The Open-Source Multimodal AI Agent Stack: Connecting Cutting-Edge AI Models and Agent Infra
- agent
- vlm
- vision
- computer-use
47,133 repositories indexed
1–17 of 17 · Most stars
The Open-Source Multimodal AI Agent Stack: Connecting Cutting-Edge AI Models and Agent Infra
Build and run agents you can see, understand and trust.
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
Platform for stateful agents: AI with advanced memory that can learn and self-improve over time.
An AI agent development platform with all-in-one visual tools, simplifying agent creation, debugging, and deployment like never before. Coze your way to AI Agent creation.
How Python does AI. Agents, realtime voice, image generation, embeddings. Every model, every interface, typed end to end.
<⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
A framework for building realtime voice AI agents 🤖🎙️📹
The ultimate LLM/AI application development framework in Go.
Open-source framework for conversational voice AI agents
Learning to Recover from LLM Agent Failures at Test Time
oar (Open Agent Runtime): programming interface for all agent harnesses
A framework for building decision-model harnesses for any kind of task. Your program offers the model a short list of next steps, and the model picks one at a time until it can answer.
Scikit-learn for Recursive Self-improvement (RSI). A modular framework for recursive harness evolution.
Official Code for OpenVLHarness: Real-World Visual Agentic Tool‑Use with Multimodal Memory
OPAL: a reactive optimizer. A coding agent rewrites the optimizer while your model trains.
Nosis is a lightweight personal agent runtime: per-session execution boundaries with OS-level sandboxing, computer use, a full built-in toolset, Skill / Plugin / MCP extensions, one runtime shared by GUI and TUI, and file-persisted sessions, plans, and memory.