Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for developer teams using Claude Code who want to A/B test the underlying model without rewriting the agent harness (Backdoor's value-add is model-agnostic switching — Claude Code is the agent, Backdoor is the model router, the user picks the model per session): Backdoor is the MIT model-agnostic OpenAI-compatible proxy that lets you run Claude Code against any OpenAI-compatible provider (D
Who should use it
Who should skip it
Pass on ajsai47/backdoor if its scope or audience does not match what your team is building right now.
About this signal
ajsai47/backdoor is tracked by RepoRadar as a model-agnostic openai-compatible in the MIT model-agnostic OpenAI-compatible proxy that section. It was first seen on 2026-06-25 and last updated on 2026-06-25. The current verdict is 'try now' with a Silver tier and easy setup difficulty. The standout signals for ajsai47/backdoor are workflow potential (8.9) and setup ease (8.8), while momentum (7.0) trails — that balance shapes where it fits best. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned ajsai47/backdoor a composite score of 7.8 out of 10, placing it in the Silver tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 300.0 and never affects the composite score or tier. The risk label of 'low' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read Local AI vs. hosted APIs: how to choose for the checklist behind this score.
Risk explanation
**Format-translation correctness is the load-bearing risk — verify tool use and streaming on the team's target provider.** The proxy translates between Anthropic's request format and OpenAI-compatible providers' formats, which are similar but not identical. A translation bug in tool use or streaming would surface as agent behavior regressions on the target model. For any production adoption, run the team's existing Claude Code eval set (the eval set that includes file reads, file writes, shell calls, grep, and multi-turn planning) against Backdoor + the target provider and confirm the agent's success rate matches the Anthropic baseline; **The 99.95% cost delta is the maintainer's benchmark on a specific workload — reproduce on the team's own workload before committing.** The headline number is the maintainer's 500M-token benchmark on a specific workload (Claude Opus 4.7 vs DeepSeek V3 Flash). The team's workload, the team's preferred non-Anthropic provider, and the team's prompt structure will produce a different number. Run the maintainer's methodology on the team's own workload before quoting the 99.95% number internally; **Anthropic's terms of service and the target provider's terms of service both apply.** Running Claude Code against a non-Anthropic provider is a deliberate choice that should be reviewed against Anthropic's terms of service and the target provider's terms of service. The MIT license covers the code; the legal posture of routing Claude Code's API calls through a third-party proxy is the team's responsibility. Confirm the legal review with the team's counsel before any production deployment.