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by Emmimal · LLM Plugin · ★ 62
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control-layer A production-grade control layer that sits between your application logic and any LLM — input validation, schema enforcement, circuit breaking, targeted retry, and audit logging in one composable pipeline. Most LLM integrations stop at: write a prompt, call the model, use the response. This library handles what prompt engineering cannot — enforcing what the model actually returns, blocking what should never reach it, and recovering cleanly when things break. Read the full write-up on Towards Data Science → Prompt Engineering Failed in Production — I Built the Control Layer That Actually Works What It Does User Input | [1] InputGuard -- injection detection (20 patterns), length check, sanitization | [2] CircuitBreaker -- stops hammering a failing LLM backend | [3] TokenBudget -- tiktoken-accurate slot allocation, priority order [4] PromptBuilder -- assembles prompt within budget, injects constraints | [5] LLMCaller -- enforces hard timeout on every call | [6] R
| Stars | 62 |
| Forks | 9 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 72.4484420627363/100 |
| Last Updated | 2026-05-25 |
| Created | 2026-05-18 |
| Platforms | python |
| Est. Tokens | ~5k |
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control-layer is A production-grade control layer that sits between your application logic and any LLM — input validation, schema enforcement, circuit breaking, targeted retry, and audit logging in one composable pipe. It is categorized as a LLM Plugin with 62 GitHub stars.
control-layer is primarily written in Python. It covers topics such as anthropic, circuit-breaker, generative-ai.
You can find installation instructions and usage details in the control-layer GitHub repository at github.com/Emmimal/control-layer. The project has 62 stars and 9 forks, indicating an active community.
control-layer is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to control-layer on Agent Skills Hub include ai-microcore, SimplerLLM, llmix. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.