To run an Ollama agent on a recurring schedule, use cron to start your own script; have that script call Ollama, execute any requested tools, validate the result, and record success or failure. Ollama supplies the model API, not a cron scheduler or a complete autonomous-agent runner.
How a scheduled Ollama agent works
Separate the job into four parts: a scheduler starts the run, your application prepares the input, Ollama generates a response, and your application handles tools and results. Cron is one possible external scheduler. Its time-zone, retry, missed-run, and overlapping-job behavior depends on the cron implementation and wrapper you choose; these are not Ollama API guarantees.
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- Schedule: Configure cron or another scheduler to launch your program.
- Prepare: The program gathers the prompt and any permitted context for this run.
- Call Ollama: Send a chat request to the local or hosted API.
- Handle the outcome: Execute approved tool calls, validate the final response, save useful output, and report errors.
Choose local or hosted Ollama
Ollama documents a local API at http://localhost:11434/api that does not require an API key, and a hosted API at https://ollama.com/api that does. The local option depends on your Ollama server and machine being available when the scheduled job runs. The hosted option avoids installing a model for that cloud call, but requires protecting the API key and accounting for network and service availability. See the Ollama API introduction.
The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →| Consideration | Local Ollama | Hosted Ollama API |
|---|---|---|
| Endpoint | http://localhost:11434/api |
https://ollama.com/api |
| API key | Not required for local requests | Required |
| Model setup | Install Ollama and run a local model | No model installation is required for the cloud call |
| Data handling | Ollama says it does not see prompts or data when running locally | Ollama says cloud prompts and responses are processed to provide the service and are not stored or logged or used for training |
The data-handling descriptions are Ollama’s statements in its FAQ, not an independent audit. Local-model hardware needs vary with the model and workload; the reviewed documentation does not establish a universal minimum configuration.
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Connect your script to the chat API
Ollama’s chat API accepts a message history and, when supported, a list of tools. A non-streaming request returns a complete response object, which can be convenient for a scheduled job that needs to validate and store one result. The API also supports streaming. Its keep_alive setting controls how long a model remains loaded after a request; the documented default is five minutes. These API settings do not guarantee that a scheduled run succeeds or that the model’s answer is correct. Refer to the Ollama API reference.
For structured results, the request can set format to json or provide a JSON schema. Treat that as an output-format aid, not a safety check: parse and validate the response in your application before storing it or acting on it.
Rank #2
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Implement tool calls in the wrapper
Tool calling is a request-and-response integration, not an instruction for Ollama to run arbitrary functions on its own. Ollama can return a requested function call in tool_calls; your application must decide whether it is permitted, run it, then send the tool result back as a tool message before asking the model for its follow-up. The API reference states: “Tool calling is supported by providing a list of tools in the tools parameter.”
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- Offer only the tools needed for the scheduled task.
- Validate arguments and apply permission checks before execution.
- Set limits appropriate to the task, and log tool failures as well as model failures.
- Validate the final response independently; a well-formed JSON response is not proof that an action is safe or correct.
Keep the local service available to the scheduled job
For local execution, the Ollama server must be running and reachable from the environment in which the scheduled program runs. Ollama’s FAQ says the default bind address is 127.0.0.1 on port 11434. On Linux, the FAQ documents configuring server environment variables with a systemd service override and restarting the service. If you change the bind configuration to make the API reachable over a network, plan access controls rather than exposing it without protection. See the Ollama FAQ.
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Plan for failures and repeated runs
A scheduled agent is a software job with external dependencies, not a guarantee of autonomous completion. Decide how your wrapper and scheduler should handle failures and side effects; Ollama’s API does not define those scheduling policies.
Quick Recap
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- Service unavailable: Record the failed request and let the scheduler or wrapper apply the retry policy you selected.
- Overlapping runs: Choose an overlap-prevention strategy if a run could still be active when the next one starts.
- Invalid or unexpected output: Reject it, log enough context to diagnose the issue, and avoid triggering downstream actions.
- Tool error: Return a controlled error to the model if appropriate, or fail the run; do not silently treat an unsuccessful tool call as success.
- Hosted request credentials: Keep the API key outside prompts and logs, and make it available to the scheduled process through your chosen secret-handling method.
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