Institutional learning

Share reviewed insights across agents and teams through a global learning namespace.

Use a shared learning store to carry reviewed insights from one research cycle into the next. Agno’s LearningMachine with a global namespace makes those learnings available to every agent and team that uses the store.

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.learn import LearnedKnowledgeConfig, LearningMachine, LearningMode
from agno.models.openai import OpenAIResponses

# team_learnings is a Knowledge instance backed by a vector database, on its own
# table so learnings stay out of the research library's search results.
from agents.settings import team_learnings

db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")


def institutional_learning() -> LearningMachine:
    return LearningMachine(
        knowledge=team_learnings,
        learned_knowledge=LearnedKnowledgeConfig(
            mode=LearningMode.AGENTIC,
            namespace="global",
        ),
    )


learning_instructions = (
    "Use save_learning to store reusable insights. "
    "Use search_learnings to find prior knowledge before forming a view."
)

analyst = Agent(
    name="Financial Analyst",
    model=OpenAIResponses(id="gpt-5.5"),
    db=db,
    instructions=learning_instructions,
    learning=institutional_learning(),
)

risk_officer = Agent(
    name="Risk Officer",
    model=OpenAIResponses(id="gpt-5.5"),
    db=db,
    instructions=learning_instructions,
    learning=institutional_learning(),
)

# The risk officer captures an insight:
risk_officer.print_response(
    "Save this insight: analyst estimates lag this sector by about a quarter."
)

# Later, a different agent reads it back from the shared store:
analyst.learning_machine.learned_knowledge_store.print(query="estimate lag")

The learning is available to every agent and team sharing the store. In Agentic mode, retrieve it with search_learnings. A configured Knowledge instance backs the store. Missing knowledge configuration causes saves and searches to log a warning; state remains unchanged.

Per-agent vs institutional

	User memory	Institutional learning
Scope	One user across agents sharing the database	Every agent and team that shares the store
Namespace	Scoped by user_id	"global"
Effect	User preferences and facts remain available across sessions	Reviewed insights remain available across research cycles

Use institutional learning to keep reviewed team judgment available across research cycles.

What to capture

Save to shared learning	Keep in context or source data
”Analyst estimates lag this sector by a quarter”	One-off query results stay with the run
”This data source double-counts renewals”	The mandate stays in static context
A correction to a conclusion that was wrong	Source summaries stay in the research library

Capture corrections and transferable insights. Leave durable facts to the research library and rules to the static context.

Modes

Mode	Behavior	Use for
ALWAYS	Start extraction from the current input on every run	Steady accumulation of observations
AGENTIC	The agent decides what is worth keeping	Research, where signal-to-noise matters
PROPOSE	The model proposes a learning and asks before saving; approval is prompt-based	Low-risk review where soft approval is sufficient

The data agent self-correction pattern uses the same machine with a per-warehouse store. This research pattern uses the shared institutional store.

Next steps

Task	Guide
Feed it grounded context too	Grounding research
Audit what changed the team’s mind	Structured deliverable

Developer Resources

• Learning Machines
• Learning modes
• Learned knowledge cookbook