LLM Context

What is LLM Context about?

LLM Context enables developers to quickly share selected portions of a codebase or document collection with any LLM chat interface. It extracts files that match project‑specific rules, formats them, and sends the content either through the system clipboard or directly over the Model Context Protocol.

How to use LLM Context?

1. Install – uv tool install "llm-context>=0.3.0" (or upgrade with uv tool upgrade llm-context).
2. Initialize – Run lc-init at the root of your project to create configuration files.
3. Select files – Use lc-sel-files (or lc-sel-outlines for outlines) to pick the files that should be part of the context.
4. Generate context – Execute lc-context (add -p for prompt instructions, -u for user notes, or -f FILE to write to a file). The result is copied to the clipboard.
5. MCP integration – Add the following snippet to claude_desktop_config.json:

{
  "mcpServers": {
    "CyberChitta": {
      "command": "uvx",
      "args": ["--from", "llm-context", "lc-mcp"]
    }
  }
}

After configuration, Claude Desktop can request the project context directly. 6. Iterative workflow – When the LLM asks for additional files, copy the file list, run lc-clip-files, and paste the returned contents back into the chat.

Key features of LLM Context

• Smart file selection using .gitignore patterns.
• Rule‑based profiles for different tasks (code review, documentation, etc.).
• Automatic code outlines that give LLMs a high‑level view of a codebase.
• Implementation extraction for specific definitions via lc-clip-implementations.
• CLI‑first workflow with clipboard fallback for any LLM.
• Extensible templates and prompts to tailor the generated context.

Use cases of LLM Context

• Code review assistance – Quickly provide the reviewer LLM with the exact files it needs to examine.
• Documentation generation – Feed selected source files to an LLM to produce API docs or markdown guides.
• Bug triage – Supply relevant snippets to an LLM for root‑cause analysis.
• Learning & onboarding – New developers can ask an LLM to explain project structure using generated outlines.
• Cross‑LLM compatibility – Works with Claude Desktop, custom GPTs, or any chat interface that accepts pasted text.

FAQ from the LLM Context

Q: Do I need to reinstall after each update? A: No, simply run uv tool upgrade llm-context to get the latest version.

Q: Will my configuration be overwritten? A: Updates may overwrite files prefixed with lc-. Keep them under version control.

Q: Can I use LLM Context with large monorepos? A: It’s optimized for projects that fit within an LLM’s context window; large‑project support is being developed.

Q: How does rule switching work? A: Run lc-set-rule <n> where <n> corresponds to a rule profile defined in the configuration. System rules are prefixed with lc-.

Q: Is there a Python API? A: The primary interface is the CLI, but the underlying library can be imported from the llm_context package for custom integrations.

LLM Context

LLM Context is a tool that helps developers quickly inject relevant content from code/text projects into Large Language Model chat interfaces. It leverages .gitignore patterns for smart file selection and provides both a streamlined clipboard workflow using the command line and direct LLM integration through the Model Context Protocol (MCP).

Note: This project was developed in collaboration with several Claude Sonnets - 3.5, 3.6 and 3.7 (and more recently Grok-3 as well), using LLM Context itself to share code during development. All code in the repository is human-curated (by me 😇, @restlessronin).

Why LLM Context?

For an in-depth exploration of the reasoning behind LLM Context and its approach to AI-assisted development, check out our article: LLM Context: Harnessing Vanilla AI Chats for Development

To see LLM Context in action with real-world examples and workflows, read: Full Context Magic - When AI Finally Understands Your Entire Project

Current Usage Patterns

• Direct LLM Integration: Native integration with Claude Desktop via MCP protocol
• Chat Interface Support: Works with any LLM chat interface via CLI/clipboard
  • Optimized for interfaces with persistent context like Claude Projects and Custom GPTs
  • Works equally well with standard chat interfaces
• Project Types: Suitable for code repositories and collections of text/markdown/html documents
• Project Size: Optimized for projects that fit within an LLM's context window. Large project support is in development

Installation

Install LLM Context using uv:

uv tool install "llm-context>=0.3.0"

To upgrade to the latest version:

uv tool upgrade llm-context

Warning: LLM Context is under active development. Updates may overwrite configuration files prefixed with lc-. We recommend all configuration files be version controlled for this reason.

Quickstart

MCP with Claude Desktop

Add to 'claude_desktop_config.json':

{
  "mcpServers": {
    "CyberChitta": {
      "command": "uvx",
      "args": ["--from", "llm-context", "lc-mcp"]
    }
  }
}

Once configured, you can start working with your project in two simple ways:

1. Say: "I would like to work with my project" Claude will ask you for the project root path.

2. Or directly specify: "I would like to work with my project /path/to/your/project" Claude will automatically load the project context.

Preferred Workflow: Combining Project UI with MCP

For optimal results, combine initial context through Claude's Project Knowledge UI with dynamic code access via MCP. This provides both comprehensive understanding and access to latest changes. See Full Context Magic for details and examples.

CLI Quick Start and Typical Workflow

1. Navigate to your project's root directory
2. Initialize repository: lc-init (only needed once)
3. Select files: lc-sel-files
4. (Optional) Review selected files in .llm-context/curr_ctx.yaml
5. Generate context: lc-context (with optional flags: -p for prompt, -u for user notes)
6. Use with your preferred interface:

• Project Knowledge (Claude Pro): Paste into knowledge section
• GPT Knowledge (Custom GPTs): Paste into knowledge section
• Regular chats: Use lc-context -p to include instructions

7. When the LLM requests additional files:
   • Copy the file list from the LLM
   • Run lc-clip-files
   • Paste the contents back to the LLM

Core Commands

• lc-init: Initialize project configuration
• lc-set-rule <n>: Switch rules (system rules are prefixed with "lc-")
• lc-sel-files: Select files for inclusion
• lc-sel-outlines: Select files for outline generation
• lc-context [-p] [-u] [-f FILE]: Generate and copy context
  • -p: Include prompt instructions
  • -u: Include user notes
  • -f FILE: Write to output file
• lc-prompt: Generate project instructions for LLMs
• lc-clip-files: Process LLM file requests
• lc-changed: List files modified since last context generation
• lc-outlines: Generate outlines for code files
• lc-clip-implementations: Extract code implementations requested by LLMs (doesn't support C/C++)

Features & Advanced Usage

LLM Context provides advanced features for customizing how project content is captured and presented:

• Smart file selection using .gitignore patterns
• Multiple rule-based profiles for different use cases
  • System rules (prefixed with "lc-") provide default functionality
  • User-defined rules can be created independently or extend existing rules
• Code Navigation Features:
  1. Smart Code Outlines: Allows LLMs to view the high-level structure of your codebase with automatically generated outlines highlighting important definitions
  2. Definition Implementation Extraction: Paste full implementations of specific definitions that are requested by LLMs after they review the code outlines, using the lc-clip-implementations command
• Customizable templates and prompts

See our User Guide for detailed documentation of these features.

Similar Tools

Check out our comprehensive list of alternatives - the sheer number of tools tackling this problem demonstrates its importance to the developer community.

Acknowledgments

LLM Context evolves from a lineage of AI-assisted development tools:

• This project succeeds LLM Code Highlighter, a TypeScript library I developed for IDE integration.
• The concept originated from my work on RubberDuck and continued with later contributions to Continue.
• LLM Code Highlighter was heavily inspired by Aider Chat. I worked with GPT-4 to translate several Aider Chat Python modules into TypeScript, maintaining functionality while restructuring the code.
• This project uses tree-sitter tag query files from Aider Chat.
• LLM Context exemplifies the power of AI-assisted development, transitioning from Python to TypeScript and back to Python with the help of GPT-4 and Claude-3.5-Sonnet.

I am grateful for the open-source community's innovations and the AI assistance that have shaped this project's evolution.

I am grateful for the help of Claude-3.5-Sonnet in the development of this project.

License

This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.