runprompt

Product Introduction

1. Definition: Runprompt is a specialized serverless execution platform and orchestration layer designed for LLM-powered automations. Often described as the "Heroku for prompts," it functions as a managed environment where users can deploy, schedule, and monitor autonomous AI agents and prompts without managing underlying infrastructure or writing custom backend code.

2. Core Value Proposition: The platform exists to bridge the gap between static LLM prompts and functional, recurring AI workflows. By providing a managed cron-based scheduling engine, native Model Context Protocol (MCP) support, and a persistent Key-Value (KV) store, Runprompt enables users to build sophisticated "fire-and-forget" automations. It eliminates the friction of setting up Docker containers, secret management systems, or recurring task runners for AI-driven tasks like daily reporting, content monitoring, and data transformation.

Main Features

1. Cron-Scheduled Execution Engine: Runprompt utilizes standard cron syntax (e.g., 0 9 * * 1-5) to trigger LLM runs at precise intervals. This feature includes full timezone support, allowing automations to execute every morning, weekly, or as frequently as every 15 minutes. This transforms a standard prompt into a reliable background service that operates without human intervention.

2. Native MCP (Model Context Protocol) Support: The platform integrates MCP servers to grant prompts "skills" or the ability to interact with external data sources. Through a standardized protocol, prompts can connect to APIs like GitHub, Datadog, or Linear. This allows the AI to fetch real-time data, perform lookups, and execute tool calls during its run, effectively turning the LLM into an active participant in a software ecosystem.

3. Sandboxed Docker Runtime: To ensure security and reliability, every prompt run occurs within an isolated Docker container. These environments are configured with specific resource limits (512MB RAM, 0.5 CPU) and network restrictions. This sandboxing prevents cross-contamination between runs and ensures that complex prompts or tool executions cannot access sensitive host infrastructure.

4. Stateful KV Store and Context Persistence: Unlike stateless API calls, Runprompt provides a Key-Value store that maintains state and context between consecutive runs. This allows the system to remember previous outputs or store configuration flags, enabling the creation of "aware" automations that can track changes over time or avoid redundant processing.

5. Encrypted Secrets Management: Security is handled through an integrated vault where users can store API keys and tokens (e.g., GITHUB_TOKEN). These secrets are encrypted at rest and injected as environment variables only at runtime. They are never exposed in execution logs, ensuring that sensitive credentials remain protected throughout the automation lifecycle.

6. Comprehensive Execution Tracing: Users have access to a complete run history, which provides visibility into every execution's output, specific tool calls made via MCP, token consumption, and associated costs. This level of transparency is essential for debugging autonomous agents and optimizing prompt performance for cost-efficiency.

Problems Solved

1. Infrastructure Overhead for AI Agents: Traditionally, running a prompt on a schedule required setting up a server, managing a cron job, and writing a Python or Node.js script to call the OpenAI or Anthropic API. Runprompt solves this "infrastructure tax" by providing a code-free, hosted environment dedicated to prompt execution.

2. Target Audience:

• DevOps and SRE Teams: For automating infrastructure health checks, SSL certificate monitoring, and security vulnerability scanning.
• Product and Engineering Managers: For generating automated daily briefings from GitHub PRs or summarizing project progress in Linear.
• Data and Marketing Analysts: For monitoring competitor website updates, changelogs, and performing automated data transformation tasks.
• Content Strategists: For periodic content auditing and automated reporting on SEO or social metrics.

3. Use Cases:

• Daily Briefing Assistants: Pulling data from GitHub and Datadog at 9 AM every weekday to summarize technical priorities.
• Content Monitoring: Checking competitor pages every 6 hours and alerting teams via webhooks when changes are detected.
• Automated Reporting: Fetching weekly API data every Friday to generate formatted business summaries.
• Security and Compliance: Running weekly dependency scans or checking public endpoints for vulnerabilities.

Unique Advantages

1. Zero-Code Logic Orchestration: While platforms like Zapier focus on rigid, connector-based workflows, Runprompt focuses on "natural language logic." Users describe the task in a prompt, and the AI determines how to use the provided MCP tools to complete it, offering significantly higher flexibility for complex reasoning tasks.

2. Optimized for the Anthropic Claude Ecosystem: The platform provides streamlined access to the Claude model family (Haiku, Sonnet, and Opus), allowing users to select the optimal balance between reasoning power and credit cost.

3. Persistence and Statefulness: Most serverless prompt runners are stateless. Runprompt’s inclusion of a KV store allows for "memory," which is a critical innovation for automations that need to compare "current state" vs "previous state," such as tracking a metric over time.

Frequently Asked Questions (FAQ)

1. What is Runprompt and how does it work? Runprompt is an automation platform that allows you to write LLM prompts and set them to run on a schedule using cron syntax. It provides a secure, sandboxed environment with built-in tools (MCP) to help the AI interact with the web and other software services autonomously.

2. Can Runprompt connect to my private data sources? Yes, Runprompt supports MCP (Model Context Protocol) servers and encrypted secrets. You can securely store your API tokens and use MCP servers to connect to your private data on platforms like GitHub, Linear, or custom databases without exposing your credentials.

3. How much does it cost to run LLM automations on Runprompt? Runprompt uses a credit-based system where different models have different costs per run. For example, a run using Claude Haiku costs approximately 4 credits, while the more powerful Claude Opus costs around 60 credits. The platform offers a free tier with 200 initial credits to get started.

4. Is Runprompt secure for enterprise use? Runprompt prioritizes security by running every prompt in an isolated Docker container with strict resource limits. It also features encrypted secrets management, ensuring that your API keys are never logged or exposed, and provides complete execution traces for auditability.