Memora

Long-term memory for AI agents.

AI assistants forget everything between sessions. Memora fixes that with a memory system that handles temporal reasoning, entity connections, and personality-aware responses.

Why Memora?

• Temporal queries — "What did Alice do last spring?" requires more than vector search
• Entity connections — Knowing "Alice works at Google" + "Google is in Mountain View" = "Alice works in Mountain View"
• Agent opinions — Agents form and recall beliefs with confidence scores
• Personality — Big Five traits influence how agents process and respond to information

5-Minute Setup

1. Start the server

# Clone and start with Docker
git clone https://github.com/anthropics/memora.git
cd memora/docker
./start.sh

Server runs at http://localhost:8080

2. Install the Python client

pip install memora-client

3. Use it

from memora_client import Memora

client = Memora(base_url="http://localhost:8080")

# Store memories
client.store(agent_id="my-agent", content="Alice works at Google")
client.store(agent_id="my-agent", content="Bob prefers Python over JavaScript")

# Search memories
results = client.search(agent_id="my-agent", query="What does Alice do?")
for r in results:
    print(f"{r['text']} ({r['weight']:.2f})")

# Generate personality-aware responses
answer = client.think(agent_id="my-agent", query="Tell me about Alice")
print(answer["text"])

Documentation

Full documentation: memora-docs

• Architecture — How ingestion, storage, and retrieval work
• Python Client — Full API reference
• API Reference — REST API endpoints
• Personality — Big Five traits and opinion formation

License

MIT