HippocampAI — Enterprise Memory Engine for Intelligent AI Systems

HippocampAI is a production-ready, enterprise-grade memory engine that transforms how AI systems remember, reason, and learn from interactions. It provides persistent, intelligent memory capabilities that enable AI agents to maintain context across sessions, understand user preferences, detect behavioral patterns, and deliver truly personalized experiences.

The name "HippocampAI" draws inspiration from the hippocampus - the brain region responsible for memory formation and retrieval - reflecting our mission to give AI systems human-like memory capabilities.

Current Release: v0.3.0 — Production-ready with 102+ methods, 50+ API endpoints, and comprehensive monitoring.

Quick Start

Installation

# Core library (lightweight - 10 dependencies)
pip install hippocampai

# With SaaS features (API, auth, background tasks)
pip install "hippocampai[saas]"

# With specific LLM providers
pip install "hippocampai[openai]"     # OpenAI support
pip install "hippocampai[anthropic]"  # Anthropic Claude
pip install "hippocampai[groq]"       # Groq support

# Everything (development, all features)
pip install "hippocampai[all,dev]"

Your First Memory (30 seconds)

from hippocampai import MemoryClient

# Initialize client
client = MemoryClient()

# Store a memory
memory = client.remember(
    "I prefer oat milk in my coffee and work remotely on Tuesdays",
    user_id="alice",
    type="preference"
)

# Recall memories
results = client.recall("work preferences", user_id="alice")
print(f"Found: {results[0].memory.text}")

That's it! You now have intelligent memory for your AI application.

Key Features

Why Choose HippocampAI?

vs. Traditional Vector Databases

• Built-in Intelligence: Pattern detection, insights, behavioral analysis
• Memory Types: Facts, preferences, goals, habits, events (not just vectors)
• Temporal Reasoning: Native time-based queries and narratives

vs. Other Memory Platforms

• 5-100x Faster: Redis caching, optimized retrieval
• Deployment Flexibility: Local, self-hosted, or SaaS
• Full Control: Complete source access and customization

vs. Building In-House

• Ready in Minutes: pip install hippocampai
• 102+ Methods: Complete API covering all use cases
• Production-Tested: Battle-tested in real applications

See detailed comparison →

Documentation

Complete documentation is available in the docs/ directory.

Quick Links

Documentation Index

Complete Documentation Index - Browse all 26 documentation files organized by topic

Core Documentation:

• API Reference - All 102+ methods with examples
• Features Overview - Comprehensive feature documentation
• Architecture - System design and components
• Configuration - All configuration options

Advanced Topics:

• Multi-Agent Features - Agent coordination
• Intelligence Features - Fact extraction, entity recognition
• Temporal Analytics - Time-based queries
• Session Management - Conversation tracking

Production & Operations:

• User Guide - Production deployment
• Security - Security best practices
• Monitoring - Observability and metrics
• Backup & Recovery - Data protection

Configuration

Local Development

# .env file
QDRANT_URL=http://localhost:6333
LLM_PROVIDER=ollama
LLM_MODEL=qwen2.5:7b-instruct

Cloud/Production

from hippocampai import MemoryClient
from hippocampai.adapters import GroqLLM

client = MemoryClient(
    llm_provider=GroqLLM(api_key="your-key"),
    qdrant_url="https://your-qdrant-cluster.com",
    redis_url="redis://your-redis:6379"
)

See all configuration options →

Deployment Options

Local Development

docker run -d -p 6333:6333 qdrant/qdrant
pip install hippocampai

Production Stack

git clone https://github.com/rexdivakar/HippocampAI.git
cd HippocampAI
docker-compose up -d  # Includes Qdrant, Redis, API, Celery, Monitoring

Includes:

• FastAPI server (port 8000)
• Celery workers with Beat scheduler
• Flower monitoring (port 5555)
• Prometheus metrics (port 9090)
• Grafana dashboards (port 3000)

Production deployment guide →

Use Cases

AI Agents & Chatbots

• Personalized assistants with context across sessions
• Customer support with interaction history
• Educational tutoring that adapts to students

Enterprise Applications

• Knowledge management for teams
• CRM enhancement with interaction intelligence
• Compliance monitoring and audit trails

Research & Analytics

• Behavioral pattern analysis
• Long-term trend detection
• User experience personalization

More use cases →

Performance

See benchmarks →

Community & Support

• Documentation: Complete guides
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Discord: Join our community

Examples

Interactive Chat Demo

Try the fully-functional chatbot with persistent memory:

# Set your Groq API key
export GROQ_API_KEY="your-api-key-here"

# Run the interactive chat
python chat.py

Features:

• Persistent memory across sessions
• Context-aware responses
• Pattern detection
• Memory search
• Session summaries

Complete chat demo guide →

Code Examples

Over 15 working examples in the examples/ directory:

# Basic operations
python examples/01_basic_usage.py

# Advanced features
python examples/11_intelligence_features_demo.py
python examples/13_temporal_reasoning_demo.py
python examples/14_cross_session_insights_demo.py

# Run all examples
./run_examples.sh

View all examples →

Contributing

We welcome contributions! See our Contributing Guide for details.

git clone https://github.com/rexdivakar/HippocampAI.git
cd HippocampAI
pip install -e ".[dev]"
pytest

License

Apache 2.0 - Use freely in commercial and open-source projects.

Star History

If you find HippocampAI useful, please star the repo! It helps others discover the project.

Built with by the HippocampAI team

Quick Reference Card

from hippocampai import MemoryClient

client = MemoryClient()

# Core operations
memory = client.remember("text", user_id="alice")
results = client.recall("query", user_id="alice", k=5)
client.update_memory(memory_id, text="new text")
client.delete_memory(memory_id)

# Intelligence
facts = client.extract_facts("John works at Google")
entities = client.extract_entities("Elon Musk founded SpaceX")
patterns = client.detect_patterns(user_id="alice")

# Analytics
habits = client.detect_habits(user_id="alice")
changes = client.track_behavior_changes(user_id="alice")
stats = client.get_memory_statistics(user_id="alice")

# Sessions
session = client.create_session(user_id="alice", title="Planning")
client.complete_session(session.id, generate_summary=True)

# See docs/LIBRARY_COMPLETE_REFERENCE.md for all 102+ methods

Full API Reference | REST API Reference