AutoAct vs Lumos
Side-by-side comparison of two AI agent tools
Short answer
- AutoAct is growing faster: +0 GitHub stars in the last 30 days vs +0 for Lumos.
- Pick AutoAct for: aCL 2024 AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning. Pick Lumos for: code and data for "Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs".
From GitHub data refreshed daily.
AutoActopen-source
[ACL 2024] AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning
Lumosopen-source
Code and data for "Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs"
Metrics
| AutoAct | Lumos | |
|---|---|---|
| Stars | 239 | 477 |
| Star velocity /mo | 0.4736842105263158 | 0.3157894736842105 |
| Commits (90d) | 0 | 0 |
| Releases (6m) | 0 | 0 |
| Overall score | 0.1440900567232822 | 0.13906464371434624 |
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
- +Modular architecture with separate planning, grounding, and execution components enables flexible customization and debugging
- +Unified data format supports multiple task types (web navigation, QA, math, multimodal) within a single framework
- +Competitive performance with much larger proprietary models while being fully open-source and based on smaller LLAMA-2 models
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
- -Based on LLAMA-2 architecture which is older and may not incorporate latest language model advances
- -Primarily research-focused with limited documentation for production deployment
- -Requires significant computational resources for training and may need fine-tuning for domain-specific applications
Use Cases
- •Building cost-effective QA agents for organizations without access to expensive closed-source language models
- •Creating reproducible agent systems in research environments with limited annotated training data
- •Developing multi-agent systems that require automatic task decomposition and specialized sub-agent coordination
- •Research into open-source language agents and comparative studies against proprietary models
- •Web navigation and automation tasks requiring multi-step planning and execution
- •Complex question answering systems that need to break down problems into actionable subgoals
FAQ
- Which is more popular, AutoAct or Lumos?
- Lumos has more GitHub stars (477 vs 239).
- Which is more actively developed, AutoAct or Lumos?
- AutoAct had more commits in the last 90 days (0 vs 0).
- Should I use AutoAct or Lumos?
- 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.