AgentBench vs Promptfoo
Side-by-side comparison of two AI agent tools
Short answer
- AgentBench has had no commit in 7 months; Promptfoo is actively maintained (920 commits in the last 90 days).
- Promptfoo is growing faster: +1,110 GitHub stars in the last 30 days vs +76 for AgentBench.
- Pick AgentBench for: a Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24). Pick Promptfoo for: open-source CLI and library for evaluating and red-teaming prompts, agents, RAG systems, and LLM apps.
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AgentBenchopen-source
A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)
Promptfooopen-source
Open-source CLI and library for evaluating and red-teaming prompts, agents, RAG systems, and LLM apps
Metrics
| AgentBench | Promptfoo | |
|---|---|---|
| Stars | 3.8k | 25.7k |
| Star velocity /mo | 76.42105263157895 | 1.1k |
| Commits (90d) | 0 | 920 |
| Releases (6m) | 0 | 10 |
| Downloads (30d, npm + PyPI) | — | 3.0M |
| Overall score | 0.25439272565218957 | 0.8639349362705032 |
Pros
- +Comprehensive evaluation across five diverse task domains with standardized metrics and reproducible containerized environments
- +Function-calling integration with AgentRL framework enables end-to-end agent training and sophisticated multiturn interactions
- +Active research community with public leaderboard, Slack workspace, and ongoing collaboration for benchmark improvements
- +Comprehensive testing suite covering both performance evaluation and security red teaming in a single tool
- +Multi-provider support with easy comparison between OpenAI, Anthropic, Claude, Gemini, Llama and dozens of other models
- +Strong CI/CD integration with automated pull request scanning and code review capabilities for production deployments
Cons
- -Complex setup requiring multiple Docker images and external data dependencies like Freebase database
- -Primarily research-focused with limited documentation for production deployment scenarios
- -Resource-intensive containerized environment may require significant computational resources for full evaluation
- -Requires API keys and credits for multiple LLM providers, which can become expensive for extensive testing
- -Command-line focused interface may have a learning curve for teams preferring GUI-based tools
- -Limited to evaluation and testing - does not provide actual LLM application development capabilities
Use Cases
- •Research teams evaluating and comparing different LLM agent architectures across standardized benchmark tasks
- •AI companies developing autonomous agents who need systematic performance assessment before deployment
- •Academic institutions studying agent capabilities in interactive environments, databases, and web-based scenarios
- •Automated testing and evaluation of prompt performance across different models before production deployment
- •Security vulnerability scanning and red teaming of LLM applications to identify potential risks and compliance issues
- •Systematic comparison of model performance and cost-effectiveness to optimize AI application architecture
FAQ
- Which is more popular, AgentBench or Promptfoo?
- Promptfoo has more GitHub stars (25,665 vs 3,759).
- Which is more actively developed, AgentBench or Promptfoo?
- Promptfoo had more commits in the last 90 days (920 vs 0).
- Should I use AgentBench or Promptfoo?
- 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.