🚀 Developer Portal Coming Soon - SDK and API Documentation in Progress

Transform Any AI into a Memory-Powered Intelligence

Add a persistent memory layer and sentiment analysis to your AI application with just a few lines of code. Caura SDK and API handle identity management, conversation persistence, and emotional context—so your AI can build real relationships.

Core Capabilities

The foundation of memory-powered AI

Identity Layer

Persistent user profiles that follow across sessions, devices, and platforms. Each user gets a unique memory graph.


                            user = caura.get_user("user_123")

                            session_id = caura.create_session(user_id)

                            user.update_profile(preferences)

Chat Continuity

Seamless conversation flow with automatic context retrieval. Your AI remembers past interactions and learns from them.


                            session_id = caura.create_session(user_id)

                            response = caura.chat(session_id, message)

                            # Auto-retrieves relevant context

Smart Memory

Automatic memory formation with semantic search. Store facts, preferences, and emotional states intelligently.


                            caura.store_memory(session_id, content)

                            memories = caura.search_memories(user_id, query)

Key Features

Advanced capabilities for intelligent AI experiences

Sentiment Layer

Track emotional patterns and adapt responses. Build empathetic AI that understands user mood and context.


                            sentiment = caura.get_sentiment(session_id)

                            caura.track_emotion(session_id, context)

Response Comparison

Compare responses from multiple LLMs and let users select the best answer. Caura learns from selections to improve future responses.


                            responses = caura.compare_llms(session_id, query)

                            caura.record_preference(session_id, selected)

Agentic Memory

Autonomous memory agents that proactively organize, update, and synthesize information. Your AI's memory evolves and improves over time.


                            agent = caura.memory_agent(user_id)

                            agent.auto_organize(session_id)

Quick Start Example

# Install Caura SDK
pip install caura-sdk


import caura
# Initialize with your API key

caura.init(api_key="your_api_key")
user_id = "user_123"

# Create a new user session
session_id = caura.create_session(user_id)

# Store memory in the session
caura.store_memory(session_id, "I prefer TypeScript over JavaScript")

# Chat with perfect memory recall
response = caura.chat(session_id, "What's my favorite language?", llm="gpt-4o")
# AI responds: "You prefer TypeScript!"

# Compare multiple LLM responses
responses = caura.compare_llms(session_id, "Explain async programming")
caura.record_preference(session_id, responses[0])

# Track sentiment and emotions for this session
sentiment = caura.get_sentiment(session_id)
caura.track_emotion(session_id, "excited about new project")

# Search across all user memories
memories = caura.search_memories(user_id, "programming languages")

Developer Toolkit

Everything you need to build memory-powered AI applications

RESTful API

Simple HTTP endpoints for all memory operations. Integrate Caura into any application with just a few API calls.

Multi-LLM Support

Works seamlessly with OpenAI, Anthropic Claude, Google Gemini, and local Ollama models. Switch models on the fly.

WebSocket Streaming

Real-time streaming responses for instant user feedback. Built-in support for both WebSocket and Server-Sent Events.

Vector Search

MCP Integration

Native Model Context Protocol support for Claude Desktop. Use Caura as a memory layer in your Claude conversations.

Python SDK

Full-featured Python client library with async support. Type hints, auto-retry logic, and comprehensive documentation.