> ## Documentation Index
> Fetch the complete documentation index at: https://browseruse-0aece648-agency-v4-agentmail-2fa-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Rerunnable scripts
> Save a Browser Use task as a rerunnable script for repeated live data extraction and self-healing runs.
Teach a Cloud agent a repeated browser task once. It can save the working code
in its [workspace](/cloud/agent/workspaces). Later runs reuse that script,
fetch fresh data from the live site, and repair the code if the site changed.
This pattern works well for repeated data extraction, checks, and reports. The
example below collects the top five Hacker News stories. The first run writes
and tests the script. Every later run sends a new request to the live page.
The file stays. The running process does not. Each later run starts an agent
again, but it can reuse the code instead of rebuilding the browser workflow.
## Save the working script
Create one workspace, then ask the first run to do the job and save a tested
script:
```python Python theme={null}
from browser_use_sdk.v4 import BrowserUse
client = BrowserUse()
workspace = client.workspaces.create(name="live-hn-data")
first = client.runs.create(
"""
Open https://news.ycombinator.com/ and return the top five story titles and
URLs as JSON.
Save a script at scripts/extract_top_stories.py that collects the same
fields. The script must send a fresh request to the live page every time it
runs. Do not hard-code story titles, save the page HTML, or read an old
result. Print JSON with source_url set to https://news.ycombinator.com/ and
include fetched_at, source_sha256, and stories.
Run the script twice and save the outputs as proof/run-1.json and
proof/run-2.json. Confirm both outputs have a fresh fetched_at value and
five stories. Add scripts/README.md with the command to run it again.
""",
model="grok-4.5",
workspace_id=workspace.id,
)
client.runs.wait_for_completion(first.id)
saved = client.workspaces.files(workspace.id, prefix="scripts/")
print([file.path for file in saved.files])
print(f"Keep this workspace ID: {workspace.id}")
```
```typescript TypeScript theme={null}
import { BrowserUse } from "browser-use-sdk/v4";
const client = new BrowserUse();
const workspace = await client.workspaces.create({ name: "live-hn-data" });
const first = await client.runs.create({
task: `
Open https://news.ycombinator.com/ and return the top five story titles and
URLs as JSON.
Save a script at scripts/extract_top_stories.py that collects the same
fields. The script must send a fresh request to the live page every time it
runs. Do not hard-code story titles, save the page HTML, or read an old
result. Print JSON with source_url set to https://news.ycombinator.com/ and
include fetched_at, source_sha256, and stories.
Run the script twice and save the outputs as proof/run-1.json and
proof/run-2.json. Confirm both outputs have a fresh fetched_at value and
five stories. Add scripts/README.md with the command to run it again.
`,
model: "grok-4.5",
workspaceId: workspace.id,
});
await client.runs.waitForCompletion(first.id);
const saved = await client.workspaces.files(workspace.id, {
prefix: "scripts/",
});
console.log(saved.files.map((file) => file.path));
console.log(`Keep this workspace ID: ${workspace.id}`);
```
## Rerun it later
Copy the workspace ID from the first process. Pass that ID without a session ID
to start a new conversation with the saved files:
```python Python theme={null}
from browser_use_sdk.v4 import BrowserUse
client = BrowserUse()
workspace_id = "your-workspace-id"
later = client.runs.create(
"""
Run python scripts/extract_top_stories.py and return its JSON. Check that
fetched_at is current, source_url is https://news.ycombinator.com/, and it
has five stories. If the script fails or the result is malformed, inspect
the live page and repair the saved script before returning the new result.
""",
model="grok-4.5",
workspace_id=workspace_id,
)
result = client.runs.wait_for_completion(later.id)
print(result.result)
```
```typescript TypeScript theme={null}
import { BrowserUse } from "browser-use-sdk/v4";
const client = new BrowserUse();
const workspaceId = "your-workspace-id";
const later = await client.runs.create({
task: `
Run python scripts/extract_top_stories.py and return its JSON. Check that
fetched_at is current, source_url is https://news.ycombinator.com/, and it
has five stories. If the script fails or the result is malformed, inspect
the live page and repair the saved script before returning the new result.
`,
model: "grok-4.5",
workspaceId,
});
const result = await client.runs.waitForCompletion(later.id);
console.log(result.result);
```
## Live data, not a cached answer
The saved script must request the source page on every execution. `fetched_at`
shows when the request ran, and `source_sha256` identifies the response body.
The data can stay the same between two runs when the page has not changed. The
script still fetched it again.
Do not put old output, page HTML, or fixed values inside the script. Save output
files only as optional history or proof.
## Can rerunnable tasks be cheaper at scale?
Reusing code can remove browser steps and model work that would otherwise be
repeated on every extraction. Every later run still starts a model and uses
tokens, so the savings depend on the task, site, and model. Compare the cost and
duration of the first run with later runs in your own project before making a
savings claim.
## Workspace or session?
* Pass `workspace_id` / `workspaceId` for a new conversation with the same
files. The new run gets a new browser.
* Pass `session_id` / `sessionId` to continue the old conversation and its
workspace. Cloud can also reuse that session's browser while it is still
alive.
A saved script can break when a site changes. Ask the agent to check the live
result and self-heal the script instead of trusting stale output.