Framework for building and orchestrating multi-agent LLM systems, with agent types, tool integration, and human-in-the-loop workflows. Supports multi-agent conversation patterns, multiple LLM providers, and RAG-style tooling for research and prototyping agentic workflows.
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.
A self-hostable virtual companion: a VRM or Live2D character you own that voice-chats in real time, plays Minecraft and Factorio, and runs models in-browser via WebGPU or across 25+ LLM providers like Ollama, OpenAI, and Claude.
Terminal-native AI coding agent that brings conversational, multi-model code assistance into your shell. Integrates with 300+ models and providers, offers an interactive TUI, Zsh ':' plugin, semantic workspace search, and Git-oriented workflows for in-terminal edits, commits, and command suggestions.
A library of specialized AI agents that automate data science steps: loading, cleaning, wrangling, feature engineering, SQL queries, EDA, and ML modeling via H2O and MLflow. Higher-level analyst workflows chain these under a supervisor agent.
Elixir-native autonomous agent framework that models state changes as pure cmd/2 operations and describes side effects with typed directives; integrates with OTP supervision and optional LLM plugins for AI-driven agents.
A 100-line LLM framework built on one graph abstraction of nodes and flows, with zero dependencies and no vendor wrappers. The tiny core composes agents, workflows, and RAG, and is small enough for a coding agent to read and extend on its own.
Full-stack AI red‑teaming platform that fingerprints AI infrastructure for known CVEs, audits MCP servers and agent skills with LLM-driven analysis, and runs cross-model jailbreak evaluations; designed for hands-on security assessment of AI deployments.
Simulates a trading firm using LLM agents in specialized roles — fundamentals, sentiment, news and technical analysts feed bull/bear researcher debates, then a trader and risk team decide. Works across US, global and crypto markets and 10+ LLM providers.
Curates 500+ open-source AI agent use cases, indexed two ways: by industry vertical (healthcare, finance, legal, retail, and more) and by framework (CrewAI, AutoGen, LangGraph, LlamaIndex, Agno). Each entry links a runnable repo.
Lets teams build, deploy, and manage AI agents from chat, visual workflows, code, knowledge bases, tables, and more than a thousand integrations.
Runs penetration tests autonomously: a multi-agent system (researcher, developer, executor) plans attacks, writes and runs exploit code, and chains 20+ tools like nmap, metasploit and sqlmap in isolated Docker containers — for authorized testing only.