Calljmp

Calljmp

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About

Calljmp is a developer-first AI agent runtime designed to build, run, and scale long-running stateful workflows written in TypeScript. While many modern tools like Mastra AI provide rich frameworks to define agents and workflows, Calljmp focuses on actually running them reliably in production. Calljmp combines agent logic, durable execution, human-in-the-loop pause/resume, retries with idempotency, and built-in observability into a unified execution environment. Developers implement agents as code, and the runtime guarantees reliable execution, state persistence, and operational visibility without gluing together custom queues, databases, and monitoring stacks. Calljmp is ideal for engineering teams, product developers, and backend architects who want to embed intelligent agents into product systems while offloading execution complexity to a purpose-built runtime.

About

boxd provides persistent Linux virtual machines for developers, engineering teams and AI-agent workloads. Each machine has its own kernel, SSH access, a public HTTPS address and persistent disk, so software that needs a full computer—including Docker, systemd, long-running agents and development servers—can run in an isolated environment. Users can fork a running machine with its memory and disk state to explore parallel approaches without rebuilding the environment; idle machines can hibernate and retain disk state. Machines are managed over SSH, a command-line interface, API, and Python and TypeScript SDKs. Organizations can connect GitHub, Linear and Slack. boxd runs as a managed cloud in Frankfurt or can be self-hosted on KVM-capable Linux infrastructure, free for deployments up to 16 vCPUs. Use cases include cloud development environments, AI-agent sandboxes, background coding agents and preview environments.

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises Not Supported
iPhone Supported
iPad Supported
Android Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

SaaS Founders, CTOs, Software Engineers, AI Developers, Product Managers

Audience

Developers, engineering teams and companies building AI agents that need persistent cloud machines, agent sandboxes or self-hosted dev environments.

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Supported
24/7 Live Support Supported
Online Supported

API

Offers API Not Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

Free
Open source
Free Version Supported
Free Trial Supported

Pricing

€0.03/hour
Cloud machines use metered, pay-as-you-go billing for vCPU, RAM in use and disk space; there is no monthly subscription or per-seat fee. New cloud accounts receive €30 in credits after adding a payment method. Self-hosted deployments are free up to 16 vCPUs; larger deployments require an annual license.
Free Version Not Supported
Free Trial Supported

Reviews/Ratings

Overall 5.0 / 5
ease 4.0 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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Pros & Cons from Real Users

Pros

  • Calljmp stands out as a true agentic backend for running AI workflows in production. It turns fragile, prompt-driven scripts into durable, stateful systems that can handle long-running tasks reliably. The biggest advantage is built-in durable execution. Every step in a workflow is checkpointed, so there’s no “state amnesia.” If an API fails or a process is interrupted, execution resumes exactly where it left off. This is critical for AI agents handling multi-step or long-duration jobs. It also acts as a centralized layer for execution state, retries, and observability. Instead of building custom infrastructure for each agent, we rely on Calljmp to manage orchestration and state persistence. That shift alone saves significant engineering time and reduces operational risk.
  • "Most ""AI agent"" frameworks are just brittle API wrappers. Calljmp’s biggest win is that it operates as a true managed agentic backend. It provides durable execution out of the box, saving state checkpoints at every step. If a task times out or a node restarts mid-workflow, the agent doesn't lose its place—it just resumes. This saved our team from having to manually build and maintain custom queues, state databases, and retry logic. Another massive plus: the workflows are fully replayable. Debugging complex, multi-step agents is actually possible because you get full observability into the execution data instead of dealing with an LLM black box. It handles the 80% of backend infrastructure plumbing that usually makes production AI so fragile, letting us focus entirely on the core logic."

Cons

  • Setup takes some effort since it’s a foundational backend layer - not a plug-and-play tool. You need to think in terms of architecture, not just prompts.
  • It’s a deep architectural layer, not a plug-and-play toy. Because it operates as a serious code-first agentic backend, the initial setup and integration take actual development time.

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Calljmp
Founded: 2025
United States
calljmp.com

Company Information

boxd
Founded: 2026
Netherlands
boxd.sh/

Alternatives

Mastra

Mastra

Mastra AI

Alternatives

KVM

KVM

Red Hat

Categories

Agentic AI Supported
AI Agent Builders Supported
AI Agents Supported

Categories

Virtual Machine Supported

Integrations

GitHub Not Supported
Linear Not Supported
Model Context Protocol (MCP) Not Supported
Slack Not Supported

Integrations

GitHub Supported
Linear Supported
Model Context Protocol (MCP) Supported
Slack Supported
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