director-ai 2.0.0

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

---

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"]

Three 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. Your data, your rules — ingest your own documents. The scorer checks against your ground truth.

Quickstart

Method	Command
pip install	pip install director-ai
CLI scaffold	director-ai quickstart --profile medical
Colab
HF Spaces	Try it live
Docker	docker run -p 8080:8080 ghcr.io/anulum/director-ai:latest

6-line guard

from director_ai import guard
from openai import OpenAI

client = guard(
    OpenAI(),
    facts={"refund_policy": "Refunds within 30 days only"},
    threshold=0.6,
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What is the refund policy?"}],
)

Score a response

from director_ai.core import CoherenceScorer, GroundTruthStore

store = GroundTruthStore()
store.add("sky color", "The sky is blue due to Rayleigh scattering.")

scorer = CoherenceScorer(threshold=0.6, ground_truth_store=store)
approved, score = scorer.review("What color is the sky?", "The sky is green.")

print(approved)     # False
print(score.score)  # 0.42

Streaming halt

from director_ai.core import StreamingKernel

kernel = StreamingKernel(hard_limit=0.4, window_size=5)
session = kernel.stream_tokens(token_generator, lambda tok: my_scorer(tok))

if session.halted:
    print(f"Halted at token {session.halt_index}: {session.halt_reason}")

Installation

pip install director-ai                      # heuristic scoring
pip install director-ai[nli]                 # NLI model (DeBERTa)
pip install director-ai[vector]              # ChromaDB knowledge base
pip install "director-ai[nli,vector,server]" # production stack

Framework integrations: [langchain], [llamaindex], [langgraph], [haystack], [crewai].

Full installation guide: docs.

Docker

docker run -p 8080:8080 ghcr.io/anulum/director-ai:latest        # CPU
docker run --gpus all -p 8080:8080 ghcr.io/anulum/director-ai:gpu # GPU

Benchmarks

Accuracy — LLM-AggreFact (29,320 samples)

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 at 17x fewer params than the leader. 14.6 ms/pair with ONNX GPU batching — faster than every competitor at this accuracy tier. Director-AI's unique value is the system: NLI + KB + streaming halt.

Full results: benchmarks/comparison/COMPETITOR_COMPARISON.md.

Domain Presets

8 built-in profiles with tuned thresholds:

director-ai config --profile medical   # threshold=0.75, NLI on, reranker on
director-ai config --profile finance   # threshold=0.70, w_fact=0.6
director-ai config --profile legal     # threshold=0.68, w_logic=0.6
director-ai config --profile creative  # threshold=0.40, permissive

Known Limitations

1. Heuristic fallback is weak: Without [nli], scoring uses word-overlap heuristics (~55% accuracy). Use strict_mode=True to return neutral 0.5 instead.
2. Summarisation is a weak spot: NLI models under-perform on summarisation (AggreFact-CNN: 68.8%, ExpertQA: 59.1%).
3. ONNX CPU is slow: 383 ms/pair without GPU. Use onnxruntime-gpu for production.
4. Weights are domain-dependent: Default w_logic=0.6, w_fact=0.4 suits general QA. Adjust for your domain.
5. Chunked NLI: Very short chunks (<3 sentences) may lose context.

Citation

@software{sotek2026director,
  author    = {Sotek, Miroslav},
  title     = {Director-AI: Real-time LLM Hallucination Guardrail},
  year      = {2026},
  url       = {https://github.com/anulum/director-ai},
  version   = {2.0.0},
  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/contact | [e-mail]

Contributing

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