Retrieve and store memories around LLM calls in Vercel AI SDK applications.
npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk
Please help me install this Agent Skill. Skill: mem0-vercel-ai-sdk Source: https://github.com/mem0ai/mem0/tree/main/skills/mem0-vercel-ai-sdk Command: npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk
Mem0 provides a memory-augmented AI provider for Vercel AI SDK applications, enabling seamless retrieval and storage of memories around LLM calls.
Install the Mem0 Vercel AI SDK provider and wrap your LLM calls to enable automatic memory retrieval and storage.
High-intent search queries and answers about installing and using this skill.
To add memory to LLM calls in a Vercel AI SDK application, you can wrap your model calls with the Mem0 provider to automatically retrieve relevant memories before generation and store new memories after the response.
Yes, memory-augmented AI supports streaming responses in Next.js API routes, allowing you to retrieve and store memories seamlessly while streaming outputs across multi-provider configurations.
Standalone memory utilities include functions to retrieve, store, and search memories independently, such as addMemories, getMemories, searchMemories, and retrieveMemories for flexible workflow integration.
Yes, the Mem0 Vercel AI provider supports multi-provider configurations, enabling memory retrieval and storage around LLM calls across different AI models within your application setup.
The wrapped model pattern handles memory by automatically injecting retrieved memories as context before an LLM call and extracting new information to store as memories after the call completes.
The best way to search stored memories in a TypeScript AI application is using the searchMemories function, which queries previously saved context to provide relevant historical data for your workflows.
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