Universal provider proxy for OpenAI Codex & Claude Code — use any LLM (Claude, Gemini, Grok, DeepSeek, Ollama…) with Codex CLI, App, SDK, and Claude Code
C-Rank Grade: A (Welcoming) - 56/100
External PR Merge Rate: 57%
Response Time: 5h
First Timer Success: 36%
lidge-jun/opencodex has a recorded first-timer success rate of 36.4%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in lidge-jun/opencodex respond to incoming external pull requests in approximately 4.9 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 56.5 places lidge-jun/opencodex in the A tier.
The external contributor pull request merge rate for lidge-jun/opencodex is 57.0%, based on public PR activity from non-core contributors.
lidge-jun/opencodex does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
Universal provider proxy for OpenAI Codex & Claude Code — use any LLM (Claude, Gemini, Grok, DeepSeek, Ollama…) with Codex CLI, App, SDK, and Claude Code
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/lidge-jun/opencodex?utm_source=github&utm_medium=badge)
Enter your branch size and test coverage below to forecast review turnaround and get 1-click git splitting commands.
Area Provider adapter What are you trying to accomplish? I use several text-only routed models through the vision sidecar, which describes images before feeding them to the text-only model. Right now my only choice for the "describer" is an OpenAI-native or Anthropic model that runs through the ChatGPT forward backend. I'd like to be able to pick any configured provider's vision model — for instance a Doubao/volcengine vision model I already have configured in opencodex — as the describer. What prevents this today? The vision sidecar (visionSidecar.model) sends the describe request through forwardProvider (the ChatGPT forward backend) to /responses, so model is effectively limited to OpenAI-native (or Anthropic) model ids. A model hosted on a custom provider (cliproxy, volcengine/doubao, etc.) can't be selected because the describe path doesn't know how to reach that provider. What should OpenCodex do? Allow the vision-sidecar description model to be resolved against any configured
What are you trying to accomplish? I need to route Codex to the Alibaba Token Plan (Qwen) endpoint and have the model picker advertise only the reasoning efforts each Qwen model actually supports. For example, qwen3.8-max should offer low/medium/xhigh and not max/ultra, so the UI reflects the real capability of each model. What prevents this today? The catalog row for any reasoning-capable routed model is padded with synthetic top rungs. In src/codex/catalog/effort.ts, applyReasoningLevels appends max and ultra to every reasoning model unless preserveExact is set, and preserveExact only applies to exact-combo models (provider === COMBO_NAMESPACE). As a result, after ocx sync the entry alibaba-token-plan-intl/qwen3.8-max emits supported_reasoning_levels = [low, medium, xhigh, max, ultra] even when modelReasoningEfforts is configured as ["low", "medium", "xhigh"]. What should OpenCodex do? Add a per-model (or per-provider) config option to suppress the synthetic top rungs so the catalog
Rate what actually matters after opening a pull request here.
Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.
When evaluating whether to contribute to lidge-jun/opencodex, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of lidge-jun/opencodex acknowledge new external contributions in approximately 4 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 57.0% were successfully merged into the primary branch.
lidge-jun/opencodex has a recorded first-timer success rate of 36.4%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~4 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 56.5 places lidge-jun/opencodex in the Welcoming tier.
The external pull request merge rate is 57.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
lidge-jun/opencodex does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.
All metrics displayed for lidge-jun/opencodex are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.