8 Best Dust Alternatives in 2026 (Open Source)
Dust — Custom AI agent platform to speed up your work.. Enterprise AI agent platform that connects to company knowledge bases (Slack, Notion, Drive) — unlike developer-focused frameworks, Dust is designed for non-technical teams to build and deploy custom AI agents
These 8 open-source tools do the same job. They are ordered by how closely they match Dust, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| Dust(original) | 1.5k | +26 | 2026-09-30 |
| LLMStack | 2.3k | +2 | 2024-12-11 |
| ChatDev | 34.4k | +407 | 2026-07-24 |
| AgentPilot | 568 | +5 | 2025-05-15 |
| MetaGPT | 70.7k | +703 | 2026-01-21 |
| iX | 1.0k | +0 | 2024-03-03 |
| OpenAgents | 4.9k | +20 | 2024-11-18 |
| AI Legion | 1.4k | +1 | 2025-05-27 |
| GPTeam | 1.7k | +1 | 2024-06-28 |
1. LLMStack
No-code multi-agent framework to build LLM Agents, workflows and applications with your data
What sets it apart: vs Flowise / Dify: no-code AI platform with multi-tenant support, built-in vector DB, and Slack/Discord integration — deploy AI agents from Google Drive/Notion data without writing code
Best for: Non-developers wanting to build AI agents and chatbots without coding; Teams needing multi-LLM chain workflows with data integration; Organizations wanting self-hosted AI platforms with multi-tenant support
2. ChatDev
ChatDev 2.0: Dev All through LLM-powered Multi-Agent Collaboration
What sets it apart: Pioneered the virtual software company paradigm with role-based agents — v2.0 evolved into a general-purpose zero-code multi-agent platform
Best for: Research on multi-agent collaboration and communication; Rapid prototyping of software via natural language descriptions
3. AgentPilot
A versatile workflow automation platform to create, organize, and execute AI workflows, from a single LLM to complex AI-driven workflows.
What sets it apart: vs ChatGPT/Claude desktop: local multi-agent workflow builder with graph-based design, 20+ LLM providers via LiteLLM, branching chats, and built-in multi-language code interpreter
Best for: Power users building complex multi-agent workflows on desktop; Developers wanting visual graph-based agent orchestration with code execution
4. MetaGPT
🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
What sets it apart: vs AutoGen/CrewAI: models entire software company with role-based SOPs (PM→Architect→Engineer), producing not just code but docs, API specs, and data structures
Best for: Automated software project generation from requirements; Research on multi-agent collaboration and SOP-driven workflows
5. iX
Autonomous GPT-4 agent platform
What sets it apart: vs LangChain/AutoGen: visual no-code drag-and-drop editor with native multi-agent orchestration and horizontal worker scaling — design complex agent workflows visually instead of writing code
Best for: Building custom multi-agent teams with visual no-code editor; Rapid prototyping of AI workflows without coding; Organizations needing self-hosted parallel agent execution at scale
6. OpenAgents
[COLM 2024] OpenAgents: An Open Platform for Language Agents in the Wild
What sets it apart: vs agent frameworks (LangChain/AutoGen): complete full-stack platform with web UI for general users, not just developers — three specialized agents (Data/Plugins/Web) ready to use
Best for: Data analysis and visualization workflows for non-technical users; Research on real-world agent evaluation and benchmarking
7. AI Legion
An LLM-powered autonomous agent platform
What sets it apart: Multi-agent platform where autonomous LLM agents with persistent memory collaborate through console interaction, learning from their own mistakes
Best for: multi-agent-experimentation; exploring-agent-self-organization; autonomous-task-delegation
8. GPTeam
GPTeam: An open-source multi-agent simulation
What sets it apart: vs single-agent systems: agents with individual memory communicate as a team using messaging as a tool — spatial simulation with location-based interaction adds a unique social dynamics layer
Best for: Multi-agent collaboration simulations and research; Exploring agent communication and coordination patterns; Simulating team dynamics with AI agents