> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-studio-tools-doc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Institutional learning

> Share what every review learns so the next one starts from the team's accumulated judgment.

A research team that does not learn re-derives the same conclusions and repeats the same mistakes. The value compounds when an insight from one review is available to every agent on the next. Agno's `LearningMachine` with a shared store and a global namespace makes the team's learning institutional, not per-agent.

```python theme={null}
from agno.agent import Agent
from agno.learn import LearnedKnowledgeConfig, LearningMachine, LearningMode
from agno.models.openai import OpenAIResponses

# Shared learnings store, imported once, used by every agent and team
from agents.settings import team_learnings

analyst = Agent(
    name="Financial Analyst",
    model=OpenAIResponses(id="gpt-5.5"),
    learning=LearningMachine(
        knowledge=team_learnings,
        learned_knowledge=LearnedKnowledgeConfig(
            mode=LearningMode.AGENTIC,
            namespace="global",
        ),
    ),
)

# Another agent built on the same team_learnings store captures an insight:
risk_officer.print_response("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")
```

Every analyst and every team writes to and reads from the same `team_learnings` store, so the capture above is already in the next agent's context.

## Per-agent vs institutional

|           | Per-agent memory       | Institutional learning                     |
| --------- | ---------------------- | ------------------------------------------ |
| Scope     | One agent              | Every agent and team that shares the store |
| Namespace | The user or agent      | `"global"`                                 |
| Effect    | This analyst remembers | The committee remembers                    |

For deep research, institutional is the point. The team's judgment should outlive any single review.

## What to capture

| Worth a learning                                 | Not worth a learning                         |
| ------------------------------------------------ | -------------------------------------------- |
| "Analyst estimates lag this sector by a quarter" | A one-off number from a single query         |
| "This data source double-counts renewals"        | A restatement of the mandate                 |
| A correction to a conclusion that was wrong      | A summary of what was already in the library |

Capture corrections and transferable insights. Leave durable facts to the [research library](/use-cases/deep-research/grounding-research) and rules to the static context.

## Modes

| Mode      | Behavior                                | Use for                                    |
| --------- | --------------------------------------- | ------------------------------------------ |
| `ALWAYS`  | Capture runs after every response       | Steady accumulation of observations        |
| `AGENTIC` | The agent decides what is worth keeping | Research, where signal-to-noise matters    |
| `PROPOSE` | A human approves before it persists     | Anything that changes how the team decides |

This is the same machine the [data agent uses to self-correct](/use-cases/data-agents/self-correcting-agents), pointed at a shared store instead of a per-warehouse one.

## Next steps

| Task                               | Guide                                                                     |
| ---------------------------------- | ------------------------------------------------------------------------- |
| Feed it grounded context too       | [Grounding research](/use-cases/deep-research/grounding-research)         |
| Audit what changed the team's mind | [Structured deliverable](/use-cases/deep-research/structured-deliverable) |

## Developer Resources

* [Learning Machines](/learning/overview)
* [Learning modes](/learning/learning-modes)
* [Learned knowledge cookbook](https://github.com/agno-agi/agno/tree/main/cookbook/08_learning/05_learned_knowledge)