Agent Storage

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.

Welcome

Get Started

Features

Use Cases

Agent Storage

What gets stored

Pick a backend

Vector storage

Splitting concerns across databases

File and blob storage

Developer Resources

​What gets stored

​Pick a backend

​Vector storage

​Splitting concerns across databases

​File and blob storage

​Developer Resources

What gets stored

Pick a backend

Vector storage

Splitting concerns across databases

File and blob storage

Developer Resources