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SelfMemory Documentation

Long-term memory for AI Agents with zero-setup simplicity

🚀 Quick Start​

Install SelfMemory with a single command:

pip install selfmemory

Start using it immediately:

from selfmemory import SelfMemory

# Works out of the box!
memory = SelfMemory()

# Add memories
memory.add("I love pizza but hate broccoli", user_id="demo")
memory.add("Meeting with Bob tomorrow at 3pm", user_id="demo")

# Search memories
results = memory.search("pizza", user_id="demo")
print(results)

🔥 Key Features​

  • Zero Setup: Install and start using immediately - no configuration required
  • Dual Architecture: Direct library usage OR managed client with REST API
  • Advanced Search: Semantic similarity with vector embeddings
  • Flexible Storage: File-based or MongoDB backend
  • NVIDIA NeMo Integration: Use Mem0, Redis, or Zep as memory backends via NeMo Agent Toolkit
  • Production Ready: Authentication, API keys, and dashboard integration

📚 Documentation Sections​

For Developers​

Integrations​

For Platform Users​

🏗️ Architecture​

SelfMemory provides two usage modes:

  1. Direct Library Usage: Import and use the SelfMemory class directly in your Python code
  2. Client/Server Mode: Run a REST API server and connect with SelfMemoryClient

Both modes support:

  • Semantic search with embeddings (Ollama, OpenAI)
  • Vector storage (Qdrant, ChromaDB)
  • Metadata and tagging
  • User isolation

💡 Use Cases​

  • Personal AI Assistants: Remember user preferences and conversation history
  • Customer Support Bots: Maintain customer interaction history
  • Research Tools: Store and retrieve research notes
  • Team Collaboration: Shared memory across AI agents

🔗 Quick Links​

🤝 Contributing​

We welcome contributions! Check out our GitHub repository to get started.

📄 License​

SelfMemory is licensed under the Apache 2.0 License.