lean-ctx

Control what your AI can see.

LeanCTX — Lean Context Engineering for AI agents

LeanCTX — short for Lean Context — is the context engineering layer for AI agents. It runs as a single local binary between your agents and everything they touch — your code, shell, data, and the model itself: it decides what they read, compresses what they send (an optional local proxy shrinks every request — system prompt, history and tool results — prompt-cache-safe), remembers what they learn, guards what they touch — and proves what they save with a signed, verifiable savings ledger. The result: 60–90% fewer tokens. Zero config required. Local-first.

Control what your AI can see. LeanCTX — short for Lean Context — is the context engineering layer for AI agents: one local Rust binary that decides what your agents read, compresses what they send to the model, remembers what they learn, guards what they touch — and proves what they save.

Token savings are the receipt. Intelligence is the product. Works with Cursor, Claude Code, Copilot, Windsurf, Codex, Gemini and 30+ other agents — no config needed.

Why developers use LeanCTX

• Longer useful coding sessions — less context waste = more room for actual code reasoning
• Lower API costs — 60–90% fewer tokens on reads and shell output, cached re-reads cost ~13 tokens
• No more "I already showed you this file" — session memory persists across chats
• Works with your existing setup — one lean-ctx setup command, no config changes needed
• Full visibility — see exactly where your context window budget goes
• Model-agnostic & yours — swap OpenAI/Anthropic/Gemini freely; your context and memory stay local and portable, never locked in a vendor's black box

Why now — own your context

Models are converging on commodity. The durable edge isn't which model you call — it's your context: what your agents read, what they remember, and what you can prove. And the layer that optimizes and owns that context can't come from the vendor that bills per token or keeps your memory in a black box — it has to sit on your side.

That's the shift behind "agent entities" that live in your chat and remember your company (Claude in Slack, ClickUp Brain): a context login, not a model login — you end up renting your own company knowledge back. LeanCTX is the opposite layer. It keeps the moat yours: local-first, portable (.ctxpkg), and model-agnostic — swap OpenAI, Anthropic or Gemini without losing context or cache. Own your context; don't rent it back.

What it does — the four dimensions of context

LeanCTX treats context as a managed resource, not an afterthought. One binary covers the four dimensions that decide how well an AI agent actually performs:

1. Compression — input efficiency

Your AI agent reads files and runs commands. LeanCTX compresses both automatically.

• File reads: 10 read modes (full, map, signatures, diff, lines:N-M, density:X, …) — cached re-reads cost ~13 tokens
• 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: 95+ shell-output patterns compress git, npm, cargo, docker, kubectl, terraform and more (270 passthrough rules)
• Tree-sitter AST: structural understanding for 27 languages — not just text compression
• Reversible by design (CCR): compression never discards content — pruned or truncated payloads move to a content-addressed store with a deterministic handle, so the model can pull the original bytes back on demand via ctx_expand, ctx_retrieve, an in-band marker, or GET /v1/references/{id}. Five recovery paths →

2. Routing — the right fidelity per read

Not every file needs the same depth. LeanCTX sends the signal, not the noise.

• 10 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. Memory — context that persists

Context doesn't disappear between chats anymore.

• 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. Verification — control what reaches the model

Performance is accuracy, not just speed. You stay in control of the window.

• 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): 4-lay