chr15m/runprompt — Run LLM prompts from your shell

Description and use

**Brief Summary (2‑3 sentences)**
chr15m/runprompt is a Python‑based CLI tool that lets you execute LLM prompts directly from the shell, making it easy to prototype AI‑powered features, RAG pipelines, or autonomous agents without building a model stack from scratch. With 438 GitHub stars, recent commits (as of 2026‑05‑11), and a clean API/SDK surface, it’s ready for rapid experimentation and early‑stage production pilots.

**Value**

• **Speed to prototype** – Invoke any supported LLM (OpenAI, Anthropic, local models, etc.) with a single command, eliminating boilerplate code and letting developers focus on prompt engineering and workflow logic.
• **Unified interface** – The tool abstracts away provider‑specific SDKs, exposing a consistent CLI/SDK that can be scripted, embedded in CI pipelines, or chained with other DevOps tools.
• **Low barrier to adoption** – Written in pure Python, it integrates smoothly with existing tooling, notebooks, or containerized environments, and its open‑source nature lets teams audit or extend functionality as needed.

**Practical Adoption Path**

1. **Evaluation** – Clone the repo, run the built‑in runprompt --help to list supported back‑ends, and test a few prompts against your preferred model API keys.
2. **Prototype** – Wrap runprompt calls in shell scripts or Makefiles to build quick RAG or agent prototypes; use the Python SDK for tighter integration with existing codebases.
3. **Pilot** – Containerize the tool (Dockerfile is provided), configure secret management for API keys, and integrate it into a staging CI/CD pipeline to validate reliability and latency under load.
4. **Productionization** – Harden the deployment by pinning dependencies, adding monitoring (e.g., Prometheus metrics via the provided hooks), and establishing a governance process for prompt versioning.

**Production Readiness**

• **Activity & Adoption** – Recent commits, a healthy star count (438) and active community forks indicate ongoing maintenance and interest.
• **Technical Maturity** – The CLI/SDK is stable, well‑documented, and written in a widely‑used language (Python), making it easy to audit and extend.
• **Risk Considerations** – No major metadata or licensing red flags have been identified, but a final security review (dependency scanning, secret handling) and confirmation of an active maintainer are recommended before mission‑critical use.

Overall, chr15m/runprompt offers a high‑signal, low‑friction way to bring LLM capabilities into existing workflows, and its current state makes it a strong candidate for both experimental and early‑production deployments.

Problem

chr15m/runprompt helps add AI capability without starting from a blank model stack.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Evidence

Run LLM prompts from your shell

Source