Self-correcting agents

Use the Learning Machine to save diagnosed query corrections and retrieve them for related requests.

uv pip install "agno[openai,pgvector,psycopg,sql]"

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.learn import LearningMachine
from agno.models.openai import OpenAIResponses
from agno.tools.sql import SQLTools
from agno.vectordb.pgvector import PgVector

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

db = PostgresDb(db_url=db_url)
knowledge = Knowledge(
    vector_db=PgVector(table_name="agent_learnings", db_url=db_url),
)

agent = Agent(
    id="data-analyst",
    model=OpenAIResponses(id="gpt-5.5"),
    db=db,
    tools=[SQLTools(db_url="postgresql+psycopg://readonly@warehouse/analytics")],
    learning=LearningMachine(knowledge=knowledge, decision_log=True),
    instructions=(
        "Before writing SQL, call search_learnings with terms from the request and "
        "apply relevant corrections. "
        "When a query errors, diagnose the cause. Call save_learning for a "
        "reusable correction. Call log_decision when you change the query shape."
    ),
)

Passing knowledge to LearningMachine enables the Learned Knowledge store. It uses AGENTIC mode by default, so the model calls save_learning when it finds a reusable correction. decision_log=True also selects AGENTIC mode and adds the log_decision and search_decisions tools. A decision is saved only when the model calls log_decision.

The loop

The agent writes SQL and runs it.
It errors, or returns a number a human flags as wrong.
The agent diagnoses the cause (wrong column, stale table, a join that double-counts).
The fix is saved according to the configured learning mode.
On the next similar request, the agent calls search_learnings before generating SQL.

In AGENTIC mode, retrieval occurs when the model calls search_learnings. The instruction in this example makes that search part of the query workflow.

Choose how learning happens

Each store has its own mode. Choose explicit tool calls, background extraction, or human approval based on the correction.

from agno.learn import LearnedKnowledgeConfig, LearningMachine, LearningMode

always_agent = Agent(
    id="data-analyst",
    model=OpenAIResponses(id="gpt-5.5"),
    db=db,
    learning=LearningMachine(
        knowledge=knowledge,
        learned_knowledge=LearnedKnowledgeConfig(mode=LearningMode.ALWAYS),
    ),
)

Mode	Behavior	Use for
`ALWAYS`	Background extraction from the pre-model message snapshot	Corrections in the current user input, plus prior user and assistant messages when history is enabled
`AGENTIC`	The agent decides what to save with explicit tools	Nuanced domain insights
`PROPOSE`	The agent proposes, a human approves before it persists	Anything that changes how numbers are computed

ALWAYS schedules background extraction after run messages are prepared and before the main model call. With the default add_history_to_context=False, this example supplies the current user message. Set add_history_to_context=True to include prior user and assistant messages in later snapshots. The extractor ignores system messages, tool calls, and tool results. It may decide there is nothing worth saving.

Audit what it learned

Inspect the Learned Knowledge store for saved corrections and the Decision Log for recorded query-shape decisions.

lm = agent.learning_machine

# What the agent has learned about the warehouse:
lm.learned_knowledge_store.print(query="MRR")

# The audit trail of why a query shape changed:
lm.decision_log_store.print(agent_id=agent.id, limit=5)

Store	Why a data agent wants it
Learned Knowledge	Corrections that transfer across users
Decision Log	An audit trail of why a query shape changed

Next steps

Task	Guide
Feed it curated context too	Grounding in context
Gate writes behind approval	Safe data access

Welcome

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Features

Use Cases

Self-correcting agents

The loop

Choose how learning happens

Audit what it learned

Next steps

Developer Resources

​The loop

​Choose how learning happens

​Audit what it learned

​Next steps

​Developer Resources

The loop

Choose how learning happens

Audit what it learned

Next steps

Developer Resources