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LeanCTX Vision

Control what your AI can see.

Stack overview (LeanCTX · CTXPKG · ctxpkg.com · CTXFabric): ECOSYSTEM.md

The Cognitive Context Layer

High performance with LLMs isn't about bigger context windows — it's about information density. LeanCTX is the cognitive context layer between your AI and your code: every token reaching the LLM carries maximum signal, and every byte of noise stripped away is a byte of reasoning gained.

The winners won't be those who can afford 1M-token contexts. They'll be those who achieve the same result with 10K.

The four dimensions

  1. Compression layer (input efficiency) — AST-based signatures, delta loading, session caching (re-reads ~13 tokens), entropy filtering, 95+ CLI compression patterns, 18 tree-sitter languages, 10 read modes.
  2. Semantic router (model selection) — intent detection, mode prediction learned per file type, LITM-aware positioning per model family.
  3. Context manager (memory architecture) — Context Continuity Protocol (~400 tokens instead of ~50K cold start), context ledger, multi-agent coordination, temporal knowledge system, property graph with hybrid search fusion.
  4. Quality guardrail (output verification) — compression safety levels, deterministic anchoring, 19 versioned contracts with CI drift gates, policy packs, tamper-evident audit trails, Ed25519-signed evidence bundles.

Technical depth: docs/cognition-interface.md · CONTRACTS.md

Principles

  • Local-first, zero telemetry. Nothing leaves your machine automatically — ever. The engine learns locally (read modes, compression thresholds, bandits); what it learns belongs to you.
  • Learned optimization is portable, not harvested. Tuned profiles can be exported as signed .ctxpkg packages and shared through the registry — a deliberate, inspectable file, not a background upload.
  • Evidence over claims. Policy decides what an agent may see; signed evidence proves what it saw. Compliance reports (EU AI Act, ISO/IEC 42001, SOC 2) are generated from real session data, offline-verifiable.
  • One binary, 30+ tools. Cursor, Claude Code, Windsurf, Copilot, Codex, Gemini, JetBrains and more — the same engine everywhere.

Direction

  • Context as Code — declarative pipelines, profiles and policies in TOML, version-controlled like infrastructure.
  • Cognition interface — constraints-aware instruction compilation, attention-aware layout, budget/SLO enforcement, proof-carrying context.
  • Unified context graph — code, tests, commits, CI runs and knowledge in one semantic graph with graph-aware reads.
  • Provider framework — issues, tickets, CI and logs flowing through the same consolidation pipeline as code.
  • Fabric primitives — agent handoffs, cross-session memory and org accounts as the substrate for fleet-level context (see ECOSYSTEM.md).

The end state: an AI that sees only what matters, remembers what's relevant, and reasons at maximum capacity — governed by policies you define.

Tokens are the new gold. Context is the new infrastructure. Spend both wisely.