lms-llmsTxt 0.1.3

LM Studio-powered llms.txt generator based on DSPy tutorials.

---

title: "LM Studio llms.txt Generator" description: "Generate llms.txt, llms-full, and fallback artifacts for GitHub repositories using DSPy with LM Studio."

Overview

Use this CLI-first toolkit to produce LLM-friendly documentation bundles (llms.txt, llms-full.txt, optional llms-ctx.txt, and fallback JSON) for any GitHub repository. The generator wraps DSPy analyzers, manages LM Studio model lifecycle with the official Python SDK, and guarantees output even when the primary language model cannot respond.

[!NOTE] The pipeline validates curated links, detects default branches automatically, and writes artifacts to artifacts/<owner>/<repo>/.

Prerequisites

• Python 3.10 or later
• LM Studio server available locally (Developer tab → Start Server) or the CLI (lms server start --port 1234)
• GitHub API token in GITHUB_ACCESS_TOKEN or GH_TOKEN
• Optional: llms_txt when you want to produce llms-ctx.txt

[!WARNING] Install dependencies inside a virtual environment to avoid PEP 668 “externally managed environment” errors.

Install

Create a virtual environment

python3 -m venv .venv
source .venv/bin/activate

Install the package with developer extras

pip install -e '.[dev]'

Installing the editable package exposes the lmstxt CLI and the lmstxt-mcp server.

[!TIP] Keep the virtual environment active while running the CLI or tests so the SDK-based unload logic can import lmstudio.

Configure LM Studio

Load the CLI and start the server

npx lmstudio install-cli
lms server start --port 1234

The server must expose an OpenAI-compatible endpoint, commonly http://localhost:1234/v1.

Ensure the target model is downloaded

Open LM Studio, download the model (for example qwen/qwen3-4b-2507), and confirm it appears in the Server tab.

Quick start

Run the CLI against any GitHub repository:

lmstxt https://github.com/owner/repo \
  --model qwen/qwen3-4b-2507 \
  --api-base http://localhost:1234/v1 \
  --stamp

The command writes artifacts to artifacts/owner/repo/. Use --output-dir to override the destination.

Environment variables

Variable	Description
LMSTUDIO_MODEL	Default LM Studio model identifier
LMSTUDIO_BASE_URL	Base URL such as http://localhost:1234/v1
LMSTUDIO_API_KEY	API key for secured LM Studio deployments
OUTPUT_DIR	Custom root directory for artifacts
ENABLE_CTX=1	Emit llms-ctx.txt using the optional llms_txt package

Generated artifacts

Artifact	Purpose
*-llms.txt	Primary documentation synthesized by DSPy or the fallback heuristic
*-llms-full.txt	Expanded content fetched from curated GitHub links with 404 filtering
*-llms.json	Fallback JSON following LLMS_JSON_SCHEMA (only when LM fallback triggers)
*-llms-ctx.txt	Optional context file created when ENABLE_CTX=1 and llms_txt is installed

[!IMPORTANT] The pipeline always writes llms.txt and llms-full.txt, even when the language model call fails.

Model Context Protocol (MCP) Server

This package includes a FastMCP server that exposes the generator as an MCP tool and provides access to generated artifacts as resources.

Features

• Asynchronous Processing: Tool calls return a run_id immediately while generation happens in the background.
• Tools:
  • lmstxt_generate_llms_txt: Trigger llms.txt generation.
  • lmstxt_generate_llms_full: Generate llms-full.txt from an existing run.
  • lmstxt_generate_llms_ctx: Generate llms-ctx.txt (requires llms_txt).
  • lmstxt_list_runs: View recent generation history and status.
  • lmstxt_read_artifact: Read generated files with pagination support.
  • lmstxt_list_all_artifacts: List all persistent .txt artifacts on disk.
• Resources:
  • Run-specific: lmstxt://runs/{run_id}/{artifact_name}
  • Persistent Directory: lmstxt://artifacts/{filename} (e.g., lmstxt://artifacts/owner/repo/repo-llms.txt)

Running the Server

# Default stdio transport
lmstxt-mcp

Client Configuration

Add to your MCP client config (e.g., claude_desktop_config.json or config.toml):

Claude Desktop / Cursor

{
  "mcpServers": {
    "lmstxt": {
      "command": "lmstxt-mcp",
      "env": {
        "GITHUB_ACCESS_TOKEN": "your_token",
        "LMSTUDIO_BASE_URL": "http://localhost:1234/v1"
      }
    }
  }
}

Codex / CLI (toml)

[mcp_servers.lmstxt]
command = "lmstxt-mcp"
startup_timeout_sec = 30
tool_timeout_sec = 30

[mcp_servers.lmstxt.env]
GITHUB_ACCESS_TOKEN = "your_token"
LMSTUDIO_BASE_URL = "http://localhost:1234/v1"

How it works

1. Collect repository material – the GitHub client gathers the file tree, README, package files, repository visibility, and default branch.
2. Prepare LM Studio – the manager confirms the requested model is loaded, auto-loading if necessary.
3. Generate documentation – DSPy produces curated content; on LM failures the fallback serializer builds markdown and JSON directly.
4. Assemble llms-full – curated links are re-fetched via raw GitHub URLs for public repos or authenticated API calls for private ones, with validation to remove dead links.
5. Unload models safely – the workflow first uses the official lmstudio SDK (model.unload() or list_loaded_models), then falls back to HTTP and CLI unload requests.

Project layout

• src/lms_llmsTxt/ – core generation library, DSPy analyzers, and LM Studio helpers.
• src/lms_llmsTxt_mcp/ – MCP server implementation, asynchronous worker, and resource providers.
• tests/ – pytest coverage for the generator pipeline and MCP server.
• artifacts/ – sample outputs from previous runs.

Verify your setup

source .venv/bin/activate
python -m pytest

All tests should pass, confirming URL validation, fallback handling, and MCP resource exposure.

Troubleshooting

[!WARNING] If pip install -e .[dev] fails with build tool errors, ensure cmake and necessary compilers are installed.

[!TIP] If the MCP server times out during generation, check lmstxt_list_runs to see if the background task is still processing. The lmstxt_generate_* tools return immediately to avoid client timeouts.

MCP Inspector

npx @modelcontextprotocol/inspector --config ./inspector.config.json --server lmstxt

Use the payloads in docs/mcp-inspector-payloads.md to verify specific tool behaviors.