Installation

Buyer-facing bundles

Three bundles compose the granular extras so you do not have to assemble them by hand. The granular extras (below) remain available for fine control.

pip install director-ai                  # core: scoring + opt-in streaming checks, zero ML deps
pip install director-ai-lite             # smallest first-run guard facade
pip install "director-ai[recommended]"   # production guardrail: NLI + RAG + REST API
pip install "director-ai[integrations]"  # framework adapters (LangChain, LlamaIndex, …)
pip install "director-ai[all]"           # the common capability set in one shot

Base Package

pip install director-ai

Includes: coherence scorer, streaming kernel, safety kernel, ground truth store, heuristic scoring.

Director-Lite Package

pip install director-ai-lite

Use this package when the first install should expose only the guard facade:

from director_ai_lite import guard

result = guard(
    token_stream,
    facts={"capital": "Paris is the capital of France."},
    prompt="What is the capital of France?",
)

director-ai-lite is a thin distribution wrapper around director_ai.lite; the halt implementation, heuristic scorer, optional model-backed scorer path, and session object are the same code used by the full director-ai package.

Supported Default Path

For new users and contributors, the supported default is Python-only:

pip install director-ai[server,vector]
director-ai quickstart --run
director-ai doctor

This path keeps setup to the Python package, FastAPI service, local Chroma persistence, and the CLI. Rust acceleration, the Go gateway, Julia tuning, Lean verification, TensorRT, and WASM are advanced optional paths. They should not be required for the base install, quickstart, or first contributor tasks.

Run director-ai doctor after enabling optional extras. It reports the active runtime stack and warns when environment settings request optional components that are not installed.

See Runtime Boundaries for the supported default path and the advanced runtime boundary.

Recommended Setup

Use the default path for first local runs, first production trials, and first contributor tasks:

pip install director-ai[server,vector]
director-ai quickstart --run
director-ai doctor

That gives one supported path before choosing specialized backends:

Layer	Default Choice	Why
Runtime	Python package	Smallest supported surface
API	FastAPI service	Local and production-compatible HTTP path
Proxy	Generated quickstart proxy	Standard guarded-chat entry point
Retrieval	Local Chroma	Persistent facts without external services
Diagnostics	director-ai doctor	Checks optional runtime drift

Advanced Backend Matrix

Pick an advanced backend only when its capability is required. The base install and quickstart must remain usable without these extras.

Scoring and Inference

Extra	Command	Adds
nli	pip install director-ai[nli]	FactCG-DeBERTa-v3-Large NLI (75.6% per-ds mean BA, 14.6ms/pair ONNX GPU)
embed	pip install director-ai[embed]	Embedding cosine-similarity scorer (~65% BA, 3ms CPU)
nli-lite	pip install director-ai[nli-lite]	Experimental distilled NLI backend; production claims require a validated student artefact
minicheck	pip install director-ai[minicheck]	MiniCheck alternative
onnx	pip install director-ai[onnx]	ONNX Runtime inference (14.6ms/pair GPU, portable CPU fallback)
quantize	pip install director-ai[quantize]	bitsandbytes 8-bit quantization

Retrieval and Knowledge Base

Extra	Command	Adds
vector	pip install director-ai[vector]	ChromaDB vector store
embeddings	pip install director-ai[embeddings]	sentence-transformers (bge-large)

Provider and Agent Integrations

Extra	Command	Adds
openai	pip install director-ai[openai]	OpenAI SDK guard
anthropic	pip install director-ai[anthropic]	Anthropic SDK guard
langchain	pip install director-ai[langchain]	LangChain integration
llamaindex	pip install director-ai[llamaindex]	LlamaIndex integration
langgraph	pip install director-ai[langgraph]	LangGraph integration
haystack	pip install director-ai[haystack]	Haystack integration
crewai	pip install director-ai[crewai]	CrewAI integration
guardrails	pip install director-ai[guardrails] guardrails-ai	Guardrails AI custom validator; install Guardrails AI from an index that provides it
Vercel AI SDK	cd packages/vercel-ai && npm install && npm run build	TypeScript AI SDK middleware

Operations, Training, and Development

Extra	Command	Adds
finetune	pip install director-ai[finetune]	Experimental model-adaptation tooling for evidence-gated evaluation work; not a default production accuracy claim
grpc	pip install director-ai[grpc]	gRPC server (grpcio, protobuf)
physical	pip install director-ai[physical]	MuJoCo adapter support; ROS 2 and CARLA use their vendor installs behind the same runtime boundary
server	pip install director-ai[server]	FastAPI server
otel	pip install director-ai[otel]	OpenTelemetry tracing
enterprise	pip install director-ai[enterprise]	Multi-tenant, audit, Redis
docs	pip install director-ai[docs]	MkDocs documentation tools
dev	pip install director-ai[dev]	pytest, ruff, mypy, sphinx

Production Presets

pip install director-ai[nli,vector,embeddings,openai]

FactCG NLI (75.6% per-dataset mean BA, 14.6ms/pair ONNX GPU batch), ChromaDB + bge-large embeddings, OpenAI SDK interception.

For managed service deployments, start from the recommended setup first and add provider, NLI, or observability extras only after director-ai doctor reports the base stack as healthy.

GPU Acceleration

For GPU-accelerated NLI scoring:

pip install director-ai[nli,quantize]

Set device and dtype:

from director_ai import CoherenceScorer

scorer = CoherenceScorer(
    use_nli=True,
    nli_device="cuda",
    nli_torch_dtype="float16",
    nli_quantize_8bit=True,  # reduces VRAM, slightly slower than fp32
)

Python Version

Requires Python 3.11+.