Choose a database for an AI agent by matching it to the data the agent must store and the way it must retrieve that data—not by choosing the product with the strongest “AI database” label. Start with what needs to persist, then test whether your existing database can handle the required search, filtering, updates, access controls, and recovery. Add a specialist system only when a representative test shows a concrete need.
What does your agent actually need to store?
“Agent memory” can refer to several different things. Treating them as one undifferentiated store makes it harder to set retention rules, control access, and keep information current. Inventory each data category before comparing products.
- Authoritative application records: The canonical business data, such as customer, order, or project records. Decide which system is allowed to change it and which other stores contain only copies or indexes.
- Session and workflow state: Information an agent needs to resume a current conversation or multi-step task. Set its lifetime and decide what must survive a process restart or service failure.
- Conversation history: Messages or summaries retained for later context. Specify what is stored, who can access it, and when it is deleted.
- Knowledge documents and chunks: Source material retrieved to answer questions. Track the source and version so edits or deletions can be reflected in indexed content.
- Durable user or task memories: Extracted facts intended to be useful across sessions. Define how a memory is created, corrected, superseded, and removed.
- Temporary cache or working state: Data that can be regenerated and need not be treated as a permanent record.
For each category, record retention, update frequency, tenant scope, access rules, deletion requirements, and whether it must survive failures. MongoDB’s agent guidance distinguishes short- and long-term memory patterns; Redis documents searchable long-term memory records. These are examples of patterns, not a rule that either product should own every category.
Should you extend the database your application already uses?
Begin with the system your team already operates. If it can meet retrieval quality, filtering, isolation, and recovery requirements, keeping related records and retrieval capabilities together may reduce integration work. That does not establish that an integrated option is always faster or cheaper; measure those outcomes for your workload.
#1 Best Overall
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PostgreSQL with pgvector
Consider PostgreSQL with pgvector when relational application data and vector retrieval belong together, especially if PostgreSQL full-text search is also relevant. The pgvector project README says its default nearest-neighbor search is exact and provides perfect recall; approximate indexes trade recall for speed. It documents HNSW and IVFFlat as approximate-index options with different speed, recall, build-time, and memory profiles. Test the particular index and settings against your data rather than assuming one is the right choice.
Filtering deserves special attention: pgvector documents that filters applied after an approximate index scan can leave fewer qualifying rows than requested. Its documentation describes iterative scans and other approaches for filtered cases. Check the results and latency for the actual filters your application uses, not only unfiltered nearest neighbors.
Rank #2
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MongoDB Vector Search
Consider MongoDB Vector Search for a document-centric application that needs semantic retrieval, full-text search, and filtering against document fields. Verify that the specific cluster or deployment supports the features you need, and confirm whether an agent integration is officially supported or community-maintained.
Redis
Consider Redis when its documented vector-search capabilities or agent-memory patterns fit the design. Decide whether Redis is a cache, a persistent memory store, or part of a wider architecture; verify that the Redis service or deployment you plan to use exposes the necessary features and meets persistence and recovery requirements.
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Qdrant
Consider Qdrant when vector retrieval and structured payload filtering are central enough to justify testing a dedicated vector database. Its documented capabilities include vector indexing and payload indexes for structured and text filtering. Include the work of synchronizing indexed content with authoritative application data in the evaluation.
How do lexical search, vector search, and filters fit together?
These retrieval mechanisms answer different questions. Lexical search finds words or phrases; vector search finds semantically similar content; metadata filters constrain results by fields such as document type, date, or tenant. An agent may need one mode or a combination, so check the exact features and deployment you intend to run.
Rank #4
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- Use lexical search when matching terms in the source text matters.
- Use vector search when a query should find related meaning even if it uses different wording.
- Use metadata filters to narrow eligible records, including records permitted for a user or tenant.
- Test combined or hybrid queries if the application depends on more than one mode. Do not infer that a database supports the combination you need from a vector-search feature alone.
PostgreSQL provides full-text search alongside pgvector; MongoDB documents vector search, full-text search, and metadata filtering; Qdrant documents payload indexes. Compare actual query behavior and access rules on the intended deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare database options?
Use the same representative evaluation set for every candidate. It should reflect real user questions and tool-generated queries, the embedding model you expect to use, the data volume and update pattern, and the deployment tier or hardware being considered.
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- Build representative test data. Include exact-match and semantic-match cases, common and highly selective metadata filters, multiple tenants, updated or stale documents, and the expected rate of concurrent writes.
- Define success before running queries. Set the required top-k relevance, acceptable latency, and the access-control behavior you expect. Include cases where the correct result is absent or not authorized.
- Run the same query mix on each candidate. Measure retrieval quality and latency for lexical, semantic, filtered, and combined queries that the product must support.
- Exercise data lifecycle behavior. Update and delete source records, change documents, and verify how indexed or extracted records are brought into line. Test retention and deletion rules for conversation history and durable memories.
- Test tenant isolation explicitly. Attempt queries across tenant boundaries and verify that filters and authorization cannot expose another tenant’s data. Treat this as a correctness requirement, not just a search-quality metric.
- Test recovery and operations. Evaluate backup and restore, persistence, monitoring, scaling, deployment geography, security controls, and the team’s ability to run the system.
- Measure cost in the planned operating model. Include the actual service tier or compute, storage, indexing, and operational labor for the expected workload. Product feature descriptions do not establish comparable total cost.
No cross-vendor performance ranking or workload benchmark is established by the product documentation reviewed for this guide. A useful result is therefore a reproducible comparison for your own query mix and operating conditions, not a universal winner.
Which option belongs on your shortlist?
| Option | Consider it when | Verify in your evaluation |
|---|---|---|
| PostgreSQL with pgvector | Relational records and vector retrieval should coexist, and PostgreSQL full-text search may also be useful. | HNSW or IVFFlat trade-offs; recall under real filters; index size and build behavior; tenant isolation; and exact PostgreSQL and extension versions. |
| MongoDB Vector Search | The application is document-centric and needs semantic retrieval, full-text search, and filtering against document fields. | Support on the required cluster or deployment; index and query behavior; and whether the desired agent integration is officially supported or community-maintained. |
| Redis | The design benefits from Redis vector search or its documented agent-memory patterns. | Persistence and recovery; its relationship to the system of record; and which Redis service or deployment exposes the needed features. |
| Qdrant | Vector retrieval and structured payload filtering are central enough to test a dedicated vector database. | Filtered recall and update behavior; deployment and operations; and the effort of synchronizing it with authoritative application data. |
What should you verify before committing?
Feature availability and integrations depend on product, version, and deployment. Confirm the exact database version, hosting tier, connector maturity, and operational model you plan to use. Check the relevant product documentation and prerequisites for that configuration; for example, Microsoft’s PostgreSQL connector documentation lists prerequisites that should be verified before relying on the integration.
- Can the planned deployment perform every required search mode and filter?
- Can it meet the application’s consistency, update, delete, retention, and recovery requirements?
- Are permissions and tenant boundaries enforced where retrieval happens, not merely assumed from a prompt?
- Can the team monitor, back up, restore, scale, and secure every system in the proposed design?
- Does the measured benefit of a separate retrieval store justify the extra synchronization and operational work?
Product documentation describes capabilities and prerequisites, but it does not by itself settle workload performance or total operating cost. Use a proof of concept to answer those questions with your data and conditions.
Quick Recap
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