Memori
ActiveDescription
Agent-native memory infrastructure that turns agent execution and conversation into structured, persistent state with an LLM-agnostic memory layer, MCP integration, and Python/TypeScript dual SDK support.
Key Features
- Agent-native memory infrastructure converting conversations and execution into structured persistent state
- LLM-agnostic design supporting OpenAI, Anthropic, and any model provider
- Python/TypeScript dual SDK with MCP one-command integration for Claude Code/Cursor/Codex
- 81.95% accuracy on LoCoMo benchmark using only 4.97% of full-context token footprint
- Entity attribution system tracking memory provenance by user/agent/process
- Memori Cloud zero-config deployment and BYODB self-hosted database modes
Use Cases
💡 Cross-session persistent memory and context recovery for AI agents
💡 Long-term tracking of user preferences and interaction history in customer service
💡 Shared coding conventions and project agreements across development teams
💡 Shared state and decision records in multi-agent collaboration
💡 Reducing LLM context window costs while maintaining reasoning quality
Strengths & Limitations
✅ Strengths
- • Actively maintained, recent updates
- • High community interest (16.3k stars)
- • Responsive to issues, low backlog
⚠️ Limitations
- • No clear open-source license
Categories
Quick Start
```bash
pip install memori
# or
npm install @memorilabs/memori
```
Set `MEMORI_API_KEY` and `OPENAI_API_KEY` environment variables, then wrap your LLM client with Memori:
```typescript
import { Memori } from '@memorilabs/memori';
const mem = new Memori().llm.register(client).attribution('user_123', 'support_agent');
```
Conversations are automatically persisted and recalled. MCP integration: `claude mcp add --transport http memori https://api.memorilabs.ai/mcp/`