Performs automated, citation-backed deep research across web, arXiv, PubMed and your private documents using configurable local or cloud LLMs. Runs locally with per-user SQLCipher encryption, Docker/pip installs, LangChain integrations, and an MCP server for assistant integration.
Desktop AI client that unifies cloud and local LLMs, tool calling (MCP), installable Skills, and ACP agent integration into a single multi-window workspace. Supports local Ollama models, multi-provider configuration, remote control, and privacy-focused local storage.
Curates 80+ hands-on LLM-powered examples, tutorials and recipes for building agents, RAG systems, voice assistants, and agentic workflows. Includes starter templates, course playlists, and reference apps for rapid prototyping and learning.
An asynchronous, high-throughput framework for large-scale reinforcement learning and agentic training that scales to 1T+ MoE models and 1000+ GPUs, with native verifiers integration, end-to-end SFT/RL/evals, and Slurm/Kubernetes deployment; requires NVIDIA GPUs.
Distributes one post across 14+ platforms (Douyin, Xiaohongshu, TikTok, X), automates likes and replies via a browser plugin, and matches creators to paid brand tasks settled by sales, views, or engagement. Drivable from Claude/Cursor via MCP.
Curated collection of production-oriented AI projects that implement OCR, RAG, multi-agent systems, and multimodal pipelines. Each entry provides runnable code, setup notes, and engineering patterns to help developers move prototypes toward production.
Scaffolds production-ready GenAI agents on Google Cloud from one CLI command, wrapping your agent logic in Terraform, CI/CD, observability, and evaluation. Ships ADK, LangGraph, and multimodal RAG templates for Cloud Run or Vertex AI Agent Engine.
Coordinates role-playing agents to automate real-world tasks — web search and browsing, code execution, document parsing, and multimodal handling. Built on the CAMEL-AI framework; scored 69.09% on the GAIA benchmark, topping open-source frameworks.
Simulates adversarial attacks against LLMs and AI agents to surface vulnerabilities (e.g., jailbreaks, prompt injection, PII leakage) and ships guardrails to block risky inputs/outputs; runs locally and can be driven from CLI or Python.
Build and run configurable multi-agent LLM workflows and personal AI agents locally or with cloud LLMs; supports simple TOML-based LLM configuration, optional browser automation, a demo on Hugging Face, and companion RL tuning (OpenManus-RL) for agent training.
Framework-agnostic library for connecting and optimizing teams of AI agents built in LangChain, LlamaIndex, CrewAI, Semantic Kernel, or Google ADK. Profiles them down to individual tokens, traces execution, and runs built-in evaluation.