols, calls, imports, dependencies, and lightweight data flow. Agents can then query focused evidence through MCP-compatible tools before generating code.

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