# How Recurring Tasks and Background Jobs Are Handled in Hiring-Agent > Discover how hiring-agent handles recurring tasks and background jobs. Learn about its synchronous processing, CLI invocation, and file-based caching for development. - Repository: [HackerRank/hiring-agent](https://github.com/interviewstreet/hiring-agent) - Tags: internals - Published: 2026-07-12 --- **Hiring-agent does not implement an internal scheduler, worker pool, or background-job framework; all processing executes synchronously when the CLI is invoked, with file-based caching available only for development workflows.** The open-source `interviewstreet/hiring-agent` repository provides a command-line resume evaluation tool that processes documents end-to-end in a single synchronous run. Unlike production-grade hiring platforms that rely on Celery, APScheduler, or async task queues, this architecture deliberately avoids background processing to maintain simplicity. Understanding this design decision clarifies why recurring workloads require external orchestration rather than internal triggering. ## Synchronous Pipeline Execution All business logic in hiring-agent runs synchronously through a single entry point. In [`score.py`](https://github.com/interviewstreet/hiring-agent/blob/main/score.py), the `if __name__ == "__main__"` block orchestrates the complete pipeline: PDF text extraction via [`pymupdf_rag.py`](https://github.com/interviewstreet/hiring-agent/blob/main/pymupdf_rag.py), section parsing through LLM calls, optional GitHub profile enrichment via [`github.py`](https://github.com/interviewstreet/hiring-agent/blob/main/github.py), and final evaluation through [`evaluator.py`](https://github.com/interviewstreet/hiring-agent/blob/main/evaluator.py). Each step executes sequentially during the CLI invocation: ```bash python score.py resume_candidate.pdf ``` The script runs to completion before returning control to the shell, with no daemon processes, threads, or coroutines remaining active after termination. ## Absence of Background-Job Frameworks A comprehensive review of the source tree reveals zero imports of scheduling or concurrency libraries. The codebase contains no references to `celery`, `apscheduler`, `cron`, `threading`, `multiprocessing`, or `asyncio`. Processing modules such as [`pdf.py`](https://github.com/interviewstreet/hiring-agent/blob/main/pdf.py), [`pymupdf_rag.py`](https://github.com/interviewstreet/hiring-agent/blob/main/pymupdf_rag.py), and [`evaluator.py`](https://github.com/interviewstreet/hiring-agent/blob/main/evaluator.py) implement pure synchronous functions that block until LLM API calls and file I/O complete. According to the repository structure, there are no worker configuration files, no task queues, and no scheduler databases—only the main execution thread handling one resume at a time. ## Development-Mode Caching as State Management The only mechanism approximating "recurring" behavior is the optional file-based cache system controlled by `DEVELOPMENT_MODE` in [`config.py`](https://github.com/interviewstreet/hiring-agent/blob/main/config.py). When enabled, intermediate results serialize to the `cache/` directory during execution: - `cache/resumecache_*.json` stores extracted resume sections - `cache/githubcache_*.json` stores fetched GitHub profile data These JSON snapshots persist between CLI invocations, allowing subsequent runs to skip expensive LLM parsing or API calls if the source data remains unchanged. However, this is pure read optimization—not background processing. The cache only refreshes when a user manually re-runs `python score.py `, and stale entries are never updated automatically. ## Implementing External Recurring Execution Because hiring-agent lacks internal scheduling, any recurring task requirement must leverage external automation. The CLI accepts file paths as arguments, making it compatible with standard Unix schedulers and CI pipelines. To process resumes on a fixed schedule, wrap the CLI in a cron job or systemd timer: ```bash # Cron entry to evaluate new resumes every hour 0 * * * * cd /opt/hiring-agent && python score.py /data/uploads/*.pdf >> /var/log/hiring-agent.log 2>&1 ``` For batch processing across multiple files, invoke the script in a loop: ```bash for resume in /path/to/resumes/*.pdf; do python score.py "$resume" done ``` ## Summary - **No internal scheduler**: Hiring-agent contains no task queues, workers, or timing mechanisms. - **Synchronous-only execution**: The [`score.py`](https://github.com/interviewstreet/hiring-agent/blob/main/score.py) entry point processes each resume in a single blocking run through the complete pipeline. - **Development caching**: The `cache/` directory provides file-based persistence for LLM results and GitHub data, but requires manual CLI re-runs to refresh. - **External dependency**: True recurring execution requires cron, systemd timers, or CI pipelines to trigger `python score.py` automatically. ## Frequently Asked Questions ### Does hiring-agent support Celery or Redis Queue for background jobs? No. The repository does not import Celery, RQ, or any distributed task queue libraries. All code executes in the main thread, making it unsuitable for asynchronous background processing without significant architectural modification. ### How can I automate hiring-agent to run on a schedule? You must use an external scheduler such as Linux cron, systemd timers, or a CI/CD pipeline (GitHub Actions, GitLab CI) to invoke `python score.py ` at your desired interval. The tool itself exposes no scheduling API or daemon mode. ### What is the purpose of the cache/ directory? The `cache/` directory stores serialized JSON outputs from expensive operations like LLM section parsing and GitHub API calls. When `DEVELOPMENT_MODE=True` in [`config.py`](https://github.com/interviewstreet/hiring-agent/blob/main/config.py), the pipeline reads these files on subsequent runs to avoid redundant API calls. This is a development optimization, not a background-job system. ### Is there any asynchronous processing in the pipeline? No. The codebase uses synchronous HTTP requests and blocking file I/O throughout. Modules like [`github.py`](https://github.com/interviewstreet/hiring-agent/blob/main/github.py) and [`evaluator.py`](https://github.com/interviewstreet/hiring-agent/blob/main/evaluator.py) make sequential API calls that block until responses return, with no `async/await` patterns or thread pools implemented.