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VideoDB Agent Toolkit

AI Agent toolkit for VideoDB
llms.txt >> llms-full.txt
MCP

# VideoDB Agent Toolkit The VideoDB Agent Toolkit exposes VideoDB context to LLMs and agents. It enables integration to AI-driven IDEs like Cursor, chat agents like Claude Code etc. This toolkit automates context generation, maintenance, and discoverability. It auto-syncs SDK versions, docs, and examples and is distributed through MCP and \`llms.txt\` ## Quick Overview The toolkit offers context files designed for use with LLMs, structured around key components: \`llms-full.txt\` — Comprehensive context for deep integration. \`llms.txt\` — Lightweight metadata for quick discovery. \`MCP (Model Context Protocol)\` — A standardized protocol. These components leverage automated workflows to ensure your AI applications always operate with accurate, up-to-date context. ## Toolkit Components ### 1. llms-full.txt ([View »](https://videodb.io/llms-full.txt)) --- \`llms-full.txt\` consolidates everything your LLM agent needs, including: - Comprehensive VideoDB overview. - Complete SDK usage instructions and documentation. - Detailed integration examples and best practices. **Real-world Examples:** - [VideoDB's Director](https://chat.videodb.io) \`code-assistant\` agent ([View Implementation ](https://github.com/video-db/Director/blob/main/backend/director/agents/code_assitant.py)) - [VideoDB's Discord Bot](https://discord.com/invite/py9P639jGz) to power customer support and community help ([View Implementation ]()) - Integrate \`llms-full.txt\` directly into your LLM-powered workflows, agent systems, or AI coding environments. ### 2. llms.txt ([View »](https://videodb.io/llms.txt)) --- A streamlined file following the [Answer.AI llms.txt proposal](https://github.com/answerdotai/llms-txt). Ideal for quick metadata exposure and LLM discovery. > **ℹ️ Recommendation**: Use \`llms.txt\` for lightweight discovery and metadata integration. Use \`llms-full.txt\` for complete functionality. ### 3. MCP (Model Context Protocol) The VideoDB MCP Server connects with the Director backend framework, providing a single tool for many workflows. For development, it can be installed and used via uvx for isolated environments. For more details on MCPs, please visit [here](https://docs.videodb.io/add-videodb-mcp-server-in-clients-108) **Install \`uv\`** We need to install uv first. For macOS/Linux: \`\`\` curl -LsSf https://astral.sh/uv/install.sh | sh \`\`\` For Windows: \`\`\` powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" \`\`\` You can also visit the installation steps of \`uv\` for more details [here](https://docs.astral.sh/uv/getting-started/installation) **Run the MCP Server** You can run the MCP server using \`uvx\` using the following command \`\`\` uvx videodb-director-mcp --api-key=VIDEODB_API_KEY \`\`\` **Update VideoDB Director MCP package** To ensure you're using the latest version of the MCP server with \`uvx\`, start by clearing the cache: \`\`\` uv cache clean \`\`\` This command removes any outdated cached packages of \`videodb-director-mcp\`, allowing \`uvx\` to fetch the most recent version. If you always want to use the latest version of the MCP server, update your command as follows: \`\`\` uvx videodb-director-mcp@latest --api-key= \`\`\`
## Anatomy of LLM Context Files LLM context files in VideoDB are modular, automatically generated, and continuously updated from multiple sources: ### Modular Structure: - **Instructions** — Best practices and prompt guidelines [View »](https://github.com/video-db/agent-toolkit/blob/main/context/instructions/prompt.md) - **SDK Context** — SDK structure, classes, and interface definitions [View »](https://github.com/video-db/agent-toolkit/blob/main/context/sdk/context/index.md) - **Docs Context** — Summarized product documentation [View »](https://github.com/video-db/agent-toolkit/blob/main/context/docs/docs_context.md) - **Examples Context** — Real-world notebook examples [View »](https://github.com/video-db/agent-toolkit/blob/main/context/examples/examples_context.md) ### Automated Maintenance: - Managed through GitHub Actions for automated updates. - Triggered by changes to SDK repositories, documentation, or examples. - Maintained centrally via a [\`config.yaml\`](https://github.com/video-db/agent-toolkit/blob/readme-refactor/config.yaml) file. --- ## ️ Automation with GitHub Actions Automatic context generation ensures your applications always have the latest information: ### SDK Context Workflow ([View](https://github.com/video-db/agent-toolkit/blob/main/.github/workflows/update_sdk_context.yml)) - **Automatically generates documentation** from SDK repo updates. - Uses [Sphinx](https://www.sphinx-doc.org/en/master/) for Python SDKs. ### Docs Context Workflow ([View](https://github.com/video-db/agent-toolkit/blob/main/.github/workflows/update_docs_context.yml)) - **Scrapes and summarizes documentation** using [FireCrawl](https://www.firecrawl.dev/) and LLM-powered summarization. ### Examples Context Workflow ([View](https://github.com/video-db/agent-toolkit/blob/main/.github/workflows/update_examples_context.yml)) - Converts and summarizes notebooks into practical context examples. ### Master Context Workflow ([View](https://github.com/video-db/agent-toolkit/blob/main/.github/workflows/update_master_context.yml)) - Combines all sub-components into unified \`llms-full.txt\`. - Generates standards-compliant \`llms.txt\`. - Updates documentation with token statistics for transparency. --- ## ️ Customization via \`config.yaml\` The [\`config.yaml\`](https://github.com/video-db/agent-toolkit/blob/readme-refactor/config.yaml) file centralizes all configurations, allowing easy customization: - **Inclusion & Exclusion Patterns** for documentation and notebook processing - **Custom LLM Prompts** for precise summarization tailored to each document type - **Layout Configuration** for combining context components seamlessly \`config.yaml\` > \`llms_full_txt_file\` defines how \`llms-full.txt\` is assembled: \`\`\`yaml llms_full_txt_file: input_files: - name: Instructions file_path: "context/instructions/prompt.md" - name: SDK Context file_path: "context/sdk/context/index.md" - name: Docs Context file_path: "context/docs/docs_context.md" - name: Examples Context file_path: "context/examples/examples_context.md" output_files: - name: llms_full_txt file_path: "context/llms-full.txt" - name: llms_full_md file_path: "context/llms-full.md" layout: | \{\{FILE1\}\} \{\{FILE2\}\} \{\{FILE3\}\} \{\{FILE4\}\} \`\`\` ## Best Practices for Context-Driven Development - **Automate Context Updates:** Leverage GitHub Actions to maintain accuracy. - **Tailored Summaries:** Use custom LLM prompts to ensure context relevance. - **Seamless Integration:** Continuously integrate with existing LLM agents or IDEs. By following these practices, you ensure your AI applications have reliable, relevant, and up-to-date context—critical for effective agent performance and developer productivity. --- ## Get Started Clone the toolkit repository and follow the setup instructions in [\`config.yaml\`](https://github.com/video-db/agent-toolkit/blob/readme-refactor/config.yaml) to start integrating VideoDB contexts into your LLM-powered applications today. **Explore further:** - [VideoDB SDK](https://github.com/video-db/videodb-python) - [Documentation](https://docs.videodb.io) - [Cookbook Examples](https://github.com/video-db/videodb-cookbook) --- [token-length-shield]: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/video-db/agent-toolkit/refs/heads/main/readme_shields.json&style=for-the-badge [token-length-url]: https://github.com/video-db/agent-toolkit/blob/main/token_breakdown.png [tag-shield]: https://img.shields.io/github/v/tag/video-db/agent-toolkit?style=for-the-badge [tag-url]: https://github.com/video-db/agent-toolkit/tags [stars-shield]: https://img.shields.io/github/stars/video-db/agent-toolkit.svg?style=for-the-badge [stars-url]: https://github.com/video-db/agent-toolkit/stargazers [issues-shield]: https://img.shields.io/github/issues/video-db/agent-toolkit.svg?style=for-the-badge [issues-url]: https://github.com/video-db/agent-toolkit/issues