Memory Utilities Reference

Complete reference for standalone utility functions exported from @mem0/vercel-ai-provider. These functions give you manual control over memory retrieval and storage, independent of the wrapped model pattern.

Source: integrations/vercel-ai-sdk/src/mem0-utils.ts

addMemories(messages, config?)

Stores messages to Mem0 as new memories.

import { addMemories } from "@mem0/vercel-ai-provider";

await addMemories(
  [
    { role: "user", content: [{ type: "text", text: "I love Italian food" }] },
    { role: "assistant", content: [{ type: "text", text: "Noted! I'll remember that." }] },
  ],
  { user_id: "alice", mem0ApiKey: "m0-xxx" }
);

Signature:

async function addMemories(
  messages: LanguageModelV2Prompt | string,
  config?: Mem0ConfigSettings
): Promise<any>;

Parameters:

Behavior:

1. If messages is a string, wraps it as a single user message
2. Otherwise, converts LanguageModelV2Prompt to Mem0 format via convertToMem0Format (handles multimodal content)
3. Calls POST /v1/memories/ with the converted messages and config

Returns: The API response from Mem0 (memory operation result).

---

retrieveMemories(prompt, config?)

Retrieves memories and returns a formatted system prompt string ready to inject into a system parameter.

import { retrieveMemories } from "@mem0/vercel-ai-provider";

const systemPrompt = await retrieveMemories("What restaurants do I like?", {
  user_id: "alice",
  mem0ApiKey: "m0-xxx",
});
// Returns: "System Message: These are the memories I have stored... Memory: User loves Italian food\n\n ..."

Signature:

async function retrieveMemories(
  prompt: LanguageModelV2Prompt | string,
  config?: Mem0ConfigSettings
): Promise<string>;

Parameters:

Behavior:

1. Flattens the prompt to a plain string (extracts text from LanguageModelV2Prompt parts)
2. Calls searchInternalMemories (POST /v2/memories/search/)
3. Formats each memory as "Memory: {memory.memory}\n\n"
4. Wraps everything in a system prompt preamble

Returns: A string containing the formatted system prompt with embedded memories. Returns "" (empty string) if no memories found.

Output format:

System Message: These are the memories I have stored. Give more weightage to the question by users and try to answer that first. You have to modify your answer based on the memories I have provided. If the memories are irrelevant you can ignore them. Also don't reply to this section of the prompt, or the memories, they are only for your reference. The System prompt starts after text System Message:

Memory: User loves Italian food

Memory: User is vegetarian

---

getMemories(prompt, config?)

Retrieves memories and returns the raw memory array.

import { getMemories } from "@mem0/vercel-ai-provider";

const memories = await getMemories("What are my preferences?", {
  user_id: "alice",
  mem0ApiKey: "m0-xxx",
});
// Returns: [{ memory: "User loves Italian food", id: "...", ... }, ...]

Signature:

async function getMemories(
  prompt: LanguageModelV2Prompt | string,
  config?: Mem0ConfigSettings
): Promise<any>;

Parameters:

Behavior:

1. Flattens the prompt to a plain string
2. Calls searchInternalMemories (POST /v2/memories/search/)
3. Returns memories.results (the array of memory objects)

Returns: Memory object array.

---

searchMemories(prompt, config?)

Retrieves the full search API response including results, relations, scores, and metadata.

import { searchMemories } from "@mem0/vercel-ai-provider";

const response = await searchMemories("cooking preferences", {
  user_id: "alice",
  mem0ApiKey: "m0-xxx",
});
// Returns: { results: [{ memory: "...", score: 0.95, ... }], relations: [...] }

Signature:

async function searchMemories(
  prompt: LanguageModelV2Prompt | string,
  config?: Mem0ConfigSettings
): Promise<any>;

Parameters:

Behavior:

1. Flattens the prompt to a plain string
2. Calls searchInternalMemories (POST /v2/memories/search/)
3. Returns the full response without any filtering

Returns: The complete API response object. On error, returns [].

Note: Unlike getMemories, this always returns the full response.

---

When to Use Which Function

Internal: searchInternalMemories(query, config?, top_k?)

Not exported. Used by all retrieval functions.

async function searchInternalMemories(
  query: string,
  config?: Mem0ConfigSettings,
  top_k: number = 5
): Promise<any>;

Behavior:

1. Builds a filters object from entity identifiers (user_id, app_id, agent_id, run_id)
2. Resolves entity identifiers
3. Loads the API key from config.mem0ApiKey or MEM0_API_KEY env var
4. Calls POST {host}/v2/memories/search/ with:
   • query: the search string
   • filters: the filter object with entity identifiers
   • top_k: from config or default 5
   • All other config fields spread into the request body

Default host: https://api.mem0.ai

Internal: convertToMem0Format(messages)

Not exported. Used by addMemories to convert LanguageModelV2Prompt messages to Mem0's format.

Multimodal content mapping:

The function handles three message content shapes:

1. String content: passed through directly
2. Array content: each element mapped individually, nulls filtered out
3. Single object content: mapped as a single element

Internal: flattenPrompt(prompt)

Not exported. Extracts plain text from LanguageModelV2Prompt for use as a search query.

• Iterates over prompt parts, extracting text from user role messages
• For text type content: extracts .text
• For file type content: returns descriptive placeholders ([PDF document], [Markdown document], [Image], [File attachment])
• For other content types: returns [multimodal content]
• Joins all parts with spaces

Mem0ConfigSettings Fields Reference

All fields are optional. Used across all utility functions.