AutoAct vs guidance
Side-by-side comparison of two AI agent tools
Short answer
- AutoAct has had no commit in 20 months; guidance is actively maintained.
- guidance is growing faster: +67 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 guidance for: a guidance language for controlling large language models.
From GitHub data refreshed daily.
AutoActopen-source
[ACL 2024] AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning
guidanceopen-source
A guidance language for controlling large language models.
Metrics
| AutoAct | guidance | |
|---|---|---|
| Stars | 239 | 21.8k |
| Star velocity /mo | 0.4736842105263158 | 66.63157894736841 |
| Commits (90d) | 0 | 0 |
| Releases (6m) | 0 | 0 |
| Downloads (30d, npm + PyPI) | — | 11.8K |
| Overall score | 0.1440900567232822 | 0.253336309574155 |
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
- +Pythonic interface that integrates naturally with existing Python workflows and familiar programming patterns
- +Constrained generation capabilities that guarantee output syntax and structure using regex and context-free grammars
- +Multi-backend support allowing seamless switching between different model providers and local/cloud deployments
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
- -Requires Python programming knowledge, limiting accessibility for non-technical users
- -Learning curve for advanced constraint features like context-free grammars and complex regex patterns
- -Dependent on backend availability and may require additional setup for specific model types
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
- •Structured data extraction from documents or conversations where output must conform to specific JSON schemas or formats
- •Building conversational AI applications that require controlled dialogue flows and predictable response structures
- •Cost-effective alternative to fine-tuning when you need specific output formatting without retraining models
FAQ
- Which is more popular, AutoAct or guidance?
- guidance has more GitHub stars (21,786 vs 239).
- Which is more actively developed, AutoAct or guidance?
- AutoAct had more commits in the last 90 days (0 vs 0).
- Should I use AutoAct or guidance?
- 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.