| Name | soika-memory JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | Long-term memory for AI Agents |
| upload_time | 2025-08-18 14:25:05 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <4.0,>=3.9 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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graph
memory
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# Soika Memory - Long-term Memory for AI Agents
Soika Memory is a comprehensive memory management system that provides long-term memory capabilities for AI agents and applications. It enables storage, retrieval, and semantic search of conversation history and contextual information using vector databases and graph networks.
## Features
### 🧠 Core Memory Management
- **Create memories**: Store conversation messages and context for users, agents, or runs
- **Retrieve memories**: Get all memories with flexible filtering options
- **Search memories**: Semantic search through stored memories using vector similarity
- **Update memories**: Modify existing memories with new information
- **Delete memories**: Remove specific memories or bulk delete with filters
### 🔗 Graph Memory
- **Knowledge Graph**: Extract entities and relationships from conversations
- **Graph Search**: Find related information through entity relationships
- **Multiple Graph Stores**: Support for Neo4j, Memgraph, and AWS Neptune
### 🚀 Multiple LLM Providers
- **OpenAI**: GPT models with structured output support
- **Azure OpenAI**: Enterprise-grade OpenAI integration
- **Anthropic**: Claude models
- **Google Gemini**: Gemini Pro models
- **Groq**: High-speed inference
- **DeepSeek**: Cost-effective models
- **XAI**: Grok models
- **SoikaStack**: Integrated AI framework with multi-model support
### 📊 Vector Store Support
- **Qdrant**: Default vector database
- **Pinecone**: Managed vector database
- **Chroma**: Open-source vector database
- **Weaviate**: Enterprise vector search
- **Google Vertex AI**: Vector search
- **Baidu**: Chinese market support
- **LangChain**: Framework integration
### 🌐 Flexible Deployment
- **REST API**: FastAPI-based server with OpenAPI documentation
- **Python Client**: Sync and async client libraries
- **Proxy Integration**: Memory-enhanced OpenAI API proxy
- **Docker Support**: Container deployment
## Quick Start
### Installation
```bash
pip install ai-memory
```
Or with optional dependencies:
```bash
# With graph support
pip install memory[graph]
# With all vector stores
pip install memory[vector_stores]
# Full installation
pip install memory[graph,vector_stores]
```
### Basic Usage
```python
from memory import Memory
# Initialize memory with SoikaStack provider
m = Memory(
config={
"llm": {
"provider": "soikastack",
"config": {
"api_key": "your-soikastack-api-key",
"model": "llama3.3",
"base_url": "http://localhost:4141/v1"
}
},
"embedder": {
"provider": "soikastack",
"config": {
"api_key": "your-soikastack-api-key",
"model": "bge-m3"
}
}
}
)
# Alternative: Use OpenAI provider
# m = Memory(
# config={
# "llm": {
# "provider": "openai",
# "config": {
# "api_key": "your-openai-api-key",
# "model": "gpt-4"
# }
# },
# "embedder": {
# "provider": "openai",
# "config": {
# "api_key": "your-openai-api-key",
# "model": "text-embedding-3-large"
# }
# }
# }
# )
# Add memories
messages = [
{"role": "user", "content": "I'm John, a software engineer from San Francisco"},
{"role": "assistant", "content": "Nice to meet you John!"}
]
result = m.add(messages, user_id="john_doe")
print(result)
# Search memories
search_results = m.search("software engineer", user_id="john_doe")
print(search_results)
# Get all memories
all_memories = m.get_all(filters={"user_id": "john_doe"})
print(all_memories)
```
### REST API Server
Start the memory server:
```bash
cd server
python main.py
```
The API will be available at `http://localhost:8000` with documentation at `/docs`.
#### API Examples
**Create Memory:**
```bash
curl -X POST "http://localhost:8000/memories" \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "user", "content": "I love pizza"},
{"role": "assistant", "content": "Great! I will remember that."}
],
"user_id": "user123"
}'
```
**Search Memories:**
```bash
curl -X POST "http://localhost:8000/search" \
-H "Content-Type: application/json" \
-d '{
"query": "food preferences",
"user_id": "user123"
}'
```
**Get All Memories:**
```bash
curl "http://localhost:8000/memories?user_id=user123"
```
## Configuration
Memory supports extensive configuration options:
```python
config = {
"llm": {
"provider": "openai", # openai, azure_openai, anthropic, gemini, etc.
"config": {
"api_key": "your-api-key",
"model": "gpt-4",
"temperature": 0.1
}
},
"embedder": {
"provider": "openai",
"config": {
"api_key": "your-api-key",
"model": "text-embedding-3-large"
}
},
"vector_store": {
"provider": "qdrant", # qdrant, pinecone, chroma, weaviate, etc.
"config": {
"host": "localhost",
"port": 6333
}
},
"graph_store": {
"provider": "neo4j", # neo4j, memgraph, neptune
"config": {
"url": "bolt://localhost:7687",
"username": "neo4j",
"password": "password"
}
}
}
```
## Advanced Features
### Graph Memory
Enable graph memory to extract and store entity relationships:
```python
# Enable graph memory
m = Memory(config=config, enable_graph=True)
# Add complex information
messages = [
{"role": "user", "content": "Alice works at Google as a software engineer and lives in Mountain View"}
]
result = m.add(messages, user_id="user123")
# Access extracted relationships
relations = result.get("relations", {})
entities = relations.get("added_entities", [])
```
### Space-based Isolation
Organize memories by workspace or project:
```python
# Add memories to different spaces
m.add(messages, user_id="user123", space_id="project_alpha")
m.add(messages, user_id="user123", space_id="project_beta")
# Search within specific space
results = m.search("query", user_id="user123", space_id="project_alpha")
```
### Async Operations
Use async client for high-performance applications:
```python
from memory import AsyncMemory
async def main():
m = AsyncMemory(config=config)
# Async operations
result = await m.add(messages, user_id="user123")
search_results = await m.search("query", user_id="user123")
all_memories = await m.get_all(filters={"user_id": "user123"})
```
## Docker Deployment
### Using Docker Compose
```yaml
version: '3.8'
services:
memory-server:
build: .
ports:
- "8000:8000"
environment:
- OPENAI_API_KEY=your-openai-api-key
- QDRANT_HOST=qdrant
- QDRANT_PORT=6333
depends_on:
- qdrant
qdrant:
image: qdrant/qdrant
ports:
- "6333:6333"
volumes:
- qdrant_storage:/qdrant/storage
```
## Architecture
```
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Client SDK │ │ REST API │ │ Proxy Server │
│ (Sync/Async) │ │ (FastAPI) │ │ (OpenAI) │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
└───────────────────────┼───────────────────────┘
│
┌─────────────────┐
│ Memory Core │
│ (Engine) │
└─────────────────┘
│
┌────────────────────┼────────────────────┐
│ │ │
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ LLM │ │ Vector │ │ Graph │
│ Providers │ │ Stores │ │ Stores │
└─────────────┘ └─────────────┘ └─────────────┘
```
## Testing
Run the test suite:
```bash
# Basic functionality test
cd server
python simple_test.py
# Advanced scenarios
python advanced_test.py
# API endpoint testing
python test_endpoints.py
# Space isolation demo
bash final_demo.sh
```
## Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
## Roadmap
- [ ] Real-time memory synchronization
- [ ] Multi-modal memory support (images)
- [ ] Advanced graph analytics
- [ ] Memory compression and archival
- [ ] Federated memory networks
---
**Memory** - Making AI agents truly intelligent with persistent, searchable, and contextual memory.
Raw data
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"description": "# Soika Memory - Long-term Memory for AI Agents\n\nSoika Memory is a comprehensive memory management system that provides long-term memory capabilities for AI agents and applications. It enables storage, retrieval, and semantic search of conversation history and contextual information using vector databases and graph networks.\n\n## Features\n\n### \ud83e\udde0 Core Memory Management\n- **Create memories**: Store conversation messages and context for users, agents, or runs\n- **Retrieve memories**: Get all memories with flexible filtering options\n- **Search memories**: Semantic search through stored memories using vector similarity\n- **Update memories**: Modify existing memories with new information\n- **Delete memories**: Remove specific memories or bulk delete with filters\n\n### \ud83d\udd17 Graph Memory\n- **Knowledge Graph**: Extract entities and relationships from conversations\n- **Graph Search**: Find related information through entity relationships\n- **Multiple Graph Stores**: Support for Neo4j, Memgraph, and AWS Neptune\n\n### \ud83d\ude80 Multiple LLM Providers\n- **OpenAI**: GPT models with structured output support\n- **Azure OpenAI**: Enterprise-grade OpenAI integration\n- **Anthropic**: Claude models\n- **Google Gemini**: Gemini Pro models\n- **Groq**: High-speed inference\n- **DeepSeek**: Cost-effective models\n- **XAI**: Grok models\n- **SoikaStack**: Integrated AI framework with multi-model support\n\n### \ud83d\udcca Vector Store Support\n- **Qdrant**: Default vector database\n- **Pinecone**: Managed vector database\n- **Chroma**: Open-source vector database\n- **Weaviate**: Enterprise vector search\n- **Google Vertex AI**: Vector search\n- **Baidu**: Chinese market support\n- **LangChain**: Framework integration\n\n### \ud83c\udf10 Flexible Deployment\n- **REST API**: FastAPI-based server with OpenAPI documentation\n- **Python Client**: Sync and async client libraries\n- **Proxy Integration**: Memory-enhanced OpenAI API proxy\n- **Docker Support**: Container deployment\n\n## Quick Start\n\n### Installation\n\n```bash\npip install ai-memory\n```\n\nOr with optional dependencies:\n\n```bash\n# With graph support\npip install memory[graph]\n\n# With all vector stores\npip install memory[vector_stores]\n\n# Full installation\npip install memory[graph,vector_stores]\n```\n\n### Basic Usage\n\n```python\nfrom memory import Memory\n\n# Initialize memory with SoikaStack provider\nm = Memory(\n config={\n \"llm\": {\n \"provider\": \"soikastack\",\n \"config\": {\n \"api_key\": \"your-soikastack-api-key\",\n \"model\": \"llama3.3\",\n \"base_url\": \"http://localhost:4141/v1\"\n }\n },\n \"embedder\": {\n \"provider\": \"soikastack\", \n \"config\": {\n \"api_key\": \"your-soikastack-api-key\",\n \"model\": \"bge-m3\"\n }\n }\n }\n)\n\n# Alternative: Use OpenAI provider\n# m = Memory(\n# config={\n# \"llm\": {\n# \"provider\": \"openai\",\n# \"config\": {\n# \"api_key\": \"your-openai-api-key\",\n# \"model\": \"gpt-4\"\n# }\n# },\n# \"embedder\": {\n# \"provider\": \"openai\", \n# \"config\": {\n# \"api_key\": \"your-openai-api-key\",\n# \"model\": \"text-embedding-3-large\"\n# }\n# }\n# }\n# )\n\n# Add memories\nmessages = [\n {\"role\": \"user\", \"content\": \"I'm John, a software engineer from San Francisco\"},\n {\"role\": \"assistant\", \"content\": \"Nice to meet you John!\"}\n]\n\nresult = m.add(messages, user_id=\"john_doe\")\nprint(result)\n\n# Search memories\nsearch_results = m.search(\"software engineer\", user_id=\"john_doe\")\nprint(search_results)\n\n# Get all memories\nall_memories = m.get_all(filters={\"user_id\": \"john_doe\"})\nprint(all_memories)\n```\n\n### REST API Server\n\nStart the memory server:\n\n```bash\ncd server\npython main.py\n```\n\nThe API will be available at `http://localhost:8000` with documentation at `/docs`.\n\n#### API Examples\n\n**Create Memory:**\n```bash\ncurl -X POST \"http://localhost:8000/memories\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"messages\": [\n {\"role\": \"user\", \"content\": \"I love pizza\"},\n {\"role\": \"assistant\", \"content\": \"Great! I will remember that.\"}\n ],\n \"user_id\": \"user123\"\n }'\n```\n\n**Search Memories:**\n```bash\ncurl -X POST \"http://localhost:8000/search\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"query\": \"food preferences\",\n \"user_id\": \"user123\"\n }'\n```\n\n**Get All Memories:**\n```bash\ncurl \"http://localhost:8000/memories?user_id=user123\"\n```\n\n## Configuration\n\nMemory supports extensive configuration options:\n\n```python\nconfig = {\n \"llm\": {\n \"provider\": \"openai\", # openai, azure_openai, anthropic, gemini, etc.\n \"config\": {\n \"api_key\": \"your-api-key\",\n \"model\": \"gpt-4\",\n \"temperature\": 0.1\n }\n },\n \"embedder\": {\n \"provider\": \"openai\",\n \"config\": {\n \"api_key\": \"your-api-key\",\n \"model\": \"text-embedding-3-large\"\n }\n },\n \"vector_store\": {\n \"provider\": \"qdrant\", # qdrant, pinecone, chroma, weaviate, etc.\n \"config\": {\n \"host\": \"localhost\",\n \"port\": 6333\n }\n },\n \"graph_store\": {\n \"provider\": \"neo4j\", # neo4j, memgraph, neptune\n \"config\": {\n \"url\": \"bolt://localhost:7687\",\n \"username\": \"neo4j\",\n \"password\": \"password\"\n }\n }\n}\n```\n\n## Advanced Features\n\n### Graph Memory\n\nEnable graph memory to extract and store entity relationships:\n\n```python\n# Enable graph memory\nm = Memory(config=config, enable_graph=True)\n\n# Add complex information\nmessages = [\n {\"role\": \"user\", \"content\": \"Alice works at Google as a software engineer and lives in Mountain View\"}\n]\n\nresult = m.add(messages, user_id=\"user123\")\n\n# Access extracted relationships\nrelations = result.get(\"relations\", {})\nentities = relations.get(\"added_entities\", [])\n```\n\n### Space-based Isolation\n\nOrganize memories by workspace or project:\n\n```python\n# Add memories to different spaces\nm.add(messages, user_id=\"user123\", space_id=\"project_alpha\")\nm.add(messages, user_id=\"user123\", space_id=\"project_beta\")\n\n# Search within specific space\nresults = m.search(\"query\", user_id=\"user123\", space_id=\"project_alpha\")\n```\n\n### Async Operations\n\nUse async client for high-performance applications:\n\n```python\nfrom memory import AsyncMemory\n\nasync def main():\n m = AsyncMemory(config=config)\n \n # Async operations\n result = await m.add(messages, user_id=\"user123\")\n search_results = await m.search(\"query\", user_id=\"user123\")\n all_memories = await m.get_all(filters={\"user_id\": \"user123\"})\n```\n\n## Docker Deployment\n\n### Using Docker Compose\n\n```yaml\nversion: '3.8'\nservices:\n memory-server:\n build: .\n ports:\n - \"8000:8000\"\n environment:\n - OPENAI_API_KEY=your-openai-api-key\n - QDRANT_HOST=qdrant\n - QDRANT_PORT=6333\n depends_on:\n - qdrant\n \n qdrant:\n image: qdrant/qdrant\n ports:\n - \"6333:6333\"\n volumes:\n - qdrant_storage:/qdrant/storage\n```\n\n## Architecture\n\n```\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Client SDK \u2502 \u2502 REST API \u2502 \u2502 Proxy Server \u2502\n\u2502 (Sync/Async) \u2502 \u2502 (FastAPI) \u2502 \u2502 (OpenAI) \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2502 \u2502 \u2502\n \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2502\n \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n \u2502 Memory Core \u2502\n \u2502 (Engine) \u2502\n \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2502\n \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n \u2502 \u2502 \u2502\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 LLM \u2502 \u2502 Vector \u2502 \u2502 Graph \u2502\n\u2502 Providers \u2502 \u2502 Stores \u2502 \u2502 Stores \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n## Testing\n\nRun the test suite:\n\n```bash\n# Basic functionality test\ncd server\npython simple_test.py\n\n# Advanced scenarios\npython advanced_test.py\n\n# API endpoint testing\npython test_endpoints.py\n\n# Space isolation demo\nbash final_demo.sh\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## License\n\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.\n\n## Roadmap\n\n- [ ] Real-time memory synchronization\n- [ ] Multi-modal memory support (images)\n- [ ] Advanced graph analytics\n- [ ] Memory compression and archival\n- [ ] Federated memory networks\n\n---\n\n**Memory** - Making AI agents truly intelligent with persistent, searchable, and contextual memory.",
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