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lean-ctx

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LeanCTX — the Context OS for AI development. One local binary that compresses, remembers, routes, and verifies every token between your code and the model. 63 MCP tools, 10 read mo

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OSS

lean-ctx

Added 1 June 2026

#agentic-coding #ai #ai-coding #claude-code #context-engineering #context-layer #copilot #cursor

Overview

LeanCTX is a local binary that compresses, remembers, routes, and verifies every token between your code and the model. It provides 63 MCP tools and 10 read modes, achieving up to 99% token savings. It integrates with Cursor, Claude Code, Copilot, Windsurf, Codex, and Gemini.

Best for

Best for
Developers using multiple AI coding assistants who want to minimize token spend and maintain consistent context

Use cases

  • Reduce token usage and costs in AI-assisted coding workflows
  • Maintain context across multiple model interactions without manual re-prompting
  • Route and verify token streams for observability and debugging

Notes

LeanCTX is a local binary that compresses, remembers, routes, and verifies every token between your code and the model. It provides 63 MCP tools and 10 read modes, achieving up to 99% token savings. It integrates with Cursor, Claude Code, Copilot, Windsurf, Codex, and Gemini.

2,330 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

  • Reduce token usage and costs in AI-assisted coding workflows
  • Maintain context across multiple model interactions without manual re-prompting
  • Route and verify token streams for observability and debugging

Pros

  • Up to 99% token savings reduces API costs significantly
  • 63 MCP tools offer extensive integration options
  • Works with major AI coding assistants out of the box

Cons

  • Requires running a local binary, adding setup overhead
  • Token compression may lose nuance in complex contexts
  • Community-maintained tool with no official vendor support

Indexed from awesome-llmops and enriched against its public facts.

Pros

  • Up to 99% token savings reduces API costs significantly
  • 63 MCP tools offer extensive integration options
  • Works with major AI coding assistants out of the box

Cons

  • Requires running a local binary, adding setup overhead
  • Token compression may lose nuance in complex contexts
  • Community-maintained tool with no official vendor support