Multi-tenant agent harness that makes enterprise knowledge retrievable, graph-reasonable, and deliverable by LLM-powered agents. Integrates RAG + a Milvus-based knowledge graph, LangGraph orchestration, and document parsing for citation-backed answers and graph reasoning; deployable via Docker (requires a compatible LLM API).
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.
Python web scraping framework that automatically relocates elements when a site's HTML changes, so selectors survive redesigns. Bundles Cloudflare Turnstile bypass, TLS fingerprint impersonation, and a Scrapy-like async spider for full crawls.
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.
Reference architectures and microservices for building GPU-accelerated vision agents that enable natural-language video search, long-video summarization, visual Q&A, and alert verification. Integrates NVIDIA NIM models, embeddings, VLMs/LLMs, and agent workflows for deployable video-analytics stacks.
Turns any website into a structured, text-like interface that LLM agents can read and act on, handling clicks, forms, scraping, anti-detection and CAPTCHAs. Ships as an open-source Python library plus a hosted cloud API for running browser agents at scale.
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.
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.