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An MCP (Model Context Protocol) server that lets AI assistants interact with Xiaohongshu (RedNote): check login, publish image/text or video posts, search and fetch feed/details, and manage comments — exposes HTTP+MCP endpoints and integrates with MCP clients via local Docker or browser automation.
Evaluates and optimizes AI agents and language models in containerized environments, supporting large-scale parallel benchmarks and RL rollouts. Integrates with third‑party providers for thousands of parallel environments and serves as the official harness for Terminal‑Bench.
Deploys autonomous AI agents that dynamically attack running apps and return validated proof-of-concept exploits instead of static-analysis noise. Specialized agents cover IDOR, injection, SSRF, XSS, and auth flaws, with HTTP proxy and CI/CD hooks.
A ~5,000-line Python LLM inference engine that re-implements SGLang's serving optimizations — radix KV-cache reuse, chunked prefill, overlap scheduling, tensor parallelism — as a fully type-annotated reference instead of a black box.
Build and self-host production voice agents with a drag-and-drop workflow builder, real-time telephony integration, and pluggable LLM/STT/TTS backends. Docker-first with an optional managed cloud offering for teams that want faster onboarding.
Open-source companion to a technical book that teaches how to design, evaluate and ship LLM-based AI agents — includes the full Chinese manuscript, community translations, chapter-aligned runnable example projects, and reproducible evaluation harnesses.
Provides Gymnasium-style APIs and tooling to run isolated, networked execution environments for agentic reinforcement learning. Offers async/sync EnvClients, Docker/Kubernetes container providers, a web UI and CLI for scaffolding and deploying environments (Hugging Face Spaces); experimental and evolving.
Provides a Gymnasium-style API and tooling to create, deploy, and interact with isolated execution environments for agentic RL training. Includes async/sync clients, a web interface, CLI, Docker-based deployment, and Hugging Face Spaces integration.
Turns clinical text into structured, de-identified clinical signals—entity extraction and PII de-identification—that run entirely on local hardware. Provides 1,000+ specialized medical NER models, multilingual support, Apple MLX acceleration, and Apache‑2.0 licensing.
Autonomously performs end-to-end data science tasks — from cleaning and exploration to modeling, visualization, and analyst-grade reports — via an agentic LLM. Open-source model, code, datasets and demos; supports vLLM deployment, Jupyter/CLI/Web UIs, and OpenAI-style APIs.
A stateful agent harness, shipped as a CLI, whose agents keep memory, skills, and prompts across sessions instead of resetting each run. Context is git-versioned via MemFS, and agents can rewrite their own prompts and skills over time.
Agent memory that learns over time instead of just recalling past chats: retain/recall/reflect primitives turn interactions into facts, experiences, and mental models. Reports top LongMemEval scores; self-hostable with Python and Node SDKs.