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About HarnessRouter

HarnessRouter is a unified API platform designed to simplify the integration of AI agent harnesses such as Codex, Claude Code, Hermes, and DeepSeek Harness into products without requiring developers to build or maintain the underlying infrastructure. Each agent session operates in an isolated serverless sandbox, with HarnessRouter handling provisioning, streaming, retries, and artifact delivery automatically. The platform includes a benchmarking tool that allows users to evaluate different harness and model combinations based on cost, latency, and success rate, enabling informed decisions about which configuration to route to in production. By abstracting the complexity of agent environments, HarnessRouter allows teams to focus on building features rather than infrastructure, significantly reducing time-to-market for AI-powered applications. It supports both managed cloud hosting and self-hosting via the open-source Community Edition, providing flexibility for different deployment needs. The Unified Harness Protocol (UHP) serves as the contract between a product and a complete agent harness, ensuring seamless integration and operation. HarnessRouter is particularly suited for teams looking to rapidly deploy agent-powered features while maintaining control over costs and performance through data-driven harness selection.

Key features

  • Unified API for multiple AI agent harnesses
  • Isolated serverless sandboxes for each agent session
  • Automatic provisioning and resource management
  • Real-time streaming and retry mechanisms
  • Built-in artifact delivery handling
  • Benchmarking and comparison tools for harnesses and models
  • Single API key integration for quick setup
  • Cost, latency, and success rate tracking

Use cases

  • Integrating AI coding assistants like Codex or Claude Code into development platforms
  • Building AI-powered workflow automation tools with isolated agent environments
  • Evaluating and comparing different AI agent harnesses for performance and cost

Pros

  • Unified API for multiple agent harnesses (e.g., Codex, Claude Code, Hermes) with a single integration
  • Serverless, isolated sandboxes for each agent session with automatic provisioning and scaling
  • Built-in benchmarking tool to compare harness and model combinations across cost, latency, and success rate
  • Open-source Community Edition available for self-hosting with Apache 2.0 license
  • Supports streaming, retries, and artifact delivery out of the box

Cons

  • Benchmarking results may vary significantly depending on the specific task and workload
  • Self-hosting requires maintaining infrastructure and managing keys, state, and files
  • Cost variability between harness and model combinations may impact budgeting

Frequently asked questions about HarnessRouter

What is an agent harness?

An agent harness provides the runtime environment and tools for an AI model to perform tasks beyond simple token generation, including memory, tools, and execution capabilities.

Who should use HarnessRouter?

Teams building agent-powered products who want to avoid the complexity of managing agent infrastructure and prefer a unified API to integrate multiple harnesses quickly.

How does HarnessRouter handle pricing?

Usage is billed only while hosted tasks run; waiting time is free. Users can bring their own API keys or use HarnessRouter's managed cloud, with plans that include model usage at catalog prices.

Can I self-host HarnessRouter?

Yes, the Community Edition is open-source under the Apache 2.0 license, allowing self-hosting on your own infrastructure with full control over keys, state, and files.

What integrations does HarnessRouter support?

HarnessRouter supports multiple agent harnesses including Codex, Claude Code, Hermes, DeepSeek Harness, and others, all accessible through a single API integration.

How do I get started with HarnessRouter?

Start by signing up for the managed cloud or downloading the Community Edition to self-host. Use the provided API key to integrate a harness into your product and begin building agent-powered features.

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