AutoAct vs CAMEL
Side-by-side comparison of two AI agent tools
Short answer
- AutoAct has had no commit in 20 months; CAMEL is actively maintained (63 commits in the last 90 days).
- CAMEL is growing faster: +205 GitHub stars in the last 30 days vs +0 for AutoAct.
- Pick AutoAct for: aCL 2024 AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning. Pick CAMEL for: cAMEL: The first and the best multi-agent framework.
From GitHub data refreshed daily.
AutoActopen-source
[ACL 2024] AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning
CAMELopen-source
π« CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents. https://www.camel-ai.org
Metrics
| AutoAct | CAMEL | |
|---|---|---|
| Stars | 239 | 17.8k |
| Star velocity /mo | 0.4736842105263158 | 204.94736842105263 |
| Commits (90d) | 0 | 63 |
| Releases (6m) | 0 | 8 |
| Downloads (30d, npm + PyPI) | β | 42.8K |
| Overall score | 0.1440900567232822 | 0.6284874280633658 |
Pros
- +Eliminates dependency on expensive closed-source models like GPT-4, making agent development more accessible and cost-effective
- +Automatically synthesizes planning trajectories without requiring human annotation or manual trajectory creation
- +Implements division-of-labor strategy with specialized sub-agents for improved task decomposition and completion
- +Comprehensive multi-agent research platform with extensive documentation and community support
- +Focuses on critical scaling law research to understand agent behavior and capabilities at scale
- +Supports diverse applications from data generation to world simulation with modular architecture
Cons
- -Primarily focused on question answering tasks, which may limit applicability to other agent use cases
- -Requires an existing tool library to function effectively, adding setup complexity
- -Performance may vary significantly depending on the quality and capabilities of the underlying open-source language model used
- -Primary focus on research may require significant technical expertise for practical implementation
- -Large framework scope could present complexity challenges for simple use cases
- -Academic orientation may not align with immediate commercial deployment needs
Use Cases
- β’Building cost-effective QA agents for organizations without access to expensive closed-source language models
- β’Creating reproducible agent systems in research environments with limited annotated training data
- β’Developing multi-agent systems that require automatic task decomposition and specialized sub-agent coordination
- β’Academic research into AI agent scaling laws and multi-agent system behaviors
- β’Synthetic dataset generation for training and testing AI models
- β’Task automation systems requiring coordination between multiple AI agents
FAQ
- Which is more popular, AutoAct or CAMEL?
- CAMEL has more GitHub stars (17,805 vs 239).
- Which is more actively developed, AutoAct or CAMEL?
- CAMEL had more commits in the last 90 days (63 vs 0).
- Should I use AutoAct or CAMEL?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.