AI Agent for Patronus MCP
A legacy integration from Patronus AI’s earlier work in LLM evaluation. The Patronus MCP Server let FlowHunt AI agents initialize projects, run single and batch evaluations, and run experiments on datasets. The Patronus evaluation API behind it has been retired.
Patronus AI is a research lab developing training data and simulated environments for frontier AI. Its work spans reinforcement learning environments and digital world models for agents operating in software. This integration reflects the company’s earlier work in LLM evaluation. The Patronus evaluation API it connects to has been retired, so this page is kept for reference.
Standardized LLM Evaluation
Quickly initialize Patronus with your project and API credentials to run single or batch evaluations. Choose from remote and custom evaluators, define criteria, and get detailed, JSON-formatted results for every test. Perfect for tracking and optimizing LLM performance at scale.
- Single and Batch Evaluations:
- Run one-off or multi-sample LLM evaluations with configurable evaluators and detailed output.
- Customizable Criteria:
- Define and manage evaluation criteria, including support for active learning and tailored pass conditions.
- Remote and Custom Evaluator Support:
- Utilize built-in remote evaluators or integrate your own custom evaluation functions.
- JSON Output for Results:
- All test results are output in structured, easy-to-parse JSON for seamless integration into your workflow.
LLM Experimentation at Scale
Run experiments on datasets with both remote and custom evaluators. Automate comparison, scoring, and explanation for every experiment. Results are grouped by evaluator family for easy analysis and tracking of model improvements over time.
- Run Dataset Experiments:
- Test LLM outputs across entire datasets, tracking performance and custom metrics.
- Evaluator Family Grouping:
- View results grouped by evaluator family, making insights and model comparisons straightforward.
- Automated Scoring & Explanations:
- Receive automated scoring, pass/fail status, and explanations for every experiment.
Custom Evaluation & Criteria Management
Leverage advanced API endpoints to create custom evaluation functions, criteria, and adapters. List all available evaluators, define new pass conditions, and use the MCP protocol for seamless test automation and resource management.
- Create Custom Evaluators:
- Easily implement, register, and test custom evaluator functions with the Patronus SDK.
- List & Manage Evaluators:
- Get a comprehensive overview of all available evaluators and their criteria for robust LLM QA.
- MCP Protocol Support:
- Seamlessly connect and automate model evaluations and experiments using the Model Context Protocol.
MCP INTEGRATION
Available Patronus MCP Integration Tools
The following tools are available as part of the Patronus MCP integration:
- initialize
Initialize Patronus with your API key and project settings to prepare for evaluations and experiments.
- evaluate
Run a single evaluation on a model output using configurable evaluators and criteria.
- batch_evaluate
Perform batch evaluations on multiple outputs or with multiple evaluators for comprehensive analysis.
- run_experiment
Launch experiments with datasets, supporting both remote and custom evaluators for advanced testing.
- list_evaluator_info
Retrieve detailed information about all available evaluators and their supported criteria.
- create_criteria
Define and add new evaluator criteria to customize evaluation behavior.
- custom_evaluate
Evaluate outputs using custom evaluator functions for specialized or user-defined logic.
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What is Patronus AI
Patronus AI is a research lab developing training data and simulated environments for frontier AI. Its work spans reinforcement learning environments and digital world models for agents operating in software. The company was founded in 2023 by Anand Kannappan and Rebecca Qian. The Patronus MCP integration on this page reflects the company's earlier work in LLM evaluation. The evaluation API that the integration relied on has since been retired.
Focus areas
What Patronus AI works on today
Patronus AI describes itself as a behavioral research lab for AI. It builds worlds for models to explore and studies what they do there and why.
- Training Data for Frontier AI
- Developing training data for frontier AI models.
- Reinforcement Learning Environments
- Building simulated environments for training AI with reinforcement learning.
- Digital World Models
- Simulated replicas of websites and internal company systems where AI agents practice tasks and learn through reinforcement learning.
- Behavioral Research
- Studying where AI models fail and how to improve them.
- Earlier Work in LLM Evaluation
- The Patronus MCP integration comes from the company's earlier LLM evaluation work. Its evaluation API has been retired.
Integration status
The Patronus MCP Server was built on the Patronus evaluation API, which has been retired. The tools listed on this page are kept for reference and no longer connect to a live service. For Patronus AI's current work, see the company's website.