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.

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.

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.

Quickstart

pip install codecortex

This installs the codeintel CLI; the semantic engine works out of the box. The graph and LSP engines use external backends (codebase-memory-mcp, and serena via uvx) — run codeintel doctor to see what's available and how to enable the rest. (On PyPI the distribution is codecortex because codeintel was taken; the CLI and import stay codeintel.)

Or from source:

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

Register with your AI agent(s):

codeintel install            # registers with Claude, Codex, Gemini, Zed

Index a project, check what's ready, and run your first query:

codeintel index /path/to/your/project
codeintel doctor /path/to/your/project    # which engines are ready + how to fix the rest
codeintel query --op search --target "authentication middleware"

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.
• 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
max_chunks   = 500                      # max chunks to embed per file
model        = "BAAI/bge-small-en-v1.5" # fastembed embedding model

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.

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

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.

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)