director-ai 3.12.0

Real-time LLM hallucination guardrail — NLI + RAG fact-checking with token-level streaming halt

Director-AI

Real-time LLM hallucination guardrail — NLI + RAG fact-checking with token-level streaming halt

Active Development — Director-AI is under intensive development. The core guardrail engine, NLI scoring pipeline, 5-SDK guard, FastAPI middleware, REST/gRPC servers, and intent-grounded injection detection are fully functional, tested (4 126+ passing tests, zero functional failures), and production-deployable via PyPI. We are currently adding Rust-accelerated signal paths and expanding adversarial robustness coverage. APIs may evolve as this work progresses.

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What It Does

Director-AI sits between your LLM and the user. It scores every output for hallucination before it reaches anyone — and can halt generation mid-stream if coherence drops below threshold.

graph LR
    LLM["LLM<br/>(any provider)"] --> D["Director-AI"]
    D --> S["Scorer<br/>NLI + RAG"]
    D --> K["StreamingKernel<br/>token-level halt"]
    S --> V{Approved?}
    K --> V
    V -->|Yes| U["User"]
    V -->|No| H["HALT + evidence"]

Ten things make it different:

1. Token-level streaming halt — not post-hoc review. Severs output the moment coherence degrades.
2. Dual-entropy scoring — NLI contradiction detection (DeBERTa) + RAG fact-checking against your knowledge base.
3. Meta-confidence — the guardrail tells you how confident it is in its own verdict. Route low-confidence results to human review.
4. Structured output verification — JSON schema validation, tool call fabrication detection, code hallucinated API detection. Zero dependencies (stdlib only).
5. Online calibration — collects human feedback, automatically adjusts thresholds for your deployment. The longer you use it, the better it gets.
6. Contradiction tracking — detects when an AI contradicts itself across conversation turns.
7. EU AI Act compliance — automated Article 15 documentation. Accuracy metrics, drift detection, feedback loop detection, audit trails, per-model breakdown with confidence intervals. Ready for August 2026 enforcement.
8. Verification gems — numeric consistency checks, reasoning chain verification, temporal freshness scoring, cross-model consensus, conformal prediction intervals. All stdlib-only, zero dependencies.
9. Agentic loop monitor — detects circular tool calls, goal drift, and budget exhaustion in AI agent loops. The first guardrail that monitors agent execution, not just individual calls.
10. Adversarial self-test — 25-pattern robustness suite tests your guardrail against zero-width chars, homoglyphs, encoding tricks, and prompt injection.
11. Intent-grounded injection detection — two-stage pipeline: regex pattern matching (fast) + bidirectional NLI divergence scoring (semantic). Detects the effect of injection in the output — works regardless of how the attack was encoded. Per-claim attribution with grounded/drifted/injected verdicts.

Scope

Pure Python core — no compiled extensions required. Optional Rust kernel (pip install director-ai[rust]) for SIMD-accelerated scoring. Works on any platform with Python 3.11+.

Layer	Packages	Install
Core (zero heavy deps)	CoherenceScorer, StreamingKernel, GroundTruthStore, HaltMonitor	pip install director-ai
NLI models	DeBERTa, FactCG, MiniCheck, ONNX Runtime	pip install director-ai[nli]
Vector DBs	ChromaDB ([vector]), Pinecone ([pinecone]), Weaviate ([weaviate]), Qdrant ([qdrant])	pip install director-ai[vector]
LLM judge	OpenAI, Anthropic escalation	pip install director-ai[openai]
Observability	OpenTelemetry spans	pip install director-ai[otel]
Server	FastAPI + Uvicorn	pip install director-ai[server]

Four Ways to Add Guardrails

A: Wrap your SDK (6 lines)

Duck-type detection for five SDK shapes: OpenAI-compatible (OpenAI, vLLM, Groq, LiteLLM, Ollama), Anthropic, AWS Bedrock, Google Gemini, and Cohere.

from director_ai import guard
from openai import OpenAI

client = guard(
    OpenAI(),
    facts={"refund_policy": "Refunds within 30 days only"},
)
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What is the refund policy?"}],
)

B: One-shot check (4 lines)

Score a single prompt/response pair without an SDK client:

from director_ai import score

cs = score("What is the refund policy?", response_text,
           facts={"refund": "Refunds within 30 days only"},
           threshold=0.3)
print(f"Coherence: {cs.score:.3f}  Approved: {cs.approved}")

C: Zero code changes (2 lines)

Point any OpenAI-compatible client at the proxy:

pip install director-ai[server]
director-ai proxy --port 8080 --facts kb.txt --threshold 0.3

Then set OPENAI_BASE_URL=http://localhost:8080/v1 in your app. Every response gets scored; hallucinations are rejected (or flagged with --on-fail warn).

D: FastAPI middleware (3 lines)

Guard your own API endpoints:

from director_ai.integrations.fastapi_guard import DirectorGuard

app.add_middleware(DirectorGuard,
    facts={"policy": "Refunds within 30 days only"},
    on_fail="reject",
)

Responses on POST endpoints get X-Director-Score and X-Director-Approved headers. Set paths=["/api/chat"] to limit which endpoints are scored.

Installation

pip install "director-ai[nli]"                    # recommended — NLI model scoring
pip install "director-ai[nli,vector,server]"       # production stack with RAG + REST API
pip install "director-ai[nli,voice]"               # voice AI with TTS adapters
pip install director-ai                            # heuristic-only (limited accuracy)

Extras: [vector] (ChromaDB), [voice] (ElevenLabs, OpenAI TTS, Deepgram), [finetune] (domain adaptation), [ingestion] (PDF/DOCX parsing), [colbert] (late-interaction retrieval). Framework integrations: [langchain], [llamaindex], [langgraph], [haystack], [crewai], Semantic Kernel, DSPy/Instructor. Kubernetes: Helm chart with GPU toggle, HPA, Sigstore-signed releases. Voice AI: VoiceGuard (sync) and AsyncVoiceGuard + voice_pipeline() (async) — real-time token filter for TTS pipelines with ElevenLabs, OpenAI TTS, and Deepgram adapters (guide).

Full installation guide: docs.

Docker

Dockerfile included for self-hosted builds. Pre-built images not yet published to a registry.

docker build -t director-ai .                                      # build locally
docker run -p 8080:8080 director-ai                                # CPU
docker build -f Dockerfile.gpu -t director-ai:gpu .                # GPU build
docker run --gpus all -p 8080:8080 director-ai:gpu                 # GPU

Benchmarks

Accuracy — LLM-AggreFact (29,320 samples)

Scoring model: yaxili96/FactCG-DeBERTa-v3-Large (0.4B params, MIT license).

Model	Balanced Acc	Params	Latency	Streaming
Bespoke-MiniCheck-7B	77.4%	7B	~100 ms	No
Director-AI (FactCG)	75.8%	0.4B	14.6 ms	Yes
MiniCheck-Flan-T5-L	75.0%	0.8B	~120 ms	No
MiniCheck-DeBERTa-L	72.6%	0.4B	~120 ms	No

75.8% balanced accuracy comes from the FactCG-DeBERTa-v3-Large model (77.2% in the NAACL 2025 paper; our eval yields 75.86% due to threshold tuning and data split version). Latency: 14.6 ms/pair measured on GTX 1060 6GB with ONNX GPU batching (16-pair batch, 30 iterations, 5 warmup). Director-AI's unique value is the system: NLI + KB + streaming halt.

Full results: benchmarks/comparison/COMPETITOR_COMPARISON.md. Performance trade-offs and E2E pipeline metrics: docs.

Domain Presets

10 built-in profiles with preset thresholds (starting points — adjust for your data):

director-ai config --profile medical   # threshold=0.30, NLI on, reranker on
director-ai config --profile finance   # threshold=0.30, w_fact=0.6
director-ai config --profile legal     # threshold=0.30, w_logic=0.6
director-ai config --profile creative  # threshold=0.40, permissive

Domain-specific benchmark scripts exist but have not yet been validated with measured results. Run them yourself (requires GPU + HuggingFace datasets):

python -m benchmarks.medical_eval   # MedNLI + PubMedQA
python -m benchmarks.legal_eval     # ContractNLI + CUAD (RAGBench)
python -m benchmarks.finance_eval   # FinanceBench + Financial PhraseBank

Known Limitations & When Not to Use

Accuracy

• Heuristic fallback is weak: Without [nli], scoring uses word-overlap heuristics (~55% accuracy). Use strict_mode=True to reject (0.9) instead of guessing.
• Summarisation FPR at 10.5%: Reduced from 95% via bidirectional NLI + baseline calibration (v3.5). AggreFact-CNN: 68.8%, ExpertQA: 59.1% (structurally expected at 0.4B params).
• NLI-only scoring needs KB grounding: Without a knowledge base, PubMedQA F1=62.1%, FinanceBench 80%+ FPR. Load your domain facts into the vector store.

Performance

• ONNX CPU is slow: 383 ms/pair without GPU. Use onnxruntime-gpu for production.
• Long documents need ≥16GB VRAM: Legal contracts and SEC filings exceed 6GB during chunked NLI inference.

Configuration

• Weights are domain-dependent: Default w_logic=0.6, w_fact=0.4 suits general QA. Adjust for your domain or use a built-in profile.
• Threshold defaults differ by API surface: guard()/score() default to threshold=0.3 (permissive). DirectorConfig defaults to coherence_threshold=0.6 (conservative). Always set the threshold explicitly.

Privacy

• LLM-as-judge sends data externally: When llm_judge_enabled=True, truncated prompt+response (500 chars) are sent to the configured provider. Do not enable in privacy-sensitive deployments without user consent. The default NLI-only mode runs entirely locally with no external calls.

Citation

@software{sotek2026director,
  author    = {Sotek, Miroslav},
  title     = {Director-AI: Real-time LLM Hallucination Guardrail},
  year      = {2026},
  url       = {https://github.com/anulum/director-ai},
  version   = {3.12.0},
  license   = {AGPL-3.0-or-later}
}

License

Dual-licensed:

1. Open-Source: GNU AGPL v3.0 — research, personal use, open-source projects.
2. Commercial: Proprietary license — removes copyleft for closed-source and SaaS.

See Licensing for pricing tiers and FAQ.

Contact: anulum.li | [e-mail]

Community

Join the Director-AI Discord for CI notifications, release announcements, and support. The Discord bot also provides /version, /docs, /install, /status, and /quickstart slash commands.

Contributing

See CONTRIBUTING.md. By contributing, you agree to AGPL v3 terms.

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Developed by ANULUM / Fortis Studio