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Basic Information

Inferable is an open source, managed durable execution runtime and control plane for building AI-driven workflows and human-in-the-loop processes. It provides a platform where developers define versioned workflows that run in their own infrastructure, enabling long-running, stateful executions with version affinity. The repository contains the core control-plane services, a web management console and playground, a command line tool, and SDKs for Node.js, Go and experimental .NET to integrate workflows into applications. Typical uses include creating structured outputs from large language models, asking humans for approvals via email or Slack, caching expensive side effects, and observing workflow timelines. The project emphasizes self-hosting, security, and backward compatibility so teams can retain control of data and models while deploying robust AI workflows across private networks.

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Features
Inferable exposes several developer-focused features: workflows that execute inside customer infrastructure using long polling to avoid inbound ports, versioned workflows so running executions keep older behavior while new executions use updated logic, and human-in-the-loop interrupts for approval or intervention delivered via Slack or email. It provides automatic parsing and validation of structured outputs from LLMs with retry logic, memoized distributed results for caching side effects, a timeline observability UI and integration points for external monitoring, and a simple SDK-driven API surface. The repo includes the control-plane, a frontend management app, a CLI tool, and SDKs for Node.js, Go and .NET. It also documents self-hosting and contribution guidelines.
Use Cases
The project helps engineering teams build reliable, auditable AI workflows by combining durable execution, schema-validated outputs, and human approvals. Running workflows in private infrastructure reduces data exposure and supports regulatory or security requirements. Versioned workflows provide backward compatibility for long-running processes and make iterative changes safer. Structured output parsing and automatic retries improve robustness when integrating LLMs. Memoized results reduce cost and latency for expensive side effects. The timeline observability and notification features make human interventions traceable and actionable. SDKs and tooling accelerate integration into existing systems so teams can deploy QA gates, approval flows, and agent-like tool chains while maintaining control over deployment, governance, and operational monitoring.

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