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[AgentMem](https://agentmem-dashboard.vercel.app) — drop-in memory tools for Mastra agents.\n\n```bash\nnpm install @agentmem/mastra @mastra/core zod\n```\n\n## Quick start\n\n```ts\nimport { Agent } from '@mastra/core/agent'\nimport {\n  AgentMemIntegration,\n  agentmemMemorize,\n  agentmemRemember,\n} from '@agentmem/mastra'\n\nconst agentmem = new AgentMemIntegration({\n  apiKey:  process.env.AGENTMEM_API_KEY!,\n  agentId: 'support-bot',            // default; tools can override per-call\n  scope:   'team',                   // optional default\n})\n\nconst agent = new Agent({\n  name: 'support',\n  instructions:\n    'You help customers. When you learn a durable fact (preference, decision, ' +\n    'constraint), call `memorize`. Before answering, call `remember` to recall ' +\n    'prior context.',\n  tools: {\n    memorize: agentmemMemorize(agentmem),\n    remember: agentmemRemember(agentmem),\n  },\n})\n```\n\n## What it gives you\n\n| Export | Purpose |\n|---|---|\n| `AgentMemIntegration` | Class wrapping the AgentMem SDK; exposes `createMemory` / `searchMemory` / `searchMemoryRaw`. |\n| `agentmemMemorize(integration, defaults?)` | Pre-built Mastra tool. Stores a fact. |\n| `agentmemRemember(integration, defaults?)` | Pre-built Mastra tool. Retrieves relevant memories. |\n| `MemoryStore`, `AgentMemError`, `Scope`, `Role`, `MemoryHit` | Re-exported from `@agentmem/sdk` for convenience. |\n\n## Defaults and overrides\n\nConstructor `AgentMemConfig` accepts default `agentId`, `scope`, `role`, `workflowId`. Every per-call site (tool input or method argument) can override. The merge order is:\n\n`tool input` > `tool factory defaults` > `integration config` > error if still missing.\n\n`agentId` is the only required field; if it isn't set on the integration AND isn't passed per-call, the tool throws a helpful error.\n\n## Using the integration without tools\n\nIf you don't want the pre-built tools (e.g., you're writing custom flows):\n\n```ts\nconst text = await agentmem.searchMemory('what does the customer prefer?', {\n  agentId: 'support-bot',\n  topK:    5,\n})\n// → prompt-prefixed string ready to inject into the model's context\n\nconst ack = await agentmem.createMemory('Customer prefers email over phone.', {\n  agentId: 'support-bot',\n  scope:   'team',\n})\n// → \"Memory saved (id: ...). Content: ...\"\n```\n\nFor raw results without the prompt prefix:\n\n```ts\nconst hits = await agentmem.searchMemoryRaw('escalation threshold', { agentId: 'support-bot' })\n// → MemoryHit[]\n```\n\n## License\n\n[Apache-2.0](../LICENSE)\n","readmeFilename":"README.md"}