Use cases · AI agents

Give your agents the rules your team already follows

Agents are quick and capable, but they don't know your conventions, your exceptions, or what changed last month. Connect them to Mnemo over MCP or the API and they can check the current rule, and its source, before they act.

Illustrative example (agent · MCP tool call): "memory_search("discount approval limit")" Mnemo: "Discounts above 20% need VP approval since the Q3 pricing change." Source: PRICING-77 · current. It replaces: 25% limit (Q2).

What your team gets

This quarter's rules, not last quarter's

Superseded decisions rank lower, so an agent doesn't act on a policy your team already changed.

Scoped to what each agent needs

Give each agent its own project and key, or limit it to the containers it needs over MCP. Keys are hashed at rest and revocable.

Fits the stack you already use

A hosted MCP server for Claude, Cursor, and other MCP clients, plus SDKs for TypeScript and Python and adapters for LangGraph, the Vercel AI SDK, and Mastra.

From the tools this work already happens in

No new process, and Mnemo needs no software on employee laptops. An admin connects each tool once, and Mnemo backfills its history and keeps syncing.

Sources for this use case: Slack, Jira, GitHub, Notion, Google Drive, Linear. All connectors: https://mnemohq.com/connectors

Common questions

Which agents can connect?

Anything that speaks MCP, including Claude Desktop, Cursor, Windsurf, VS Code, and Zed, or any code that can call a REST API.

How fast is a lookup?

Warm searches on the production API return in about 150 ms at the median.

Can agents add to the memory?

Yes. Agents can add memories through the API or MCP. Include source metadata and each one carries where it came from.

See it on your own work

Book a demo (https://mnemohq.com/contact#demo) and we'll walk through it with your tools and your questions, or start free and connect your first source today.