[docs] platform core concept revamp and overview update (#3664)
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Parth Sharma
@@ -5,43 +5,52 @@ icon: "plus"
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iconType: "solid"
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---
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# How Mem0 Adds Memory
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## Overview
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Adding memory is how Mem0 captures useful details from a conversation so your agents can reuse them later. Think of it as saving the important sentences from a chat transcript into a structured notebook your agent can search.
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The `add` operation stores 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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<Info>
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**Why it matters**
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- Preserves user preferences, goals, and feedback across sessions.
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- Powers personalization and decision-making in downstream conversations.
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- Keeps context consistent between managed Platform and OSS deployments.
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</Info>
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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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## Key terms
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Mem0 offers two implementation flows:
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- **Messages** – The ordered list of user/assistant turns you send to `add`.
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- **Infer** – Controls whether Mem0 extracts structured memories (`infer=True`, default) or stores raw messages.
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- **Metadata** – Optional filters (e.g., `{"category": "movie_recommendations"}`) that improve retrieval later.
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- **User / Session identifiers** – `user_id`, `session_id`, or `run_id` that scope the memory for future searches.
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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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## How does it work?
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Each supports the same core memory operations, but with slightly different setup.
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Mem0 offers two flows:
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- **Mem0 Platform** – Fully managed API with dashboard, scaling, and graph features.
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- **Mem0 Open Source** – Local SDK that you run in your own environment.
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## Architecture
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Both flows take the same payload and pass it through the same pipeline.
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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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<Steps>
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<Step title="Information extraction">
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Mem0 sends the messages through an LLM that pulls out key facts, decisions, or preferences to remember.
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</Step>
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<Step title="Conflict resolution">
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Existing memories are checked for duplicates or contradictions so the latest truth wins.
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</Step>
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<Step title="Storage">
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The resulting memories land in managed vector storage (and optional graph storage) so future searches return them quickly.
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</Step>
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</Steps>
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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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You trigger this pipeline with a single `add` call—no manual orchestration needed.
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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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## Add with Mem0 Platform
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<CodeGroup>
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```python Python
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@@ -79,9 +88,11 @@ await client.add({
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```
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</CodeGroup>
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---
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<Info icon="check">
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Expect a `memory_id` (or list of IDs) in the response. Check the Mem0 dashboard to confirm the new entry under the correct user.
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</Info>
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## Example: Mem0 Open Source
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## Add with Mem0 Open Source
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<CodeGroup>
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```python Python
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@@ -125,7 +136,9 @@ const result = memory.add(messages, {
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```
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</CodeGroup>
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---
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<Tip>
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Use `infer=False` only when you need to store raw transcripts. Most workflows benefit from Mem0 extracting structured memories automatically.
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</Tip>
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## When Should You Add Memory?
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@@ -145,9 +158,38 @@ Storing this context allows the agent to reason better in future interactions.
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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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## Managed vs OSS differences
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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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| Capability | Mem0 Platform | Mem0 OSS |
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| --- | --- | --- |
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| Conflict resolution | Automatic with dashboard visibility | SDK handles merges locally; you control storage |
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| Graph writes | Toggle per request (`enable_graph=True`) | Requires configuring a graph provider |
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| Rate limits | Managed quotas per workspace | Limited by your hardware and provider APIs |
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| Dashboard visibility | Yes — inspect memories visually | Inspect via CLI, logs, or custom UI |
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<Snippet file="get-help.mdx"/>
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## Put it into practice
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- Review the <Link href="/platform/advanced-memory-operations">Advanced Memory Operations</Link> guide to layer metadata, rerankers, and graph toggles.
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- Explore the <Link href="/api-reference/memory/add-memories">Add Memories API reference</Link> for every request/response field.
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## See it live
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- <Link href="/examples/customer-support-agent">Customer Support Agent</Link> shows add + search powering a support flow.
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- <Link href="/examples/personal-ai-tutor">Personal AI Tutor</Link> uses add to personalize lesson plans.
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{/* DEBUG: verify CTA targets */}
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<CardGroup cols={2}>
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<Card
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title="Explore Search Concepts"
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description="See how stored memories feed retrieval in the Search guide."
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icon="search"
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href="/core-concepts/memory-operations/search"
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/>
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<Card
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title="Build a Support Agent"
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description="Follow the cookbook to apply add/search/update in production."
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icon="rocket"
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href="/examples/customer-support-agent"
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/>
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</CardGroup>
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