AI agent platform with memory management, document processing, and function orchestration.
- Long-term memory with conversation summarization
- Document ingestion and semantic search
- Dynamic function discovery and execution
- Web content processing and analysis
- Rate limiting and caching
- User preferences management
AgentManager- Memory and conversation handlingFunctionsManager- Function discovery and executionDocumentManager- Document processing and searchWebManager- Web content analysisMemorySummarizer- Conversation optimizationPreferencesResolver- User preference storage
- Python 3.11+
- Qdrant vector database
- MongoDB instance
git clone https://github.com/SuperDappAI/AI.git
cd AI
pip install -r requirements.txtCopy and edit the environment file:
cp .env.example .envRequired environment variables:
QDRANT_API_KEY=your_qdrant_api_key
QDRANT_URL=https://your_qdrant_instance.com
MONGODB_URL=mongodb://localhost:27017/preferencesNote: OpenAI API keys are passed via API requests, not environment variables.
Start the server:
uvicorn main:app --host 0.0.0.0 --port 8000API documentation available at http://localhost:8000/docs
POST /push_memory/- Store memoriesPOST /pull_memory/- Retrieve memoriesPOST /push_functions/- Register functionsPOST /get_functions/- Query functionsPOST /add_doc/- Add documentsPOST /search_doc/- Search documentsPOST /semantic_search_html/- Search web contentPOST /get_preferences/- Get user preferences
Run tests:
python -m pytest tests/Format code:
black .
isort .docker build -t superdappai .
docker run -p 8000:80 --env-file .env superdappaiSee CONTRIBUTING.md for development guidelines.
MIT License. See LICENSE for details.