> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bindai.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# 06.8 timeout

# Timeout Policies

Timeout policies protect workflows from running indefinitely.

A timeout defines the maximum amount of time a workflow or operation is allowed to execute before it is considered to have exceeded its execution limit.

Timeouts improve system stability by preventing stalled or excessively long-running tasks from consuming resources indefinitely.

***

# What is a Timeout?

A timeout specifies the maximum execution duration.

Conceptually:

```text id="timeout1" theme={null}
Start Task

↓

Execute

↓

Time Limit Reached?

      ↙        ↘

     No        Yes

     ↓          ↓

 Continue    Timeout
```

If execution exceeds the configured limit, the timeout policy is applied.

***

# Why Use Timeouts?

Not every operation finishes successfully.

Sometimes tasks become stuck because of:

* unresponsive services
* infinite loops
* blocked network requests
* external API delays
* infrastructure failures
* unexpected system behavior

Timeouts prevent these situations from affecting the entire workflow.

***

# Execution Timeline

A timeout measures elapsed execution time.

```text id="timeout2" theme={null}
Start

↓

Running

↓

Running

↓

Time Limit

↓

Timeout
```

If the task finishes before the limit, execution continues normally.

***

# Timeout Policy

BindAI provides a timeout policy object.

Example:

```python id="timeout3" theme={null}
TimeoutPolicy(
    seconds=30,
    fail_workflow=True,
)
```

The timeout specifies:

* maximum execution time
* behavior after timeout

***

# Successful Completion

If execution completes within the allowed duration:

```text id="timeout4" theme={null}
Start

↓

Execute

↓

Complete

↓

Continue Workflow
```

The timeout policy has no effect.

***

# Timeout Triggered

If execution exceeds the configured limit:

```text id="timeout5" theme={null}
Start

↓

Execute

↓

Time Limit

↓

Timeout
```

The workflow responds according to the configured policy.

***

# Failing the Workflow

A common configuration is to fail the workflow immediately.

```text id="timeout6" theme={null}
Timeout

↓

Workflow Failed
```

This prevents additional processing after an unrecoverable delay.

***

# Continuing After Timeout

Some workflows may choose not to fail immediately.

Conceptually:

```text id="timeout7" theme={null}
Timeout

↓

Record Event

↓

Continue
```

This behavior depends on workflow requirements and timeout configuration.

***

# Timeout vs Retry

Timeouts and retries address different concerns.

| Timeout                 | Retry                      |
| ----------------------- | -------------------------- |
| Limits execution time   | Repeats failed operations  |
| Prevents hanging tasks  | Handles transient failures |
| Based on elapsed time   | Based on execution errors  |
| Stops long-running work | Attempts recovery          |

Both policies are often used together.

***

# Timeout vs Loop

Loops intentionally repeat execution.

Timeouts prevent excessive execution time.

```text id="timeout8" theme={null}
Loop

↓

Timeout

↓

Exit
```

A timeout provides protection if loop conditions never become false.

***

# Common Use Cases

Timeout policies are useful for:

* external API calls
* AI model inference
* document processing
* long-running workflows
* scheduled jobs
* network communication
* background automation

Any operation with uncertain execution time can benefit from a timeout.

***

# Combining Policies

Production workflows often combine multiple reliability mechanisms.

Example:

```text id="timeout9" theme={null}
Execute

↓

Timeout

↓

Retry

↓

Failure Handler
```

This allows temporary problems to recover while preventing indefinite execution.

***

# Monitoring

Timeout events should be monitored in production systems.

Useful information includes:

* execution duration
* node identifier
* workflow identifier
* timeout threshold
* retry attempts
* error details

Monitoring helps identify performance bottlenecks.

***

# Best Practices

* Set realistic timeout values.
* Use shorter timeouts for external services.
* Combine timeouts with retry policies.
* Monitor timeout frequency.
* Avoid excessively large timeout limits.
* Design workflows so interrupted tasks can be restarted safely when appropriate.

***

# Summary

Timeout policies protect BindAI workflows from stalled or excessively long-running execution.

By limiting execution duration, they improve system stability, prevent resource exhaustion, and provide predictable behavior when operations fail to complete within acceptable time limits.