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docs(skills): refresh mem0 skills against SDK 2.2.1 / TS 3.3.1 (#7548)

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Kartik
2026-10-07 18:36:15 +05:30
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skills/mem0-vercel-ai-sdk/SKILL.md
+38 -20
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@@ -12,10 +12,11 @@ description: >
license: Apache-2.0
metadata:
author: mem0ai
version: "1.1.0"
version: "2.0.0"
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), MEM0_API_KEY + LLM provider API key
mem0_tested_versions: "@mem0/vercel-ai-provider (npm) >=3.0.0,<4.0.0; ai (npm) >=6.0.0,<7.0.0; mem0ai (npm) >=3.0.0,<4.0.0"
compatibility: Node.js 18+, npm install @mem0/vercel-ai-provider, Vercel AI SDK v6 (ai package ^6), MEM0_API_KEY + LLM provider API key
---
# Mem0 Vercel AI SDK Provider
@@ -25,9 +26,11 @@ Memory-enhanced AI provider for Vercel AI SDK. Automatically retrieves and store
## Step 1: Install
```bash
npm install @mem0/vercel-ai-provider ai
npm install @mem0/vercel-ai-provider ai@^6
```
`ai` and the `@ai-sdk/*` provider packages ship as regular dependencies of `@mem0/vercel-ai-provider`, so nothing else needs installing. The only peer dependency is `zod` (optional, `^3.0.0`).
## Step 2: Set up environment variables
```bash
@@ -53,10 +56,23 @@ const { text } = await generateText({
```
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)
1. The prompt is stored to Mem0 (`POST /v3/memories/add/`), awaited before the LLM call (a failed write is logged and ignored)
2. The prompt is sent to Mem0 search (`POST /v3/memories/search/`) to retrieve relevant memories
3. If any memories are found, they are injected as a system message at the start of the prompt
4. The underlying LLM (e.g., OpenAI gpt-5-mini) generates a response using the enriched prompt
For `generateText`, the retrieved memories are also attached to the result as a source:
```typescript
const { text, sources } = await generateText({
model: mem0("gpt-5-mini", { user_id: "alice" }),
prompt: "Recommend a restaurant",
});
console.log(sources.find((s) => s.title === "Mem0 Memories")?.providerMetadata?.mem0);
```
The source has `title: "Mem0 Memories"` and `providerMetadata.mem0` holds `memories` (array of memory objects) and `memoriesText`. It is only present when at least one memory was retrieved.
## Pattern 2: Standalone Utilities
@@ -111,7 +127,7 @@ for await (const chunk of result.textStream) {
}
```
The wrapped model handles memory retrieval before streaming begins and stores the conversation after.
The wrapped model stores the conversation and retrieves memories before streaming begins.
## Supported Providers
@@ -119,7 +135,7 @@ The wrapped model handles memory retrieval before streaming begins and stores th
|----------|-------------|------------------|
| OpenAI (default) | `"openai"` | `OPENAI_API_KEY` |
| Anthropic | `"anthropic"` | `ANTHROPIC_API_KEY` |
| Google | `"google"` | `GOOGLE_GENERATIVE_AI_API_KEY` |
| Google | `"google"` (alias `"gemini"`) | `GOOGLE_GENERATIVE_AI_API_KEY` |
| Groq | `"groq"` | `GROQ_API_KEY` |
| Cohere | `"cohere"` | `COHERE_API_KEY` |
@@ -128,7 +144,7 @@ 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" }),
model: mem0("claude-sonnet-4-20250514", { user_id: "alice" }),
prompt: "Hello!",
});
```
@@ -139,11 +155,11 @@ const { text } = await generateText({
```
User prompt
--> searchInternalMemories (POST /v3/memories/search/)
--> memories injected as system message at start of prompt
--> processMemories: addMemories (POST /v3/memories/add/, awaited)
--> processMemories: getMemories (POST /v3/memories/search/)
--> memories (if any) 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
--> response returned to caller (doGenerate also attaches a "Mem0 Memories" source)
```
### Standalone flow
@@ -162,18 +178,20 @@ User controls each step:
|----------|---------|----------|
| `retrieveMemories` | Formatted system prompt **string** | Injecting directly into `system` parameter |
| `getMemories` | Raw memory **array** | Processing memories programmatically |
| `searchMemories` | Full search **response** (results + relations) | Need relations, scores, metadata |
| `searchMemories` | Raw search **response** (as returned by the API) | Need scores and full metadata |
| `addMemories` | API response | Storing new messages to Mem0 |
All four accept `LanguageModelV2Prompt | string` as the first argument and optional `Mem0ConfigSettings` as the second.
`retrieveMemories`, `getMemories`, and `searchMemories` accept `LanguageModelV3Prompt | string` as the first argument; `addMemories` is typed as `LanguageModelV3Prompt` (a string also works at runtime). All four take optional `Mem0ConfigSettings` as the second argument.
## Common Edge Cases and Tips
- **Always provide `user_id`** (or `agent_id`/`app_id`/`run_id`) for consistent memory retrieval. Without an entity identifier, memories cannot be scoped.
- **Always provide `user_id`** (or `agent_id`/`app_id`/`run_id`) for consistent memory retrieval. The search endpoint requires at least one entity ID in `filters`; the provider places these IDs there for you.
- **Standalone utilities require explicit API key**: pass `mem0ApiKey` in the config object, or set the `MEM0_API_KEY` environment variable.
- **This uses Vercel AI SDK v5** (LanguageModelV2 / ProviderV2 interfaces). It is not compatible with AI SDK v3 or v4.
- **`processMemories` fires `addMemories` as fire-and-forget** (`.then()` without `await`). Memory storage happens asynchronously and does not block the LLM response.
- **The `"gemini"` alias** exists in the provider switch but is NOT in the `supportedProviders` list. Use `"google"` instead.
- **This uses Vercel AI SDK v6** (LanguageModelV3 / ProviderV3 interfaces, `@mem0/vercel-ai-provider` 3.x). Provider 2.x targeted AI SDK v5. It is not compatible with AI SDK v4 or earlier.
- **`processMemories` awaits `addMemories`** before searching and calling the LLM, so each wrapped call includes one memory write and one memory search. If either request fails, the error is logged and the LLM call proceeds without memories.
- **`"google"` and `"gemini"`** are both accepted and map to `@ai-sdk/google`.
- **Removed in 3.0.0**: `org_id`, `project_id`, `org_name`, `project_name`, `output_format`, `filter_memories`, `async_mode`, `enable_graph`, `version`, `api_version`. Graph memory is now a Mem0 Platform project setting, not a provider option.
- **Default `top_k` is 10.** `threshold` and `rerank` are only sent when set (the API default for `rerank` is `false`; `threshold` is a server-side cutoff, not a floor on the returned score).
- **Custom host**: set `host` in the config to point to a different Mem0 API endpoint (default: `https://api.mem0.ai`).
## References