codeintel

One MCP tool that lets a coding agent search, trace, and understand a codebase — structurally, not by grepping. codeintel unifies three engines — a call/import graph, an LSP for exact symbols, and semantic embedding search — behind a single code.query call that routes to the right engine, caches the answer, and never throws. The agent always gets back a clean, well-formed result to reason over.

codeintel visualizing its own codebase. One command — codeintel graph <repo> --html — turns any indexed repo into a self-contained, interactive call graph you can open offline or share as a file. Layouts, complexity-sized nodes, click-to-inspect metrics, and JSON/Markdown/SVG/PNG export. See docs/graph-viewer.md.

Prefer plain text? codeintel map writes a readable architecture overview to CODE_INTEL.md — node/edge counts, ranked symbols by caller count, and entry points — for skimming or for MCP hosts that can't render a graph:

What CODE_INTEL.md is for. It's a static, committable snapshot of a codebase's shape — meant to be read (by a person or an agent) first, instead of reconstructing structure by grepping. It covers the cases the live code.query tool doesn't:

• Agents & hosts that don't speak MCP. Not every agent supports MCP, and the server isn't always running. codeintel map writes a plain file any agent can read; codeintel map --inject also drops a pointer into CLAUDE.md / AGENTS.md, so an agent picks up the codebase's structure automatically at the start of a session.

• A committed, diffable overview. It lives in the repo — reviewable in a PR, browsable on GitHub, available offline. Re-run codeintel map after codeintel index to refresh it.

• The load-bearing code at a glance. Ranking symbols by caller count surfaces what most of the codebase depends on (the risky-to-change core) plus the entry points — the first things a newcomer, or an agent, should understand before touching anything.

See docs/map-file.md for the format and the --inject flow.

Why an agent needs it

Without structural tools, an agent dropped into unfamiliar code falls back on grep and reads whole files to reconstruct relationships by hand — burning tokens, missing call sites, and guessing at blast radius before it edits anything. codeintel answers those questions directly instead:

• "What calls this? What breaks if I change it?" → the real call graph, which catches cross-file and module-level callers a text search silently misses.

• "Where is this symbol defined, and everywhere it's used?" → the language server, with exact locations.

• "Where's the code that does X?" (when you don't know the name) → semantic search over the repo.

• Always a clean answer. Every call returns the same JSON envelope. A missing or broken backend degrades to a safe null with a reason — so the agent falls back to grep instead of crashing on an exception it can't reason its way out of.

Net effect: fewer, sharper tool calls, less re-reading, and an agent that can see structure — callers, impact, call chains — that plain search can't.

Related MCP server: codemap

What your agent can ask

It's one call: code.query(op, target, engine="auto"). In auto mode (the default) codeintel picks the engine per operation:

Pin one engine with --engine graph│lsp│semantic, or fan out with --engine both / all to merge results.

Example — "who uses safe_null_result?"

// request
{ "op": "callers", "target": "safe_null_result", "engine": "auto" }

// response — always this exact envelope; `result` is ready-to-read markdown
{
  "ok": true, "op": "callers", "target": "safe_null_result",
  "engine": "graph", "cached": false,
  "result": "## Callers of safe_null_result (7)\n- …gateway [USAGE] (src/codeintel/gateway.py)\n- …providers.graph [USAGE] (src/codeintel/providers/graph.py)\n- …server [USAGE] (src/codeintel/server.py)\n- … (4 more)"
}

The agent hands result straight to the model. If the graph backend isn't installed, the identical call returns "result": null, "reason": "engine-unavailable" — no exception, and the agent just falls back to its own search.

What makes it good

• Local-first and private. One process on your machine — no cloud service, no API keys, no telemetry, no per-query network. Safe to point at a private repo, even with --engine all. (The one-time exception: fastembed downloads its embedding model once, then runs fully offline.)

• It never throws. Every call returns the same JSON envelope; a missing or broken backend degrades to null with a reason. No exceptions, no 500s, no malformed output for the agent to trip over — so you never wrap code.query in a try.

• One tool, not three. Register a single MCP server and it auto-routes each question to graph, LSP, or semantic — instead of wiring up three backends with three response shapes and three failure modes.

• Degrades instead of breaking. No graph backend installed? That engine returns null and the agent falls back to grep. The semantic engine needs nothing external, so codeintel is useful the moment it's installed and only gets sharper as you add backends.

• Fast on repeat, never stale. A content-hash cache returns instantly for unchanged code and self-invalidates when a background reindex advances the index — answers stay both quick and fresh. The cache is bounded (LRU), so a long-running server holds steady memory.

• Concurrency-safe. The HTTP transport handles requests on threads, so one slow query (an LSP session warming, a first-time index) can't block every other agent.

• Honest about its own health. codeintel doctor reports exactly which engines are ready for a repo and the single command to fix each gap — no guessing why a query came back empty.

Quickstart

pip install codecortex

This installs the codeintel CLI; the semantic engine works out of the box. (On PyPI the distribution is codecortex because codeintel was taken; the CLI and import stay codeintel.)

One command prepares the rest and indexes your repo:

codeintel setup --all /path/to/your/project

This installs uv (for the LSP engine), warms serena, downloads the embedding model, indexes the repo, and prints a health report ending in a Next: list — exactly what's ready and the one remaining step. It's idempotent, so re-running is safe. The graph engine (codebase-memory-mcp) is an optional external binary that adds who-calls / impact / hotspots / changed; codeintel is fully usable without it.

Or from source:

git clone https://github.com/hamilton-sky/codeintel.git
cd codeintel
pip install -e .

Register with your AI agent(s), then query:

codeintel install            # registers with Claude, Codex, Gemini, Zed
codeintel query --op search --target "authentication middleware"

Enable native Codex integration

codeintel is an MCP server, so Codex can call its tools directly rather than invoking the CLI. After installing the package, explicitly register it with Codex:

codeintel install --agent codex

This safely adds a [mcp_servers.codeintel] entry to ~/.codex/config.toml without changing your other Codex settings. Registration is deliberately opt-in: installing a Python package should not silently modify an agent's configuration. Start a new Codex task (or restart Codex) after registration; the refreshed task will have native code.query, code.status, code.doctor, and code.map MCP tools available.

For a fully prepared local setup, run:

codeintel setup --all /path/to/your/project && codeintel install --agent codex

Enable native Claude Code integration

After installing the package, explicitly register it with Claude Code:

codeintel install --agent claude

This adds the codeintel MCP server to ~/.claude/settings.json while preserving your existing settings. Start a new Claude Code session after registration so it can load the native code.query, code.status, code.doctor, and code.map MCP tools.

For a fully prepared local setup, run:

codeintel setup --all /path/to/your/project && codeintel install --agent claude

How it works

A Gateway receives every query and dispatches it to one of three providers — graph (structural relationships), LSP (precise symbol resolution), or semantic (embedding-based search) — based on the operation type. Each provider is fully isolated: if it is unavailable or raises an exception, the gateway catches it and returns a safe-null envelope. The caller always gets a well-formed response with no exception to catch.

flowchart LR
    A["AI agent · MCP"] --> GW
    H["Harness · HTTP"] --> GW
    C["Developer · CLI"] --> GW
    GW["Gateway<br/>route · cache · safe-null"] -->|"auto: search"| SP[SemanticProvider]
    GW -->|"auto: impact / callers / …"| GP[GraphProvider]
    GW -->|"auto: symbol"| LP[LspProvider]
    GP --> GB[("codebase-memory-mcp")]
    LP --> LB[("language server")]
    SP --> SB[("fastembed + sqlite-vec")]

Full walkthrough: docs/architecture.md · docs/query-flow.md.

Safe-null contract

Every Gateway.query() call returns a dict with exactly these keys:

{"ok": true, "op": "search", "target": "auth", "result": null, "engine": "semantic", "cached": false}

ok is always true. result is null when no provider has an answer — never an exception, never a 500. An optional reason key explains null results (e.g. "engine-unavailable", "no-result"). Callers must check result is not None before using the value.

Engines

Run codeintel doctor at any time to see which engines are actually ready for a repo and how to fix the ones that aren't.

Pass --engine auto (the default) and codeintel chooses the best engine per operation. Pass --engine both or --engine all to fan out to multiple engines and merge results.

Documentation

Full system docs live in docs/ — start with the index:

• Architecture — layers, the CodeProvider protocol, the safe-null contract, caching, freshness (ASCII + Mermaid).

• Query flow — request lifecycle, engine selection, fan-out & merge, and why it never throws.

• Map file — the static CODE_INTEL.md orientation layer for hosts with no MCP support.

• Benchmarks — real numbers at scale: 25 k chunks indexed in ~8 min, ~235 ms warm queries, 60 MB index.

• Engine references: graph · lsp · semantic.

CLI reference

Human-facing commands (doctor, status, query, setup, reset) honor --no-color / NO_COLOR and --ascii, and auto-degrade to plain text when piped.

Config

Create .codeintel.toml at your project root to override defaults:

backend          = "auto"                   # auto | graph | lsp | semantic
semantic         = "on"                     # on | off
reindex          = "on-demand"              # on-demand | never
cosine_floor     = 0.25                     # minimum similarity score for semantic hits (0–1)
max_chunks       = 500                      # max chunks to embed per file
max_total_chunks = 100000                   # safety ceiling on chunks embedded in one index pass
model            = "BAAI/bge-small-en-v1.5" # fastembed embedding model

Config is validated on load — an out-of-range number, a misspelled enum, or a wrong type falls back to that key's default (with a logged warning) instead of breaking every query.

Environment variables:

Privacy & dependencies

codeintel is local-first — one local process, no cloud service, no API keys, no telemetry, and no per-query network. Its own code makes zero outbound HTTP calls, and the HTTP transport binds to 127.0.0.1 only by default — binding a non-loopback host requires --allow-remote, and --token (or CODEINTEL_HTTP_TOKEN) then gates every request behind a bearer token. The server bounds concurrent connections, but for exposure to a hostile network you should still front it with a reverse proxy (TLS, rate-limiting) — the built-in http.server is not hardened for the open internet.

Bundled (installed with the package, run locally): mcp (the tool interface) · sqlite-vec (the semantic index, a local DB file) · fastembed (the local embedding model).

Optional external backends — auto-detected on PATH; if one is absent, that engine returns a safe-null and the agent simply degrades to grep:

Not sure what's installed? codeintel doctor reports exactly which backends are present, whether this repo is indexed, and the command to fix each gap.

The only network touch is first-run setup: fastembed downloads the BAAI/bge-small-en-v1.5 weights once (cached under ~/.cache, fully offline thereafter); the optional backends also install on first use if you opt in. After that, no code or data leaves your machine — which is what makes --engine all safe to run on a private repo.

For agents

Register codeintel as an MCP server (codeintel install) and the agent gets four tools:

Over MCP the agent calls code.query directly. Over HTTP, start the server and POST to /code/query:

codeintel serve-http &   # listens on 127.0.0.1:8766 by default

For a shared or remote deployment, start it with --allow-remote --token "$CODEINTEL_HTTP_TOKEN" and send Authorization: Bearer <token> on each request — a missing or wrong token gets a clean 401. Requests are handled concurrently, so one slow query never blocks another.

import urllib.request, json

def code_query(op: str, target: str, engine: str = "auto") -> dict:
    body = json.dumps({"op": op, "target": target, "engine": engine}).encode()
    req = urllib.request.Request(
        "http://127.0.0.1:8766/code/query",
        data=body,
        headers={"Content-Type": "application/json"},
    )
    with urllib.request.urlopen(req) as resp:
        return json.loads(resp.read())

result = code_query("search", "authentication middleware")
if result["result"] is not None:
    print(result["result"])   # ranked semantic matches

The response is always JSON-safe. Check result["result"] is not None before use. Never catch an exception from the gateway — it never raises.

Operations & deployment

Running codeintel as a shared service? It ships with what ops teams expect:

Plus bearer-token auth — or RBAC (per-token roles + op scopes via auth.toml; a disallowed op returns 403, and the role is server-authoritative so a client can't escalate) — structured JSON logs (CODEINTEL_LOG_FORMAT=json) with optional per-request access logs, graceful SIGTERM shutdown, a bounded connection pool, and a non-root Dockerfile with a healthcheck.

Full guide → docs/deploy.md: systemd, Docker / Compose, Kubernetes (liveness + readiness probes, token from a Secret), reverse-proxy TLS, RBAC + SSO-via-auth-proxy, a Prometheus scrape config, and a security checklist.

docker build -t codeintel . && docker run -p 127.0.0.1:8766:8766 \
  -e CODEINTEL_HTTP_TOKEN="$(openssl rand -hex 32)" codeintel

Development

git clone https://github.com/hamilton-sky/codeintel.git
cd codeintel
pip install -e .[dev]
pytest tests/ -q            # full suite (~15s — includes live graph/LSP backend tests)