Evaluating LLMs as Agents
We introduce AgentBench, a multi-dimensional evolving benchmark consisting of 8 distinct environments, to assess LLMs' reasoning and decision-making abilities in a multi-turn open-ended generation setting. Our extensive test over 25 available LLMs shows that top commercial LLMs excel in complex environments, but there is a significant disparity between them and open-sourced competitors. Datasets, environments, and an integrated evaluation package for AgentBench are released at https://github.com/THUDM/AgentBench.
Add your AI agent to our curated directory
Handle multi-step workflows autonomously
Example
Schedule meeting → Find time → Send invite → Confirm attendees
Save 5-10 hours/week on routine coordination tasks
Gather data from multiple sources and summarize
Example
Research competitor pricing across 5 websites, create comparison table
Reduce research time from hours to minutes
Analyze options and recommend actions
Example
Review 20 vendor proposals, score against criteria, rank top 3
Make data-driven decisions faster
AI agents combine large language models with tools, memory, and decision-making logic to autonomously complete multi-step tasks without constant human guidance.
Large language model for reasoning and decision-making
Understand tasks, plan steps, generate responses
APIs, databases, external services the agent can call
Take actions beyond text generation (search, compute, write files)
Short-term (conversation) and long-term (persistent) memory
Maintain context across interactions and learn from past actions
Decision engine for choosing next action
Plan multi-step workflows and handle errors/edge cases
Prerequisites
Steps
✓ Do
✗ Don't
Key Metrics
Optimization Tips
Solid agent profile: LLMBench links out cleanly and the on-site reviews add signal beyond marketing copy.
LLMBench reduced evaluation time — saves/upvotes on explainx.ai correlated with fewer surprises in the trial.
We compared LLMBench with three neighbors in the same category; this one had the most concrete “what it does” framing.
Good discoverability: LLMBench shows up in the agents directory with enough detail to pre-qualify buyers.
LLMBench is a strong agent listing on explainx.ai — the profile made it easy to compare capabilities before we signed up on the vendor site.
LLMBench has been stable for production-ish demos; the explainx.ai page was a useful single link to share internally.
Good discoverability: LLMBench shows up in the agents directory with enough detail to pre-qualify buyers.
I recommend LLMBench for teams already running multiple AI agents; the listing helped us narrow the short list quickly.
Solid agent profile: LLMBench links out cleanly and the on-site reviews add signal beyond marketing copy.
We compared LLMBench with three neighbors in the same category; this one had the most concrete “what it does” framing.
showing 1-10 of 67
Key Considerations
