Claude-Mem is a persistent memory compression system built for Claude Code. It automatically captures tool usage observations during coding sessions, generates semantic summaries using Claude's agent-sdk, and injects relevant context into future sessions to maintain continuity of project knowledge.
Orchestrates teams of role-based autonomous agents that collaborate on multi-step tasks, plus event-driven Flows for deterministic control. Built from scratch with no LangChain dependency; runs 450M+ agentic workflows monthly.
Generates interactive codebase wikis from GitHub, GitLab, or Bitbucket repositories by analyzing structure, writing documentation, and creating diagrams for navigation.
Organizes reusable AI prompts as Markdown 'Patterns' you run from the CLI — summarize a video, extract claims, rate content. Switch among 20+ providers (OpenAI, Claude, Gemini, Ollama) and reach them via CLI, web UI, or REST API.
AI Hedge Fund is a proof-of-concept for an AI-powered hedge fund. It employs multiple AI agents modeled after renowned investors to analyze stocks, perform valuations, sentiment analysis, and generate trading signals. Designed for educational purposes only, it supports CLI and web interfaces, requiring API keys for LLMs and financial data.
Python framework for building and serving LLM agents in production: a unified event bus for real-time frontends and human-in-the-loop, fine-grained tool permissions, multi-tenant serving, and tool/code execution sandboxed via Docker or E2B.
Curates 300+ agentic AI projects, tools, and resources into a categorized, regularly updated list — includes top projects, rising projects, and curated links for building or researching autonomous LLM agents.
Curated developer resources that demonstrate building RAG systems, multi-agent workflows, and memory-augmented AI using Oracle AI Database and OCI — includes end-to-end reference apps, notebooks, guides, and workshops for hands-on prototyping.
Gives developers low-level primitives for building stateful single-agent, multi-agent, and graph-based control flows, with built-in human-in-the-loop checkpoints, persistent cross-session memory, and token-level streaming.
A family of GUI agents that operate phones, desktops, and browsers by perceiving the screen visually rather than reading app code. Ships open GUI-Owl vision-language models (7B/32B) plus a multi-agent framework for planning, reflection, and tool use.
Controls customer-facing LLM agents turn-by-turn against deterministic guidelines instead of one big system prompt, surfacing only the rules and tools that apply each turn. Adds journeys, pre-approved canned responses, and traces for auditable behavior.
Turns repeatable business workflows into versioned agent skills that can run in Refly, ship to coding agents, or be exposed as APIs and webhooks.