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A vector database alone is rarely enough for an AI agent, and no single database type is the universal answer. The more useful starting point is to separate three storage jobs: what must persist as memory, how the agent retrieves knowledge, and what execution state must survive an interruption such as a crash, restart, timeout or redeployment. Each job has different correctness and retrieval requirements, so the strongest design is often a composition of capabilities, either inside one multi-model database or across a few specialized systems.
The short answer to “Where do AI agents store memory?” is that they usually store it in more than one place. Short-term conversation context may sit in a session store, long-term facts in a database or extraction layer, and reference documents in a retrieval index. Asking whether a vector database is enough is really asking whether one store can cover all three jobs. It can cover some of them well, and the rest of this article explains which ones.
Three questions, three different storage jobs
Before comparing products, write down the answer to each of these questions for your agent. The answers usually point to different storage.
The Tool Desk
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|---|---|---|---|
| What must persist as memory? | Recent conversation, active task context, and selected facts kept across sessions | The current session’s messages; a user’s stated preferences | Ordered session history for short-term context; editable, deletable records for long-term facts |
| How does the agent retrieve knowledge? | Material the agent searches to answer a question or take an action | Help articles, policy documents, past cases, linked product records | Similarity search, keyword matching, hybrid ranking, metadata filters, and freshness |
| What execution state must survive interruptions? | Where a task stands and what its tools returned | Task status, checkpoints, tool outcomes, records that receive exact updates | Transactions, write ordering, concurrent-write handling, and recovery after failure |
Where memory fits: short-term context and long-term facts
MongoDB’s agent documentation separates short-term session context from long-term memory. For short-term interactions, an application can store a session identifier and keep the recent exchange under it. For long-term memory, it extracts selected information from conversations and stores that for later sessions. MongoDB describes its own platform this way:
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As both a vector and document database, MongoDB supports various search methods for agentic RAG, as well as storing agent interactions in the same database for short and long-term agent memory.
That sentence is the vendor’s description of its capabilities, not an independent test.
The two kinds of memory fail in different ways, which is why they deserve different storage decisions. If a session store loses recent turns, the user may have to repeat a request. If a long-term store keeps a wrong fact or cannot delete one, that fact can shape every later answer. Long-term memory therefore needs a correction and deletion path from the start, as covered in the governance section below.
How agents retrieve knowledge
Retrieval is a separate question from memory. An agent may need a policy document it has never seen, which is a search problem rather than a record of what happened in its own sessions. Three retrieval methods come up most often.
Vector search finds similar meaning
Vector search embeds content and ranks items by semantic similarity to the query. It handles paraphrases well, so a user who describes a problem in their own words can still reach the right article. It is weaker when an exact string decides relevance: a product code or clause number can lose to a looser match that is closer in meaning. Vector dimensions, the embedding model and the metadata filters you apply all shape results, so each belongs in your test set.
Full-text search matches terms
Full-text search matches words and phrases rather than meaning. It is the right tool when identifiers, names or exact terminology determine relevance. It does not help when the user’s wording differs from the document’s wording.
Hybrid retrieval and tool choice
Hybrid search combines the two approaches. MongoDB’s documentation describes vector, full-text and hybrid retrieval as tools an agent can use, with the agent choosing among them according to the task. That gives the agent more options, but it also means retrieval behavior depends on how the tools are described and prompted. Test the combined behavior, not each tool in isolation.
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Execution state is where a task stands: which step ran, what a tool returned, whether a ticket update or payment went through, and what should happen next. This state has to be exact. Semantic retrieval can tell an agent that a similar past case involved a refund, but it cannot reliably tell the agent whether the refund request it sent a minute ago succeeded. That answer lives in a record that is updated, ordered and read back exactly.
Any store that holds execution state should meet these requirements:
- Atomic updates: a status change and the related record change either both commit or neither does.
- Ordering: the events of a task replay in the order they happened, so a restart does not reorder tool calls.
- Concurrent writes: two workers handling the same task cannot silently overwrite each other.
- Recovery: after a crash, the agent reads the last committed state and resumes or compensates.
The storage options and when each fits
Each option below suits a particular mix of the three jobs. The side-by-side table that follows summarizes them.
Relational database
Use a relational store when agent state and business records have defined structures, when transactions matter, or when joins are already central to the application. An order, a ticket and a refund step are natural rows with constraints. PostgreSQL extensions can add vector search, graph queries and full-text search to the same engine. Microsoft’s Microsoft Learn guidance on AI agents in Azure HorizonDB describes PostgreSQL, pgvector, Apache AGE and full-text search as options for agent workloads; read that as Microsoft’s description of its product. Having these features in one engine does not show that it will meet your scale or latency targets.
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Use a key-value store when the main need is keyed session state, or when several workers must reach the same session with low latency. The OpenAI Agents SDK’s session documentation lists Redis sessions for shared memory across workers and services and describes them as suitable for low-latency distributed deployments. It also lists Dapr sessions, which let a team switch the configured state-store backend while keeping agent code stable. These are SDK guidance points. The page does not establish durability, consistency or failover behavior for your deployment, so check those yourself before relying on a session store for execution state.
Rank #3
Vector database or hybrid index
A dedicated vector index fits when similarity retrieval dominates the workload and relationships between records matter little. It is usually best treated as a retrieval index over content whose source of truth lives elsewhere, which makes it a better fit for knowledge retrieval than for execution state. Filtering support, update behavior, scale and your own evaluation results decide whether a given vector system is adequate. Keep a keyword path alongside it when identifiers or exact terms matter.
Graph database
Use a graph when the agent must follow connections among people, events, entities or records, especially when the question is how several links connect. A graph makes those relationships explicit and traversable. It is less compelling when the application mostly updates keyed state or runs similarity search with few relational hops. Neo4j’s graph memory architecture guidance says the choice depends on the application’s actual queries and operating requirements. Joins and vector filters can answer some relationship questions too, so the test is whether your traversal logic stays simple and fast enough as the number of hops grows.
Markdown files and SQLite
A small Markdown file or a SQLite database suits a local prototype, a single-user assistant or a compact memory profile. Microsoft’s memory architecture patterns describe structured relational profiles and small Markdown files as transparent, cheap and auditable, and as sufficient in many cases. The OpenAI Agents SDK lists in-memory SQLite for temporary conversations and file-backed SQLite for persistent ones. Move to a shared service when several processes write concurrently, when availability matters, or when access boundaries need enforcement that a local file cannot provide.
Extract-and-update memory service
A separate memory layer can extract candidate facts from conversations, decide whether to add, update, merge or delete them, summarize interactions asynchronously, and serve retrieval through vector search, optionally augmented by a graph. Microsoft’s patterns describe this design for production deployments where several agents share memory and cost matters. The trade-offs are another service to operate and an extraction step whose quality you must evaluate. A wrong fact written by the extractor becomes a wrong fact in every later answer that retrieves it.
Side-by-side view
| Option | Data shape it suits | Strongest at | Main thing to test |
|---|---|---|---|
| Relational database | Structured records linked through joins | Transactions, constraints, exact updates | Scale and latency when vector, graph or full-text features run in the same engine |
| Key-value or session store | Keyed session state and ordered history per session | Low-latency access shared across workers | Durability, consistency and failover in your deployment; not stated by the SDK documentation |
| Vector or hybrid index | Embeddings alongside text and metadata | Semantic and keyword retrieval over reference content | Filtering, update behavior and relevance on your own queries |
| Graph database | Connected entities and events | Multi-hop traversal | Whether the value holds when most queries use few hops |
| Markdown file or SQLite | Small structured profiles or documents | Transparency, simplicity and local use | Concurrent writes, availability and access control |
| Extract-and-update memory service | Candidate facts, summaries and their indexes | Long-term memory shared across several agents | Operating an extra service and the quality of extracted facts |
One multi-model database or several systems
A multi-model database reduces integration work. You have one engine to back up, one access-control model to manage, and fewer data paths to keep in step. MongoDB’s documentation describes storing vector, document and agent-interaction data in the same database. PostgreSQL extensions bring vector, graph and full-text capabilities into one engine in the same way. The trade-off is that a single system must handle each workload well enough, and a broad feature list does not guarantee that.
Separate systems make sense when a specialized system clearly serves your workload better, or when your team already runs one. The cost is consistency across copies. When a source record is corrected or deleted, the derived vector entry, the cached session and any extracted memory must all follow. Multiple systems also mean multiple backup, monitoring and scaling plans.
The following is an illustrative composition for a customer-support agent, not a recommendation for any particular product:
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- A relational database holds tickets, refund steps and their status, updated inside transactions.
- A hybrid index over help articles and policy documents supports retrieval, filtered by product and region.
- A session store keeps the active chat so that any worker can serve the next message.
- Extracted customer preferences are stored with source references, so they can be corrected or removed.
Governance and deletion shape the design
Microsoft’s guidance argues that retrieving from governed enterprise systems, rather than copying content into the agent’s own store, helps keep source data fresh, reduces leakage and makes deletion tractable. It also notes that a permission-aware index and good retrieval quality remain requirements, so this approach does not remove them.
Settle these questions before persisting user facts or indexing internal content:
- Identity scoping: which user, tenant or agent can read each memory item.
- Permission-aware retrieval: results must respect the caller’s access rights, not only the index’s contents.
- Retention: how long each memory type is kept.
- Correction and deletion: how a user or administrator fixes a wrong fact, and how that change reaches indexes, caches and extracted copies.
- Audit trail: which memory was written, from which source, and when it was read or changed.
Measuring candidates without relying on rankings
Storage category alone does not decide performance. Neo4j’s architecture guidance states that it does not provide a reproducible PostgreSQL-versus-Neo4j benchmark for the workloads it describes, and that it does not imply measured latency, a storage estimate or a universal asymptotic comparison. The official documentation for these systems does not include an independent cross-database benchmark that you can apply to your own data. Feature pages describe what a system can do, not how quickly it does it on your schema and volume.
Microsoft’s guidance includes cost figures for context management. It says summarization gives “Roughly a 43% token reduction while retaining most of the context,” and it describes fact extraction as “Around 2K tokens per query in published benchmarks.” The page does not name the original benchmark publisher, and both figures describe token volume in the model’s context rather than database speed. Use them to reason about prompt cost, not to rank databases.
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When you run your own tests, record the details that change results:
Quick Recap
- Schema, index definitions and the exact queries the agent issues.
- Representative data volume and, for vector search, the embedding dimensions.
- Concurrency level and cache state, such as cold versus warm.
- Result quality, latency and resource use measured on equivalent queries, so the systems are compared on the same task.
Decision sequence
- Identify what must survive a restart: transcript, checkpoint, task state, source records, extracted facts, or some combination.
- Classify the operations each piece needs: exact keyed access, transactional writes, ordered history, keyword search, semantic similarity or relationship traversal.
- Start with the fewest systems that meet your correctness and retrieval requirements. A multi-model database can cut integration work; add a separate system only when its specialized capability justifies the added consistency and operational overhead.
- Define permissions, retention, correction and deletion before persisting user facts or indexing governed enterprise content.
- Run a representative test set against the shortlisted systems. Compare equivalent results, latency and resource use, and include the concurrent-write, stale-data and restart-recovery cases your product depends on.
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