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A benchmark evaluating the capabilities of autonomous AI agents across varied tasks and environments.
DESCRIPTION
AgentBench is a comprehensive benchmark that measures how well language models act as autonomous agents on diverse tasks. It assesses tool use, planning, memory, and environment interaction. Researchers use it to compare agent frameworks and training methods. Results guide reliability improvements and inform deployment decisions for agentic systems.
ALTERNATIVE DEFINITIONS
AgentBench Authors
We introduce AgentBench, a comprehensive benchmark for evaluating LLMs as agents across diverse tasks.
Positions AgentBench as a broad, multi-task agent evaluation suite.
AgentBench Project
AgentBench is a benchmark suite to test general-purpose AI agents in realistic, diverse scenarios.
Emphasizes realism and diversity in task environments.
RELATED TERMS
Autonomous Agent LLM-as-Agent Function Calling Planning Memory ReAct Evaluation Benchmarks
COMPETING TERMS
SWE-bench OSWorld AgentBoard
RELATIONS
- References: Autonomous Agent Function Calling Memory Planning ReAct
KNOWLEDGE AREAS
AI Data Science Testing & Validation MLOps
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer AI Product Manager
EXTERNAL SOURCES
- Wikipedia - Benchmark (computing)
- AgentBench Authors - Original benchmark paper
- AgentBench Project - Benchmark overview and resources
- THUDM - Code and datasets for AgentBench
TOP QUESTIONS
What does AgentBench measure in AI agents?
It measures planning, tool use, memory, reliability, and success across diverse scenarios. It focuses on real-world, multi-step tasks to stress agentic capabilities.
Category: Overview
Why is AgentBench relevant today?
Agentic workflows are increasingly deployed. A standardized benchmark is essential to compare systems, identify failure modes, and guide safer designs.
Category: Relevancy
How do teams apply AgentBench?
They run benchmark tasks on candidate agents, collect metrics, analyze errors, and iterate on architectures, tools, and prompts to improve performance.
Category: HowTo
What are common mistakes using AgentBench?
Overfitting to benchmark tasks, ignoring environment configuration details, and misinterpreting aggregate scores without task-level diagnostics are typical pitfalls.
Category: Mistakes
What risks come from relying on AgentBench alone?
Benchmarks can miss domain-specific hazards. Real deployments need additional evaluations, safety tests, and monitoring beyond benchmark coverage.
Category: Risk
What alternatives compete with AgentBench?
SWE-bench, OSWorld, and AgentBoard provide complementary agent evaluation perspectives and tasks.
Category: Alternatives
How is AgentBench maintained?
Authors publish updates, tasks, and evaluation scripts. The community contributes scenarios, bug fixes, and results to the public repository.
Category: Management
Where should I start with AgentBench?
Read the paper and documentation site, then clone the repository to run baseline evaluations on your agent system.
Category: HowTo
What’s a hot topic around AgentBench?
Evaluating multimodal agents and integrating safety testing, red-teaming, and guardrails directly into agent benchmarks are active areas.
Category: Topic