Soumil Rathi
59880a6f8f
TypeScript SDK v3 pipeline (full parity with Python): - Single-pass additive extraction with ADDITIVE_EXTRACTION_PROMPT - Hybrid search (semantic + BM25 + entity boost) with additive scoring - 8-phase batch pipeline (batch embed, persist, entity linking) - New utils: scoring.ts, lemmatization.ts (natural), entity_extraction.ts (compromise) - keywordSearch() on 8 vector stores (3 full: PGVector, Memory, Azure AI Search) - Message persistence in SQLiteManager (rolling window of 10) - Entity store as second vector collection - MAX_BATCH=100 chunking guard on OpenAI/Azure embedBatch - Updated default LLM model to gpt-4.1-nano-2025-04-14 - compromise + natural added as peer dependencies Graph store removal (Python + TypeScript): - Removed Neo4j, Memgraph, Kuzu, Neptune, Apache AGE integrations - Deleted 18 graph-related files across both SDKs - Removed GraphStoreFactory, GraphStoreConfig, graph_store config field - Removed "relations" key from all API responses - Removed graph optional dependency group from pyproject.toml - Removed neo4j-driver from TS peerDependencies - Simplified add/search/delete/reset (no more parallel graph operations) Client SDK v3 API migration: - add() endpoint: /v1/memories/ -> /v3/memories/ (async response) - search() endpoint: /v2/memories/search/ -> /v3/memories/search/ - Removed output_format injection and v1.1 unwrapping logic - Applied to both Python (sync + async) and TypeScript clients Review feedback fixes: - Removed deprecated custom_update_memory_prompt from MemoryConfig - Added MAX_BATCH=100 chunking to Python + TS embed_batch - Moved all inline imports to top level in main.py - Cleaned up GraphStoreError dead code from exceptions.py Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Mem0 - The Memory Layer for Your AI Apps
Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. We offer both cloud and open-source solutions to cater to different needs.
See the complete OSS Docs. See the complete Platform API Reference.
1. Installation
For the open-source version, you can install the Mem0 package using npm:
npm i mem0ai
2. API Key Setup
For the cloud offering, sign in to Mem0 Platform to obtain your API Key.
3. Client Features
Cloud Offering
The cloud version provides a comprehensive set of features, including:
- Memory Operations: Perform CRUD operations on memories.
- Search Capabilities: Search for relevant memories using advanced filters.
- Memory History: Track changes to memories over time.
- Error Handling: Robust error handling for API-related issues.
- Async/Await Support: All methods return promises for easy integration.
Open-Source Offering
The open-source version includes the following top features:
- Memory Management: Add, update, delete, and retrieve memories.
- Vector Store Integration: Supports various vector store providers for efficient memory retrieval.
- LLM Support: Integrates with multiple LLM providers for generating responses.
- Customizable Configuration: Easily configure memory settings and providers.
- SQLite Storage: Use SQLite for memory history management.
4. Memory Operations
Mem0 provides a simple and customizable interface for performing memory operations. You can create long-term and short-term memories, search for relevant memories, and manage memory history.
5. Error Handling
The MemoryClient throws errors for any API-related issues. You can catch and handle these errors effectively.
6. Using with async/await
All methods of the MemoryClient return promises, allowing for seamless integration with async/await syntax.
7. Testing the Client
To test the MemoryClient in a Node.js environment, you can create a simple script to verify the functionality of memory operations.
Getting Help
If you have any questions or need assistance, please reach out to us:
- Email: founders@mem0.ai
- Join our discord community
- GitHub Issues: Report bugs or request features