benchmark· Stanford University
Stanford AgentBench Framework
AgentBench is a comprehensive benchmarking framework developed by Stanford researchers to evaluate LLM-based agents on real-world interactive tasks. It tests agents across operating systems, databases, knowledge graphs, and web environments, providing standardized metrics for comparing agent capabilities.
Key Highlights
- Tests agents across 8 distinct environments
- Evaluates multi-turn reasoning and tool use
- Provides reproducible evaluation protocols
- Open-source implementation available
- Leaderboard tracks state-of-the-art performance
How to Access & Use
- 1.Clone the AgentBench repository from GitHub
- 2.Set up the evaluation environments (Docker recommended)
- 3.Configure your agent's API endpoint
- 4.Run the benchmark suite against your agent
- 5.Submit results to the public leaderboard
Applications for AI Agents
- Comparing different LLM backends for agent tasks
- Measuring regression in agent capabilities across versions
- Identifying specific weaknesses in agent reasoning
- Validating agent improvements before production deployment
- Research publications on agent architectures
Explore AgentBench↗
Research from Stanford University