docs: navigation (#5900)

This commit is contained in:
Kartik
2026-07-01 22:48:23 +05:30
committed by GitHub
parent f2532f072f
commit 207f65deda
107 changed files with 951 additions and 1296 deletions
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@@ -60,6 +60,10 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
| **Multimodal support** | ✅ | ✅ |
| **Custom categories** | ✅ | Limited |
| **Advanced retrieval** | ✅ | ✅ |
| **Criteria retrieval** | ✅ | ❌ |
| **Temporal reasoning** | ✅ (v3) | ❌ |
| **Memory decay** | ✅ (v3) | ❌ |
| **Graph memory** | ✅ Built-in | ✅ External graph store |
| **Memory filters v2** | ✅ | ⚠️ (via metadata) |
| **Webhooks** | ✅ | ❌ |
| **Memory export** | ✅ | ❌ |
@@ -71,8 +75,8 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
| **Hosting** | Managed by Mem0 | Self-hosted |
| **Auto-scaling** | ✅ | Manual |
| **High availability** | ✅ Built-in | DIY setup |
| **Vector DB choice** | Managed | Qdrant, Chroma, Pinecone, Milvus, +20 more |
| **LLM choice** | Managed (optimized) | OpenAI, Anthropic, Ollama, Together, +10 more |
| **Vector DB choice** | Managed | 20+ stores: Qdrant, Pinecone, Chroma, Weaviate, Milvus, pgvector |
| **LLM choice** | Managed (optimized) | 15+ providers: OpenAI, Anthropic, Gemini, Groq, Ollama, Together |
| **Data residency** | US (expandable) | Your choice |
</Accordion>
@@ -88,10 +92,10 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
<Accordion title="Development & Integration" icon="code">
| Feature | Platform | Open Source |
|---------|----------|-------------|
| **REST API** | ✅ | ✅ (via feature flag) |
| **REST API** | ✅ | ✅ (self-hosted server) |
| **Python SDK** | ✅ | ✅ |
| **JavaScript SDK** | ✅ | ✅ |
| **Framework integrations** | LangChain, CrewAI, LlamaIndex, +15 | Same |
| **Framework integrations** | LangChain, CrewAI, LlamaIndex, and 20+ more | Same |
| **Dashboard** | ✅ Web-based | ❌ |
| **Analytics** | ✅ Built-in | DIY |
</Accordion>
@@ -101,45 +105,17 @@ Mem0 offers two powerful ways to add memory to your AI applications. Choose base
## Decision Guide
### Choose **Platform** if you want:
**Choose Platform if you want:**
- Fast time to market: get your AI app with memory live in hours, not weeks.
- Production-ready hosting: auto-scaling, high availability, and managed infrastructure.
- Built-in analytics: track memory usage, query patterns, and user engagement through the dashboard.
- Advanced features: webhooks, memory export, custom categories, and priority support.
<CardGroup cols={2}>
<Card icon="bolt" title="Fast Time to Market">
Get your AI app with memory live in hours, not weeks. No infrastructure setup needed.
</Card>
<Card icon="shield" title="Production-Ready">
Auto-scaling, high availability, and managed infrastructure out of the box.
</Card>
<Card icon="chart-line" title="Built-in Analytics">
Track memory usage, query patterns, and user engagement through our dashboard.
</Card>
<Card icon="webhook" title="Advanced Features">
Access to webhooks, memory export, custom categories, and priority support.
</Card>
</CardGroup>
### Choose **Open Source** if you need:
<CardGroup cols={2}>
<Card icon="lock" title="Full Data Control">
Host everything on your infrastructure. Complete data residency and privacy control.
</Card>
<Card icon="wrench" title="Custom Configuration">
Choose your own vector DB, LLM provider, embedder, and deployment strategy.
</Card>
<Card icon="code" title="Extensibility">
Modify the codebase, add custom features, and contribute back to the community.
</Card>
<Card icon="dollar-sign" title="Cost Optimization">
Use local LLMs (Ollama), self-hosted vector DBs, and optimize for your specific use case.
</Card>
</CardGroup>
**Choose Open Source if you need:**
- Full data control: host everything on your infrastructure with complete data residency.
- Custom configuration: choose your own vector DB, LLM provider, embedder, and deployment strategy.
- Extensibility: modify the codebase, add custom features, and contribute back to the community.
- Cost optimization: use local LLMs (Ollama), self-hosted vector DBs, and optimize for your use case.
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