Choose the database by separating the kinds of information your agents use. Shared state during a running workflow, durable business records, semantic or episodic memory, and relationship data have different consistency and retrieval requirements. Start by mapping those requirements, then test a candidate platform—or a small combination of stores—under your real concurrency, query mix, security model, and recovery targets. There is no evidence-based universal winner.
Start with the four kinds of agent data
Parallel agents rarely need one undifferentiated bucket called “memory.” Treating every record the same can create unnecessary latency, weak isolation, or an awkward schema. Microsoft’s multi-agent reference architecture puts the principle plainly: “Storage strategy should be chosen per memory sub-type, not for memory as a whole.”
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Shared workflow and session state
This is the information agents need while a task is in progress: a plan, tool results, checkpoints, messages, locks, retry counters, and the current status of each worker. It is usually read and written frequently, often by several processes. The important questions are whether updates must be atomic, how conflicts are resolved, and how state is recovered after a worker or service restart.
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Orders, permissions, customer profiles, billing events, inventory, and audit records normally require durable storage and transactional rules. These records are the system of record even when an agent is the writer. Constraints, foreign keys, conditional updates, and an auditable history may matter more than vector similarity.
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Semantic or episodic memory
Past conversations, observations, documents, and extracted preferences can be recalled by meaning rather than exact key. Embeddings enable vector similarity, but useful agent retrieval commonly combines vectors with metadata filters and full-text search. You may need to distinguish short-lived conversation history from long-lived preferences or knowledge.
Relationship and graph context
Some questions are fundamentally about connections: which services depend on a component, how entities are linked, or what path joins two facts. Graph traversal can answer those questions more directly than a nearest-neighbor search. A vector-plus-graph design is justified when relationship queries are frequent enough to offset the extra data model and consistency work.
Turn the workflow into selection requirements
Write down the answers before comparing products. The same platform can be excellent for one workload and unsuitable for another.
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| Decision axis | Questions to answer |
|---|---|
| Data model | Are records relational, document-shaped, key-value, graph-shaped, or mixed? Which fields are updated together? |
| Concurrency and correctness | How many agents and workers can act at once? Which writes must be atomic? What should happen when two agents update the same record or a retry repeats a request? |
| Retrieval | Do you need exact filters, full-text matching, vector similarity, hybrid search, graph traversal, or several of these in one request? |
| Latency and scale | What are the p95 and p99 targets during read/write bursts? How will index growth, larger prompts, and more tenants change tail latency? |
| Persistence and recovery | Which state must survive a process crash, workflow restart, or regional failure? What backup, restore, and point-in-time recovery behavior is required? |
| Security and isolation | How are tenant, user, session, agent, and tool permissions enforced? Can queries be constrained so one tenant cannot retrieve another’s memory? |
| Operations and cost | Who provisions, scales, patches, monitors, migrates, and reconciles the stores? What are the actual storage, compute, request, backup, and data-transfer costs? |
| Platform limits | Which regions, integrations, APIs, service quotas, preview features, and portability options apply to your deployment? |
A session identifier is not an authorization mechanism. The OpenAI Agents SDK documentation explicitly says applications must authorize access to each session; a caller who knows an ID must not automatically receive its contents.
Match the workload to a database pattern
Relational database with a vector extension
A relational engine is a strong starting point when agents manipulate structured records and semantic retrieval must observe the same transactional boundaries. AWS lists Aurora PostgreSQL with pgvector as an option for relational applications that need vector search alongside transactional data. Read the recommendation as a candidate pattern, not a cross-provider ranking; validate the joins, filters, index behavior, and write contention in your own environment.
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This pattern can keep an order update, an access-control check, and an embedding reference under one durability and backup regime. It is less attractive when your dominant workload is document ingestion with rapidly changing fields, or when graph traversal is central. Decide whether embedding generation belongs in the write transaction or in an asynchronous pipeline; coupling a slow model call to a business transaction can hold locks longer than intended.
Document database with integrated search
MongoDB’s agent guidance describes storing interactions in collections, using session identifiers for short-term history, and keeping extracted preferences or context for longer-term memory. Its documentation presents vector, full-text, and hybrid search as retrieval choices. This fits applications whose records are naturally nested or evolve as tools and agents add fields.
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Key-value or in-memory platform
Redis documents a design in which application state and vector search live in one database, with streaming, publish/subscribe, caching, and context retrieval as related use cases. Its documentation also describes a complementary architecture where PostgreSQL remains the system of record while Redis serves low-latency reads or real-time coordination.
Redis publishes sub-millisecond operation claims for appropriate deployments. Treat that as a vendor statement, not a guaranteed result: network distance, persistence settings, data size, commands, contention, and failover determine your latency. A key-value store is useful for leases, short-lived checkpoints, rate limits, fan-out notifications, and hot retrieval results. Set explicit expiration and recovery policies so temporary state does not become an accidental permanent record.
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Graph-enhanced retrieval
A graph store earns its place when agents must traverse relationships rather than only rank similar text. AWS lists Neptune Analytics for vector similarity combined with graph analytics and graph-enhanced retrieval. Microsoft’s architecture reference describes a hybrid vector-plus-graph approach with metadata filters.
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Separate vector or search service
A specialized search tier can isolate embedding indexes from transactional traffic. AWS’s decision guide lists OpenSearch for k-nearest-neighbor and hybrid search, and S3 Vectors for cost-oriented bulk vector storage and query. These are AWS portfolio options with different workload positions, not neutral performance rankings.
Separate storage introduces an ingestion and consistency boundary. Define how a newly created record becomes searchable, what happens when indexing fails, and how deletions propagate. Keep the source record’s version or sequence number in the search document so an agent can reject stale results or re-fetch authoritative data before taking an irreversible action.
Session persistence supplied by an application framework
The OpenAI Agents SDK lists in-memory and file-based SQLite sessions, Redis, SQLAlchemy-backed databases, MongoDB, Dapr state stores, and OpenAI-hosted conversations. In-memory sessions are convenient for a single process but disappear on restart and do not automatically coordinate multiple workers. File-based SQLite can work for local or low-concurrency deployments; protect the file and its backups because they contain conversation data.
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The SDK’s guidance points to Redis-backed sessions for shared, low-latency memory and MongoDB sessions for teams already using MongoDB or needing horizontally scalable, multi-process storage. SQLAlchemy provides a path to relational backends, while Dapr can abstract a state store across deployments. Hosted conversations can reduce application-side session storage, but you still need to evaluate data residency, retention, access controls, export, and failure behavior for your application.
Use a workload matrix instead of a winner-takes-all list
| Pattern | Best fit | Retrieval strengths | Main trade-offs to test |
|---|---|---|---|
| Relational plus vector extension | Structured business data and semantic recall that should share transactions | SQL predicates, joins, constraints, and vector search in one data platform | Lock contention, vector-index tuning, schema changes, and mixed OLTP/search tail latency |
| Document plus integrated search | Nested, evolving agent records and context documents | Document queries with vector, full-text, or hybrid retrieval | Index size, metadata-filter selectivity, schema governance, and promotion of memory into durable profiles |
| Key-value or in-memory store | Hot session state, coordination, streaming, and cache-heavy workflows | Fast key access, pub/sub, streams, and (where supported) vector search | Eviction and durability settings, hot keys, failover behavior, and vendor latency claims |
| Graph-enhanced retrieval | Questions requiring multi-hop relationships | Graph traversal combined with vector similarity and filters | Graph modeling, synchronization lag, operational overhead, and consistency across stores |
| Separate search or vector service | Large semantic indexes or search traffic that should be isolated from transactions | k-nearest-neighbor and hybrid search tuned for retrieval | Dual writes, indexing lag, deletion handling, data transfer, and extra monitoring |
| Framework-managed sessions | Teams that want session lifecycle code supplied by the agent SDK | Standardized session APIs across SQLite, Redis, SQLAlchemy, MongoDB, Dapr, or hosted conversations | Backend-specific durability, authorization, scaling, portability, and backup responsibilities |
“One database” is a valid outcome when it covers the required data model and retrieval modes at the required latency. A hybrid is valid when the additional capability materially improves the workflow and the team can operate the synchronization and failure paths.
Design a fair performance test
Vendor comparisons and feature checklists cannot predict your agent workload. Build a small representative benchmark before committing to a migration or a second store.
- Freeze the test data. Use a corpus with the same record sizes, tenant distribution, metadata cardinality, embedding dimensions, and expected index growth as production. Include updates and deletes, not just initial loading.
- Use the production retrieval mix. Reproduce exact-key reads, metadata filters, full-text queries, vector searches, hybrid queries, graph traversals, and the writes generated by checkpoints and tool results. Include empty-result and highly selective cases.
- Hold model and infrastructure constant. Keep the embedding model, chunking, query rewriting, client connection pools, network placement, instance class, replicas, and persistence settings comparable. Record software versions and configuration.
- Apply realistic parallelism. Run the number of concurrent agents, workers, retries, and background indexers expected at normal and peak load. Include bursts when many agents finish a planning phase together.
- Measure distributions, not averages. Capture throughput, p50, p95, and p99 latency for each operation, error and timeout rates, queue depth, CPU and memory, index build time, and storage growth. Separate cold-cache from warm-cache results.
- Test correctness under contention. Force two workers to update the same record, replay an idempotent request, kill a worker during a transaction, and retry after a timeout. Verify that invariants, versions, and authorization boundaries remain intact.
- Test recovery. Restore a backup, rebuild an index, fail a replica or node, and restart workers with unfinished checkpoints. Measure recovery time and identify which memory types can be regenerated versus which require authoritative backups.
- Record operational effort. Include provisioning, schema migration, observability, alerting, backup verification, secret rotation, and the work required to reconcile a dual-write or asynchronous index pipeline.
Do not publish a single throughput number as if it were portable. A result is meaningful only with its dataset, query mix, concurrency, consistency level, deployment, and software versions.
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Build security and isolation into the data path
Authorize every session access
Map the authenticated principal to an allowed tenant, user, workflow, and session before reading or writing. Treat session IDs as opaque references, not bearer credentials. Enforce the check in the service that calls the database, and use database-level row, document, or key policies where available as a second boundary.
Separate memory by sensitivity and retention
Conversation transcripts may contain secrets or personal data that should expire, while billing or audit records may require years of retention. Apply different encryption, backup, deletion, and export rules. Store tool credentials outside agent memory and pass only scoped, short-lived capabilities to workers.
Prevent retrieval leakage
Apply tenant and authorization filters before vector ranking whenever possible. A semantically similar document from another tenant must never enter the candidate set merely because it scores highly. For hybrid or graph retrieval, enforce the same policy on every underlying collection, index, and edge traversal.
Make agent writes accountable
Record the acting agent, user or service principal, workflow run, tool, timestamp, and source version for material changes. Use optimistic versions or conditional writes to prevent a late retry from overwriting a newer decision. Keep a review or compensation path for actions that cannot be safely reversed.
Account for operations, cost, and failure modes
A second database is not free even when its managed service looks inexpensive. Budget for data movement, duplicate storage, backups, monitoring, incident response, schema evolution, and engineers who understand both systems. A single store may cost more per operation but remove an entire synchronization class of failures.
- Dual-write failure: the primary commit succeeds while the search or cache write fails. Use an outbox or change-data-capture process, record lag, and make indexing idempotent.
- Stale retrieval: an agent sees an old preference or permission. Attach versions or timestamps to retrieved records and re-check sensitive facts against the source of truth.
- Hot partition or key: one popular session or tenant overloads a shard. Distribute keys, cap fan-out, and test burst behavior rather than relying on average traffic.
- Unbounded memory: every tool result is retained forever. Define summarization, compaction, archival, and deletion policies for each memory subtype.
- Restore mismatch: the transactional database is restored to one point while a vector index reflects another. Rebuild or replay derived indexes from versioned source data and document the recovery order.
Ask each provider for current region coverage, quotas, backup guarantees, API stability, migration tooling, and pricing for your actual data and request volume. These details change more quickly than the underlying architectural patterns.
Check product maturity and explicit limits
Feature availability can be decisive. MongoDB’s Atlas Agent Engine documentation was marked Public Preview on the page accessed October 13, 2026, and says MongoDB does not recommend production workloads on the service during preview. Its limitations page lists a limit of 50 concurrent requests per second across agents in an orchestration project. That is an orchestration-service limit, not a benchmark of the MongoDB database engine, and it should be rechecked before deployment because preview terms and quotas can change.
Do not generalize that preview warning to MongoDB database services as a whole. Evaluate the exact product, API, region, support commitment, and exit path you intend to use.
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- Inventory data by memory subtype. Label each item as live session state, durable business data, semantic or episodic memory, or graph context. Mark its owner, retention, sensitivity, and source of truth.
- Write invariants and access rules. State which updates must be atomic, which retries must be idempotent, who can read each record, and what stale data is acceptable.
- Choose the simplest existing platform that meets those rules. An already supported relational, document, or key-value system reduces integration, monitoring, and staff burden when its retrieval and scale characteristics are adequate.
- Add a specialized store only for a measured gap. Introduce a vector, graph, cache, or search tier when it solves a concrete latency, relevance, or query-shape problem that the primary store cannot meet economically.
- Define synchronization and recovery before launch. Specify event flow, lag indicators, replay, deletion propagation, backup scope, and the behavior when a derived store is unavailable.
- Run the representative workload test. Compare tail latency, correctness, recovery, and operational effort under the same conditions. Keep the test artifacts so future scale or version changes can be evaluated.
- Recheck volatile limits. Confirm preview status, quotas, supported regions, API versions, and pricing immediately before production approval.
Bottom line for parallel agent teams
Begin with the database your team already operates if it can provide the required transactions, isolation, persistence, and retrieval modes. Keep live coordination state close to the workers that need low-latency shared access, keep durable business facts in an authoritative store, and add vector or graph capabilities where the query genuinely demands them. Choose a hybrid only after measuring the benefit against synchronization, security, backup, and operational complexity. The winning design is the one that preserves correctness and recovery under your real parallel workload—not the one with the longest feature list.
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