Langchain Nobulex

B 81 completed
Library
unknown / python · tiny
16
Files
965
LOC
1
Frameworks
3
Languages

Pipeline State

completed
Run ID
#365942
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
24.55
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47778
Member of a group with 1 similar repo(s) — canonical #22814 view group →
Top concepts (2)
Project DescriptionTesting
Repobility — the code-quality scanner for AI-generated software · https://repobility.com

AI Prompt

Build me a Python library that acts as a compliance middleware for LangChain pipelines. I need it to generate hash-chained audit trails to help meet EU AI Act readiness requirements. The middleware should allow defining rules using a covenant DSL—specifically, I need to be able to `permit` actions, `forbid` actions (optionally with conditions), and `require` certain behaviors. It must intercept tool calls, block forbidden actions, and log everything into a tamper-proof audit log containing fields like `timestamp`, `action`, and `prev_hash`. Finally, include a function to independently verify the integrity of the entire audit chain.
python langchain ai-act middleware audit-log security llm-ops compliance
Generated by gemma4:latest

Catalog Information

A Python library that adds compliance middleware to LangChain pipelines, generating hash‑chained audit trails for EU AI Act readiness.

Description

This library provides a middleware layer for LangChain applications, automatically recording every interaction with AI models and data sources. It creates a hash‑chained audit trail that preserves the integrity and order of events, enabling transparent verification of model usage. The middleware can enforce compliance rules, flag non‑conforming inputs, and log decisions for regulatory review. Designed for developers building AI services, it simplifies the integration of audit and governance features into existing LangChain workflows. By aligning with the EU AI Act requirements, it helps organizations demonstrate accountability and traceability of AI outputs.

الوصف

توفر هذه المكتبة طبقة وساطة للخطوط البينية في LangChain، تقوم بتسجيل تلقائيًا كل تفاعل مع نماذج الذكاء الاصطناعي ومصادر البيانات. تُنشئ سجلات تدقيق متسلسلة بالهش تحافظ على سلامة وتسلسل الأحداث، مما يتيح التحقق الشفاف من استخدام النماذج. يمكن للوسيط تطبيق قواعد الامتثال، وتحديد المدخلات غير المتوافقة، وتسجيل القرارات للمراجعة التنظيمية. صممت للمطورين الذين يبنون خدمات ذكاء اصطناعي، وتبسط دمج ميزات التدقيق والحكم في سير العمل الحالي لـ LangChain. من خلال التوافق مع متطلبات قانون AI الأوروبي، تساعد المؤسسات على إظهار المساءلة وسلاسل تتبع نواتج الذكاء الاصطناعي.

Novelty

7/10

Tags

compliance audit-trail hash-chaining ai-governance data-integrity transparency traceability regulatory-readiness

Technologies

langchain

Claude Models

claude-opus-4.6

Quality Score

B
80.9/100
Structure
78
Code Quality
95
Documentation
65
Testing
70
Practices
76
Security
100
Dependencies
60

Strengths

  • Good test coverage (71% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected
  • Properly licensed project

Weaknesses

  • No CI/CD configuration \u2014 manual testing and deployment

Recommendations

  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment

Security & Health

4.1h
Tech Debt (E)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (10)
Repobility · code-quality intelligence platform · https://repobility.com
MIT
License
0.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
90.7%
markdown
6.1%
toml
3.2%

Frameworks

pytest

Concepts (2)

Findings produced by Repobility · scan your repo at https://repobility.com/scan/
CategoryNameDescriptionConfidence
Powered by Repobility — scan your code at https://repobility.com
auto_descriptionProject DescriptionCompliance middleware for LangChain agents with hash-chained audit trails.80%
auto_categoryTestingtesting70%

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1 quality score recorded.

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