~/neuroforgelabs (main) $
open source

We build
infra
for AI.

Open-source

Tools for engineers building production-grade RAG systems, LLM pipelines, and secure smart contracts. All MIT licensed.

3+
Projects
5
Focus Areas
MIT
License
100%
Open Source
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/// design_philosophy

AI assists.
Logic decides.

Every tool we build rests on one conviction: deterministic systems make guarantees. AI cannot. When you need a reproducible, auditable answer, rules beat reasoning.

AI belongs in the explanation layer — translating results into natural language, surfacing semantic context, suggesting next steps. Not computing the answer itself.

“AI should assist engineering, not replace deterministic systems.”

— Core Design Principle

01

Deterministic Core

Static analysis, rule engines, AST parsing. Guaranteed, reproducible, auditable outcomes every time.

static_analysis()rule_engine.eval()ast.parse()
02

AI Explanation Layer

LLM-powered natural language summaries, semantic search, context-aware suggestions on top.

llm.explain(result)embed.search(query)ctx.window(4096)
03

Trustworthy Output

Explainable findings with auditable traces. Engineers act on results with full confidence.

audit.trace()explain.why()verify.result()

/// engineering_focus

Where we
build.

Five domains where open-source tooling has the highest leverage for AI engineers.

01

AI Infrastructure

Foundational abstractions for deploying, managing, and scaling AI systems in production.

02

RAG Pipelines

Building, debugging, and optimizing Retrieval-Augmented Generation for accuracy and speed.

03

LLM Debugging

Diagnosing hallucinations, context failures, and retrieval errors in language model pipelines.

04

Developer Tooling

CLI tools, SDKs, and integrations making AI workflows native to the engineering lifecycle.

05

Smart Contract Security

Automated vulnerability detection and AI-enhanced audit reports for Solidity contracts.

/// open_source

Built in the open.
For everyone.

Every line of code is public. MIT licensed. No paywalls, no enterprise tiers. If it's useful to AI engineers, it belongs on GitHub.

View on GitHub
MIT License — use it anywhere, forever.
Open PRs and issues — community-first roadmap.
No commercial upsells — no enterprise tier.
bash — rag-doctor
~/projects git clone github.com/NeuroForgeLabs/rag-doctor
Cloning into 'rag-doctor'...
✓ done
~/projects/rag-doctor pip install -e . && rag-doctor diagnose
Successfully installed rag-doctor-0.2.1
✓ Retrieval stage: OK
✓ Chunking strategy: OK
⚠ Context window: 78% full
✗ Reranker: score collapse detected
→ Fix: lower top_k from 20 to 8
~/projects/rag-doctor