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Calljmp

TypeScript-native agentic backend with managed execution, state, HITL approvals and observability

paidUsage-based pricing on two published subscription tiers, both starting with $25 in free credits and no credit card. Solo is $20/month for evaluation and single-developer use: 1 seat with no additional seats available, $25 of included usage credits and 1,000 included actions per month. Pro is $99/month for teams: 2 seats included with additional seats at $20/month each, $25 of included credits, 10,000 included actions per month, plus the prompt studio and priority support. Above the included allowance, actions are metered individually — agent runs at $0.01 per run, dataset and vector RAG queries at $0.01 per query, web scrapes at $0.01 per scrape, LLM inference at $0.011 per 1,000 tokens, and indexed dataset segments (pages, sheets or documents) at $0.05 per segment. Multi-step workflow phases are free. Dedicated support, custom SLAs and MSAs, and custom deployment are quoted separately and are not included on Solo or Pro.View full pricing →

About Calljmp

Calljmp is a dedicated backend for running AI agents in production, sold to teams that already have a working product and do not want to grow an agent runtime inside it. Agents and workflows are written as plain TypeScript rather than assembled in a visual builder or wrapped in a framework, and they run edge-deployed alongside the existing application backend rather than replacing it — the vendor's architecture diagram is explicit that product logic, auth and billing stay where they are and Calljmp owns execution, state, human-in-the-loop approvals and observability. The three shapes the vendor targets are embedded product copilots that read live product data and take actions, support automation that queries a knowledge base and CRM and routes to a human with full context, and long-running internal workflow agents with approvals, retries and traceable runs. The second audience is agencies and studios, on the argument that code-native agents can be handed to a client and still be debuggable six months later, whereas a visual-builder workflow cannot. The site publishes head-to-head comparisons against LangChain, n8n, Mastra and Agno, keeps a public changelog and open-source repositories on GitHub, and runs a Discord community. Retrieval and web scraping are billed as first-class actions alongside agent runs, so a RAG-shaped agent does not need a second vendor.

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Key Features

✓Agents and workflows written as plain TypeScript, no framework lock-in
✓Managed execution, state and retries next to your existing backend
✓Human-in-the-loop approvals with full run traces
✓Metered actions covering agent runs, RAG queries and web scrapes
✓Open-source repositories and a public changelog

Tags

ai-agentstypescriptedgeobservabilityhuman-in-the-loop
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