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Redis Agent Memory Server

Give your AI agents persistent memory and context that gets smarter over time.

Transform your AI agents from goldfish 🐠 into elephants 🐘 with Redis-powered memory that automatically learns, organizes, and recalls information across conversations and sessions.

What is Redis Agent Memory Server?

Redis Agent Memory Server is a production-ready memory system for AI agents and applications that:

  • 🧠 Remembers everything: Stores conversation history, user preferences, and important facts across sessions
  • 🔍 Finds relevant context: Uses semantic, keyword, and hybrid search to surface the right information at the right time
  • 📈 Gets smarter over time: Automatically extracts, organizes, and deduplicates memories from interactions
  • 🔌 Works with any AI model: REST API and MCP interfaces compatible with OpenAI, Anthropic, and others
  • 🌐 Multi-provider support: Use 100+ LLM providers via LiteLLM (OpenAI, Anthropic, AWS Bedrock, Ollama, Azure, Gemini, and more)

Why Use It?

=== "For AI Applications"

- Never lose conversation context across sessions
- Provide personalized responses based on user history
- Build agents that learn and improve from interactions
- Scale from prototypes to production with authentication and multi-tenancy

=== "For Developers"

- Drop-in memory solution with REST API and MCP support
- Works with existing AI frameworks and models
- Production-ready with authentication, background processing, and vector storage
- Extensively documented with examples and tutorials

Quick Example

from agent_memory_client import MemoryAPIClient, MemoryClientConfig

client = MemoryAPIClient(MemoryClientConfig(base_url="http://localhost:8000"))

# Store a user preference
await client.create_long_term_memory([{
    "text": "User prefers morning meetings and hates scheduling calls after 4 PM",
    "memory_type": "semantic",
    "topics": ["scheduling", "preferences"],
    "user_id": "alice"
}])

# Later, search for relevant context
results = await client.search_long_term_memory(
    text="when does user prefer meetings",
    limit=3
)

print(f"Found: {results.memories[0].text}")
# Output: "User prefers morning meetings and hates scheduling calls after 4 PM"

Core Features

🧠 Two-Tier Memory System

!!! info "Working Memory (Session-scoped)" - Current conversation state and context - Automatic summarization when conversations get long - Durable by default, optional TTL expiration

!!! success "Long-Term Memory (Persistent)" - User preferences, facts, and important information - Semantic, keyword, and hybrid search with vector embeddings - Advanced filtering by time, topics, entities, users

🔍 Intelligent Search

  • Multiple search modes: Semantic (vector similarity), keyword (full-text), and hybrid (combined) search
  • Advanced filters: Search by user, session, time, topics, entities
  • Query optimization: AI-powered query refinement for better results
  • Recency boost: Time-aware ranking that surfaces relevant recent information

✨ Smart Memory Management

  • Automatic extraction: Pull important facts from conversations
  • Contextual grounding: Resolve pronouns and references ("he" → "John")
  • Deduplication: Prevent duplicate memories with content hashing
  • Memory editing: Update, correct, or enrich existing memories

🚀 Production Ready

  • Multiple interfaces: REST API, MCP server, Python client
  • Authentication: OAuth2/JWT, token-based, or disabled for development
  • Scalable storage: Redis (default), Pinecone, Chroma, PostgreSQL, and more
  • Background processing: Async tasks for heavy operations
  • Multi-tenancy: User and namespace isolation

Get Started

Ready to give your AI agents perfect memory?

**New to memory systems?**

Start with our quick tutorial to understand the basics and see immediate results.

🚀 Quick Start Guide{ .md-button .md-button--primary }

**Ready to integrate?**

Jump into the Developer Guide for memory patterns and integration strategies.

🧠 Developer Guide{ .md-button }

Community & Support

Ready to transform your AI agents? Start with the Quick Start Guide and build smarter agents in minutes! 🧠✨