Developer FAQ

How does Caura differ from RAG or vector databases?

While RAG (Retrieval-Augmented Generation) and vector databases handle document retrieval, Caura provides a complete memory ecosystem:

• Identity Management — Each user has their own persistent memory graph, not just shared documents
• Contextual Intelligence — Memories are linked with relationships, emotions, and temporal context
• Automatic Memory Formation — No manual chunking or embedding required; memories form naturally from conversations
• Sentiment & Personality Tracking — Beyond facts, Caura tracks emotional patterns and user preferences
• Cross-Session Continuity — Seamless memory persistence across platforms, devices, and time

Think of RAG as giving AI access to a library, while Caura gives it a brain with personal memories and relationships.

What's the performance impact and latency?

Minimal overhead with intelligent caching and optimization:

• Sub-100ms memory retrieval — Powered by Pinecone's vector search and edge caching
• Async memory storage — Memories are stored in the background without blocking responses
• Smart context selection — Only relevant memories are retrieved, reducing token usage by up to 70%
• Parallel processing — Memory operations run alongside LLM inference
• CDN-backed infrastructure — Global edge locations ensure low latency worldwide

Benchmark results show no perceptible difference in response time for 95% of queries, with massive improvements in contextual accuracy.

How does the memory architecture work?

Caura uses a dual-layer architecture optimized for both semantic search and relationship mapping:

User → Memory Layer → LLM
         ↓
    ┌─────────────┬──────────────┐
    Vector Store  Graph Database  Time Index    
                

• Vector Layer — Semantic embeddings using text-embedding-3-small for similarity search
• Graph Layer — Entity relationships and knowledge connections
• Temporal Index — Chronological ordering and time-based retrieval
• Metadata Store — User preferences, emotional states, and context flags

How do I handle memory conflicts and updates?

Caura automatically resolves conflicts using a multi-factor resolution system:

# Override with custom resolution
caura.set_conflict_strategy(
    strategy="weighted",
    factors={
        "recency": 0.4,
        "confidence": 0.3,
        "source_authority": 0.3
    }
)
                

• Temporal Precedence — Recent memories typically override older ones
• Confidence Scoring — Memories with higher confidence take priority
• Source Authority — User-provided facts override inferred information
• Manual Override — API endpoints for explicit memory updates and deletions

Can I integrate Caura with my existing product?

Yes, multiple integration options:

• REST API — Standard HTTPS endpoints with JSON payloads
• WebSocket — Real-time bidirectional streaming
• GraphQL — Flexible queries for complex data requirements
• gRPC — High-performance binary protocol for microservices
• SDK Support — Python, JavaScript/TypeScript, Go, Java, .NET
• MCP Support Model Context Protocol

Enterprise features include VPC peering, private endpoints, on-premise deployment, and custom integrations with your existing auth, monitoring, and data pipeline systems.

How do I implement authentication and user isolation?

Caura provides multiple authentication methods:

# API Key authentication
client = Client(api_key="sk-...")

# OAuth 2.0 flow
client = Client(
    client_id="...",
    client_secret="...",
    redirect_uri="..."
)

# JWT with custom claims
client = Client(jwt_token=token)
                

• User Isolation — Complete data segregation at the infrastructure level
• Multi-tenancy — Logical separation with performance isolation
• SSO Integration — SAML, OAuth, OpenID Connect support
• API Scopes — Granular permission control for different operations

What SDKs and tools are available?

We will start with SDKs for the following:

• Python — pip install caura-sdk (async/sync support)
• JavaScript/TypeScript — npm install @caura/sdk (Node & browser)
Later we plan to publish MCP Server, Java, .NET and Go

All SDKs include type definitions, auto-retry logic, connection pooling, and comprehensive documentation with examples.

How do I monitor and debug memory operations?

Comprehensive observability tools:

• Debug Mode — Detailed logs of memory retrieval and storage
• Memory Inspector — Web UI to visualize user memory graphs
• API Analytics — Request metrics, latency tracking, error rates
• Webhooks — Real-time notifications for memory events
• OpenTelemetry — Full tracing support for distributed systems

# Enable debug mode
client = Client(api_key="...", debug=True)

# Set up webhook notifications
caura.webhooks.subscribe(
    events=["memory.created", "memory.updated"],
    url="https://your-server.com/webhook"
)