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Use SQL as the authoritative store for most AI-agent state, and add vector search for information that must be recalled by meaning. In practice, that usually means PostgreSQL (optionally with pgvector) for checkpoints, workflow records, identities, permissions, tool results and canonical facts, plus a vector index for semantic memories and document retrieval. A dedicated vector database is worthwhile when embedding volume, latency tuning or operational isolation has outgrown the SQL system. Redis can combine hot session state and vector search when every reasoning step has a tight latency budget, but durable, auditable records should still have an authoritative store.
Start by defining what “agent state” means
“State” is often used as shorthand for embeddings, but an agent needs several kinds of information. A checkpoint that lets a workflow resume after an approval pause is not the same thing as a paragraph retrieved because it is semantically similar to the current question. Treating both as interchangeable is the source of many fragile designs.
| State class | Typical contents | Primary requirement | Best initial home |
|---|---|---|---|
| Thread checkpoint | Current messages, graph step, pending tool call, retry metadata and interrupt status | Atomic updates and reliable resume after failure | SQL checkpointer |
| Workflow and business records | Runs, jobs, invoices, tickets, tool receipts, approvals and status transitions | Transactions, constraints, joins and audit history | SQL |
| Identity and policy data | Users, teams, roles, tenant boundaries and consent | Exact authorization decisions and controlled retention | SQL or the system that owns identity |
| Durable user or application facts | Preferences, profile attributes and confirmed facts shared across threads | Provenance, correction, deletion and cross-thread access | SQL record, with an optional embedding for recall |
| Episodic memory | Summaries of prior interactions, observations and successful or failed episodes | Semantic retrieval, freshness and relevance ranking | Vector index backed by a durable record |
| Document retrieval index | Chunked manuals, policies, tickets or code with embeddings and metadata | Nearest-neighbor search, filtering and index maintenance | pgvector or a vector service |
Keep these classes explicit in your schema and APIs. Each class needs its own owner, retention period, deletion behavior and provenance fields. A single “memory” table or collection makes it too easy to mix a temporary checkpoint with a user fact that must survive for months.
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| Decision axis | SQL database (such as PostgreSQL) | Vector database or service |
|---|---|---|
| Primary query | Exact predicates, joins, ordering, constraints and transactions | Nearest-neighbor similarity over embeddings, commonly combined with metadata filters |
| Agent data that fits naturally | Checkpoints, threads, users, permissions, tool receipts, workflow records and canonical facts | Semantic memories, document chunks, similar past episodes and retrieval indexes |
| Correctness model | Transactional updates and relational integrity | Retrieval relevance, index freshness and recall/latency behavior |
| Main scaling concern | Transaction throughput, joins, write contention, storage and replica design | Vector-index size, recall, latency, rebuilds, sharding and memory cost |
| Operational trade-off | Fewer systems when vector workload fits the existing database, with mature SQL tooling | Specialized retrieval performance, but an additional system to synchronize, secure, delete and observe |
A similarity score cannot enforce that only an authorized tenant’s records are returned, that a payment is recorded once, or that a workflow moves through legal status transitions. Conversely, a relational query does not discover that two differently worded notes describe the same concept unless you add a semantic representation. The storage choice should follow the query and correctness requirement, not the popularity of a product category.
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Why SQL should usually be the source of truth
Checkpoints make workflows resumable
Agents that call tools, wait for people, or run for longer than one request need durable checkpoints. The checkpoint records the thread identifier, current state and the point at which execution can continue. If a process crashes after a tool call, the system can inspect the recorded step instead of guessing whether the call happened. Atomic SQL writes and constraints are well suited to this boundary.
LangGraph’s official persistence guidance captures the separation precisely: “Most applications can use both: a checkpointer tracks the current thread, and a store tracks durable information across threads.” A checkpointer supports conversation continuity, interruption recovery, human-in-the-loop workflows and fault tolerance; a store holds information that should be available to later threads.
Relational records protect business invariants
Users, tenants, roles, approvals, tool receipts and workflow transitions have relationships that should be validated by the database. Foreign keys can prevent an orphaned tool receipt; unique constraints can stop duplicate idempotency keys; transactions can update a run and its audit event together. These guarantees are more important than approximate relevance for execution state.
SQL gives one authoritative history
When a user asks why an agent took an action, you need the exact input, policy decision, tool response and actor identity—not merely a nearby embedding. Store the canonical event or fact in SQL, including timestamps, tenant ownership, source and revision. You may create an embedding from that record for recall, but the embedding is a derived index that can be regenerated.
PostgreSQL is already a documented agent persistence layer
LangSmith’s data-plane documentation describes PostgreSQL storing user, run and memory data and serving as the default backend for graph checkpoints. The same documentation notes that PostgreSQL remains required even when MongoDB is selected for checkpointing. That pattern reflects a broader principle: checkpoint persistence and operational metadata are foundational infrastructure, not optional vector-search features.
What vector search contributes
Semantic recall over long-term information
Vector retrieval compares an embedding of the current query with embeddings of stored items and returns the closest matches, usually subject to metadata filters. It can find a preference expressed as “keep meetings short” when the current request says “schedule a brief call,” or locate a relevant paragraph without an exact keyword match.
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Document and episodic retrieval are natural fits
Chunked policies, manuals, support cases and summaries of prior episodes are commonly read by similarity rather than by primary key. A vector index can return a small candidate set for the model to inspect, reducing the need to place an entire corpus in the prompt. Store chunk text, document version, tenant, access labels and source identifiers alongside each embedding so retrieval can be filtered and explained.
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Vector search should not decide whether a user is an administrator, whether a job is complete, whether a refund has been issued, or which checkpoint is the latest. Those decisions require exact predicates and transactional ownership. Use retrieved text as context for reasoning, then write the resulting state through the authoritative workflow path.
A practical hybrid architecture
The common production design has a durable SQL core and a semantic index as a derived service. The two layers have clear responsibilities:
- Write the authoritative event or fact to SQL. Include tenant or user ownership, source, timestamp, revision and retention metadata. For a checkpoint, write the new thread state in the same transaction as any execution record that must be consistent with it.
- Publish an indexing task. An outbox row or equivalent durable queue entry identifies the record and the embedding model version. Do not make a successful vector write the only proof that the business event occurred.
- Generate or update the embedding. Embed the approved text or summary, attach metadata used for authorization and filtering, and upsert it with a stable identifier derived from the SQL record.
- Retrieve with filters first. Apply tenant, visibility, document-version and deletion rules before or during nearest-neighbor search. Similarity alone is not an access-control mechanism.
- Fetch canonical details when needed. The vector result can carry a record identifier and a short preview; load the authoritative row from SQL before taking an action or presenting a consequential fact.
- Handle deletion and re-embedding as data changes. Mark records deleted or superseded in SQL, propagate that state to the index, and reprocess items when the embedding model or chunking policy changes.
This arrangement lets the SQL database remain the source of truth even when indexing is delayed. A temporary retrieval miss is visible as stale search; it does not silently create or lose workflow state.
When PostgreSQL alone is enough
Start with PostgreSQL when the agent needs resumable workflows, transactional state, joins, audit trails or a single source of truth. Add pgvector when the semantic workload is moderate enough to share the same operational database and the team values one backup, one permission model and one change-management path.
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Signs the combined approach fits
- Checkpoints and business records are the dominant writes.
- Embeddings belong to the same tenants and retention policies as relational rows.
- Vector queries can share existing CPU, memory and storage without harming transaction latency.
- Your team prefers SQL migrations, backups and observability over operating another service.
- Consistency between a fact and its embedding matters more than independently scaling retrieval.
Design details for pgvector
Keep the canonical text and embedding metadata in a table linked to the source row. Record the embedding model and dimensionality so a future model can coexist during migration. Use metadata predicates for tenant and visibility boundaries, and monitor index build time, query latency and the effect of vector scans on ordinary transactions. If vector operations begin to consume the resources needed for checkpoints, isolate read replicas or move retrieval to a separate service rather than allowing execution reliability to degrade.
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When a dedicated vector database is justified
A separate vector service is a scale and operations decision, not a prerequisite for every agent. It becomes reasonable when embeddings dominate the workload, when index capacity or recall/latency tuning needs independent scaling, or when several applications share a large retrieval corpus that should not compete with transactional traffic.
Workload indicators
- The corpus and index memory grow faster than the primary SQL workload and require their own sharding or capacity plan.
- Retrieval traffic has latency or availability targets that differ from the agent’s transactional database.
- You need specialized controls for approximate-nearest-neighbor indexes, recall tuning or rebuilds.
- Multiple products consume the same corpus while transactional records remain owned by different systems.
- Bulk ingestion and re-embedding would create unacceptable write contention in SQL.
Costs beyond the service bill
The second datastore adds synchronization, deletion propagation, tenancy enforcement, backup and restore procedures, schema evolution and observability work. You must decide which system wins when writes arrive out of order, how retries are deduplicated, how an index is rebuilt, and how to prove that a deleted or restricted record is no longer retrievable. If those controls are not designed up front, a faster search engine can create a less trustworthy agent.
Where Redis fits
Redis is useful when the same low-latency layer must provide key-value session state, caching and vector search. Redis documents a memory-layer pattern in which application state and vector retrieval coexist, and recommends it when each reasoning step must recall current-session events and learned information under a strict latency budget.
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Use Redis as a hot or session tier when rapid access is the main constraint. Keep authoritative records in a durable store whenever auditability, relational integrity or long retention matters. A common arrangement is SQL for checkpoints and canonical facts, Redis for the active context window and frequently accessed embeddings, and an asynchronous process to refresh or evict the hot tier. Define what happens when Redis is unavailable or loses an entry; the agent should be able to rebuild hot state from the durable source.
Separate memory classes and retention rules
Memory quality improves when the agent knows what each item represents. A useful model has four explicit classes:
Thread checkpoint state
Keep only what is required to resume the current thread: messages or references to them, graph position, pending actions and interruption metadata. Apply thread-level access controls and a retention period appropriate to operational recovery.
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Durable user and application facts
Store confirmed preferences and facts as structured records with provenance, confidence or confirmation status, owner and deletion behavior. If an embedding is created for recall, retain the structured value as the authority and update or remove the embedding when the fact changes.
Episodic memories
Represent notable interactions or outcomes as dated episodes linked to the user, tenant and source thread. Retrieval should consider recency and validity, not similarity alone. When an episode is corrected, superseded or withdrawn, mark that relationship in SQL and propagate it to the index.
Document RAG indexes
Track document identity, version, chunk boundaries, source location, access labels and embedding model. Re-indexing a new document version should not leave old restricted text searchable. Keep a manifest so operators can determine which chunks are current.
Synchronization rules for two datastores
Make synchronization a first-class design rather than an incidental background script.
- Ownership: name the system that owns each field. The vector store should not become the editor of canonical user facts unless it can provide the required transactional guarantees.
- Ordering: write SQL first, then enqueue indexing. Include a monotonically increasing revision or updated timestamp so an older retry cannot overwrite a newer embedding.
- Retries: use stable record identifiers and idempotent upserts. Record failures and retry counts; do not silently drop an indexing task.
- Deletion: propagate hard deletes, tombstones and access changes. Test that a revoked document disappears from retrieval, not just from the SQL UI.
- Embedding versions: store model name, dimensions and chunking version. Re-embedding should be a controlled migration with coexistence or a rebuild plan.
- Observability: measure indexing lag, failed tasks, stale-result rate, retrieval latency, filter errors and the age of the newest indexed revision.
- Recovery: be able to rebuild the vector index entirely from SQL or the original document store. Treat the index as replaceable infrastructure.
Implementation blueprint for an SQL-first agent
The following sequence works whether the semantic index is pgvector, a dedicated service or Redis:
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- Create relational ownership boundaries. Add tenant, user, thread and revision identifiers. Enforce uniqueness and foreign-key relationships for records that must not be duplicated or orphaned.
- Make execution idempotent. Give tool calls and external side effects idempotency keys. Record request, response, status and retry information before allowing the workflow to advance.
- Choose the semantic projection. Decide which text is embedded, how it is chunked, which metadata is filterable and which source row is canonical. Do not embed secrets or fields that a requester must never retrieve.
- Introduce an outbox or durable queue. Commit the source change and indexing instruction together. A worker can then retry embedding without rerunning the business transaction.
- Implement filtered retrieval. Require tenant and permission filters in the retrieval API, set a result limit appropriate to the prompt budget, and return source identifiers with every hit.
- Validate before action. For tool calls or policy-sensitive answers, load the current SQL record and re-check authorization and status after retrieval. Retrieved text is context, not permission.
- Test failure paths. Kill workers between SQL commit and vector upsert, replay messages, delete a source document, change its permissions and rebuild the index. Verify that the agent remains resumable and that stale results cannot authorize an action.
Common mistakes and their fixes
Putting checkpoints only in a vector store
Symptom: a restart retrieves a similar message but cannot resume the exact graph step or determine whether a tool already ran. Fix: persist checkpoint state transactionally in SQL; use vectors only to recall related history.
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Using similarity as authorization
Symptom: a search returns a semantically close document from another tenant or an outdated policy. Fix: store ownership and visibility metadata, enforce filters, and verify the canonical row before presenting or acting on the result.
Writing to both stores synchronously without a recovery plan
Symptom: a failed vector request causes the user transaction to fail, or a retry creates duplicate memories. Fix: commit SQL first, use an outbox, and make vector upserts idempotent.
Allowing stale embeddings to outrank current facts
Symptom: the agent recalls an old preference after the user changed it. Fix: represent validity and revision in SQL, filter superseded records, and propagate updates and deletions to the index.
Ignoring operational contention
Symptom: embedding searches slow checkpoint writes or ordinary business queries. Fix: monitor resource use, isolate read capacity or move retrieval to a dedicated service while preserving SQL ownership.
Keeping every memory forever
Symptom: retrieval becomes noisy, deletion requests are difficult and storage grows without bound. Fix: assign retention and review rules per memory class, archive or delete expired items, and remove their vectors.
A decision framework
| If your dominant requirement is… | Prefer… | Why |
|---|---|---|
| Resumable workflows, approvals, exact status and audit trails | PostgreSQL as source of truth | Transactions and relational constraints protect execution correctness |
| Semantic recall over a moderate corpus owned by the same application | PostgreSQL plus pgvector | One operational system can hold canonical rows and their derived embeddings |
| Very large shared corpora or independently scaled retrieval | SQL plus a dedicated vector database | Index capacity and retrieval tuning can scale separately from transactions |
| Strict per-step latency with hot session context and vectors | Redis as a hot tier, with durable backing | Key-value access and vector search share a low-latency layer |
| Regulated or highly auditable facts | Durable SQL record, optional vector projection | Provenance, deletion and exact authorization remain explicit |
If two rows seem to point to different choices, prioritize the correctness requirement. You can add semantic retrieval later; repairing an architecture that let an approximate match stand in for an authorization or workflow record is much harder.
Bottom line
For most production AI agents, choose SQL first: it should own checkpoints, workflow state, identities, permissions, tool receipts and canonical facts. Add vector search for semantic memories and document retrieval, using pgvector when the workload fits comfortably beside transactions. Move to a dedicated vector database only when scale, latency isolation or shared-corpus operations justify the extra synchronization burden. Use Redis when hot state and semantic retrieval must coexist under a strict latency budget, while retaining a durable authority for records that must be correct, explainable and deletable.
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