The Tool Desk
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First decide what “memory” means in your application
An agent may use several kinds of data that have different lifetimes and access rules. Calling all of them “memory” can lead to a database choice that handles semantic search well but leaves workflow state, permissions, or deletion requirements unresolved.
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- Authoritative application records: The durable facts your application treats as correct, such as accounts, orders, or project records.
- Session and workflow state: Temporary context needed to resume a conversation or an in-progress task.
- Conversation history: Prior messages that must be retained, searched, or made available to the agent.
- Knowledge documents and chunks: Source material prepared for retrieval, often with metadata and embeddings.
- Durable user or task memories: Extracted facts intended to remain useful across sessions.
- Cache or working state: Short-lived data that can be rebuilt or discarded under defined conditions.
For each category, specify retention, update frequency, deletion behavior, tenant scope, access controls, and whether it must survive a failure. MongoDB’s agent guide describes short- and long-term memory patterns; Redis documents long-term memory records that can be searched. Those are implementation patterns, not evidence that either product should own every data role in an application.
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Choose between extending your current database and adding another
Before introducing a new service, check whether the database already in your architecture can support the retrieval and operational requirements. Keeping records and retrieval together can reduce synchronization and integration work, but it does not guarantee lower cost or better performance. Measure both rather than assuming either outcome.
PostgreSQL with pgvector
Consider PostgreSQL with pgvector when relational application data and vector retrieval belong together, or when PostgreSQL full-text search is useful alongside semantic retrieval. The pgvector project supports exact nearest-neighbor search and approximate HNSW and IVFFlat indexes. Its documentation describes different trade-offs in recall, speed, build time, and memory use; select an index using your workload rather than its name alone. PostgreSQL also provides full-text search tools, which can complement vector search when exact terms matter.
MongoDB Vector Search
Consider MongoDB Vector Search for a document-centric application that needs semantic retrieval, full-text search, and filtering on document fields. Confirm that the specific MongoDB deployment or cluster supports the features you need, and check whether an agent integration is officially supported or community-maintained. A product’s general feature description does not establish availability on every hosting tier.
Rank #2
Redis
Redis documents vector search as well as patterns for agent memory. It may fit a design that benefits from those capabilities, but decide explicitly how it relates to the authoritative store. Verify persistence, recovery, and feature availability for the particular Redis service or deployment you plan to use.
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Qdrant
Consider Qdrant when vector retrieval and structured payload filtering are central enough to justify evaluating a dedicated vector database. Its documentation describes vector indexing and payload indexes for structured and text filtering. Include the work of synchronizing indexed records with authoritative application data in the evaluation.
Rank #3
| Option | Consider it when | Verify before choosing |
|---|---|---|
| PostgreSQL with pgvector | Relational records and vector retrieval should coexist, and PostgreSQL full-text search is useful. | HNSW or IVFFlat behavior, recall under filters, index size and build behavior, tenant isolation, and the 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. | Deployment support, index and query behavior, and whether the 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 role relative to the system of record, and feature availability in the selected service or deployment. |
| Qdrant | Vector retrieval and payload filtering are important enough to test a dedicated vector database. | Filtered recall, update behavior, deployment and operations, and synchronization with authoritative application data. |
These are candidates, not a universal ranking. The product and project documentation considered here does not establish a cross-vendor winner for every agent workload.
Test the retrieval behavior your agent actually needs
Vector search is a retrieval feature, not a complete data architecture. Decide separately where the authoritative records, workflow state, permissions, and history live. Then evaluate lexical search, semantic search, and metadata filters as distinct capabilities: exact names or phrases may call for lexical matching, conceptually similar content may call for vector retrieval, and tenant or document attributes may require filters. Check that the particular product and deployment combine the modes your application needs.
Rank #4
Use representative questions and data
Build one evaluation set for every candidate. Include user questions and tool-generated queries, exact-term and semantic matches, common and highly selective metadata filters, stale or updated documents, and the expected pattern of concurrent writes. Use the same source data, embedding model, query mix, filters, and comparable hardware or service tier where possible.
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A nearest-neighbor demo without filters can conceal a production problem. The pgvector project documentation explains that approximate indexes trade recall for speed and that filtering after an approximate index scan can reduce the number of qualifying rows returned. It documents iterative scans and other approaches for filtered cases. Test realistic access rules and tenant boundaries, measuring both recall and latency; verify isolation rather than treating a filter result as proof of access control.
Compare these outcomes
- Retrieval quality: Are relevant results present at the required top-k for lexical, semantic, and any hybrid queries?
- Filter behavior: How do recall and latency change with common and highly selective filters? Are tenant boundaries enforced as intended?
- Data behavior: How do consistency, updates, deletes, retention, and source-document changes propagate into indexed records?
- Agent integration: What connectors are available, and how much application code is needed to persist sessions, workflows, and memory lifecycle rules?
- Operations: Can your team manage backups, recovery, monitoring, scaling, deployment options, and security controls?
- Cost: What do the actual service tier, compute, storage, indexing, and operational labor cost at the expected load?
Check the full operating model before committing
Database selection affects more than query speed. Document consistency and write behavior, persistence and recovery, security controls, deployment geography, observability, scaling, staffing experience, and connector support can all change whether a candidate is practical for your application. Verify the exact database version, hosting tier, and integration maturity rather than relying on a general product description. For example, Microsoft’s PostgreSQL connector documentation lists prerequisites; an integration should be checked against the version and deployment you will actually run.
Product documentation establishes documented features and prerequisites, but it does not supply comparable total-cost or workload-performance results for these candidates. Treat those as proof-of-concept measurements. Do not infer a performance ranking from feature lists or from a benchmark that does not match your data, filters, update rate, isolation needs, and service configuration.
Quick Recap
Turn the proof of concept into a decision
- Write down the data roles and constraints. For each category, record retention, update and deletion needs, tenant scope, access rules, and recovery expectations.
- Shortlist the smallest viable set. Start with the existing database if it appears capable; include a specialized candidate only to test a specific requirement it may meet.
- Run the same evaluation against each candidate. Keep the dataset, embeddings, query set, filters, concurrency, and deployment conditions comparable.
- Set acceptance thresholds before reviewing results. Define the retrieval quality, latency, isolation, recovery, and operating-cost limits the application must meet.
- Choose the simplest option that clears those thresholds. If none does, identify which unmet requirement warrants a different architecture or another system.
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