docs: update zellij-ps to reflect project switcher functionality
- Update package description and fix mainProgram typo - Rewrite documentation to describe project switching, not process viewing - Add PROJECT_FOLDERS configuration and usage examples - Update all references across docs (README, guides, module overviews)
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docs/packages/mem0.md
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docs/packages/mem0.md
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# mem0
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Long-term memory layer for AI agents with REST API support.
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## Description
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Mem0 provides a sophisticated memory management system for AI applications and agents. It enables AI assistants to maintain persistent memory across conversations and sessions using vector storage.
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## Features
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- 💾 **Long-term Memory**: Persistent memory across conversations
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- 🔍 **Semantic Search**: Vector-based similarity search
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- 🤖 **Multiple LLM Support**: OpenAI, Anthropic, Groq, Ollama, etc.
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- 📊 **Vector Stores**: Qdrant, Chroma, Pinecone, pgvector, etc.
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- 🌐 **REST API**: Easy integration via HTTP endpoints
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- 🎯 **Multi-modal Support**: Text and image memories
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- 🔧 **Configurable**: Flexible LLM and embedding models
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## Installation
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### Via Overlay
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```nix
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{pkgs, ...}: {
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environment.systemPackages = with pkgs; [
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mem0
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];
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}
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```
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### Direct Reference
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```nix
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{pkgs, ...}: {
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environment.systemPackages = with pkgs; [
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inputs.m3ta-nixpkgs.packages.${pkgs.system}.mem0
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];
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}
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```
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### Run Directly
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```bash
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nix run git+https://code.m3ta.dev/m3tam3re/nixpkgs#mem0
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```
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## Usage
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### Command Line
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```bash
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# Start mem0 server
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mem0-server
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# With custom port
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MEM0_PORT=8080 mem0-server
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# With LLM provider
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MEM0_LLM_PROVIDER=openai OPENAI_API_KEY=sk-xxx mem0-server
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```
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### Python Library
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```python
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from mem0 import Memory
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# Initialize with OpenAI
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memory = Memory(
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llm_provider="openai",
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llm_model="gpt-4o-mini",
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vector_store="qdrant",
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openai_api_key="sk-xxx"
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)
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# Add a memory
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result = memory.add(
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"I prefer coffee over tea",
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metadata={"user_id": "123"}
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)
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# Search memories
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memories = memory.search("What does the user prefer?")
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```
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### REST API
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```bash
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# Start server
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mem0-server
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# Add memory
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curl -X POST http://localhost:8000/v1/memories \
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-H "Content-Type: application/json" \
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-d '{"content": "User likes Python"}'
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# Search memories
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curl http://localhost:8000/v1/memories/search?q=Python
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```
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## Configuration
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### Environment Variables
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```bash
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# LLM Configuration
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export MEM0_LLM_PROVIDER=openai
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export MEM0_LLM_MODEL=gpt-4o-mini
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export MEM0_LLM_TEMPERATURE=0.7
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export OPENAI_API_KEY=sk-xxx
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# Vector Store
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export MEM0_VECTOR_PROVIDER=qdrant
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export QDRANT_HOST=localhost
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export QDRANT_PORT=6333
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# Server
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export MEM0_HOST=0.0.0.0
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export MEM0_PORT=8000
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export MEM0_WORKERS=4
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export MEM0_LOG_LEVEL=info
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```
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### NixOS Module (Recommended)
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Use the NixOS module for production:
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```nix
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{config, ...}: {
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imports = [m3ta-nixpkgs.nixosModules.mem0];
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m3ta.mem0 = {
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enable = true;
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port = 8000;
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llm = {
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provider = "openai";
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apiKeyFile = "/run/secrets/openai-api-key";
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model = "gpt-4o-mini";
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};
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vectorStore = {
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provider = "qdrant";
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config = {
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host = "localhost";
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port = 6333;
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};
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};
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};
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}
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```
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See [mem0 Module](../modules/nixos/mem0.md) for full module documentation.
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## Supported LLM Providers
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| Provider | Model Examples | Notes |
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|----------|---------------|-------|
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| `openai` | gpt-4, gpt-3.5-turbo | Most tested |
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| `anthropic` | claude-3-opus, claude-3-sonnet | Requires key |
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| `groq` | mixtral-8x7b-32768 | Fast inference |
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| `ollama` | llama2, mistral | Local only |
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| `together` | llama-2-70b | API access |
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## Supported Vector Stores
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| Provider | Requirements | Notes |
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|----------|--------------|-------|
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| `qdrant` | qdrant server | Recommended |
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| `chroma` | chroma server | Simple setup |
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| `pgvector` | PostgreSQL + pgvector | SQL-based |
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| `pinecone` | Pinecone API | Cloud only |
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| `redis` | Redis stack | Fast |
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| `elasticsearch` | ES cluster | Scalable |
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## Use Cases
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### AI Chatbot with Memory
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```python
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from mem0 import Memory
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memory = Memory()
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# During conversation
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memory.add("User works at Google as a software engineer")
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# Later conversations
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memories = memory.search("Where does the user work?")
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# Returns: ["User works at Google as a software engineer"]
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```
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### Personal Assistant
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```python
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memory = Memory()
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# Store preferences
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memory.add("User prefers dark mode", metadata={"category": "preferences"})
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memory.add("User is vegetarian", metadata={"category": "diet"})
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# Retrieve relevant info
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memories = memory.search("What should I cook for the user?")
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```
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### Code Assistant
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```python
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memory = Memory()
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# Store project context
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memory.add("This is a NixOS project with custom packages")
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# Later analysis
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memories = memory.search("What kind of project is this?")
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```
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## Requirements
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### Vector Store
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You need a vector store running. Example with Qdrant:
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```nix
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services.qdrant = {
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enable = true;
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port = 6333;
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};
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```
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### LLM Provider
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You need API keys or local models:
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```nix
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# OpenAI
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services.mem0.llm.apiKeyFile = "/run/secrets/openai-api-key";
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# Or use agenix
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age.secrets.openai-api-key.file = ./secrets/openai.age;
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```
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## Build Information
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- **Version**: 1.0.0
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- **Language**: Python
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- **License**: Apache-2.0
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- **Source**: [GitHub](https://github.com/mem0ai/mem0)
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## Python Dependencies
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- `litellm` - Multi-LLM support
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- `qdrant-client` - Qdrant client
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- `pydantic` - Data validation
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- `openai` - OpenAI client
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- `fastapi` - REST API
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- `uvicorn` - ASGI server
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- `sqlalchemy` - Database ORM
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## Platform Support
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- Linux (primary)
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- macOS (may work)
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- Windows (not tested)
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## Related
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- [mem0 Module](../modules/nixos/mem0.md) - NixOS module documentation
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- [Adding Packages](../guides/adding-packages.md) - How to add new packages
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- [Port Management](../guides/port-management.md) - Managing service ports
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- [Quick Start](../QUICKSTART.md) - Getting started guide
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