NEAT

NEAT solves the AI coding context problem. It constructs a live deterministic model of your codebase — static code and live runtime behavior fused into one graph — and hands your AI agents the grounded, full-stack context they need to query, code, debug, and write rules against it. This achieves the following:

• Coding LLMs hallucinate less & are more accurate.
• Rather than endlessly grepping files and guessing problems, NEAT provides time-travelling error logs along the model's nodes and edges so the LLM can infer exactly what's wrong.
• Rules & Policies allow agents to write new features while adhering to rules set by previous features, other LLMs, or engineers. For example, only use postgres for services x and y, and mongoDB for services p and q (the possibilities are endless).

TL;DR The deterministic grounded truth of code for your agents.

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

Run it from inside your project (or npx neat.is <path>). It discovers your services, extracts the static graph, wires in OpenTelemetry, starts the daemon, and opens the dashboard — no config. Then run your app and watch the live edges populate.

On Windows, use the neat command, not npx neat.is. npm generates a shim literally named neat.is, and Windows won't execute a .is file — it hands it to a file association instead. Install once and run the dotless binary: npm i -g neat.is, then neat (or neat <path>). Without a global install: npx -p neat.is neat. Every npx neat.is <verb> below becomes neat <verb> this way.

A more in-depth explanation:

At the center of NEAT is one live graph of your system, fused from two streams into a single model you can query many ways:

• Static analysis — tree-sitter over your source (JavaScript, TypeScript, Python), package.json, and yaml / env config. Every source file becomes a node; imports between them become edges; the calls each file makes to databases, queues, and external hosts are extracted from the code.
• Runtime telemetry — OpenTelemetry spans, attributed back to the exact file and line that made the call. NEAT wires the instrumentation for you, so the runtime edge lands on the same file node the static edge does.

Both streams land on the same nodes, so the graph holds what your code declares and what your system does side by side. From there, the useful questions fall out of one model: what would break if this node dies (blast radius), what broke first (root cause), which architectural rules a change would violate (policies), and where declared intent and observed reality part ways (divergence). Same graph, different traversals.

The file is the primary unit. A relationship in the graph runs from a file — src/services/billing.ts ──CALLS──▶ api.stripe.com — not from a vague service blob. Anchoring relationships to files and lines is what keeps every one of those answers sharp: a finding names this file, calling this target, rather than a service-shaped shrug.

Every edge carries a provenance tag so a consumer knows exactly how much weight a claim deserves:

• EXTRACTED from source. No clock decay.
• OBSERVED from a span. Carries lastObserved and callCount.
• INFERRED by the trace stitcher where OTel coverage has gaps. Confidence is capped.
• STALE because runtime stopped speaking. Preserves the original lastObserved.

The graph is exposed to AI agents through sixteen MCP tools. Ten read the graph — get_root_cause, get_blast_radius, get_dependencies, get_observed_dependencies, get_incident_history, get_divergences, get_graph_diff, get_recent_stale_edges, check_policies, semantic_search — and six (neat extend) let an agent close instrumentation gaps for libraries the bundled OTel set doesn't cover, driven by a versioned instrumentation registry.

CLI

The same neat binary handles every verb. After a global install (npm i -g neat.is) or via npx neat.is:

neat <path>              orchestrator: extract, instrument, spawn daemon, open dashboard
neat init <path>         extract only; patch-by-default, --apply to write
neat watch <path>        keep the graph live as files change
neat deploy              emit deployment artifacts for a hosted target
neat sync --to <url>     push the local EXTRACTED snapshot to a remote daemon (v0.3.9)
neat divergences         where your code and your production traffic disagree
neat root-cause <id>     walk inbound edges to find what broke first
neat blast-radius <id>   BFS outbound; what would break if this node dies
neat dependencies <id>   transitive outbound dependencies
neat incidents           recent error events
neat policies            current policy violations
neat search <query>      semantic match on node names and ids

Every query verb honors --json and --project <name>. Exit codes branch on success (0), server error (1), misuse (2), and daemon unreachable (3).

One example: what a divergence looks like

A divergence is one of the questions the graph answers, and the one that's hardest to get any other way — it needs both streams at once. Once your app has run, neat divergences reports where declared intent and observed behavior part ways:

[missing-extracted] src/services/prices.ts ──CALLS──▶ folio-api.example.com   confidence 0.87
  Production observed this call, but static analysis never surfaced the edge.
  → dynamic dispatch or a coverage gap. The code reaches a host it doesn't visibly name.

[missing-observed]  src/db/client.ts ──CONNECTS_TO──▶ postgres:primary        confidence 0.85
  Code declares this connection, but no production traffic has exercised it.
  → dead path, feature flag, or an unshipped branch. The declared dependency is idle.

Two findings, two different bugs. The first is a call your code makes without saying so — worth knowing before it surprises you. The second is a dependency your code carries but never uses — dead weight, or a path you thought was live and isn't. Both come from comparing the same file against itself: what it says, versus what it did.

Policies: rules over the graph

Divergence reports what is. Policies let you assert what should be. A policy.json in your project declares architectural rules as assertions over the same graph — for example, "only service:billing and service:orders may connect to postgres:primary," or "no file may call legacy-api.internal." Because the rules run against the live graph, they evaluate against both what your code declares and what production actually does.

NEAT evaluates every policy continuously as the graph changes. When an edge violates a rule, the violation is surfaced — not buried in a one-off lint run. Two surfaces expose it:

• neat policies lists what's currently violating, scoped to a node with --node, or dry-run a change with --hypothetical-action.
• check_policies hands the same answer to an AI agent over MCP, so an agent writing a new feature can see which rules it would cross and the assertions it's working within — the rules previous features, other agents, or your engineers already set.

A block action gates promotion of a FrontierNode (an external host the graph has newly seen) so unsanctioned external dependencies don't quietly settle into the model. The throughline: the graph already knows your architecture, so the rules you care about become assertions over it that stay true as the system moves.

Run NEAT on a server

The container image at ghcr.io/neat-technologies/neat:latest boots neatd start and exposes REST on :8080, OTLP on :4318, and the web UI on :6328. Generate a token, run the image, point your OTel SDKs at it:

NEAT_AUTH_TOKEN=$(openssl rand -hex 32)
docker run -d --name neat \
  -e NEAT_AUTH_TOKEN="$NEAT_AUTH_TOKEN" \
  -p 8080:8080 -p 4318:4318 -p 6328:6328 \
  -v /var/lib/neat:/neat-out \
  ghcr.io/neat-technologies/neat:latest

NEAT_AUTH_TOKEN is required on every public interface. The daemon refuses to bind on non-loopback addresses without one. REST and SSE callers send the token in Authorization: Bearer <token>; OTel exporters send the same header. Rotate the OTLP token independently with NEAT_OTEL_TOKEN.

Easier path: neat deploy generates the token, writes a docker-compose.neat.yml, and prints the env block your application's deploy platform needs:

OTEL_EXPORTER_OTLP_ENDPOINT=https://<your-host>:4318
OTEL_EXPORTER_OTLP_HEADERS=Authorization=Bearer <generated-token>

Behind a reverse proxy

When TLS termination and authentication already live in a proxy upstream, set NEAT_AUTH_PROXY=true so the daemon skips the request-side bearer check. The bind-authority gate still refuses public binds without NEAT_AUTH_TOKEN, so set both:

neat.example.com {
  reverse_proxy localhost:8080 {
    header_up Authorization "Bearer {env.NEAT_AUTH_TOKEN}"
  }
  reverse_proxy /events localhost:8080
  @otlp path /v1/traces
  reverse_proxy @otlp localhost:4318 {
    header_up Authorization "Bearer {env.NEAT_AUTH_TOKEN}"
  }
}

Then docker run … -e NEAT_AUTH_TOKEN=… -e NEAT_AUTH_PROXY=true … and let Caddy gate the public surface.

Wire NEAT into your agent