> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-codex-docs-audit-20260719-0149.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent Storage

> Store sessions, memory, knowledge, and traces in a supported database backend.

Agents persist the data they generate and use in a database, set by the `db` param: sessions, memory, knowledge metadata, traces, schedules, approvals, learnings, and usage metrics.

The primitives (agents, teams, workflows) and the AgentOS accept a `db` param. Pick from JSON files (local or cloud), embedded (SQLite), relational (Postgres, MySQL), document (MongoDB, Firestore), key-value (Redis, Valkey, DynamoDB), or distributed (SingleStore).

```python theme={null}
from agno.db.postgres import PostgresDb
from agno.os import AgentOS

db = PostgresDb(db_url="postgresql+psycopg://user:pass@host:5432/agno")

agent_os = AgentOS(agents=[agent], db=db)
```

AgentOS creates the tables and indexes on first boot. Set `auto_provision_dbs=False` on `AgentOS` when you manage the schema yourself.

## What gets stored

| Table                                  | Holds                                                            |
| -------------------------------------- | ---------------------------------------------------------------- |
| `agno_sessions`                        | Conversation history per `(user_id, session_id)`                 |
| `agno_memories`                        | User memories the agent decides to keep                          |
| `agno_learnings`                       | Learnings captured from runs                                     |
| `agno_knowledge`                       | Knowledge content metadata (embeddings live in the vector store) |
| `agno_traces`, `agno_spans`            | OpenTelemetry traces                                             |
| `agno_approvals`                       | Pending and resolved HITL requests                               |
| `agno_schedules`, `agno_schedule_runs` | Cron jobs                                                        |
| `agno_metrics`, `agno_eval_runs`       | Metrics and eval results                                         |

* Backend-specific names may vary.
* Schema changes are generally additive.

## Pick a backend

Most tutorials use `PostgresDb`. Pair it with `PgVector` when you want relational data and embeddings on the same Postgres instance.

| Backend                                                     | When to use                                       |
| ----------------------------------------------------------- | ------------------------------------------------- |
| [`PostgresDb`](/database/providers/postgres/overview)       | Production. Vector + relational on one box.       |
| [`SqliteDb`](/database/providers/sqlite/overview)           | Local dev, single-user demos, edge deployments    |
| [`MongoDb`](/database/providers/mongo/overview)             | Already on Mongo                                  |
| [`MySQLDb`](/database/providers/mysql/overview)             | Already on MySQL                                  |
| [`SingleStoreDb`](/database/providers/singlestore/overview) | Vector + analytics on one engine, high-throughput |
| [`RedisDb`](/database/providers/redis/overview)             | Cache-friendly, ephemeral sessions                |
| [`ValkeyDb`](/database/providers/valkey/overview)           | Cache-friendly, ephemeral sessions                |
| [`DynamoDb`](/database/providers/dynamodb/overview)         | AWS-native, serverless                            |
| [`FirestoreDb`](/database/providers/firestore/overview)     | GCP-native, serverless                            |
| [`JsonDb`](/database/providers/json/overview)               | Local JSON files, no server to run                |
| [`GcsJsonDb`](/database/providers/gcs/overview)             | JSON-backed records in Google Cloud Storage       |
| [`InMemoryDb`](/database/providers/in-memory/overview)      | Tests, ephemeral demos                            |

Postgres-compatible managed services like [Neon](/database/providers/neon/overview) and [Supabase](/database/providers/supabase/overview) work with `PostgresDb` directly. Point `db_url` at the managed instance. Async variants (`AsyncPostgresDb`, `AsyncSqliteDb`, `AsyncMongoDb`, `AsyncMySQLDb`) are documented under [Database](/database/overview).

## Vector storage

Knowledge needs a vector store, and Agno supports 15+ vector databases out of the box.

```python theme={null}
from agno.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.vectordb.search import SearchType

agent = Agent(
    db=db,
    knowledge=Knowledge(
        vector_db=PgVector(
            table_name="my_kb",
            db_url=DB_URL,
            search_type=SearchType.hybrid,   # vector + full-text search
        ),
    ),
)
```

Other options: LanceDB, Qdrant, Weaviate, Pinecone, Chroma, MongoDB Atlas, Cosmos, Cassandra, ClickHouse, SurrealDB, Milvus. See [Vector Stores](/knowledge/vector-stores/index).

For most production AgentOS deployments, **PgVector + PostgresDb on the same Postgres** is the right default: one database with hybrid search and transactional reads, and no extra service to operate.

## Splitting concerns across databases

Every agent, team, and workflow can take its own `db`, overriding the AgentOS default.

Use the AgentOS `db` for shared state and hand individual components a separate database when they need isolation:

```python theme={null}
shared_db = PostgresDb(db_url="postgresql+psycopg://shared/...")
tenant_db = PostgresDb(db_url="postgresql+psycopg://tenant-a/...")

tenant_agent = Agent(name="tenant-a-support", db=tenant_db)
internal_agent = Agent(name="ops", db=shared_db)

agent_os = AgentOS(
    agents=[tenant_agent, internal_agent],
    db=shared_db,
)
```

Common splits include separate tenant databases, a high-traffic agent on its own engine, or one workflow's session history on a different backend. Database-level tenant isolation also requires separate credentials and grants.

## File and blob storage

For media that doesn't belong in the relational store (generated images, audio, large PDFs), store them in object storage and reference paths in `agno_knowledge` or `agno_sessions`.