AI SummaryPrismBench enables developers to create specialized LLM agents through YAML configuration for systematic evaluation of model capabilities using Monte Carlo Tree Search. Useful for ML engineers, researchers, and teams building production LLM systems who need comprehensive benchmarking and evaluation frameworks.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to set up the "Custom Agents" agent in my project. Please run this command in my terminal: # Add AGENTS.md to your project root curl --retry 3 --retry-delay 2 --retry-all-errors -o AGENTS.md "https://raw.githubusercontent.com/PrismBench/PrismBench/main/docs/Custom-Agents.md" Then explain what the agent does and how to invoke it.
Description
PrismBench: A comprehensive framework for evaluating Large Language Model capabilities through Monte Carlo Tree Search. Systematically maps model strengths, automatically discovers challenging concept combinations, and provides detailed performance analysis with containerized deployment and OpenAI-compatible API support.
Overview
Agents in PrismBench are defined through YAML configuration files that specify: • LLM Configuration: Model, provider, and parameters • System Prompts: Specialized instructions and expertise • Interaction Templates: Structured input/output patterns • Output Formatting: Response parsing and extraction ---
Custom Agents
> Creating custom LLM agents with specialized prompts, behaviors, and interaction patterns Custom agents allow you to extend PrismBench with domain-specific expertise, specialized prompts, and tailored interaction patterns. Agents are the building blocks of all environment workflows.
**Configuration Structure**
`yaml agent_name: my_custom_agent configs: model_name: gpt-4o-mini provider: openai params: temperature: 0.7 max_tokens: 2048 local: false system_prompt: > Your specialized agent instructions here... interaction_templates: • name: basic required_keys: [input_param1, input_param2] template: > Template with {input_param1} and {input_param2} output_format: response_begin: <tag> response_end: </tag> `
**Component Breakdown**
| Component | Purpose | Required | |-----------|---------|----------| | agent_name | Unique identifier for the agent | ✅ | | configs | LLM provider and model settings | ✅ | | system_prompt | Agent's expertise and instructions | ✅ | | interaction_templates | Input/output patterns | ✅ | ---
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