lean-ctx

yvgudeApache-2.0活跃维护

为 AI 编码代理提供上下文网关，按任务筛选相关上下文、接入本地知识并记录可审计的证据链，支持 CLI、MCP 和代理路径。

LeanCTX is the Context Gateway for AI Systems. Select task-relevant context, apply supported controls, connect local knowledge and inspect recorded evidence. Open-source Engine: CLI, MCP, hooks and supported proxy paths.

v3.12.2发布于 2026-11-05 07:05 UTC3,941star +6 / 7 天365fork同步于 2026-11-10 17:40 UTC

上下文工程上下文网关 AI 代理审计追踪本地知识库

对比 GitHub 官网 agentic-coding ai ai-agents ai-coding ai-governance

怎么上手

由 README 提炼，执行前请确认

原文

lean-ctx setup

硬件要求按 README 说明

README 未说明

下载：源码 zip·GitHub Releases（安装包）本站不镜像安装包

项目说明

由 README 自动翻译提炼

没人工点评？写下你的经验

在 AI 工具与数据之间做上下文网关，按任务筛选内容、施加访问控制并留下可查证据

为 AI 编码代理提供上下文网关，按任务筛选相关上下文、接入本地知识并记录可审计的证据链，支持 CLI、MCP 和代理路径。

核心能力

用 16 种读取模式（full、map、signatures、diff 等）返回聚焦的文件视图
对支持的调用施加路径权限、内容过滤和上下文预算控制
本地保留任务、发现与决策记录，跨会话延续上下文
用 savings、shadow、gain 等命令查看 token 用量与模拟成本对比
通过 CLI、MCP、hooks 和代理路径接入现有 AI 工具

容易踩的坑

部分能力（Addon、Context Packages、Time Machine、多 Agent 交接）标注为 Research 或实验性接口，不要当作稳定功能；Shadow Mode 的节省数据是基于未压缩 token 数的模拟基线，不是第二次实测；provider 实测节省需要代理的 counterfactual holdout，仅接入上下文路径时只记录准备结果

内容由模型根据 README 生成，可能不完整或不准确，以官方文档为准。看原文 README

用户点评

登录用户的真实使用记录，可按版本和硬件筛选

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还没有人点评。第一个说出真实体验的人最有价值。

README 原文

未翻译

在 GitHub 打开

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

Where it sits

Files · repositories · tool results · configured providers
                          │
                          ▼
                LeanCTX Context Gateway
             Select · Control · Learn · 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.
Learn: retain local task records and connect code and knowledge across sessions, within the configured access and retention rules.
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

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.

Inside LeanCTX | OSS

The Engine puts Select, Control, Learn and Prove into the context paths of your existing AI tools. Your host keeps control of the task, agent loop and model.

1. Select — the context each task needs

Search and structural views help an agent find relevant information before reading entire files. Supported read and shell paths can return focused views of files and command output, with references for recovering detail.

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 or the reference API. Recovery depends on permissions, retention and the source remaining available. Read modes and detail →

2. Control — configured rules on supported paths

PathJail and roles: constrain tool filesystem access to the configured scope.
Content filters: configured detectors can warn, redact or block on supported paths. Detection coverage and enforcement depend on the integration and settings.
Context budgets: bound selected input and supported tool output.
Host-owned model choice: the host chooses the model and performs any model calls outside the Engine's observed path.

Review the security policy for defaults, supported controls and coverage limits. A filesystem boundary is not an operating-system sandbox.

3. Learn — connect work across sessions

Keep useful task records and discover relationships between code and local knowledge. Learning here means context continuity; it does not train your model.

Session memory (CCP): persist task/facts/decisions across chats — structured recovery queries survive compaction
Knowledge graph: temporal facts with validity windows, relations and lifecycle
Property Graph: multi-edge code graph (imports, calls, exports, type_ref) powers impact analysis and search ranking
Permission-bound reuse: access and retention rules still apply when stored context is retrieved. Experimental .ctxpkg export is described under Research below.

4. Prove — inspect what the Engine recorded

Inspect source references, recorded policy decisions and delivery evidence from supported paths. lean-ctx inspect explains observed context operations. A context-only integration records its prepared result; it cannot prove a later model call made by the host.

Cockpit (lean-ctx cockpit): local browser view with token tracking, compression stats, spend and recorded calls
Audit export: retain the recorded evidence for review within its stated scope.
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)

Usage and cost — measure the result

Context controls and evidence come first. Usage estimates and calculated costs help you assess their operational effect; they do not prove answer quality.

Shadow Mode — a simulated baseline

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.

LeanCTX records local cost and outcome signals without adding those reports to agent context. CPAO (cost per accepted outcome) is one comparison metric; Shadow Mode provides simulated savings estimates. Keep estimates, provider-measured usage and outcome quality separate.

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: usage 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

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-backed symbols: ctx_patch op=replace_symbol edits a whole symbol body and ctx_callgraph finds references, through rust-analyzer, typescript-language-server, pylsp or 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
Observability: lean-ctx cockpit shows local activity and recorded context metrics (Savings · Spend · Report · Proof, recorded calls in the Ledger); lean-ctx gain is the terminal summary (--json for scripts), and gain --svg/--share creates a shareable card or self-hostable page
Usage ledger: 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.
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
Cockpit (lean-ctx cockpit): local browser view 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 cockpit          # Savings · Spend · Report · Proof in the browser (also: lean-ctx dashboard)
lean-ctx gain --terminal  # the same numbers as a terminal summary (--json for scripts)
lean-ctx cockpit stop     # stop the background Cockpit server (--foreground keeps it in the terminal)

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.

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).

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.

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

🏢 Headless in CI

"The same engine, headless in pipelines."

lean-ctx bootstrap            # zero-prompt CI setup

Everything local works without an account. LeanCTX Hive (shared knowledge for teams and your own machines) follows with 3.12.1.

🎚️ 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

📊 Measure usage and cost

"Show me the numbers."

lean-ctx cockpit              # Savings · Spend · Report · Proof in the browser
lean-ctx gain --deep --terminal  # terminal: savings, cost, per-agent, heatmap (--json for scripts)
lean-ctx gain --svg           # shareable recap card (also: gain --share / gain --wrapped)
lean-ctx savings              # verified per-event ledger (auditable; savings verify)

All analytics live in the Cockpit and the CLI — 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                                   # terminal summary
lean-ctx cockpit                                # Savings · Spend · Report · Proof (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. The on/off comparison the Engine offers is the proxy's opt-in compression holdout ([proxy] compression_holdout).

Proxy rewrites preserve stable output for prompt-cache eligibility under the same inputs and settings, and the compression engine checks that critical facts (failing tests, error codes, locations) survive or it delivers the original. Actual cache hits, prices, and discounts depend on the provider. Quality results state their evidence tier (A mechanism … E production) — 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

LeanCTX | OSS and its open interfaces (rust/, packages/, docs/, scripts/) are Apache-2.0. LICENSE.md and LICENSE_MATRIX.toml are the path-level authority for that Apache host.

The consolidated development tree also carries sdk/ (client bindings) and ee/ (commercial Runtime, Suite and services) next to the host. Both keep their own existing terms, are not Apache-2.0 and are not approved for publication; the host-only mirror contains neither directory. Their presence in one checkout relicenses nothing. SOURCE_LAYOUT.md lists each path with its current terms.

怎么上手

项目说明

核心能力

容易踩的坑

最近发版

Added

Security

Fixed

LeanCTX | OSS 3.12.1

Highlights

Security

🚀 LeanCTX 3.12.0: meet the Cockpit

✨ The Cockpit

📚 Knowledge from your repository

🔒 Security: eight advisories fixed

🧭 Focus

Fixed

Changed

Changed

Fixed

用户点评

README 原文

LeanCTX

Control what your AI can see.

Where it sits

Why developers use LeanCTX

Inside LeanCTX | OSS

1. Select — the context each task needs

2. Control — configured rules on supported paths

3. Learn — connect work across sessions

4. Prove — inspect what the Engine recorded

Usage and cost — measure the result

Shadow Mode — a simulated baseline

Quick start: usage tracking

Addons — Research preview

Research directions

How it works

Get started

Real-world scenarios

🟢 Your first setup

📖 Coding every day

🧠 Resume where you left off

🗺️ Understand a new codebase

🔌 Providers & multi-repo

🛠️ Keep it healthy

🎛️ Take control of the window

🤝 Run a team of agents

🏢 Headless in CI

🎚️ Tune & govern

📊 Measure usage and cost

📚 The full reference

Supported IDEs & AI tools

Agent compatibility matrix

When to use (and when not to)

Demo

Benchmarks

Adoption and compatibility

Docs

Privacy & security

Uninstall

Star History

Contributing

License

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