engramIQ

Memory Infrastructure for Learning Agents

Every feature designed to transform stateless AI into intelligent agents that remember, learn, and improve. Complete memory platform that makes agents smarter with every interaction.

🧠 Four Types of Agent Memory

Complete memory infrastructure that gives agents access to all the memory types they need to understand context, build relationships, and learn over time.

From simple facts to complex temporal patterns, agents get the full picture to make intelligent decisions and provide personalized experiences.

Structured

Facts, metadata, user preferences, transaction records

Semantic

Concepts, context, document understanding, similarity

Graph

Entity relationships, connections, reasoning paths

Temporal

Time-series data, event sequences, causality

🔄 Agent Learning Loop

Agents that learn from every interaction. Capture outcomes, refine understanding, and continuously improve responses based on what works.

Learning Capabilities
  • • Track interaction outcomes and success patterns
  • • Refine memory based on user feedback
  • • Generate insights for agent improvement
  • • Adapt to user preferences over time
Interaction
User request
→
Memory Query
Context retrieval
Response
Agent action
→
Learning
Memory update

🎯 Intelligent Memory Orchestration

Smart routing and optimization that automatically selects the right memory types, plans efficient queries, and learns from access patterns to improve performance.

Memory operations optimized for agent workflows with predictive pre-fetching, intelligent caching, and automated memory lifecycle management.

Smart Memory Features

🧭 Memory Router

Automatically selects optimal memory types based on query patterns

⚡ Query Optimizer

Parallel execution and cache-aware planning for sub-100ms responses

📈 Learning Engine

Analyzes access patterns to optimize memory placement and retention

📡 Universal Data Ingestion

Connect any data source to your agent's memory. 500+ connectors for SaaS tools, databases, files, and APIs with automatic format normalization and semantic enhancement.

Popular Sources: Slack, Notion, Google Workspace, Salesforce, HubSpot, Jira, GitHub, custom APIs, and more.

Ingestion Modes

📁 Batch Processing

CSV, PDF, documents with full semantic enhancement and entity extraction

⚡ Real-Time Streaming

Live data feeds with immediate processing and memory updates

🔄 Scheduled Sync

Automated data ingestion from your favorite tools

🔍 Advanced Memory Queries

Sophisticated query capabilities that combine semantic search, graph traversal, and temporal reasoning to give agents complete context for intelligent responses.

Query Types
  • • Semantic similarity across all content
  • • Graph relationship traversal
  • • Time-based memory windows
  • • Hybrid multi-modal queries

Smart Query Features

🎯 Contextual Relevance

AI-powered ranking with domain-specific scoring

⏰ Temporal Awareness

Time-sensitive queries with recency weighting

🔗 Relationship Discovery

Follow connections across different data types

⚡ Enterprise Performance

Sub-100ms query latency with intelligent caching, predictive pre-fetching, and horizontal scaling designed specifically for agent memory access patterns.

Multi-level cache hierarchy with agent-specific optimization ensures your agents always have instant access to the memories they need.

Performance Features
  • • L1 Agent Cache: In-memory hot paths for <10ms access
  • • L2 Regional Cache: Distributed Redis for <50ms queries
  • • L3 Global Cache: ElastiCache persistence for <100ms
  • • Predictive Pre-fetching: Anticipate memory needs

🤖 Agent Memory SDK

Developer-friendly SDK designed for AI agents. LangChain and LangGraph compatible with intuitive memory operations that feel natural for agent developers.

# Remember user context
agent.remember(context, tags=["user:123"])
# Recall relevant memories
memories = agent.recall("purchase history")
# Learn from outcomes
agent.learn(interaction, outcome="success")

Developer Experience

  • • Simple API: Single endpoint for all memory operations
  • • Native SDKs: Python, TypeScript, Java with full type safety
  • • Framework Integration: Works with LangChain, LangGraph, AutoGPT
  • • Local Development: Test agents with persistent memory locally

🛡️ Enterprise Security & Compliance

Built-in security and compliance features that make it safe to deploy learning agents in regulated industries. Complete audit trails and access controls.

Security Features

Zero-trust architecture with encryption at rest and in transit, fine-grained access controls, and comprehensive audit logging for regulatory compliance.

Compliance Standards

HIPAA
Healthcare
SOX
Finance
GDPR
Privacy
SOC2
Security

🧬 Memory Synthesis Engine

Intelligent memory synthesis that combines insights from all memory types to create rich context for agents. Generate abstractions and compress memories while preserving meaning.

Cross-modal integration ensures agents get complete context by automatically connecting related memories across structured, semantic, graph, and temporal stores.

Synthesis Capabilities

🔗 Cross-Modal Integration

Combine insights from all memory types for complete context

📊 Abstraction Generation

Create higher-level concepts from raw memories

🗜️ Smart Compression

Optimize storage while preserving semantic meaning

🔧 Complete Developer Toolkit

SDKs & APIs

Language-native SDKs with comprehensive documentation and examples for quick integration.

Local Development

Full memory persistence in development with debugging tools and memory inspection.

Observability

Comprehensive monitoring, analytics, and debugging tools for agent memory patterns.