> ## 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.

# Workflow History & Continuous Execution

> Build conversational workflows that maintain context across multiple executions, creating truly intelligent and natural interactions.

<Badge icon="code-branch" color="orange">
  <Tooltip tip="Introduced in v2.1.4" cta="View release notes" href="https://github.com/agno-agi/agno/releases/tag/v2.1.4">v2.1.4</Tooltip>
</Badge>

Workflow History enables your Agno workflows to remember and reference previous conversations, transforming isolated executions into continuous, context-aware interactions.

Instead of starting fresh each time, with Workflow History you can:

* **Build on previous interactions** - Reference the context of past interactions
* **Avoid repetitive questions** - Avoid requesting previously provided information
* **Maintain context continuity** - Create a conversational experience
* **Learn from patterns** - Analyze historical data to make better decisions

<Tip>
  Note that this feature is different from `add_history_to_context`.
  This does not add the history of a particular agent or team, but the full workflow history to either all or some steps.
</Tip>

## How It Works

When workflow history is enabled, previous messages are automatically injected into agent/team inputs as structured context:

```xml theme={null}
<workflow_history_context>
[run-1]
input: Create content about AI in healthcare
response: # AI in Healthcare: Transforming Patient Care...

[run-2] 
input: Make it more family-focused
response: # AI in Family Healthcare: A Parent's Guide...
</workflow_history_context>

Your current input goes here...
```

Along with this, in using Steps with custom functions, you can access this history in the following ways:

1. As a formatted context string as shown above
2. In a structured format as well for more control

```bash theme={null}
[
    (<workflow input from run 1>)(<workflow output from run 1>),
    (<workflow input from run 2>)(<workflow output from run 2>),
]
```

<Note>
  A database is required to use Workflow history. Runs across different executions will be persisted there.
</Note>

Example-

```python theme={null}
def custom_function(step_input: StepInput) -> StepOutput:
    # Option 1: Structured data for analysis
    history_tuples = step_input.get_workflow_history(num_runs=3)
    for user_input, workflow_output in history_tuples:
        # Process each conversation turn

    # Option 2: Formatted context for agents  
    context_string = step_input.get_workflow_history_context(num_runs=3)

    return StepOutput(content="Analysis complete")
```

<Note>
  You can use these helper functions to access the history:

  * `step_input.get_workflow_history(num_runs=3)`
  * `step_input.get_workflow_history_context(num_runs=3)`
</Note>

Refer to [StepInput](/reference/workflows/step_input) reference for more details.

## Control Levels

You can be specific about which Steps to add the history to:

### Workflow-Level History

Add workflow history to **all steps** in the workflow:

```python theme={null}
workflow = Workflow(
    steps=[research_step, analysis_step, writing_step],
    add_workflow_history_to_steps=True  # All steps get history
)
```

### Step-Level History

Add workflow history to **specific steps** only:

```python theme={null}
Step(
    name="Content Creator", 
    agent=content_agent,
    add_workflow_history=True  # Only this step gets history
)
```

<Note>
  You can also put `add_workflow_history=False` to disable history for a specific step.
</Note>

## Precedence Logic

**Step-level settings always take precedence over workflow-level settings**:

```python theme={null}
workflow = Workflow(
    steps=[
        Step("Research", agent=research_agent),                              # None → inherits workflow setting
        Step("Analysis", agent=analysis_agent, add_workflow_history=False),  # False → overrides workflow  
        Step("Writing", agent=writing_agent, add_workflow_history=True),     # True → overrides workflow
    ],
    add_workflow_history_to_steps=True  # Default for all steps
)
```

### History Length Control

**By default, all available history is included** (no limit). It is recommended to use a fixed history run limit to avoid bloating the LLM context window.

You can control this at both levels:

```python theme={null}
# Workflow-level: limit history for all steps
workflow = Workflow(
    add_workflow_history_to_steps=True,
    num_history_runs=5  # Only last 5 runs
)

# Step-level: override for specific steps
Step("Analysis", agent=analysis_agent, 
     add_workflow_history=True,
     num_history_runs=3  # Only last 3 runs for this step
)
```

## Developer Resources

<CardGroup cols={2}>
  <Card title="Single Step Continuous Execution" icon="rotate" iconType="duotone" href="/history/workflow/single-step-continuous-execution-workflow">
    Single step workflow with continuous execution and history awareness.
  </Card>

  <Card title="Workflow History for Steps" icon="list-check" iconType="duotone" href="/history/workflow/workflow-with-history-enabled-for-steps">
    Add workflow history to all steps in the workflow.
  </Card>

  <Card title="Enable History for Specific Step" icon="toggle-on" iconType="duotone" href="/history/workflow/enable-history-for-step">
    Enable workflow history for a specific step only.
  </Card>

  <Card title="Get History in Function" icon="code" iconType="duotone" href="/history/workflow/get-history-in-function">
    Access workflow history in custom functions for analysis.
  </Card>
</CardGroup>