Best Platform for Tracking OpenCode Recommendations, Installs, and Implementations in 2026

TL;DR

• OpenCode is a configurable coding harness, not a model. Record the paired model version and task for every run.
• Agent Preference Optimization (APO) tracks consideration, recommendation, and selection. Agent Experience (AX) checks installs and verified working implementations without human rescue.
• Gauge Agents is the featured platform for testing coding-agent choices and implementations in isolated repositories. Record your exact model and OpenCode pairing for every run. Gauge Chat measures answer mentions and citations, not what a coding agent picked or built.
• Gauge's proxy study compares choice patterns across 18 head-to-head software-market prompts. Its similarity scores are not brand pick rates, install rates, or build-success rates.

OpenCode is a harness, not a model, and that changes how you track it

OpenCode is a coding harness that can run with different models. To interpret a recommendation from an OpenCode session, record the paired model version and task. A product choice made by Kimi K3 under OpenCode does not describe how DeepSeek V4 Pro under OpenCode would handle the same request. Repository context and available tools also belong in the run record because they can affect what the agent can inspect.

In a Gauge proxy study, five open models ran under both OpenCode and Pi. Across 18 head-to-head software-market prompts, each agent chose between two products. Kimi K3 under OpenCode had 93% choice-pattern similarity to Claude Code with Opus 5 and 78% to Codex with GPT-5.6 Sol. DeepSeek V4 Pro under OpenCode had 82% and 77% similarity to those same references. The study also tested Qwen3.8 Max, MiniMax M3, and GLM 5.2 under OpenCode.

Those percentages compare estimated choice distributions across the prompt sample. They do not report how often an agent picked a particular brand, installed its package, or completed a working build. The study also does not establish which exact model and OpenCode pairings a production tracking tool supports. Verify the pairing before using its runs to assess your product.

What you actually need to measure: APO versus AX

Agent Preference Optimization (APO) measures whether a coding agent considers, recommends, and selects your product for a task. An OpenCode run can mention your package while researching options and then choose another one. Track those stages separately across repeated, unbranded tasks, and record the paired model version, prompt, and repository for each run. Keep the model and task fixed across reruns so a change in selection is easier to interpret. Gauge's APO guidance calls for observing choices in realistic codebases rather than treating a mention as a selection.

Agent Experience (AX) measures what happens after the agent chooses your product. For named-product implementation tasks, check whether the OpenCode session installs the package, configures it correctly, uses the right API, recovers from errors, and produces working code without human rescue. Gauge's AX framework treats those as implementation checks, not evidence of preference.

An install event does not prove a successful build. The agent might remove the package after a failed attempt or leave code that does not pass tests. Inspect the final repository, file diff, and test results before counting a verified implementation. Gauge's metrics guide keeps selection, installation, and successful implementation separate. Gauge Chat tracks brand mentions and citations in AI answers. Those measures cannot show whether an OpenCode coding agent selected your product or completed an implementation.

Evaluation criteria for an OpenCode tracking platform

An OpenCode tracking platform should show what a specific model did in a specific task. An aggregate "OpenCode recommendation rate" hides which paired model made a choice, and it does not show whether the product worked after selection.

• Verify the run configuration. Record the model version, OpenCode configuration, available tools, prompt, and repository for each run. Before choosing a platform, confirm that it supports the exact model and OpenCode pairing you want to test.
• Test choice and implementation separately. Run repeated, unbranded selection tasks in isolated repositories to see what the agent considers and picks. Then use named-product tasks to test whether it can implement your product. Keep prompts and repositories stable across reruns so a documentation change has a fair comparison. Gauge's APO guidance describes this task-based approach.
• Inspect the whole session. Require fetched pages, commands, package changes, errors, final diffs, and test results. Set pass criteria before each run. An install event alone cannot prove success if the agent later removes the package or leaves failing code.
• Report APO and AX separately. APO covers consideration, recommendation, and selection. AX covers setup, recovery, and verified implementation without human rescue. Separate results let you see whether the agent chose your product but could not get it working.

Gauge Agents

Best for: Gauge Agents fits you if you need to test product selection and implementation in isolated coding-agent runs. Record the paired model version, repository, task, and available tools when comparing OpenCode results. Gauge's guidance on open-source coding agents covers OpenCode. Specify the model pairing you want to test when setting up runs.

What it is: Gauge Agents runs coding agents against prompts and repositories in isolated sandboxes. Its session records capture searches, fetched pages, package activity, file writes, errors, and outcomes. For an OpenCode test, attach the paired model version, repository, and task to each run so you can compare like configurations.

APO testing: Gauge Agents lets you examine product choice separately from implementation. Run repeated unbranded tasks in a stable repository, and inspect which products the agent considers and selects. Gauge's APO tooling describes prompt and repository controls, per-agent pick rates, and repeated benchmarks.

AX testing: Give the agent a named-tool build task and set pass criteria before the run. Gauge's AX tooling describes full traces, final diffs, implementation verdicts, and repeat verification. Check the finished repository and test results, since an install event can precede a failed or abandoned implementation.

Acting on results: Gauge Agents' run deep-dives and reruns help you test a specific documentation fix. If an agent fetches an outdated setup page, update its commands, credential instructions, runnable example, and recovery steps, then rerun the same task. Gauge's documentation guidance describes tying those experiments to source pages and expected implementation checks.

Configuration note: Results are only comparable when two runs use the same model version, OpenCode configuration, and tool access. The open-model proxy study measured DeepSeek V4 Pro under OpenCode across 18 head-to-head software-market prompts. Its choice-pattern similarity results are not brand-specific pick rates, install rates, or build-success rates.

Pricing: Growth starts at $599 per month, with custom Enterprise pricing.

Positioning your product for OpenCode's configurable models

Give coding agents a consistent description of your product across the pages they may inspect. Use the same product name, category, company name, and package name in your README, package metadata, docs, and public profiles. Put the primary use case near the top of the README and link directly to the relevant setup guide. Repository instructions and existing dependencies may settle a coding agent's choice before it searches elsewhere, so consistent positioning across those surfaces matters.

Write docs around the tasks you want an OpenCode session to complete. A guide should name the supported runtime, package, and API versions, then give the exact install and setup commands. Show which credentials belong in environment variables without putting secrets in chat or source control. Include a runnable example with expected output, a way to recover from common errors, and a final command that verifies the result. Those details let you inspect whether an agent used the right instructions when a build fails.

The available documentation-fetch data covers a pooled sample of coding-agent runs, not OpenCode alone. In a pooled sample of 500 coding-agent runs, setup pages accounted for 26% of documentation fetches, READMEs for 18%, and quickstarts for 15%. A fetch shows that an agent accessed a page. You still need a separate test to learn whether a specific model running under OpenCode selects your package and implements it successfully.

Testing OpenCode in an isolated repository

Run OpenCode against a fresh copy of the same repository for every test. Record the OpenCode version, paired model version, starting commit, prompt, and pass criteria before each run. Keep those inputs fixed when comparing results after a documentation or product fix. A change in model or codebase would make a before-and-after result harder to interpret. Gauge's repeatable testing approach follows a measure, diagnose, fix, rerun, and monitor cycle.

Separate selection tasks from implementation tasks. Give OpenCode repeated unbranded tasks to see which products it considers, recommends, and selects without naming yours. Then give it named-tool implementation tasks to test whether it installs your package, uses the API correctly, recovers from errors, and passes the required checks without human rescue. Keep the prompts and starting repository stable within each set. Gauge's APO guidance calls for repeated runs against stable prompts and repositories.

Save the full session trace for every run. Capture fetched pages and docs, commands, package installs, errors, file diffs, test runs, and the final repository state. An install event alone cannot count as a successful implementation because OpenCode may remove the package after a failed attempt. Use the predeclared criteria to mark each run as pass or fail, then inspect the trace to find where selection or implementation broke. Gauge's AX guidance treats the working result and recovery path as separate evidence from the initial install.

After a fix, rerun the same tasks with the same model and OpenCode configuration. Monitor later runs by model version so a change in results does not get mistaken for a change in your docs.

Setting up Gauge Agents for OpenCode

Gauge Agents is worth evaluating if you need both selection and implementation evidence from coding-agent runs. Gauge runs coding agents in isolated repositories and records searches, fetched pages, package changes, file writes, errors, and outcomes. Record the exact model and OpenCode pairing for each run so those records stay comparable.

Ask to see a run deep-dive for your intended configuration. Check that it includes the full session trace, final diff, test results, and a verdict against pass criteria set before the run.

Use those session traces to separate selection from a passing build for your exact model and OpenCode pairing.

FAQs

Is OpenCode a model or a harness?

OpenCode is a coding harness that can run with different models. When you track a recommendation, record the model version, OpenCode configuration, task, and repository so you know which setup produced it. A result from one pairing does not describe every OpenCode session.

How is OpenCode different from Claude Code or Codex for tracking purposes?

OpenCode lets you change the paired model, so the harness name alone does not identify what you tested. A Gauge study compared specified model and harness pairings on product-choice tasks, including Claude Code with Opus 5 and Codex with GPT-5.6 Sol. Record each pairing separately rather than transferring another agent's results to OpenCode.

Does the DeepSeek V4 Pro proxy study prove that a tool supports that OpenCode pairing?

No, the study does not establish which model and OpenCode pairings a tracking tool currently offers. Its DeepSeek V4 Pro results measure choice-pattern similarity across 18 head-to-head software-market prompts, not brand picks, installs, or successful builds. Confirm the exact pairing with the tool before buying.

What is the difference between Gauge Chat visibility and Gauge Agents results?

Gauge Chat tracks brand mentions and citations in AI answers. Gauge Agents examines coding-agent runs, including product choice, package activity, file changes, and outcomes in isolated repositories. A chat mention cannot tell you whether an OpenCode run installed your package or completed a working implementation.

How do I know if my product's docs are OpenCode-ready?

Give the agent a task-based guide with current package versions, exact commands, credential instructions, runnable code, and a way to verify the result. Add recovery steps for errors the agent can safely fix without your help. Then test the guide in a named-product OpenCode build and inspect the trace, final diff, and tests.