Platform feature docs revamp (#3007)
This commit is contained in:
Antaripa Saha
152
docs/core-concepts/memory-operations/add.mdx
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152
docs/core-concepts/memory-operations/add.mdx
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---
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title: Add Memory
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description: Add memory into the Mem0 platform by storing user-assistant interactions and facts for later retrieval.
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icon: "plus"
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iconType: "solid"
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---
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## Overview
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The `add` operation is how you store memory into Mem0. Whether you're working with a chatbot, a voice assistant, or a multi-agent system, this is the entry point to create long-term memory.
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Memories typically come from a **user-assistant interaction** and Mem0 handles the extraction, transformation, and storage for you.
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Mem0 offers two implementation flows:
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- **Mem0 Platform** (Managed, scalable, with dashboard + API)
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- **Mem0 Open Source** (Lightweight, fully local, flexible SDKs)
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Each supports the same core memory operations, but with slightly different setup. Below, we walk through examples for both.
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## Architecture
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<Frame caption="Architecture diagram illustrating the process of adding memories.">
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<img src="../../images/add_architecture.png" />
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</Frame>
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When you call `add`, Mem0 performs the following steps under the hood:
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1. **Information Extraction**
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The input messages are passed through an LLM that extracts key facts, decisions, preferences, or events worth remembering.
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2. **Conflict Resolution**
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Mem0 compares the new memory against existing ones to detect duplication or contradiction and handles updates accordingly.
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3. **Memory Storage**
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The result is stored in a vector database (for semantic search) and optionally in a graph structure (for relationship mapping).
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You don’t need to handle any of this manually, Mem0 takes care of it with a single API call or SDK method.
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---
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## Example: Mem0 Platform
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<CodeGroup>
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```python Python
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from mem0 import MemoryClient
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client = MemoryClient(api_key="your-api-key")
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messages = [
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{"role": "user", "content": "I'm planning a trip to Tokyo next month."},
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{"role": "assistant", "content": "Great! I’ll remember that for future suggestions."}
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]
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client.add(
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messages=messages,
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user_id="alice",
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version="v2"
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)
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```
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```javascript JavaScript
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import { MemoryClient } from "mem0ai";
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const client = new MemoryClient({apiKey: "your-api-key"});
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const messages = [
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{ role: "user", content: "I'm planning a trip to Tokyo next month." },
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{ role: "assistant", content: "Great! I’ll remember that for future suggestions." }
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];
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await client.add({
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messages,
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user_id: "alice",
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version: "v2"
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});
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```
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</CodeGroup>
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---
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## Example: Mem0 Open Source
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<CodeGroup>
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```python Python
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import os
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from mem0 import Memory
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os.environ["OPENAI_API_KEY"] = "your-api-key"
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m = Memory()
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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# Store inferred memories (default behavior)
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result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"})
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# Optionally store raw messages without inference
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result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"}, infer=False)
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```
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```javascript JavaScript
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import { Memory } from 'mem0ai/oss';
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const memory = new Memory();
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const messages = [
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{
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role: "user",
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content: "I like to drink coffee in the morning and go for a walk"
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}
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];
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const result = memory.add(messages, {
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userId: "alice",
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metadata: { category: "preferences" }
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});
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```
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</CodeGroup>
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---
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## When Should You Add Memory?
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Add memory whenever your agent learns something useful:
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- A new user preference is shared
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- A decision or suggestion is made
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- A goal or task is completed
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- A new entity is introduced
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- A user gives feedback or clarification
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Storing this context allows the agent to reason better in future interactions.
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### More Details
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For full list of supported fields, required formats, and advanced options, see the
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[Add Memory API Reference](/api-reference/memory/add-memories).
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---
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## Need help?
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If you have any questions, please feel free to reach out to us using one of the following methods:
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<Snippet file="get-help.mdx"/>
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141
docs/core-concepts/memory-operations/delete.mdx
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141
docs/core-concepts/memory-operations/delete.mdx
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---
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title: Delete Memory
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description: Remove memories from Mem0 either individually, in bulk, or via filters.
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icon: "trash"
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iconType: "solid"
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---
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## Overview
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Memories can become outdated, irrelevant, or need to be removed for privacy or compliance reasons. Mem0 offers flexible ways to delete memory:
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1. **Delete a Single Memory** – using a specific memory ID
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2. **Batch Delete** – delete multiple known memory IDs (up to 1000)
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3. **Filtered Delete** – delete memories matching a filter (e.g., `user_id`, `metadata`, `run_id`)
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This page walks through code example for each method.
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## Use Cases
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- Forget a user’s past preferences by request
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- Remove outdated or incorrect memory entries
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- Clean up memory after session expiration
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- Comply with data deletion requests (e.g., GDPR)
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---
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## 1. Delete a Single Memory by ID
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<CodeGroup>
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```python Python
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from mem0 import MemoryClient
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client = MemoryClient(api_key="your-api-key")
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memory_id = "your_memory_id"
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client.delete(memory_id=memory_id)
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```
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```javascript JavaScript
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import MemoryClient from 'mem0ai';
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const client = new MemoryClient({ apiKey: "your-api-key" });
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client.delete("your_memory_id")
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.then(result => console.log(result))
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.catch(error => console.error(error));
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```
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</CodeGroup>
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---
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## 2. Batch Delete Multiple Memories
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<CodeGroup>
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```python Python
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from mem0 import MemoryClient
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client = MemoryClient(api_key="your-api-key")
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delete_memories = [
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{"memory_id": "id1"},
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{"memory_id": "id2"}
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]
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response = client.batch_delete(delete_memories)
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print(response)
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```
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```javascript JavaScript
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import MemoryClient from 'mem0ai';
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const client = new MemoryClient({ apiKey: "your-api-key" });
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const deleteMemories = [
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{ memory_id: "id1" },
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{ memory_id: "id2" }
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];
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client.batchDelete(deleteMemories)
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.then(response => console.log('Batch delete response:', response))
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.catch(error => console.error(error));
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```
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</CodeGroup>
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---
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## 3. Delete Memories by Filter (e.g., user_id)
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<CodeGroup>
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```python Python
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from mem0 import MemoryClient
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client = MemoryClient(api_key="your-api-key")
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# Delete all memories for a specific user
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client.delete_all(user_id="alice")
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```
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```javascript JavaScript
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import MemoryClient from 'mem0ai';
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const client = new MemoryClient({ apiKey: "your-api-key" });
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client.deleteAll({ user_id: "alice" })
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.then(result => console.log(result))
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.catch(error => console.error(error));
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```
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</CodeGroup>
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You can also filter by other parameters such as:
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- `agent_id`
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- `run_id`
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- `metadata` (as JSON string)
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---
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## Key Differences
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| Method | Use When | IDs Needed | Filters |
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|----------------------|-------------------------------------------|------------|----------|
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| `delete(memory_id)` | You know exactly which memory to remove | ✔ | ✘ |
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| `batch_delete([...])`| You have a known list of memory IDs | ✔ | ✘ |
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| `delete_all(...)` | You want to delete by user/agent/run/etc | ✘ | ✔ |
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### More Details
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For request/response schema and additional filtering options, see:
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- [Delete Memory API Reference](/api-reference/memory/delete-memory)
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- [Batch Delete API Reference](/api-reference/memory/batch-delete)
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- [Delete Memories by Filter Reference](/api-reference/memory/delete-memories)
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You’ve now seen how to add, search, update, and delete memories in Mem0.
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---
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## Need help?
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If you have any questions, please feel free to reach out to us using one of the following methods:
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<Snippet file="get-help.mdx"/>
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124
docs/core-concepts/memory-operations/search.mdx
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124
docs/core-concepts/memory-operations/search.mdx
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---
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title: Search Memory
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description: Retrieve relevant memories from Mem0 using powerful semantic and filtered search capabilities.
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icon: "magnifying-glass"
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iconType: "solid"
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---
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## Overview
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The `search` operation allows you to retrieve relevant memories based on a natural language query and optional filters like user ID, agent ID, categories, and more. This is the foundation of giving your agents memory-aware behavior.
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Mem0 supports:
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- Semantic similarity search
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- Metadata filtering (with advanced logic)
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- Reranking and thresholds
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- Cross-agent, multi-session context resolution
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This applies to both:
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- **Mem0 Platform** (hosted API with full-scale features)
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- **Mem0 Open Source** (local-first with LLM inference and local vector DB)
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## Architecture
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<Frame caption="Architecture diagram illustrating the memory search process.">
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<img src="../../images/search_architecture.png" />
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</Frame>
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The search flow follows these steps:
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1. **Query Processing**
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An LLM refines and optimizes your natural language query.
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2. **Vector Search**
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Semantic embeddings are used to find the most relevant memories using cosine similarity.
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3. **Filtering & Ranking**
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Logical and comparison-based filters are applied. Memories are scored, filtered, and optionally reranked.
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4. **Results Delivery**
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Relevant memories are returned with associated metadata and timestamps.
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---
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## Example: Mem0 Platform
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<CodeGroup>
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```python Python
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from mem0 import MemoryClient
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client = MemoryClient(api_key="your-api-key")
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query = "What do you know about me?"
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filters = {
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"OR": [
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{"user_id": "alice"},
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{"agent_id": {"in": ["travel-assistant", "customer-support"]}}
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]
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}
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results = client.search(query, version="v2", filters=filters)
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```
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```javascript JavaScript
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import { MemoryClient } from "mem0ai";
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const client = new MemoryClient({apiKey: "your-api-key"});
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const query = "I'm craving some pizza. Any recommendations?";
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const filters = {
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AND: [
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{ user_id: "alice" }
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]
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};
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const results = await client.search(query, {
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version: "v2",
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filters
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});
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```
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</CodeGroup>
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---
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## Example: Mem0 Open Source
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<CodeGroup>
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```python Python
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from mem0 import Memory
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m = Memory()
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related_memories = m.search("Should I drink coffee or tea?", user_id="alice")
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```
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```javascript JavaScript
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import { Memory } from 'mem0ai/oss';
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const memory = new Memory();
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const relatedMemories = memory.search("Should I drink coffee or tea?", { userId: "alice" });
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```
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</CodeGroup>
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---
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## Tips for Better Search
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- Use descriptive natural queries (Mem0 can interpret intent)
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- Apply filters for scoped, faster lookup
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- Use `version: "v2"` for enhanced results
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- Consider wildcard filters (e.g., `run_id: "*"`) for broader matches
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- Tune with `top_k`, `threshold`, or `rerank` if needed
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### More Details
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For the full list of filter logic, comparison operators, and optional search parameters, see the
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[Search Memory API Reference](/api-reference/memory/v2-search-memories).
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---
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## Need help?
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If you have any questions, please feel free to reach out to us using one of the following methods:
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<Snippet file="get-help.mdx"/>
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117
docs/core-concepts/memory-operations/update.mdx
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117
docs/core-concepts/memory-operations/update.mdx
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@@ -0,0 +1,117 @@
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---
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title: Update Memory
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description: Modify an existing memory by updating its content or metadata.
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icon: "pencil"
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iconType: "solid"
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---
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## Overview
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User preferences, interests, and behaviors often evolve over time. The `update` operation lets you revise a stored memory, whether it's updating facts and memories, rephrasing a message, or enriching metadata.
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Mem0 supports both:
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- **Single Memory Update** – for one specific memory using its ID
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- **Batch Update** – for updating many memories at once (up to 1000)
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This guide includes usage for both single update and batch update of memories through **Mem0 Platform**
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## Use Cases
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- Refine a vague or incorrect memory after a correction
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- Add or edit memory with new metadata (e.g., categories, tags)
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- Evolve factual knowledge as the user’s profile changes
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- A user profile evolves: “I love spicy food” → later says “Actually, I can’t handle spicy food.”
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Updating memory ensures your agents remain accurate, adaptive, and personalized.
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---
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## Update Memory
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<CodeGroup>
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```python Python
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from mem0 import MemoryClient
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client = MemoryClient(api_key="your-api-key")
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memory_id = "your_memory_id"
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client.update(
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memory_id=memory_id,
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text="Updated memory content about the user",
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metadata={"category": "profile-update"}
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)
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```
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```javascript JavaScript
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import MemoryClient from 'mem0ai';
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const client = new MemoryClient({ apiKey: "your-api-key" });
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const memory_id = "your_memory_id";
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client.update(memory_id, {
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text: "Updated memory content about the user",
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metadata: { category: "profile-update" }
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})
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.then(result => console.log(result))
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.catch(error => console.error(error));
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```
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</CodeGroup>
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---
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## Batch Update
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Update up to 1000 memories in one call.
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<CodeGroup>
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```python Python
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from mem0 import MemoryClient
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client = MemoryClient(api_key="your-api-key")
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update_memories = [
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{"memory_id": "id1", "text": "Watches football"},
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{"memory_id": "id2", "text": "Likes to travel"}
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]
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response = client.batch_update(update_memories)
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print(response)
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```
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```javascript JavaScript
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import MemoryClient from 'mem0ai';
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const client = new MemoryClient({ apiKey: "your-api-key" });
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const updateMemories = [
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{ memoryId: "id1", text: "Watches football" },
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{ memoryId: "id2", text: "Likes to travel" }
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];
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client.batchUpdate(updateMemories)
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.then(response => console.log('Batch update response:', response))
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.catch(error => console.error(error));
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```
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</CodeGroup>
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---
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## Tips
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- You can update both `text` and `metadata` in the same call.
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- Use `batchUpdate` when you're applying similar corrections at scale.
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- If memory is marked `immutable`, it must first be deleted and re-added.
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- Combine this with feedback mechanisms (e.g., user thumbs-up/down) to self-improve memory.
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### More Details
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Refer to the full [Update Memory API Reference](/api-reference/memory/update-memory) and [Batch Update Reference](/api-reference/memory/batch-update) for schema and advanced fields.
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---
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||||
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## Need help?
|
||||
If you have any questions, please feel free to reach out to us using one of the following methods:
|
||||
|
||||
<Snippet file="get-help.mdx"/>
|
||||
Reference in New Issue
Block a user