8 Best AutoAct Alternatives in 2026 (Open Source)
AutoAct β [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 β zero dependency on closed-source model data or human annotations
Short answer
- Closest match to AutoAct: GPTSwarm.
- Most actively developed: DeerFlow (1,274 commits in the last 90 days).
- Fastest growing: DeerFlow (+5,271 GitHub stars in the last 30 days).
- No commit in 6+ months: GPTSwarm, Multi-GPT, TaskWeaver and BabyAGI.
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.
By package downloads AgentScope is the most used here (296.7K in the last 30 days), even though DeerFlow has the most GitHub stars. See all agent tools by downloads.
| Tool | GitHub stars | Stars / 30d | Last commit | Downloads / 30d |
|---|---|---|---|---|
| AutoAct(original) | 239 | 0 | 2025-01-13 | β |
| GPTSwarm | 1.1k | +5 | 2026-02-05 | β |
| CAMEL | 17.8k | +205 | 2026-09-30 | 42.8K |
| ChatDev | 34.4k | +402 | 2026-07-24 | β |
| Multi-GPT | 565 | +1 | 2023-05-26 | β |
| AgentScope | 32.7k | +1,829 | 2026-09-30 | 296.7K |
| DeerFlow | 83.3k | +5,271 | 2026-10-03 | β |
| TaskWeaver | 6.2k | +6 | 2026-03-23 | β |
| BabyAGI | 22.4k | +24 | 2026-01-31 | 95 |
1. GPTSwarm
π The First Self-Improving Agentic Solution
What sets it apart: vs CrewAI / LangGraph / OpenAI Swarm: graph-based agent framework with automatic edge optimization β 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
π« 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 β 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 β 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 β 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 β 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
Open-source agent harness for long-horizon research, coding, and content creation
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 β 'the simplest thing that can build itself'
Best for: Exploring autonomous agent architecture concepts; Educational experimentation with self-building AI systems
FAQ
- What are the best alternatives to AutoAct?
- The closest open-source alternatives to AutoAct are GPTSwarm, CAMEL and ChatDev, followed by Multi-GPT, AgentScope and DeerFlow. They are ranked by how closely they match what AutoAct does.
- Which AutoAct alternative is the most popular?
- DeerFlow has the most GitHub stars among AutoAct alternatives, with 83,349 stars.
- Which AutoAct alternative is the most actively maintained?
- By recent activity, DeerFlow (1,274 commits in the last 90 days) is the most actively developed alternative.
Maintain AutoAct or one of these alternatives?
Each tool page has a maintainer box: a README badge with your live rank and stars, or a homepage + category feature for $49 / 7 days.
AutoAct Β· GPTSwarm Β· CAMEL Β· ChatDev Β· Multi-GPT Β· AgentScope