Share code with LLMs via Model Context Protocol or clipboard. Rule-based customization enables easy switching between different tasks (like code review and documentation). Code outlining support is included as a standard feature.
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# LLM Context [](https://opensource.org/licenses/Apache-2.0) [](https://pypi.org/project/llm-context/) [](https://pepy.tech/project/llm-context) **Reduce friction when providing context to LLMs.** Share relevant project files instantly through smart selection and rule-based filtering. ## The Problem Getting project context into LLM chats is tedious: - Manually copying/pasting files takes forever - Hard to identify which files are relevant - Including too much hits context limits, too little misses important details - AI requests for additional files require manual fetching - Repeating this process for every conversation ## The Solution ```bash lc-select # Smart file selection lc-context # Instant formatted context # Paste and work - AI can access additional files seamlessly ``` **Result**: From "I need to share my project" to productive AI collaboration in seconds. > **Note**: This project was developed in collaboration with several Claude Sonnets (3.5, 3.6, 3.7 and 4.0), as well as Groks (3 and 4), using LLM Context itself to share code during development. All code in the repository is heavily human-curated (by me 😇, @restlessronin). ## Installation ```bash uv tool install "llm-context>=0.5.0" ``` ## Quick Start ### Basic Usage ```bash # One-time setup cd your-project lc-init # Daily usage lc-select lc-context ``` ### MCP Integration (Recommended) ```jsonc { "mcpServers": { "llm-context": { "command": "uvx", "args": ["--from", "llm-context", "lc-mcp"] } } } ``` With MCP, AI can access additional files directly during conversations. ### Project Customization ```bash # Create project-specific filters cat > .llm-context/rules/flt-repo-base.md << 'EOF' --- compose: filters: [lc/flt-base] gitignores: full-files: ["*.md", "/tests", "…
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