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Cortex

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One binary. Zero dependencies. 29 languages. 32 MCP tools. Local code intelligence for AI coding agents.

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Works with

Claude Code   Cursor   GitHub Copilot   Windsurf   VS Code   OpenAI Codex   Cline   Kiro   JetBrains   Neovim

Any MCP-compatible AI agent, IDE, or CLI tool connects out of the box. cortex install auto-detects your setup and writes the config.

All 25 supported platforms
Platform Install command
Claude Code (Linux/Mac) cortex install
Claude Code (Windows) cortex install --platform claude-code
Codex cortex install --platform codex
OpenCode cortex install --platform opencode
GitHub Copilot CLI cortex install --platform copilot
VS Code Copilot Chat cortex vscode install
Aider cortex install --platform aider
OpenClaw cortex install --platform openclaw
Factory Droid cortex install --platform droid
Trae cortex install --platform trae
Trae CN cortex install --platform trae-cn
Gemini CLI cortex install --platform gemini
Hermes cortex install --platform hermes
Kimi Code cortex install --platform kimi
Kiro IDE/CLI cortex kiro install
Pi coding agent cortex install --platform pi
Cursor cortex cursor install
Google Antigravity cortex antigravity install
Windsurf cortex install --platform windsurf
Zed cortex install --platform zed
JetBrains cortex install --platform jetbrains
Cline/Roo cortex install --platform cline
Continue.dev cortex install --platform continue
Supermaven cortex install --platform supermaven
Tabnine cortex install --platform tabnine

Why Cortex

An agent asking "what calls processOrder" gets a 200-token graph result instead of burning 20,000 tokens reading files. That's 100x fewer tokens on a single structural question.

Cortex indexes your repository into a SQLite call graph and exposes it over the Model Context Protocol. 127 files indexed in 535ms. Incremental re-index in 13ms. Handles 100K to 1M+ line repositories.

v1.1 adds multi-source confidence-aware intelligence. Cortex now ingests SCIP indexes for precise symbol resolution, runs framework-specific adapters (FastAPI, Express, NestJS, Spring, Django, React) to detect dependency injection and routing wiring, tags every edge with a confidence tier, and falls back honestly when it can't give a confident answer. The ask and get_task_context tools are completely overhauled with evidence-fusion ranking and structured fallback suggestions.

Install

npx @1337xcode/cortex@latest install
Other methods

Shell script

curl -fsSL https://raw.githubusercontent.com/1337Xcode/cortex/main/install.sh | sh

Build from source

git clone https://github.com/1337Xcode/cortex.git && cd cortex
cargo build --release
cp target/release/cortex ~/.local/bin/

Quick start

cortex index    # parse your repo, build the graph
cortex install  # detect Claude Code / Cursor, write MCP config
cortex serve    # start the MCP server

Your agent queries the graph instead of reading raw files.

Demo commands

cortex impact UserService.getUser       # blast radius: what breaks if I change this?
cortex explain DatabasePool.acquire     # offline function explanation, zero LLM calls
cortex security report                  # taint flows, OWASP patterns, dependency vulns
cortex diff main feature-branch         # call graph diff between branches
cortex viz --export graph.html          # interactive 3D graph in a standalone HTML file
cortex ci --fail-on-taint               # CI quality gate with exit codes
cortex hotspots --months 6              # high-churn risky code ranked by risk score
cortex coverage --lcov coverage.lcov    # populate coverage field, rank untested functions
cortex modules                          # Leiden community detection module boundaries
cortex federate add ../auth-service     # query across repos with unified graph
cortex benchmark                        # run correctness suite, report pass rate + token savings
cortex status --savings                 # token savings dashboard with honest net-negative reporting
cortex index --repair                   # rebuild index from scratch, preserve observations
cortex semantic enable                  # build embedding index for semantic search

What you get

  • 29 languages parsed via tree-sitter
  • 32 MCP tools exposed over stdio, or 5 in smart mode (the ask meta-tool routes internally)
  • SCIP index ingestion for precise symbol resolution — SCIP edges win over tree-sitter on conflicts
  • 6 framework adapters (FastAPI, Express, NestJS, Spring, Django, React) detect DI wiring, middleware chains, and routing that tree-sitter cannot see
  • Confidence-tagged edges — every edge carries edge_source and confidence (1.0 SCIP / 0.8 framework / 0.5 AST / 0.3 name-match); queries default to confidence ≥ 0.7
  • Evidence-fusion get_task_context — multi-signal ranking (BM25 + SCIP distance + git recency + edge confidence + file size penalty) with per-file reasons and guaranteed non-empty results
  • Honest fallbackask returns structured grep suggestions when confidence is below MEDIUM; never returns confidently wrong data from an unhealthy index
  • Index health gate — all tools return a structured error with fallback suggestions when the index is empty or corrupt
  • Token savings dashboardcortex status --savings shows cumulative net saved, average per query, and net-negative queries (reported honestly)
  • get_repo_brief — zero-parameter cold-start summary under 400 tokens: languages, frameworks, entry points, hotspots, security patterns, test shape
  • Tool surface management — 10 default tools, 7 experimental (opt-in), 5 smart-tools mode; semantic_search only listed when embeddings are built
  • Incremental embeddings — only re-embeds functions whose content hash changed; cortex semantic enable builds the initial index
  • Correctness benchmarkscortex benchmark validates tool accuracy against ground-truth; CI gate fails the build if pass rate drops below 70%
  • Sub-second incremental re-indexing via native OS file watcher (inotify, FSEvents, ReadDirectoryChangesW)
  • Configurable model pricing via ~/.cortex/pricing.toml with longest-prefix matching
  • Taint flow analysis, OWASP Top 10 detection, SBOM generation all running locally, no cloud
  • Cross-session memory that marks observations stale when linked code changes
  • Multi-repo federation for querying across repositories with a unified graph
  • CI quality gates with configurable thresholds and exit codes
  • 3D interactive graph visualization exportable to standalone HTML
  • Portable JSON bundle (cortex.json) for team sharing via git
  • Works offline. No cloud, no API keys, no Docker, no language runtimes.
All 29 supported languages
Language Language Language
Python TypeScript JavaScript
Rust Go Java
C C++ C#
Ruby PHP Swift
Kotlin Scala Lua
Zig Haskell Elixir
Dart R Julia
OCaml Bash HCL/Terraform
Perl Objective-C SQL
YAML TOML

Architecture

graph TD
    A[Repository Files] --> B[File Watcher<br/>notify crate]
    B --> C[Indexer Pipeline<br/>Rayon + tree-sitter]
    C --> D[SQLite Graph Store<br/>WAL + FTS5 + sqlite-vec]
    D --> E[MCP Server<br/>Tokio + JSON-RPC 2.0]
    E --> F[AI Coding Agent]

    C --> G1[SCIP Ingester<br/>Protobuf → HIGH edges]
    G1 --> D

    C --> G2[Framework Adapters<br/>FastAPI · Express · NestJS<br/>Spring · Django · React]
    G2 --> D

    C --> G3[Security Pass<br/>Taint + OWASP + SBOM]
    G3 --> D

    D --> H[Bundle Exporter<br/>cortex.json]
    H --> I[Git Repository<br/>Team Sharing]

    D --> J[Memory Layer<br/>Staleness-aware observations]

    D --> K[Evidence Fusion<br/>BM25 + SCIP + git + embeddings]
    K --> E
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Confidence tiers

Every edge in the graph carries a source and confidence value. Queries default to confidence ≥ 0.7 (MEDIUM), filtering out heuristic name-match edges.

Source Confidence How produced
scip 1.0 HIGH SCIP index (precise symbol resolution)
framework_adapter 0.8 MEDIUM FastAPI/Express/NestJS/Spring/Django/React pattern matching
ast_direct 0.5 LOW tree-sitter AST extraction
name_match 0.3 VERY_LOW heuristic name-based resolution

Benchmark results

Cortex ships with a self-referential benchmark suite (benchmark/cortex_self_benchmark.json, 24 cases) covering trace_callers, blast_radius, get_task_context, and ask. Run it against your index:

cortex benchmark

The CI pipeline runs this on every release and fails the build if pass rate drops below 70%.

Comparison

Feature Cortex codebase-memory-mcp Srclight Serena mcp-codebase-index
Architecture Rust binary, SQLite C binary, SQLite Python, SQLite Python, LSP Python, SQLite
Languages 29 (tree-sitter) 66 (tree-sitter) 10 (tree-sitter) 40+ (LSP) 4
MCP tools 32 (or 5 smart mode) 14 42 ~20 17
Smart tool routing Yes (ask meta-tool) No No No No
SCIP precise resolution Yes No No Yes (LSP) No
Framework adapters 6 (DI, routing, middleware) No No No No
Confidence-tagged edges Yes (4 tiers) No No No No
Honest fallback Yes (FallbackSuggestion) No No No No
Index health gate Yes No No No No
Token savings dashboard Yes (honest math) No No No No
Cold-start repo brief Yes (get_repo_brief) No No No No
Correctness benchmarks Yes (CI gate, 70% threshold) No No No No
Call graph Function-level Full chains Callers/callees LSP-precise Cross-file
Hybrid search FTS5 + sqlite-vec Graph only RRF fusion Keyword Structural
Token reduction 100x on structural queries 121x avg Not measured Not measured 87%
Incremental update 13ms (no changes) Milliseconds Git hooks LSP live Hash-based
Security (taint/OWASP/SBOM) Yes No No No No
Cross-session memory Staleness-aware No No No No
Multi-repo federation Yes No SQLite ATTACH Partial No
Community detection Leiden algorithm No Leiden No No
Coverage gap analysis Yes (LCOV cross-ref) No No No No
Single binary, zero deps Yes Yes No (Python) No (Python+LSP) No (Python)
Auto IDE config 25 agents No No No No
Air-gap compatible Yes Yes Yes Yes Yes
License MIT MIT MIT MIT MIT

Documentation

Full docs site: 1337xcode.github.io/cortex

License

MIT

Documentation · npm · Issues