AGNT5

AGNT5 provides a durable runtime environment specifically designed for building and operating AI agents and agentic workflows. It addresses the challenges of maintaining long-running, stateful workflows that can branch, loop, call external services, and pause for human input—scenarios where traditional request-response systems often lose state and require complete restarts after failures. AGNT5 ensures that every tool call, branch, and pause in an agent’s execution is recorded in a single durable journal, serving as the recovery layer, execution trace, and evaluation harness.

Developers can write agents and workflows in Python or TypeScript using AGNT5’s SDKs. By adding a decorator, each completed step in the workflow is automatically checkpointed; if a crash occurs, execution resumes at the last completed step, with the runtime managing state, recovery, and coordination. The platform also supports human-in-the-loop workflows, allowing agents to pause for human approval and seamlessly resume with full state persistence once a decision is made. This approach enables robust error recovery, execution replay, and effective debugging, as the exact sequence of decisions, tool calls, and state changes is always available.

AGNT5’s runtime is implemented in Rust and is delivered as a single binary, making it deployable on a laptop for development, a VPS for production, or scalable behind a Kubernetes operator for larger deployments. The platform provides the ability to replay any production run locally or in a dedicated studio, enabling developers to step through and analyze every aspect of an agent’s execution. It also allows prompt versions to be updated and tested against historical runs, with built-in evaluators to assess the effectiveness of fixes before deploying them to production.

The tool is positioned as a solution for developers and teams building production-grade AI agents and workflows that require durability, observability, and reliable recovery from failures. AGNT5 offers a free tier for getting started, with every run journaled from the outset.