The Tool Desk
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To run an Ollama agent on a schedule, use cron to start a script; the script calls Ollama’s chat API, carries out any requested tool calls, checks the result, and records success or failure. Ollama supplies model interaction, not the scheduling or the surrounding agent workflow.
How scheduled Ollama runs fit together
A scheduled agent is a small application made of four parts:
- Scheduler: cron starts the job at the times you configure.
- Job script or service: prepares the task input and sends it to Ollama.
- Ollama API: returns a model response, which may include a tool call.
- Application logic: executes approved tools, validates or stores outputs, and logs failures.
The scheduler does not make the model autonomous by itself. Your program must decide what the model may do and implement the steps needed to carry out its requests. Ollama’s API reference explains that tool calling is enabled by providing tools in the tools parameter: Ollama API reference.
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Ollama documents two API routes. Choose based on where you want inference served and how you want to handle credentials and data.
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| Deployment | API endpoint | API key | Setup and data handling |
|---|---|---|---|
| Local | http://localhost:11434/api |
Not needed for local requests | Install Ollama and run a local model. Ollama’s FAQ says it does not see prompts or data when running locally. |
| Hosted | https://ollama.com/api |
Required | No local installation is required for the cloud call. Ollama says hosted requests are processed to provide the service and that prompts and responses are not stored or logged or used for training. |
The hosted data-handling statements are Ollama’s own current FAQ claims, not an independent audit. See the API introduction and Ollama FAQ.
What the job script must do
The script should submit a chat request to the chosen endpoint, inspect the response, and control any follow-up work. A request can include message history and, where supported, a list of tools. If Ollama returns a tool call, the application—not Ollama or cron—must run the corresponding function and send its result back as a tool message before requesting the model’s next response.
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Keep tool execution bounded
Expose only the functions the task actually needs, and validate their inputs before execution. Treat model output as a request to your application, not as authorization to perform an action. Use application-side permission checks, especially for operations that change files, send messages, or affect external systems.
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Ollama supports JSON mode or a JSON schema through the API’s format parameter. Structured output can reduce parsing ambiguity, but it does not make an answer correct or an action safe. Validate the returned data against your application’s requirements before using or storing it.
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Choose how responses arrive
Chat responses may stream. If the job needs one complete response object for parsing, disable streaming in the request. The API’s keep_alive setting controls how long a model remains loaded after a request; its documented default is five minutes. These settings affect API behavior, not whether a scheduled run succeeds.
These behaviors are documented in the Ollama API reference.
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Keep the Ollama service reachable
A scheduled local job can call Ollama only while the local server and host are available. On Linux, Ollama documents configuring server environment variables through a systemd service override and restarting the service. Its FAQ gives the default bind address as 127.0.0.1 on port 11434; exposing the service to a network requires changing configuration. Do not expose it without considering authentication and access controls. Refer to the Ollama FAQ for the documented service setup.
Plan for operational failures
Cron starts the job according to its configuration, but the Ollama documentation does not define cron behavior or guarantee handling for time zones, missed runs, retries, or overlapping executions. Those are choices for your scheduler and wrapper application.
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- Log the scheduled run, API errors, tool calls, validation failures, and final outcome.
- Decide whether a failed request should be retried and how many attempts are appropriate.
- Prevent a new run from interfering with an earlier one if the task must not overlap.
- Define what the job should do if Ollama or the network is unavailable.
- Protect hosted API credentials and avoid putting secrets in logs.
What to check before choosing local hardware
Ollama supports running local models on a computer, but the cited documentation does not establish minimum CPU, memory, GPU, or storage requirements for a particular model or workload. Select a model and task first, then check their requirements; do not assume a generic machine specification will be sufficient.
Quick Recap
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