Add Vercel to SDKs

--- name: mem0-vercel-ai-sdk description: > Mem0 provider for Vercel AI SDK (@mem0/vercel-ai-provider). TRIGGER when: user mentions "vercel ai sdk", "@mem0/vercel-ai-provider", "createMem0", "retrieveMemories", "addMemories", "getMemories", "searchMemories", "mem0 vercel", "AI SDK provider", "AI SDK memory", or is using generateText/streamText with mem0. Also triggers for Next.js apps needing memory-augmented AI. DO NOT TRIGGER when: user asks about direct Python/TS SDK calls without Vercel (use mem0 skill), or CLI terminal commands (use mem0-cli skill). license: Apache-2.0 metadata: author: mem0ai version: "1.1.0" category: ai-memory tags: "vercel, ai-sdk, memory, nextjs, typescript, provider" compatibility: Node.js 18+, npm install @mem0/vercel-ai-provider, Vercel AI SDK v5 (ai package), MEM0APIKEY + LLM provider API key --- # Mem0 Vercel AI SDK Provider Memory-enhanced AI provider for Vercel AI SDK. Automatically retrieves and stores memories during LLM calls. ## Step 1: Install ``bash npm install @mem0/vercel-ai-provider ai ` ## Step 2: Set up environment variables `bash export MEM0_API_KEY="m0-xxx" export OPENAI_API_KEY="sk-xxx" # or ANTHROPIC_API_KEY, GOOGLE_API_KEY, etc. ` Get a Mem0 API key at: https://app.mem0.ai/dashboard/api-keys?utm_source=oss&utm_medium=skill-mem0-vercel-ai-sdk ## Pattern 1: Wrapped Model The wrapped model approach is the simplest. createMem0 returns a provider that wraps any supported LLM with automatic memory retrieval and storage. `typescript import { generateText } from "ai"; import { createMem0 } from "@mem0/vercel-ai-provider"; const mem0 = createMem0(); const { text } = await generateText({ model: mem0("gpt-5-mini", { user_id: "alice" }), prompt: "Recommend a restaurant", }); ` What happens under the hood: 1. The prompt is sent to Mem0 search (POST /v3/memories/search/) to retrieve relevant memories 2. Retrieved memories are injected as a system message at the start of the prompt 3. The underlying LLM (e.g., OpenAI gpt-5-mini) generates a response using the enriched prompt 4. The conversation is stored back to Mem0 (POST /v3/memories/add/) as a fire-and-forget async call (no await) ## Pattern 2: Standalone Utilities Use standalone utilities when you want full control over the memory retrieve/store cycle, or you want to use a provider that is already configured separately. `typescript import { openai } from "@ai-sdk/openai"; import { generateText } from "ai"; import { retrieveMemories, addMemories } from "@mem0/vercel-ai-provider"; const prompt = "Recommend a restaurant"; // Retrieve memories -- returns a formatted system prompt string const memories = await retrieveMemories(prompt, { user_id: "alice", mem0ApiKey: "m0-xxx", }); // Generate using any provider with injected memories const { text } = await generateText({ model: openai("gpt-5-mini"), prompt, system: memories, }); // Optionally store the conversation back await addMemories( [ { role: "user", content: [{ type: "text", text: prompt }] }, { role: "assistant", content: [{ type: "text", text }] }, ], { user_id: "alice", mem0ApiKey: "m0-xxx" } ); ` ## Pattern 3: Streaming Use streamText for streaming responses with memory augmentation: `typescript import { streamText } from "ai"; import { createMem0 } from "@mem0/vercel-ai-provider"; const mem0 = createMem0(); const result = streamText({ model: mem0("gpt-5-mini", { user_id: "alice" }), prompt: "What should I cook for dinner?", }); for await (const chunk of result.textStream) { process.stdout.write(chunk); } ` The wrapped model handles memory retrieval before streaming begins and stores the conversation after. ## Supported Providers | Provider | Config value | Required env var | |----------|-------------|------------------| | OpenAI (default) | "openai" | OPENAIAPIKEY | | Anthropic | "anthropic" | ANTHROPICAPIKEY | | Google | "google" | GOOGLEGENERATIVEAIAPIKEY | | Groq | "groq" | GROQAPIKEY | | Cohere | "cohere" | COHEREAPIKEY | Select a provider when creating the Mem0 instance: `typescript const mem0 = createMem0({ provider: "anthropic" }); const { text } = await generateText({ model: mem0("gpt-5-mini", { user_id: "alice" }), prompt: "Hello!", }); ` ## How It Works Internally ### Wrapped model flow ` User prompt --> searchInternalMemories (POST /v3/memories/search/) --> memories injected as system message at start of prompt --> underlying LLM generates response (doGenerate or doStream) --> processMemories fires addMemories as fire-and-forget (no await) --> response returned to caller ` ### Standalone flow ` User controls each step: 1. retrieveMemories / getMemories / searchMemories -> fetch memories 2. inject into system prompt manually 3. call generateText / streamText with any provider 4. addMemories -> store new conversation to Mem0 `` ## Key Differences Between the 4 Utility