mem0/skills/mem0-vercel-ai-sdk/references/memory-utilities.md

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: 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:

Parameter	Type	Description
messages	LanguageModelV2Prompt | string	Messages to store. If a string, wrapped as [{ role: "user", content: string }]
config	Mem0ConfigSettings	Optional. Must include entity scope (user_id, etc.) and API key

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:

Parameter	Type	Description
prompt	LanguageModelV2Prompt | string	The query to search memories for
config	Mem0ConfigSettings	Optional. Entity scope and API key

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. If enable_graph: true, also appends graph relations as "Relation: {source} -> {relationship} -> {target}\n\n"
5. 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

HERE ARE THE GRAPHS RELATIONS FOR THE PREFERENCES OF THE USER:

Relation: Alice -> likes -> Italian cuisine

---

getMemories(prompt, config?)

Retrieves memories and returns the raw memory array (or full response if graph is enabled).

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: "...", ... }, ...]

// With graph enabled:
const graphMemories = await getMemories("What are my preferences?", {
  user_id: "alice",
  mem0ApiKey: "m0-xxx",
  enable_graph: true,
});
// Returns: { results: [...], relations: [...] }

Signature:

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

Parameters:

Parameter	Type	Description
prompt	LanguageModelV2Prompt | string	The query to search memories for
config	Mem0ConfigSettings	Optional. Entity scope and API key

Behavior:

1. Flattens the prompt to a plain string
2. Calls searchInternalMemories (POST /v2/memories/search/)
3. If enable_graph is not set: returns memories.results (the array of memory objects)
4. If enable_graph is set: returns the full response object (with both results and relations)

Returns: Memory object array, or full response object when graph is enabled.

---

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:

Parameter	Type	Description
prompt	LanguageModelV2Prompt | string	The query to search memories for
config	Mem0ConfigSettings	Optional. Entity scope and API key

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 regardless of enable_graph setting.

---

When to Use Which Function

Function	Returns	Use when
retrieveMemories	Formatted system prompt string	Injecting directly into a system parameter for generateText/streamText
getMemories	Memory array (or full response if enable_graph)	Processing memories programmatically (filtering, transforming, counting)
searchMemories	Full API response (results + relations)	Need relations, similarity scores, or complete metadata regardless of graph setting
addMemories	API response	Storing new conversation messages as memories

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 an OR filter from entity identifiers (user_id, app_id, agent_id, run_id)
2. Resolves org/project identifiers (org_id takes precedence over org_name)
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 OR filter object
   • top_k: from config or default 5
   • version: "v2"
   • output_format: "v1.1"
   • 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:

Input type	Input format	Output type	Output format
Text	{ type: "text", text: "..." }	Plain string	{ role, content: "..." }
Image	{ type: "image_url", image_url: { url } } or { type: "image", ... }	Image URL	{ role, content: { type: "image_url", image_url: { url } } }
PDF file	{ type: "file", data: url, mediaType: "application/pdf" }	PDF URL	{ role, content: { type: "pdf_url", pdf_url: { url } } }
Markdown file	{ type: "file", data: url, mediaType: "text/markdown" } or "application/mdx"	MDX URL	{ role, content: { type: "mdx_url", mdx_url: { url } } }
Image file	{ type: "file", data: url, mediaType: "image/*" }	Image URL	{ role, content: { type: "image_url", image_url: { url } } }
MDX content	{ type: "mdx_url", mdx_url: { url } } or { type: "mdx", ... }	MDX URL	{ role, content: { type: "mdx_url", mdx_url: { url } } }
PDF content	{ type: "pdf_url", pdf_url: { url } } or { type: "pdf", ... }	PDF URL	{ role, content: { type: "pdf_url", pdf_url: { url } } }

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.

Field	Type	Default	Description
user_id	string	--	Scope memories to a user
app_id	string	--	Scope memories to an application
agent_id	string	--	Scope memories to an agent
run_id	string	--	Scope memories to a session/run
org_name	string	--	Organization name (fallback if org_id not set)
project_name	string	--	Project name (fallback if org_id not set)
org_id	string	--	Organization ID (takes precedence)
project_id	string	--	Project ID
metadata	Record<string, any>	--	Custom metadata
filters	Record<string, any>	--	Custom search filters
infer	boolean	--	Enable inference
page	number	--	Pagination page number
page_size	number	--	Results per page
mem0ApiKey	string	MEM0_API_KEY env	Mem0 API key
top_k	number	5	Number of memories to retrieve
threshold	number	--	Minimum similarity score
rerank	boolean	--	Enable re-ranking
enable_graph	boolean	--	Enable graph memory (relations)
host	string	https://api.mem0.ai	Custom API host
output_format	string	--	Output format version
filter_memories	boolean	--	Enable memory filtering
async_mode	boolean	--	Enable async processing