Continuously records your screen and audio 24/7 to a local, searchable timeline you can query in natural language. Stores screenshots with accessibility data in SQLite, and a plugin system runs scheduled AI agents on what it captures.
Packs a Git repository into a single AI-friendly file for easy ingestion by LLMs. Offers per-file and total token counts, optional Tree-sitter compression, secret scanning, and multiple interfaces (CLI, web, browser extension, Docker, MCP) for AI-driven code review and analysis.
Runs SQL queries against 40+ data sources — local files, databases, and apps like Notion, GitHub, and Google — through one SQLite-based engine. Doubles as an MCP server, so LLMs like Claude or ChatGPT can query that data directly via SQL.
Official Python implementation of the Model Context Protocol. Build servers that expose tools, resources, and prompts to any MCP host, or clients that connect to any server; type hints and docstrings become the schemas, so a server fits in ~15 lines.
An open protocol that standardizes how LLM applications connect to external data sources, tools, and services via JSON-RPC — TypeScript-first schema with JSON Schema exports, SDKs, and centralized documentation to enable interoperable integrations.
Implements the Model Context Protocol in TypeScript, providing server and client libraries to expose tools, resources, and prompts to LLM hosts. Ships Streamable HTTP and stdio transports, optional middleware for Express/Fastify/Hono, and runnable examples for Node/Bun/Deno.
Agentive operating system for physical robots that lets developers compose agent-native modules in Python to connect perception, spatial memory, and control across humanoids, quadrupeds, drones, and simulators.
Converts PDF, Office docs, EPUB, images, audio, HTML and ZIP archives into structured Markdown for LLM pipelines, preserving headings, tables and links instead of visual layout. Adds optional OCR, audio transcription and LLM image captions.
Orchestrates configurable deep-research agent workflows that combine LLMs, web search, and MCP tools to produce structured research reports and evaluation outputs. Supports LangGraph Studio, multiple model providers (OpenAI, Anthropic, local models), and Deep Research Bench evaluation for benchmarked comparisons.
Official remote MCP servers that let AI agents read and change Cloudflare config in natural language — managing Workers and bindings, querying observability and DNS analytics, searching docs. Each capability is a separate scoped server.
Expose Python functions as MCP‑compliant servers and clients so LLMs can call tools and resources directly; includes automatic schema generation, input validation, transport negotiation, authentication, and in‑conversation interactive UIs.
Provides a local-first Markdown knowledge graph that LLMs and humans can both read and write via the Model Context Protocol (MCP). Features two-way, editable notes, semantic search (embeddings + hybrid ranking), and optional cloud sync and team workspaces.