8 Best AutoAct Alternatives in 2026 (Open Source)
AutoAct — [ACL 2024] AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning. vs ReAct/Reflexion/BOLAA: division-of-labor strategy automatically creates specialized Plan/Tool/Reflect sub-agents from self-synthesized trajectories — zero dependency on closed-source model data or human annotations
These 8 open-source tools do the same job. They are ordered by how closely they match AutoAct, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| AutoAct(original) | 239 | +0 | 2025-01-13 |
| GPTSwarm | 1.1k | +5 | 2026-02-05 |
| CAMEL | 17.8k | +207 | 2026-09-30 |
| ChatDev | 34.4k | +407 | 2026-07-24 |
| Multi-GPT | 565 | +1 | 2023-05-26 |
| AgentScope | 32.6k | +1,846 | 2026-09-30 |
| DeerFlow | 83.3k | +5,343 | 2026-09-30 |
| TaskWeaver | 6.2k | +6 | 2026-03-23 |
| BabyAGI | 22.4k | +24 | 2026-01-31 |
1. GPTSwarm
🐝 The First Self-Improving Agentic Solution
What sets it apart: vs CrewAI / LangGraph / OpenAI Swarm: graph-based agent framework with automatic edge optimization — agents self-organize by pruning/creating inter-agent connections, backed by ICML 2024 research
Best for: Researchers building optimizable multi-agent LLM systems; Complex tasks requiring agent coordination and graph-based workflows; Teams wanting self-improving agent swarms with edge optimization
2. CAMEL
🐫 CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents. https://www.camel-ai.org
What sets it apart: Purpose-built for studying agent scaling laws with million-agent simulation support — vs other frameworks focused on practical deployment
Best for: Research on multi-agent collaboration and emergent behaviors; Synthetic data generation for model training
3. 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
4. Multi-GPT
An experimental open-source attempt to make GPT-4 fully autonomous.
What sets it apart: vs AutoGPT (single-agent): multiple specialized GPT-4 agents with independent memory collaborating on tasks — early pioneer of multi-agent architecture
Best for: Experimenting with multi-agent AI collaboration patterns; Research on autonomous agent systems with shared memory
5. AgentScope
Build and run agents you can see, understand and trust.
What sets it apart: Unlike LangGraph (stateful graph orchestration) and CrewAI (role-based crews), AgentScope uniquely combines realtime voice agents, A2A protocol, agentic RL fine-tuning, and Kubernetes-native deployment — designed for the rising capability of agentic LLMs
Best for: Teams building production multi-agent systems with realtime voice and A2A interoperability; Chinese-market developers wanting first-class DashScope/Qwen integration
6. DeerFlow
An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of ta
What sets it apart: vs AutoGPT: purpose-built for deep research with sub-agent orchestration and sandbox; vs LangGraph: higher-level harness with built-in memory, sandbox, and skill system rather than bare graph framework
Best for: Deep research and exploration tasks; Building multi-agent systems with sub-agent orchestration; Teams wanting coding agent integration (Claude Code/Codex)
7. TaskWeaver
The first "code-first" agent framework for seamlessly planning and executing data analytics tasks.
What sets it apart: Unlike text-only agent frameworks like AutoGen, TaskWeaver preserves full code execution state and in-memory data across turns, enabling seamless multi-step data analytics that manipulate DataFrames and complex structures directly
Best for: Data scientists needing automated multi-step analytics pipelines with code generation; Teams building AI agents that must handle complex data structures like DataFrames natively
8. BabyAGI
What sets it apart: vs static agent frameworks (LangChain/CrewAI): focuses on self-building capability where agents autonomously generate and improve their own functions — 'the simplest thing that can build itself'
Best for: Exploring autonomous agent architecture concepts; Educational experimentation with self-building AI systems