by frckeepit · Agent Tool · ★ 135
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LLM Production Toolkit A Python toolkit for evaluating, monitoring, and ensuring the safety of LLM deployments in production. The problem: 95% of enterprise AI pilots fail to deliver value — not because the models are bad, but because organizations lack the production engineering to deploy them reliably. The solution: Concrete, runnable tools that address the most common failure modes: hallucination, bias, lack of feedback loops, and operational unreadiness. Quick Start For ML-powered modules (hallucination detection): For everything: Modules Hallucination Grounding Check Evaluate whether LLM output is grounded in source documents. Uses embedding similarity + NLI entailment for robust detection. Bias Evaluation Test any LLM for de
| Stars | 135 |
| Forks | 6 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 68.66497566802/100 |
| Last Updated | 2026-04-09 |
| Created | 2026-04-09 |
| Platforms | python |
| Est. Tokens | ~4k |
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llm-production-toolkit is Production-ready toolkit for evaluating, monitoring, and ensuring safety of LLM deployments. Hallucination detection, bias evaluation, feedback loops, and production readiness assessment.. It is categorized as a Agent Tool with 135 GitHub stars.
llm-production-toolkit is primarily written in Python.
You can find installation instructions and usage details in the llm-production-toolkit GitHub repository at github.com/frckeepit/llm-production-toolkit. The project has 135 stars and 6 forks, indicating an active community.
llm-production-toolkit is released under the MIT license, making it free to use and modify according to the license terms.
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