██╗     ███████╗ █████╗ ███╗   ██╗     ██████╗████████╗██╗  ██╗
██║     ██╔════╝██╔══██╗████╗  ██║    ██╔════╝╚══██╔══╝╚██╗██╔╝
██║     █████╗  ███████║██╔██╗ ██║    ██║        ██║    ╚███╔╝
██║     ██╔══╝  ██╔══██║██║╚██╗██║    ██║        ██║    ██╔██╗
███████╗███████╗██║  ██║██║ ╚████║    ╚██████╗   ██║   ██╔╝ ██╗
╚══════╝╚══════╝╚═╝  ╚═╝╚═╝  ╚═══╝     ╚═════╝   ╚═╝   ╚═╝  ╚═╝

LeanCTX

Context Gateway for AI Systems.

Control what your AI can see.

LeanCTX sits between AI tools and the information they read. It selects task-relevant context, applies supported access and content controls before delivery, and records the context operations and delivery evidence it can observe. Your application, agent loop and model stay yours.

The open-source LeanCTX Engine runs locally through CLI, MCP, hooks and proxy paths. The LeanCTX SDK embeds supported Engine capabilities in your application.

---

  

Website  ·  Docs  ·  Install  ·  Scenarios  ·  Demo  ·  Benchmarks  ·  Cookbook  ·  Security  ·  Changelog

---

Where it sits

Files · repositories · tool results · configured providers
                          │
                          ▼
                LeanCTX Context Gateway
                Select → Control → Prove
                          │
                          ▼
                Your AI application / agent
                          │
                          ▼
                     Your model

• Select: give AI the context the task needs, with search, structural views, compression and reuse.
• Control: apply configured path permissions, content filters and context budgets to supported calls.
• Prove: inspect source references, policy decisions and usage evidence. A context-only integration records its prepared result; the host owns the subsequent model call.

For coding workflows, start with Claude Code, Cursor or Codex. The installation matrix distinguishes first-class paths from other compatibility references.

Your work	Start here
Improve the context path of existing AI tools	Community setup
Apply shared context controls across an organization	Enterprise — licensed controls and deployment scope
Build context control into your application	LeanCTX SDK — stable interfaces and separate SDK/OEM terms

What is LeanCTX? · Where LeanCTX fits · Architecture

See it in action:

Read + Shell Map-mode reads + compressed CLI output

Gain (live) Token usage + estimated cost differences

Benchmark proof Measure compression by language + mode

_{All GIFs are generated from reproducible VHS tapes in demo/.}

Why developers use LeanCTX

• Focused input — use less of the context window for repeated reads and noisy output.
• Configured controls — bound file access and filter supported results before delivery.
• Continuity — retain local task, finding and decision records across sessions.
• Existing tools — configure a supported integration with lean-ctx setup.
• Inspectable usage — separate token estimates, provider observations and calculated costs.
• Model choice — keep your own model calls and application workflow.

---

Saves you tokens? Give it a star — it helps others discover LeanCTX.

---

Inside the LeanCTX Engine

Context Intelligence is how LeanCTX selects and prepares information for a task. These mechanisms support the Gateway's Select → Control → Prove flow.

1. Context Compression — input efficiency

For supported read and shell paths, LeanCTX can select compact representations of the files and command output your AI agent uses.

• Workload-specific token reduction on eligible context, with recovery paths and a local Shadow Mode baseline for measurement

• File reads: 16 read modes (full, map, signatures, diff, lines:N-M, density:X, …) — eligible cached re-reads return a compact reference instead of repeating content

• Target density (density:0.4): SDE-style budget compression — keeps the highest-entropy lines until ~40% of the original tokens remain, deterministic

• JIT disclosure: signatures carries line spans and points at lines:N-M for targeted expansion — outline first, bodies on demand

• Shell output: 85+ shell-output patterns compress git, npm, cargo, docker, kubectl, terraform and more (250+ passthrough rules)

• Tree-sitter AST: structural understanding for 27 languages — not just text compression

• Source recovery (CCR): supported compact views retain source or archive references for expansion through ctx_expand, ctx_retrieve or the reference API. Recovery depends on permissions, retention and the source remaining available. Read modes and detail →

2. Intelligent Triage — task understanding

Not every task or file needs the same depth. LeanCTX classifies the task, then sends the signal rather than the noise.

• 16 read modes: from full content down to AST signatures and entropy-filtered views
• Adaptive ModePredictor: learns the optimal read mode per file type from past sessions
• IntentEngine: classifies query complexity so simple lookups stay cheap

3. Knowledge Routing — cross-source context

Relevant code, sessions, and connected sources become focused context instead of a larger prompt.

• Session memory (CCP): persist task/facts/decisions across chats — structured recovery queries survive compaction
• Knowledge graph: temporal facts with validity windows, episodic + procedural memory
• Property Graph: multi-edge code graph (imports, calls, exports, type_ref) powers impact analysis and search ranking
• Yours, not the vendor's: memory stays local and portable — export it as a .ctxpkg package and move it across machines or models, instead of locking it in a vendor's black box

4. Usage and outcome evidence

Performance is the cost of a useful result, not just speed. LeanCTX records costs and outcomes locally; CPAO (Cost per Accepted Outcome) is the north-star metric for comparing useful AI work.

• Context Manager: browser dashboard with real-time token tracking, compression stats, utilization gauge
• Budgets & SLOs: profiles, roles, per-agent budgets, and throttling policies
• Context Proof (ctx_proof, ctx_verify): exportable audit trail (verifier, SLO, pipeline and provenance records) plus runtime-checked policy claims (PathJail per touched file, budget)

5. Shadow Recommendations — savings proof

Shadow Mode estimates what the same work would have cost without LeanCTX, without changing the active workflow. The baseline is simulated from the uncompressed token counts and the same outcome signals, not a second measured run, so its reports estimate cost, tokens and CPAO deltas; they do not measure answer quality.

Cost Intelligence

LeanCTX automatically tracks local cost and outcome signals; it does not add those reports to agent context. CPAO — cost per accepted outcome — is the north-star metric, while Shadow Mode provides a simulated baseline for savings estimates.

lean-ctx savings --period week                 # costs, token savings, and CPAO
lean-ctx value-report --format markdown --last 20  # recent outcome quality
lean-ctx shadow --latest                       # latest baseline comparison

Quick start: value tracking

# Enable shadow mode for savings comparison
lean-ctx config set shadow.enabled true

# After using LeanCTX for a while:
lean-ctx savings
lean-ctx shadow --latest

Feature overview (see generated MCP registry for the current count)

• Web & Research (ctx_url_read): pull a public web page, PDF, or YouTube transcript into context as compressed, citation-backed text — facts/quotes return claims with a confidence score + source URL, relevance-ranked research-compression distils to a token budget, SSRF-guarded (http/https only)
• Graph-Powered Intelligence: hybrid search (BM25 + embeddings + graph proximity via RRF), incremental git-diff updates
• LSP Refactoring (ctx_refactor): language-server-powered rename, references, go-to-definition via rust-analyzer, typescript-language-server, pylsp, gopls
• Multi-Agent — Research (ctx_agent, ctx_handoff): experimental local agent handoff with context transfer bundles, diary system, and shared state; not a hosted or generally available team service
• Archive Full-Text Search (ctx_expand search_all): FTS5-powered cross-archive search over all previously archived tool outputs
• PR Context Packs: lean-ctx pack --pr builds a PR-ready context pack (changed files, related tests, impact, artifacts)
• Context Packages — Research: lean-ctx pack create bundles local Knowledge, Graph and Session state into .ctxpkg files with SHA-256 integrity; this is experimental local packaging, not a generally available Context Kits or hosted sharing product
• Context Time Machine — Research: lean-ctx snapshot create|list|show|verify|restore|publish|import handles git-anchored, signed local snapshots and file-based sharing; dashboard replay and restore are experimental, and hosted history or a hosted registry is not a generally available product (concept →)
• Observability: lean-ctx gain --live, lean-ctx wrapped, lean-ctx watch and the browser dashboard show local activity and recorded context metrics; gain --svg/--share creates a shareable card or self-hostable page
• Verified savings: lean-ctx savings is a local per-event ledger with tokenizer transparency, bounce-netting and a tamper-evident SHA-256 chain; provider-measured savings require the proxy's counterfactual holdout
• HTTP mode: lean-ctx serve for Streamable HTTP MCP + /v1/tools/call (used by the Cookbook and external clients)

Addons — Research preview

Addon manifests and gateway integration remain experimental Research interfaces. Local addon release|add|list commands handle signed packages; they do not provide a public marketplace, hosted registry, managed distribution or addon search. A package can carry a sandboxed WASM module or an [mcp] declaration for an external server.

lean-ctx addon release ./my-addon   # build a signed .ctxpkg — no artifact host, no CI
lean-ctx addon add ./my-addon-1.0.0.ctxpkg   # verify, disclose, ask, install
lean-ctx addon list                 # what's installed, what loads, what's wired

• Publish it yourself — the module travels inside the signed package, so the signature covers the executable bytes. Nothing external to host, hash or serve, and no pipeline of your own.
• Verified locally, then asked — add re-checks the signature on your machine rather than trusting its source, checks every module against its pinned SHA-256, prints the publisher key and the exact command any declared server would run, and only then prompts.
• LeanCTX never installs the server for you — no uv tool install, no npx. Fetching a declared tool stays your step, where your own package manager's trust model applies. The manifest says how to run it.
• Folded in, not just proxied — opt-in post-processing runs addon output through the same pipeline as your code: compress to a budget, spill oversized blobs to a ctx_expand handle, index into BM25 / graph / knowledge. A typed integration routes specific tools straight into ctx_expand, ctx_callgraph and ctx_knowledge.
• Untrusted by default — addon results pass through secret scrubbing and are tagged untrusted in the integration pipeline. Scrubbing covers configured detection patterns, not every possible secret.

The local workflow verifies signatures and module hashes, shows the publisher key and declared command before installation, and never installs the external server. Opt-in post-processing can send addon results through compression, ctx_expand, BM25, graph and knowledge. Results remain untrusted; configured secret scrubbing does not cover every possible secret. See the status-qualified addon guide for the boundary.

Research directions

LeanCTX remains the Context Gateway for AI Systems. These research directions extend its context capabilities; they are not supported product commitments.

• Hosted context history — local snapshot create/show/verify/restore and signed file-based sharing are experimental Research; a hosted registry and model-view comparison remain future work. (concept →)
• Context as Code — declarative pipelines, profiles, and policies in TOML, versioned like infrastructure
• Unified Context Graph — code, tests, commits, CI runs, and knowledge entries in a single semantic graph
• Cross-agent context controls — explore context roles, budgets, and permissions while the host retains agent scheduling and workflow execution
• Context Observability — SLOs on context consumption, anomaly detection, OpenTelemetry / Prometheus export

The full roadmap lives in VISION.md.

How it works

LeanCTX works on two planes — what your agents read and what they send to the model:

read path:   AI tool  →  (MCP tools + shell)  →  lean-ctx  →  your repo + CLI
wire path:   AI tool  →  lean-ctx proxy        →  model provider   (supported, configured requests)

• MCP server (read path): exposes ctx_* tools (read modes, caching, deltas, search, memory, multi-agent)
• Shell hook (read path): transparently compresses common commands so the LLM sees less noise
• Request proxy (wire path, opt-in): lean-ctx proxy enable routes supported requests through a local proxy. Configured transformations can reduce eligible prompt, history and tool-result content while respecting supported provider-cache boundaries. Provider-specific effort mapping (proxy.effort), verbosity controls and volatile-prefix handling have their own compatibility limits. Recovery requires retained, authorized artifacts; usage and cost evidence depend on the provider data the path observes.
• Property Graph: multi-edge code graph powers impact analysis, related file discovery, and search ranking
• Session memory: persists selected session state for recovery when authorized records remain available
• Context Manager: browser dashboard for inspecting context activity and records visible to LeanCTX

Get started

# 1) Install (pick one)
curl -fsSL https://leanctx.com/install.sh | sh      # universal (no Rust needed)
brew tap yvgude/lean-ctx && brew install lean-ctx    # macOS / Linux
npm install -g lean-ctx-bin                          # Node.js
cargo install lean-ctx                               # Rust

# 2) One-command setup for your agent
lean-ctx wrap cursor      # or: wrap claude / wrap codex

# Inspect recorded context metrics after your AI's first lean-ctx call.
lean-ctx gain

lean-ctx wrap registers the MCP server and configures the supported local transport for that agent. Undo anytime with lean-ctx unwrap cursor.

Claude Pro/Max: subscription OAuth cannot use a custom ANTHROPIC_BASE_URL. lean-ctx wrap claude therefore adds no proxy redirect while enabling the ctx_* tools and shell-output compression; an existing custom endpoint remains untouched. Claude wire-level request compression requires ANTHROPIC_API_KEY. See advanced proxy setup.

Alternative: full control

lean-ctx onboard          # connect all detected AI tools (zero prompts)
lean-ctx setup            # interactive wizard with every option

Building from source on Windows? Clone the repo and run ./install.ps1 in PowerShell — it builds the release binary and installs it into Cargo's bin directory (pass -BuildOnly to build without installing).

Windows code signing and Smart App Control

The current release workflow signs new Windows binaries as Thinkery AG, including the binaries inside ZIP archives and Python companion wheels. Earlier releases may still contain unsigned executables. Signing does not guarantee acceptance by every Windows security policy.

If Windows blocks an older unsigned download, use WSL or build locally with cargo install lean-ctx; do not disable system-wide security protections for this tool. Windows-side MCP clients can launch the WSL installation with wsl lean-ctx mcp.

See Windows signing and verification and #1820 for verification status.

Troubleshooting / Safety

• Disable immediately (current shell): lean-ctx-off
• Run a single command uncompressed: lean-ctx -c --raw "git status"
• Only activate in AI agent sessions: set shell_activation = "agents-only" in ~/.config/lean-ctx/config.toml
• Per-project config override: create .lean-ctx.toml in your project root (auto-merged with global config)
• Docker projects sharing /workspace: create .lean-ctx-id with a unique name to prevent context collisions
• Update: lean-ctx update
• Diagnose (shareable): lean-ctx doctor --json

Real-world scenarios

LeanCTX grows with you. Below are the journeys most people actually take — each links to a complete, function-by-function walkthrough in the Reference (every CLI command and the complete MCP tools are documented there).

🟢 Your first setup "I just installed it — now what?" lean-ctx wrap cursor # one-command setup for your agent lean-ctx doctor # confirm you're wired up wrap configures the supported integration; doctor checks the resulting setup. → Journey 1 — Setup & Onboarding	📖 Coding every day "Stop re-reading the same files." lean-ctx read src/server.rs -m map # API surface, ~13 tok on re-read lean-ctx -c "git status" # compressed shell output Your agent reads less and searches smarter — automatically. → Journey 2 — Daily Use
🧠 Resume where you left off "My new chat forgot everything." lean-ctx overview # task-aware project recap lean-ctx knowledge recall "auth" # facts that survive resets lean-ctx knowledge consolidate # import session + compact lifecycle lean-ctx knowledge consolidate --all # compact every project store Session memory + a project knowledge graph persist across chats. → Journey 3 — Memory & Knowledge	🗺️ Understand a new codebase "Where does this function ripple to?" lean-ctx graph impact src/auth.rs # blast radius lean-ctx smells scan # code-smell hotspots A multi-edge property graph powers impact analysis + ranked search. → Journey 4 — Code Intelligence
🔌 Providers & multi-repo "Pull in GitHub issues and our Postgres schema." lean-ctx provider list lean-ctx serve --root ./api --root ./web # multi-repo External data flows through the same consolidation pipeline. → Journey 5 — Advanced & Integrations	🛠️ Keep it healthy "Update, fix, or cleanly remove." lean-ctx doctor --fix lean-ctx update Self-healing diagnostics; surgical uninstall that only removes its own blocks. → Journey 6 — Lifecycle & Troubleshooting
🎛️ Take control of the window "Budget my context like a pro." lean-ctx plan "refactor billing" --budget 8000 lean-ctx compile --mode balanced Phi-scored planning + knapsack compilation + a context ledger. → Journey 7 — Context Engineering	🤝 Run a team of agents "Planner + coder + reviewer on one repo." ctx_agent action=register role=dev ctx_handoff action=create # baton-pass with full context Shared message bus, diaries, knowledge, and deterministic handoffs. → Journey 8 — Multi-Agent Collaboration
🏢 Explore team and CI context — Research "One shared index, headless in pipelines." lean-ctx team serve --config team.toml lean-ctx bootstrap # zero-prompt CI setup Experimental local team-server path with scoped tokens and verifiable context gates; no hosted team or cloud service is publicly available. → Journey 9 — Team, Cloud & CI	🎚️ Tune & govern "Make it behave exactly how we want." lean-ctx compression standard lean-ctx harden # enforce token discipline Compression levels, tool profiles, themes, and rules governance. → Journey 10 — Customization & Governance
📊 Prove the payoff "Show me the numbers." lean-ctx gain --deep # savings, cost, per-agent, heatmap lean-ctx wrapped # shareable recap (also: gain --svg / gain --share) lean-ctx savings # verified per-event ledger (auditable; savings verify) All analytics live in the CLI/dashboard — never burning agent tokens. → Journey 11 — Analytics & Insights	📚 The full reference "I want to read everything." Every command and the generated MCP registry, organized as user journeys, plus appendices for the CLI map, MCP tools, and paths & config. → Reference index

Supported IDEs & AI tools

LeanCTX provides an MCP server. Client support depends on transport, tool handling, and configuration; the matrix below records the integration scope. Two integration modes are selected for supported agents:

Mode	How it works	Best for
Hybrid	MCP for cached reads and references + shell hooks for command compression	Agents with shell access (Cursor, Claude Code, Codex, ...)
MCP	Complete tool set via MCP protocol, no shell hooks	Protocol-only agents (JetBrains, VS Code, Zed, ...)

Agent compatibility matrix

Agent	Hybrid	MCP	Setup
Cursor	●		lean-ctx init --agent cursor
Claude Code	●		lean-ctx init --agent claude
CodeBuddy	●		lean-ctx init --agent codebuddy
Augment CLI / VS Code	●		lean-ctx init --agent augment
Codex CLI	●		lean-ctx init --agent codex
Grok	●		lean-ctx init --agent grok
Gemini CLI	●		lean-ctx init --agent gemini
Windsurf	●		lean-ctx init --agent windsurf
GitHub Copilot	●		lean-ctx init --agent copilot
CRUSH	●		lean-ctx init --agent crush
Hermes	●		lean-ctx init --agent hermes
OpenCode	●		lean-ctx init --agent opencode
Pi	●		lean-ctx init --agent pi
Qoder	●		lean-ctx init --agent qoder
Amp	●		lean-ctx init --agent amp
Cline	●		lean-ctx init --agent cline
Roo Code	●		lean-ctx init --agent roo
Kiro	●		lean-ctx init --agent kiro
Antigravity	●		lean-ctx init --agent antigravity
Amazon Q	●		lean-ctx init --agent amazonq
Qwen	●		lean-ctx init --agent qwen
Trae	●		lean-ctx init --agent trae
Verdent	●		lean-ctx init --agent verdent
Aider		●	lean-ctx init --agent aider
Mistral Vibe		●	lean-ctx init --agent vibe
Continue		●	lean-ctx init --agent continue
JetBrains IDEs		●	lean-ctx init --agent jetbrains
QoderWork		●	lean-ctx init --agent qoderwork
VS Code		●	lean-ctx init --agent vscode
Zed		●	lean-ctx init --agent zed
Neovim		●	lean-ctx init --agent neovim
Emacs		●	lean-ctx init --agent emacs
Sublime Text		●	lean-ctx init --agent sublime

MCP clients need compatible transport, tool support, and configuration. The table lists setup targets; the client constraints distinguish verified integrations from generic compatibility.

When to use (and when not to)

Great fit if you...

• use AI coding tools daily and your sessions are shell-heavy (git/tests/builds)
• work in medium/large repos (50+ files / monorepos)
• want local context processing with inspectable network and telemetry controls

Skip it if you...

• mostly work in tiny repos and rarely call the shell from your AI tool
• always need raw/unfiltered logs (you can still use --raw, but ROI is lower)

The honest fine print: the payoff depends on three levers — reach (own the window via the proxy/engine, not just the ctx_* tool layer), context lifetime (one long-lived session vs. a fresh process per phase), and provider pricing (prompt-cache-priced vs. re-billed every turn). They stack into a clear win where they line up and net to break-even where they don't. See the win vs. break-even matrix for the full breakdown and how to tune for each case.

Demo

Try these in any repo:

lean-ctx read rust/src/server/mod.rs -m map
lean-ctx -c "git log -n 5 --oneline"
lean-ctx gain --live
lean-ctx dashboard                              # Context Manager (browser)
lean-ctx benchmark report .

• The repo ships the exact tapes used to render the GIFs in demo/
• Regenerate locally:

vhs demo/leanctx.tape
vhs demo/gain.tape
vhs demo/benchmark.tape

Benchmarks

Measurements and estimates require a declared workload and method. The earlier per-read-mode compression table (map / signatures over 50 files) is withdrawn along with the other historical figures in BENCHMARKS.md; measure your own repository with lean-ctx benchmark report . instead. Cache references can reduce repeated context; their token cost depends on the emitted result and tokenizer.

Measure the Engine's own advertised schema, instruction, and briefing overhead with lean-ctx doctor overhead --gate. The deterministic lean-ctx benchmark dual-arm --json replay reports a synthetic upper bound: its baseline never uses the provider's prompt cache, while agent hosts can cache the prefix with or without LeanCTX. It is not a provider invoice, an on/off comparison or an outcome-quality evaluation. Available comparison methods include the lean-ctx eval ab subcommand (requires a suite file via --suite), the injected-context footprint comparison in lean-ctx eval footprint (requires a suite via --suite and a baseline via --compare), and the proxy's opt-in compression holdout ([proxy] compression_holdout).

Accuracy is gated, within stated limits. A model-free A/B gate checks that the JSON crusher keeps every gold answer in its fixtures while cutting tokens, and proxy rewrites preserve stable output for prompt-cache eligibility under the same inputs and settings. Actual cache hits, prices, and discounts depend on the provider. The off-vs-on testbench (lean-ctx eval testbench) runs pinned real repos through a raw-dump baseline and through lean-ctx at an identical token budget, grades free-form QA with an LLM judge and code with each repo's own tests, and emits FINDINGS.md (tokens / turns / walltime / quality) plus a regressions file. What CI replays is a small committed recording: it is a mechanism gate (it catches a broken pipeline or a changed grade), not evidence that compression preserves answer quality. Every eval report states its evidence tier (A mechanism … E production); a run below 30 paired tasks is UNDERPOWERED and fails --gate unless run as an explicit --mechanism check — see context-quality-v1. A powered with/without quality study has not been run yet; the proxy's compression holdout measures prompt size on real traffic and reports answer quality as unknown. What each number can and cannot show, per data path: measurement scope.

• Latest snapshot: BENCHMARKS.md
• Reproduce: lean-ctx benchmark report .

Adoption and compatibility

• GitHub activity and releases show the current repository counts and release history.
• Supported integration paths distinguish verified setups from protocol compatibility.
• Generated MCP registry shows the current tool count and status.
• Published metrics state their measurement scope; adoption counts do not establish savings or outcome quality.

Docs

• Reference (every function, by user journey): docs/reference/ — 11 journeys + CLI/MCP/config appendices
• For AI agents / LLMs: llms.txt — a curated, machine-readable map of lean-ctx (per the llms.txt convention)
• Getting started: https://leanctx.com/docs/getting-started
• Tools reference: https://leanctx.com/docs/tools/
• CLI reference: https://leanctx.com/docs/cli-reference/
• What is LeanCTX: https://leanctx.com/what-is-leanctx/
• Comparison (vs RTK, Context+, MemGPT): https://leanctx.com/compare/
• Community and commercial options: https://leanctx.com/pricing/
• FAQ: discord-faq.md
• Feature catalog (SSOT snapshot): LEANCTX_FEATURE_CATALOG.md
• Monorepo guide: docs/guides/monorepo.md
• Semantic code intelligence: docs/guides/semantic-intelligence.md
• Architecture: ARCHITECTURE.md
• Vision: VISION.md

Privacy & security

• Inspectable telemetry controls: run lean-ctx telemetry status to see the effective setting and lean-ctx telemetry show to inspect the payload; lean-ctx telemetry disable turns reporting off.
• Disableable update check (config update_check_disabled = true or LEAN_CTX_NO_UPDATE_CHECK=1)
• 40+ security hardening fixes in v3.5.16 (path traversal, injection, CSPRNG, CSP, resource limits — details)
• Context Governance Benchmark self-assessment: graded C2 — Managed against the 32-control CGB v1.0-draft spec, gaps declared — docs/compliance/cgb-self-assessment.md
• Context processing runs locally; configured source providers, proxy/model requests, submitted feedback, updates, and enabled telemetry have their own network paths.

See SECURITY.md.

Uninstall

One command removes everything — it stops all processes, then deletes hooks, editor configs, rules, autostart (LaunchAgent/systemd), the data dir, and the binary itself:

lean-ctx uninstall                 # full clean removal
lean-ctx uninstall --dry-run       # preview every change, write nothing
lean-ctx uninstall --keep-config   # keep MCP configs + rules (for reinstall)
lean-ctx-off                       # or just disable for the current shell session

No binary on PATH (or you used the curl installer)? Run the same removal from the installer:

curl -fsSL https://leanctx.com/install.sh | sh -s -- --uninstall

If you installed via a package manager, uninstall removes everything it wrote and tells you the one command to finish removing the binary:

brew uninstall lean-ctx        # Homebrew
cargo uninstall lean-ctx       # cargo install
npm uninstall -g lean-ctx-bin  # npm

Star History

Contributing

Start with CONTRIBUTING.md. Easy first PR: propose a new CLI compression pattern via the issue template.

License

Mixed licensing: the Trust Core remains Apache-2.0 while explicitly mapped v4 modules may use commercial source-visible terms. See LICENSE.md and LICENSE_MATRIX.toml.