Your agents aren't failing. Their context is.
REDIS AGENT MEMORY
A managed memory layer that gives agents intelligent short-term memory and persistent context across conversations.
Get session-scoped working memory by storing each conversation's live messages for that session.
Configurable LLM-based extraction policies pull out scoped facts, preferences, and episodic events from conversations, then embed them as vectors and store them in Redis.
Automatically summarize and trim session history, promote high-signal facts from short-term to long-term memory, and use semantic + metadata retrieval so agents always see the most relevant context without custom promotion or pruning logic.
The 2025 Stack Overflow Developer Survey showed more AI agent devs trust us for memory and data storage. That's because we're fast, flexible, and reliable for everything from AI copilots and chatbots to internal assistants and agentic workflows.
Automatic extraction
Pull important facts from every agent conversation.
Contextual grounding
Resolve pronouns and references ("he" → "John").
Deduplication
Prevent duplicate memories with content hashing.
Multiple interfaces
REST API, MCP server, Python client.
Authentication
OAuth2/JWT, token-based, or disabled for development.
Scalable storage
Use Redis (default), Pinecone, Chroma, PostgreSQL, and more.
