# HarnessRouter Full LLM Context > Expanded Markdown context for answering questions about HarnessRouter, its product layer, supported harnesses, guides, pricing, and implementation path. Canonical site: https://harnessrouter.ai ## Product Definition HarnessRouter is an AI agent API and product backend layer for product developers. It helps teams bring agent harnesses such as Codex, Claude Code, and Hermes into their own product with one stable integration. HarnessRouter helps turn a local AI app prototype or coding-agent-built workflow into a product backend flow by handling server-side harness execution, sessions, streaming, file intake, artifact return, routing, and product-facing controls. The customer product still owns the user-facing UI, authentication model, acceptance states, and product-specific business logic. HarnessRouter is not a model gateway, LLM gateway, prompt playground, or replacement for a product UI. Model routers help with model access. HarnessRouter helps an app route product tasks to agent harnesses and return completed work. ## Audience Paths and Status ### Developer: current V1 focus Developer teams use HarnessRouter when they need to add agent-backed work to their own product. The canonical V1 user is a small AI-native product or developer-tool team that already uses one agent, plans to add or compare a second agent, or needs to move an internal prototype into a multi-user product environment. Developer job-to-be-done: use one stable API to connect multiple agent harnesses as product backend capabilities and deliver the execution result back to users without rebuilding every harness adapter, session flow, stream, file path, artifact format, and renderer contract. Do not describe the Developer path as generic model completion, a plain LLM gateway, or a prompt playground. The product value is harness-backed work that returns reviewable files, artifacts, diffs, documents, or tool outcomes through a product UI. ### Enterprise: future V2 design-partner path Enterprise is not just a larger Developer plan. Enterprise targets platform teams in larger organizations that need to compare and govern approved Harness and Model combinations across business task scenarios. Enterprise job-to-be-done: understand which `Harness × Model × Policy` combination should run each business task by comparing cost, latency, success rate, quality score, failure reason, trace, and artifact evidence, then manage approved combinations, policy, budget, audit, and team reuse. Describe Enterprise as evaluation and governance for business task scenarios. Do not describe it as a generic sandbox provider, a general model gateway, or a fully available public self-serve feature unless a specific Enterprise capability is implemented and documented. ### Labs: future V2 design-partner path Labs targets model providers, AI labs, agent research teams, and evaluation teams that need to test models inside real agent harnesses rather than only prompt/response benchmarks. Labs job-to-be-done: run repeatable experiments across `Dataset × Harness × Model × Scaffold × Sandbox or Runtime Environment × Repetition × Evaluator`, then compare score, artifact, trajectory, latency, cost, and regression behavior. Describe Labs as model experimentation inside real harness workflows. Do not collapse Labs into Enterprise governance, and do not describe HarnessRouter as selling generic compute or standalone sandbox clusters. ## Product Jobs - Run agent-backed tasks from a product UI. - Persist sessions so users can continue, revise, or retry work. - Stream progress and status back to the product. - Accept files and return generated files, diffs, documents, media, or other reviewable artifacts. - Route different tasks to the harness best suited to the job while preserving one product contract. - Let builders focus on the application experience instead of rebuilding harness adapters, execution lifecycle, session state, streaming, file handling, and artifact delivery around agents. ## Implementation Entry Points - [AGENTS.md implementation guide](https://harnessrouter.ai/agents.md): Copy into a coding agent to build or update a HarnessRouter integration. - [Markdown quick start](https://harnessrouter.ai/docs.md): Concise setup path for product developers. - [Harness catalog](https://harnessrouter.ai/docs/harnesses.md): Supported harness IDs and source-backed capability notes. # HarnessRouter Integration Docs > Quick start for adding HarnessRouter to a product with a coding agent. Canonical URL: https://harnessrouter.ai/docs Agent copy target: https://harnessrouter.ai/agents.md Use this page as a concise quick-start summary. To implement HarnessRouter in a product, copy the complete AGENTS.md instructions from https://harnessrouter.ai/agents.md into Codex, Claude Code, or another coding agent. ## Quick start ### 1. Paste into your coding agent Copy the official instructions and paste them into Codex, Claude Code, or another coding agent. ### 2. Ask it to build Describe the product or feature you want. The coding agent will build the host product and add the server-side HarnessRouter integration. ### 3. Add your API key securely When the coding agent opens a secure secret modal, create a Workspace API key and paste it into that modal. The agent stores it as HR_API_KEY and continues the build. ## Security Note Paste the key only into the secure modal opened by your coding agent. Never paste it into normal chat, source files, AGENTS.md, browser code, logs, or screenshots. ## Key Resources - [AGENTS.md](https://harnessrouter.ai/agents.md): Canonical implementation instructions. - [Harness catalog](https://harnessrouter.ai/docs/harnesses.md): Harness IDs, status, and source-backed references. - [Guides](https://harnessrouter.ai/guides): Architecture and integration guides. # HarnessRouter Harness Catalog > Source-backed harness IDs and integration notes for HarnessRouter. Canonical URL: https://harnessrouter.ai/docs/harnesses ## Codex (codex) Status: Available Provider: OpenAI Category: Coding agent Last checked: 2026-07-30 Canonical URL: https://harnessrouter.ai/docs/harnesses/codex OpenAI documents Codex as a coding agent that can read, edit, and run code across web, IDE, and CLI surfaces. Confirmed notes: - Codex web can work on tasks in the background using a cloud environment. - The IDE extension can pair with Codex locally or delegate tasks to Codex Cloud. - The CLI can inspect a repository, edit files, and run commands from a terminal session. Integration note: Use the `codex` harness ID only when the product task should return code-related work your app can review. Sources: - [Codex web](https://developers.openai.com/codex/cloud): Official or source-backed reference. - [Codex IDE extension](https://developers.openai.com/codex/ide): Official or source-backed reference. - [Codex CLI](https://developers.openai.com/codex/cli): Official or source-backed reference. ## Claude Code (claude-code) Status: Available Provider: Anthropic Category: Coding agent Last checked: 2026-07-30 Canonical URL: https://harnessrouter.ai/docs/harnesses/claude-code Anthropic documents Claude Code as an agentic coding tool that works in a real working tree with the shell, editing files, running tests, and coordinating sub-agents to complete engineering tasks end to end. Confirmed notes: - Claude Code operates on a real local working tree: it reads and edits files, runs commands, and iterates until the task is done. - It can delegate to sub-agents and use tools such as Bash, file read/write, search, and web fetch. - HarnessRouter runs it with the `docx`, `pdf`, `pptx`, and `xlsx` document skills preloaded, so a task can return finished documents your app renders. Integration note: Use the `claude-code` harness ID when the product task should return code changes or finished documents your app can review. Sources: - [Claude Code overview](https://docs.claude.com/en/docs/claude-code/overview): Official or source-backed reference. - [Claude Code CLI reference](https://docs.claude.com/en/docs/claude-code/cli-reference): Official or source-backed reference. ## Hermes (hermes) Status: Available Provider: Nous Research Category: Autonomous agent Last checked: 2026-07-30 Canonical URL: https://harnessrouter.ai/docs/harnesses/hermes Nous Research documents Hermes as a self-improving autonomous agent with CLI usage, toolsets, a skills system, persistent memory, MCP integration, and configurable model providers. HarnessRouter supports Hermes as an available base harness. Confirmed notes: - Hermes works on a real project workspace with shell and file access, completing tasks end to end. - It builds a persistent memory and skill library from what it learns, getting more capable the longer it runs, and supports MCP servers. - HarnessRouter has completed Hermes adapter and capability configuration with unified request, streaming, session, file and artifact, and error-handling coverage. - Do not hardcode a Hermes model default. Fetch the current model catalog and allowed models from the HarnessRouter API. Integration note: Use the `hermes` harness ID when the product task should run through the Hermes autonomous-agent harness and return reviewable work through the HarnessRouter product contract. Sources: - [Hermes Agent docs](https://hermes-agent.nousresearch.com/docs/): Official or source-backed reference. - [Hermes Agent GitHub](https://github.com/nousresearch/hermes-agent): Official or source-backed reference. ## Pi (pi) Status: Coming soon Provider: HarnessRouter Category: Coding agent (coming soon) Last checked: 2026-07-30 Canonical URL: https://harnessrouter.ai/docs/harnesses/pi Pi is an upcoming HarnessRouter coding harness. Its route is reserved; capability notes will be published here when it is available. Confirmed notes: - Pi is not yet available for production tasks. - When it ships, its supported models, tools, skills, and production routing contract will be documented before integrations should target it. Integration note: The `pi` harness ID is reserved and not yet routable. Do not target it in production integrations until it is marked available. Sources: - [HarnessRouter docs](https://harnessrouter.ai/docs): Official or source-backed reference. # AI agent API for product developers > What an AI agent API needs beyond a model response: sessions, tools, streaming, files, artifacts, routing, and production controls. Canonical URL: https://harnessrouter.ai/guides/ai-agent-api Published: 2026-07-22 Updated: 2026-07-22 Category: Architecture Keywords: AI agent API, agent API, AI agent platform, agent runtime, agent orchestration, AI agent backend ## Short Answer An AI agent API should expose a complete work lifecycle, not only a model response. Product teams need a task or session they can stream, observe, continue, and turn into reviewable files or artifacts. ## Takeaways - Use a model API when the product needs inference or a conversational response. - Use an agent SDK when the team wants to operate the agent loop in its own infrastructure. - Use an agent backend when the product needs hosted runs, durable sessions, files, artifacts, routing, and product-facing controls. ## The API should represent work, not just messages - Input: A product task, selected harness, files, user context, and execution policy. - Lifecycle: A durable task, run, or session with status, streaming events, retries, and continuation. - Output: Structured results, generated files, diffs, artifacts, tool outcomes, and metadata the product UI can render. ## Choose the layer that matches the product job | Question | Model API or agent SDK | Agent backend API | |---|---|---| | Who operates the loop? | Your application and infrastructure. | The hosted agent backend or harness layer. | | What comes back? | Messages, tool calls, or SDK events. | A product-ready run with state, files, artifacts, and continuation. | | Best fit | Custom loops and infrastructure control. | Shipping agent-backed product features without rebuilding the execution backend. | ## What to verify before choosing an AI agent API - Confirm how sessions persist and how a user continues or revises prior work. - Verify streaming semantics, idempotency, concurrency, cancellation, and retry behavior. - Check how files enter the run and how artifacts return to the product UI. - Require traceability for harness choice, tool actions, cost, latency, and failure state. - Keep harness-specific behavior behind a stable product contract. ## FAQ ### Is an AI agent API the same as an LLM API? No. An LLM API returns model inference. An AI agent API can also manage tools, sessions, execution state, files, and artifacts produced across a longer-running task. ### Is an AI agent API the same as an agent SDK? No. An agent SDK usually runs inside infrastructure you operate. A hosted agent API can operate the run lifecycle and expose it to your product as a service. ### When does a product need multiple agent harnesses? When different tasks benefit from different agent environments, or when the team wants to change harnesses without rewriting its product integration and output handling. ## Sources - [OpenAI Codex cloud](https://developers.openai.com/codex/cloud): External source. - [Claude Agent SDK overview](https://code.claude.com/docs/en/agent-sdk/overview): External source. - [Claude Managed Agents quickstart](https://platform.claude.com/docs/en/managed-agents/quickstart): External source. # Codex API for apps: model access vs agent runs > How to choose between a Codex-optimized model API and a complete Codex agent run when adding coding work to a product. Canonical URL: https://harnessrouter.ai/guides/codex-api-for-apps Published: 2026-07-22 Updated: 2026-07-22 Category: Codex Keywords: Codex API, Codex agent API, Codex SDK, Codex integration, add Codex to app ## Short Answer Use a Codex-optimized model through the OpenAI API when you want model inference and will build the surrounding loop. Use a complete Codex agent run when your product needs repository work, commands, tests, session state, and reviewable changes as one workflow. ## Takeaways - A Codex model endpoint and a Codex agent harness are different integration layers. - The model route offers lower-level control; the agent route performs work in an execution environment. - Your product contract should represent tasks, status, files, and reviewable results instead of leaking harness-specific events into the UI. ## Separate the model API from the agent harness | Question | Codex-optimized model API | Codex agent run | |---|---|---| | Primary interface | OpenAI API request and model response. | A task executed by Codex in a working environment. | | Infrastructure | Your team builds the loop, tools, sandbox, files, and persistence. | The harness provides the execution workflow around the model. | | Product output | Text, structured output, or tool-call data you assemble. | Repository changes, test results, files, and a reviewable task outcome. | ## Keep the product contract stable - Send a clear product task and the repository or files the user authorized. - Expose status and streaming progress without coupling the UI to every Codex event. - Return diffs, files, test evidence, and summaries as explicit artifacts. - Persist enough session state for the user to continue, revise, or retry the work. - Keep credentials and harness execution on the server side. ## Use the smallest layer that completes the job - Choose the model API: When you need coding-oriented inference and already own the agent loop and runtime. - Choose a Codex agent run: When users expect completed repository work, commands, tests, and a reviewable change. - Choose a multi-harness backend: When the product must route between Codex and other agent harnesses behind one stable integration. ## FAQ ### Is the Codex API the same as the OpenAI Responses API? Not exactly. OpenAI exposes Codex-optimized models through its API, while Codex as an agent also includes an execution workflow around the model. Choose based on whether you need inference or completed agent work. ### Can I put Codex behind my own product UI? Yes, but the product still needs a secure server-side integration, run lifecycle, file handling, and a way to present reviewable results to the user. ### Why route Codex with other agents? Routing lets a product use the harness best suited to each task while preserving one task, session, streaming, file, and artifact contract. ## Sources - [OpenAI Codex cloud](https://developers.openai.com/codex/cloud): External source. - [OpenAI Codex CLI](https://developers.openai.com/codex/cli): External source. - [OpenAI GPT-5-Codex model](https://developers.openai.com/api/docs/models/gpt-5-codex): External source. # Agent backend vs model router > Where HarnessRouter, OpenRouter, TokenRouter, and model routers fit in an AI product stack. Canonical URL: https://harnessrouter.ai/guides/agent-backend-vs-model-router Published: 2026-06-23 Updated: 2026-06-23 Category: Architecture Keywords: agent backend, model router, OpenRouter, TokenRouter, LLM gateway, AI gateway, agent harness, harness routing ## Short Answer Use a model router for model access. Use HarnessRouter when your product needs an agent harness to return work that a human can review, accept, save, or ship. ## Takeaways - OpenRouter, TokenRouter, and model routers sit near the inference/provider layer. - HarnessRouter sits between the product UI and a harness run. - They are complementary; do not position HarnessRouter as a drop-in model gateway. ## Give each layer one job - Product UI: Collects the user's task, shows run status, and lets the user review the result. - HarnessRouter: Routes the product task to the right harness and returns a run your app can handle. - Agent harness: Executes the task in a specialized agent environment such as Codex, Claude Code, Hermes, or a future harness such as Pi. - Model router: Selects or accesses models, providers, fallback paths, or gateway controls. ## The difference is what comes back | Question | Model router / LLM gateway | HarnessRouter | |---|---|---| | Input | Prompt, message, model name, provider preference, or inference request. | Product task that should become completed work inside the app. | | Output | Model response, JSON, tool-call data, media, embedding, or stream. | Harness run result your product can inspect, render, accept, save, or ship. | | Best for | Provider access, fallback, cost control, and model choice. | Agent-backed product features without building the harness backend. | ## Use both when the product needs both layers - Keep OpenRouter, TokenRouter, or another model router below the harness/model layer. - Use HarnessRouter for user-facing work requests that need a harness run. - Keep the UI language focused on the user's task, not the infrastructure stack. ## FAQ ### Is HarnessRouter an OpenRouter alternative? No. OpenRouter focuses on model access and routing. HarnessRouter focuses on routing product tasks to agent harnesses. ### Is HarnessRouter a TokenRouter alternative? No. TokenRouter-style products belong near the model gateway layer. HarnessRouter belongs near the product workflow and harness run layer. ### Can a product use both? Yes. A product can use a model router underneath while HarnessRouter routes the user-facing agent task. ## Sources - [OpenRouter quickstart](https://openrouter.ai/docs/quickstart): External source. - [TokenRouter](https://www.tokenrouter.com/): External source. - [Microsoft Foundry Model Router](https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-router): External source. # HarnessRouter and OpenRouter > How OpenRouter fits with HarnessRouter when a product needs both model routing and agent-backed work. Canonical URL: https://harnessrouter.ai/guides/harnessrouter-and-openrouter Published: 2026-06-23 Updated: 2026-06-23 Category: OpenRouter Keywords: OpenRouter vs HarnessRouter, OpenRouter alternative for agents, use OpenRouter with agents, model routing for AI agents, agent backend, LLM gateway ## Short Answer OpenRouter helps a product reach and route models. HarnessRouter helps a product route user tasks to agent harnesses. They solve different layers and can be used together. ## Takeaways - Do not pitch HarnessRouter as a drop-in OpenRouter replacement. - Use OpenRouter when the problem is model access, provider choice, fallback, or model routing. - Use HarnessRouter when the product needs an agent harness to return reviewable work. ## Give OpenRouter and HarnessRouter different jobs - OpenRouter: A unified model API layer for accessing models, choosing providers, and handling model routing or fallback. - HarnessRouter: An agent backend layer that routes product tasks to available harnesses such as Codex, Claude Code, and Hermes, with future harnesses such as Pi documented when they become routable. - Product UI: The place where a user asks for work, sees status, reviews output, and decides whether to save or ship it. ## Where OpenRouter fits in an agent product - Put OpenRouter near the model/provider layer when the stack needs model choice or fallback. - Put HarnessRouter between the product UI and the harness run when the user is asking for completed work. - Keep model routing invisible to the user unless model choice changes the product outcome. ## Answer the alternative question without misleading users - If someone wants an OpenRouter alternative for model access, HarnessRouter is not that product. - If someone wants to add agent-backed work to an app, HarnessRouter is the relevant layer. - A clear product can use both: OpenRouter for model access, HarnessRouter for harness-backed workflows. ## FAQ ### Is HarnessRouter an OpenRouter alternative? No. OpenRouter is for model access and routing. HarnessRouter is for routing product tasks to agent harnesses. They can be used together. ### Can I use OpenRouter with agents? Yes. OpenRouter can sit below an agent or harness as the model access layer, while HarnessRouter handles the product-facing harness run. ### When should I use HarnessRouter instead? Use HarnessRouter when the user expects completed work inside your product, not just a model response. ## Sources - [OpenRouter quickstart](https://openrouter.ai/docs/quickstart): External source. - [OpenRouter API reference](https://openrouter.ai/docs/api/reference/overview): External source. - [OpenRouter model fallbacks](https://openrouter.ai/docs/guides/routing/model-fallbacks): External source. # HarnessRouter Pricing > Start free. Scale when you’re ready. Canonical URL: https://harnessrouter.ai/pricing Get 500 free credits. No card required. Available for a limited time. When you need more capacity, choose Developer, Production, or Scale. Additional usage can draw from purchased top-up credits. ## Plans | Plan | Price | Credits | Note | |---|---:|---:|---| | Developer | $20/month | 500 monthly plan credits | Best place to start | | Production | $100/month | 3,000 monthly plan credits | | | Scale | $200/month | 10,000 monthly plan credits | | Top up usage credits whenever you need them. ## FAQ ### Can I start without a card? Yes. Create an account and you will receive 500 free credits. No card is required to register or use your free balance. ### How many credits renew with each plan? Developer includes 500 monthly credits, Production includes 3,000, and Scale includes 10,000. Plan credits refresh with each billing period. ### How do plan changes work? Upgrades take effect immediately. Before you confirm, you will see the prorated amount due for the rest of your current billing period and your next renewal price. Additional monthly plan credits are prorated for the same period. Downgrades take effect at your next renewal. ### Can I add credits without changing plans? Yes. Top-ups add prepaid usage credits without changing your plan, renewal date, or monthly plan credits.