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Live code graph that fuses static analysis (tree-sitter) with OpenTelemetry runtime traces, queried over MCP so AI coding agents get real full-stack context.
Обзор
Agents are not good at investigation, and they are not good at reasoning. Given the right context, they are very good at writing code. NEAT does the investigation and the reasoning, so your agent can do the coding. The clearest example is the . When something in your system fails, NEAT assembles one work order for it: where the failure is (down to the file and line when the runtime recorded one), the chain of what caused it, everything a fix will reach, the architectural rules that govern that code, and where the code and production disagree. Every claim carries how NEAT knows it — read from source, seen at runtime, or inferred — and nothing is guessed: a cause NEAT can't reach is left blank, not filled in. The agent reads the card and writes the fix, instead of grepping files and guessing at the cause. How NEAT does the investigation: it keeps a live graph of your running system.
README
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NEAT
Agents are not good at investigation, and they are not good at reasoning. Given the right context, they are very good at writing code. NEAT does the investigation and the reasoning, so your agent can do the coding.
The clearest example is the incident card. When something in your system fails, NEAT assembles one work order for it: where the failure is (down to the file and line when the runtime recorded one), the chain of what caused it, everything a fix will reach, the architectural rules that govern that code, and where the code and production disagree. Every claim carries how NEAT knows it — read from source, seen at runtime, or inferred — and nothing is guessed: a cause NEAT can’t reach is left blank, not filled in. The agent reads the card and writes the fix, instead of grepping files and guessing at the cause.
How NEAT does the investigation: it keeps a live graph of your running system. The real things in it are nodes — files and the symbols inside them, services, databases, routes, config, and infrastructure — joined by edges that each record how NEAT learned them: EXTRACTED from source, OBSERVED from OpenTelemetry, INFERRED by the stitcher that bridges gaps, STALE when the runtime stopped confirming an edge, and FRONTIER for a staged proposal. Fusion joins what the code declares to what production does at the same grain — file and line, symbol, host, database table — and those joins are where the answers are. The graph is deterministic and calls no LLM anywhere in the product: method, not model.
Every NEAT capability is a read of that one graph, not a layer on top of it — root cause, blast radius, dependencies, the divergence between code and production, policy checks, incidents, and grades. When you are working a task of your own, ask the graph first — incidents, divergences, root cause, blast radius, dependencies — before you grep. The incident card above is the one NEAT hands you when it finds a failure worth it, not something you fetch.
NEAT is in active development. Capability ships as patch releases on the npx neat.is surface; see open issues for what’s on deck.
One command
npx neat.is
Установка
npx neat.isКонфигурация
{
"mcpServers": {
"neat": {
"command": "neat-mcp",
"env": { "NEAT_CORE_URL": "http://localhost:8080" }
}
}
}