About Postman AI features

Postman’s AI features enable you to perform tasks faster, automate your workflows, and build your own AI models.

Postman Agent Mode

With Agent Mode, you can turn your words into action across the API lifecycle. Send requests, fix errors, update tests, and more, using natural language.

AI models and Model Context Protocol (MCP) servers

You can experiment, test, and evaluate AI models and Model Context Protocol (MCP) servers. Use Postman to add an AI model of your choice to your project, such as one from OpenAI, Anthropic, or Google. Or, add an MCP server from the community–or build your own.

Start by sending requests to AI models. You can compare models based on their responses, response times, and token counts. Then, send requests to MCP servers. You can compare servers based on their tools, resources, and prompts. You can also combine the two and see how an AI model uses an MCP server to enhance a response.

With Postman’s MCP Generator, you can create your own MCP server with public APIs from the Postman API Network, and use Postman to improve your server’s developer experience.

Postman Flows AI

If you prefer a visual, low-code editor, you can use Postman Flows to experiment, test, and evaluate AI models and MCP servers. Or use Flows to build your own MCP server.

Postman AI demos and examples

See Postman’s AI tools in action in the Postman AI Tool Builder public workspace. Then, explore the following guided demos and start building your next AI agent.

• Streamline LLM Integration with Postman’s AI Protocol
• Evaluate LLM Performance in Postman
• Build Multi-Stage AI Agents: Vector Search + LLMs in Postman Flows
• Build Event-Driven AI Workflows in Postman
• Build Autonomous AI Agents for Incident Management in Postman Flows
• What Is MCP? The Matrix for AI Tools Explained in 60 Seconds