Best databases for agent memory: Redis vs. Pinecone vs. MongoDB vs. Weaviate

AI agents don't have one memory requirement. They have three: recalling what happened seconds ago, retrieving what they learned weeks ago, and tracking what they're doing right now. Most databases handle one of those patterns well, which is why so many agent architectures end up stitching together a vector store, a cache, and an operational database just to keep a single agent coherent. Coverage is what separates the options below. Each one handles a different number of those three patterns before you need another system alongside it. This guide compares Redis, Pinecone, MongoDB, and Weaviate as agent memory databases: what each is built for, where each falls short, and what it takes to cover the full memory stack in one system.

What does agent memory actually require?

Agent memory isn't a single workload. When an agent runs, it hits the data layer with three distinct access patterns, and each one has different performance and structural requirements:

• Short-term recall: The working context of the current session. Conversation turns, recent tool results, and intermediate reasoning need to load on every step of the agent loop, so retrieval speed here compounds across the whole task.
• Long-term retrieval: Knowledge that persists across sessions. This is semantic memory, stored as vector embeddings and retrieved by meaning rather than exact match, so the agent can surface a relevant conversation from three weeks ago.
• Operational state: Everything the agent needs to act. Task queues, locks, rate limits, session data, and coordination between sub-agents are structured operations, not similarity lookups.

A database built for one of these patterns doesn't automatically serve the other two. That's the trap behind most agent memory architectures: teams pick a vector database for long-term retrieval, then discover they still need a cache for working context and an operational store for state. Three systems means three things to sync, three failure modes, and added latency at every boundary.

Redis: one platform for the full memory stack

Redis is built to cover all three patterns. Redis stores all data in memory, so core reads and writes complete in sub-millisecond time. Working context lives in native data structures like hashes and sorted sets, operational state uses the same structures plus streams, and coordination between sub-agents runs over pub/sub.

Long-term retrieval runs through the Redis Query Engine, with Hierarchical Navigable Small World (HNSW), FLAT, and SVS-VAMANA vector indexing, and it holds up at scale: in a billion-vector benchmark, Redis reported 90% precision at roughly 200ms median latency under 50 concurrent queries. One deployment handles the vector database, the cache, and the state store that agent architectures otherwise stitch together.

Pinecone: vector-native & vector-only

Pinecone takes the opposite approach: a fully managed database built for one of the three patterns. It stores vector embeddings, indexes them for approximate nearest neighbor search, and scales retrieval without you managing infrastructure. Namespaces isolate memory per agent or tenant, metadata filtering narrows results before similarity ranking, and the managed model takes capacity planning off your plate. For the long-term retrieval slice of agent memory, that's a complete offering: semantic search over past interactions and knowledge bases, handled end to end.

That focus is also the constraint. Pinecone has no session or operational state layer: no data structures for queues, counters, or locks, and no in-memory working context for the agent loop. An agent built on Pinecone alone can remember what it learned but can't track what it's doing, so in practice teams pair it with a separate cache for short-term recall and another store for operational state. One Redis deployment handles vector retrieval, the agent's working context, and its queues without a paired system to sync with.

MongoDB: document storage with vector search added on

MongoDB approaches the problem from the record-keeping side. As a document database, it's a natural fit for conversation logs, tool call metadata, and user profiles, which map cleanly to JSON documents with a flexible schema, and devs already know the query model. Native vector search and hybrid retrieval through $rankFusion mean semantic memory lives alongside document data, and the durability and transactional guarantees of a general-purpose database matter when agent history doubles as a compliance record.

The tradeoff is speed. MongoDB stores data on disk, and disk-based reads accumulate latency in a way that matters when an agent hits the data layer on every reasoning step. Document databases are a solid fit for durable conversation archives, but they aren't built for the sub-millisecond recall working memory demands, so you end up putting a cache in front anyway. Because Redis is in-memory to begin with, long-term memory and working context both read at cache speed instead of routing through a cache layer bolted on top.

Weaviate: open-source vector search & hybrid retrieval

Weaviate is an open-source vector database with hybrid retrieval, combining vector search with keyword matching and metadata filtering, plus built-in vectorization modules that generate embeddings at ingestion. For semantic memory specifically, hybrid retrieval helps when pure vector search misses exact terms like error codes or product names.

Weaviate covers long-term semantic retrieval and stops there: no operational state layer, no in-memory structure for the agent's working context, so it needs a paired cache and state store just like any other purpose-built vector database. Redis Open Source packs vector search, streams, and pub/sub into the same runtime, giving open source teams coverage and control from a single system.

How these databases compare for agent memory

Capability coverage across the three access patterns is what separates these options:

Many teams evaluating this list already run Redis somewhere in their stack for caching or session management, so adding agent memory doesn't require standing up a new database, just new capabilities on the one they're already running.

Is Redis just for session state?

No. Redis Agent Memory, currently in public preview on Redis Cloud, is a managed service that runs two memory tiers in one system. Session memory holds the active conversation state, so working context loads at cache speed on every step of the agent loop. Long-term memory stores facts extracted from those conversations as vector embeddings, so the agent retrieves semantically relevant history across sessions, channels, and agents.

Promotion between the tiers is automatic: as conversation events land in session memory, the service extracts important information in the background and stores it in long-term memory without blocking the live interaction. Native TTL support lets each memory type age out on its own schedule, so ephemeral working context expires within minutes while long-term user preferences persist as long as they're relevant. And when a user invokes their right to be forgotten, structured key patterns make targeted memory deletion straightforward without touching unrelated agents or sessions.

Where does Redis fit across the full agent memory stack?

Redis Agent Memory is part of Redis Iris, a context engine that combines memory, retrieval, fresh operational data, and semantic caching for AI agents. The caching piece matters for cost as much as speed: Redis LangCache recognizes when queries mean the same thing despite different wording and measured up to 73% lower LLM inference costs in benchmarks.

Storage costs stay controlled through tiering. Redis Flex combines RAM and SSD storage to cut memory costs by up to 80%, so long-term memory that's rarely touched doesn't pay RAM prices. Integration runs through the tooling agent teams already use: LangChain, LangGraph, and 30+ agent frameworks, plus the RedisVL Python client for ML engineers who've never touched a Redis command. The practical difference is consolidation. Every system you remove from the agent's data path is one less thing to sync, secure, and page someone about at 3 AM.

Redis Iris serves agent context in milliseconds

Redis Iris connects memory, live data, and retrieval in one place.

Agent memory is a stack, not a single index

The comparison comes down to coverage. Pinecone and Weaviate handle semantic retrieval well but leave short-term recall and operational state to other systems. MongoDB stores conversation history in a familiar document model but wasn't built for retrieval at agent-loop speed. Each is a reasonable component. None is the full memory stack, and the integration work between components is where agent architectures get fragile.

Redis covers short-term memory, long-term retrieval, and operational state in one real-time platform, with Redis Iris packaging that stack into a context engine that keeps agents grounded in fresh, relevant context. If you're deciding how to architect memory for agents your business will depend on, book a meeting with our team to walk through your architecture and where consolidation pays off. Or see it yourself first: try Redis Iris free and run your own workload against it.