Loading...
🎯
AgentBench(AgentBench)
The first comprehensive benchmark to evaluate LLMs as agents across 8 diverse environments, assessing reasoning and decision-making in multi-turn open-ended settings.
Complexity: highEvaluation and Monitoring
🎯 30-Second Overview
Pattern: First comprehensive benchmark evaluating LLMs as agents across 8 diverse environments
Why: Systematic assessment of reasoning, decision-making, and multi-turn interaction capabilities in realistic settings
Key Insight: Reveals significant performance gaps between commercial and open-source models in complex agent tasks
⚡ Quick Implementation
1Install:git clone https://github.com/THUDM/AgentBench
2Setup:Configure API keys and environment dependencies
3Select:Choose evaluation environments (1-8)
4Run:Execute benchmark against your LLM agent
5Analyze:Review performance across environments
Example: python run.py --model gpt-4 --environments all --output results.json
📋 Do's & Don'ts
✅Test across all 8 environments for comprehensive evaluation
✅Allow sufficient time for multi-turn interactions (4k-13k generations)
✅Compare results against established baselines (GPT-4, Claude)
✅Focus on long-term reasoning and decision-making capabilities
✅Analyze failure modes in complex multi-step tasks
❌Rely on single environment results for overall capability assessment
❌Skip proper environment setup and dependency configuration
❌Ignore instruction following quality in favor of task completion
❌Compare models without controlling for prompt engineering
❌Assume good performance in one domain transfers to others
🚦 When to Use
Use When
- • Comprehensive LLM agent capability assessment
- • Research on agent reasoning and decision-making
- • Comparing multiple models across diverse tasks
- • Identifying specific weaknesses in agent performance
- • Academic research and model development
Avoid When
- • Quick single-domain performance checks
- • Resource-constrained environments (requires 4k-13k generations)
- • Real-time evaluation needs
- • Domain-specific benchmarking only
- • Models without multi-turn conversation support
📊 Key Metrics
Overall Success Rate
Aggregate performance across all 8 environments
Per-Environment Score
Domain-specific capability assessment
Multi-turn Coherence
Consistency across conversation turns
Instruction Following
Adherence to task specifications
Long-term Reasoning
Performance on extended reasoning tasks
API vs OSS Gap
Commercial vs open-source model comparison
💡 Top Use Cases
LLM Agent Research: Comprehensive evaluation of reasoning and decision-making across diverse domains
Model Comparison: Systematic benchmarking of API-based vs open-source models (up to 70B parameters)
Capability Assessment: Identifying specific strengths/weaknesses in SQL, gaming, web, and OS environments
Academic Research: Supporting publications on agent capabilities and multi-turn interaction quality
Agent Development: Guiding improvements in long-term reasoning and instruction following
References & Further Reading
Deepen your understanding with these curated resources
Related Research
Contribute to this collection
Know a great resource? Submit a pull request to add it.
🎯
AgentBench(AgentBench)
The first comprehensive benchmark to evaluate LLMs as agents across 8 diverse environments, assessing reasoning and decision-making in multi-turn open-ended settings.
Complexity: highEvaluation and Monitoring
🎯 30-Second Overview
Pattern: First comprehensive benchmark evaluating LLMs as agents across 8 diverse environments
Why: Systematic assessment of reasoning, decision-making, and multi-turn interaction capabilities in realistic settings
Key Insight: Reveals significant performance gaps between commercial and open-source models in complex agent tasks
⚡ Quick Implementation
1Install:git clone https://github.com/THUDM/AgentBench
2Setup:Configure API keys and environment dependencies
3Select:Choose evaluation environments (1-8)
4Run:Execute benchmark against your LLM agent
5Analyze:Review performance across environments
Example: python run.py --model gpt-4 --environments all --output results.json
📋 Do's & Don'ts
✅Test across all 8 environments for comprehensive evaluation
✅Allow sufficient time for multi-turn interactions (4k-13k generations)
✅Compare results against established baselines (GPT-4, Claude)
✅Focus on long-term reasoning and decision-making capabilities
✅Analyze failure modes in complex multi-step tasks
❌Rely on single environment results for overall capability assessment
❌Skip proper environment setup and dependency configuration
❌Ignore instruction following quality in favor of task completion
❌Compare models without controlling for prompt engineering
❌Assume good performance in one domain transfers to others
🚦 When to Use
Use When
- • Comprehensive LLM agent capability assessment
- • Research on agent reasoning and decision-making
- • Comparing multiple models across diverse tasks
- • Identifying specific weaknesses in agent performance
- • Academic research and model development
Avoid When
- • Quick single-domain performance checks
- • Resource-constrained environments (requires 4k-13k generations)
- • Real-time evaluation needs
- • Domain-specific benchmarking only
- • Models without multi-turn conversation support
📊 Key Metrics
Overall Success Rate
Aggregate performance across all 8 environments
Per-Environment Score
Domain-specific capability assessment
Multi-turn Coherence
Consistency across conversation turns
Instruction Following
Adherence to task specifications
Long-term Reasoning
Performance on extended reasoning tasks
API vs OSS Gap
Commercial vs open-source model comparison
💡 Top Use Cases
LLM Agent Research: Comprehensive evaluation of reasoning and decision-making across diverse domains
Model Comparison: Systematic benchmarking of API-based vs open-source models (up to 70B parameters)
Capability Assessment: Identifying specific strengths/weaknesses in SQL, gaming, web, and OS environments
Academic Research: Supporting publications on agent capabilities and multi-turn interaction quality
Agent Development: Guiding improvements in long-term reasoning and instruction following
References & Further Reading
Deepen your understanding with these curated resources
Related Research
Contribute to this collection
Know a great resource? Submit a pull request to add it.