Agent Storage

Persist agent sessions, memory, knowledge, traces, approvals, schedules, evaluations, and metrics.

Agent state has to remain available across conversations, restarts, and replicas. Agents, teams, workflows, and AgentOS share a db interface for sessions, memory, learnings, knowledge metadata, traces, schedules, approvals, evaluations, and metrics.

The db parameter accepts JSON file, embedded, relational, document, key-value, and distributed backends.

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 table and collection names may vary.

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	Production runtime state; pair with PgVector for embeddings
SqliteDb	Local dev, single-user demos, edge deployments
MongoDb	Already on Mongo
MySQLDb	Already on MySQL
SingleStoreDb	Existing SingleStore infrastructure and high-throughput runtime state
RedisDb	Existing Redis infrastructure and high-throughput key-value access
ValkeyDb	Existing Valkey infrastructure and high-throughput key-value access
DynamoDb	AWS-native, serverless
FirestoreDb	GCP-native, serverless
JsonDb	Local JSON file storage
GcsJsonDb	JSON-backed records in Google Cloud Storage
InMemoryDb	Tests, ephemeral demos

Postgres-compatible managed services like Neon and Supabase work with PostgresDb directly. Point db_url at the managed instance. Async variants (AsyncPostgresDb, AsyncSqliteDb, AsyncMongoDb, AsyncMySQLDb) are documented under Database.

Vector storage

Knowledge uses a vector store for embedding search.

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.

For production deployments already using Postgres, pair PostgresDb with PgVector to keep runtime state and hybrid search in one Postgres service.

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:

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

Store generated images, audio, and large PDFs in object storage, then reference their paths in agno_knowledge or agno_sessions.

Developer Resources

• Database overview
• Vector stores
• Database migrations