Optional extras

The core autoair package installs only what every mode needs (FastAPI, Pydantic, httpx2, fsspec, Jinja, jedi, Typer, PyYAML, cryptography, Pillow and the Prometheus client). Everything else is an extra. A feature whose extra is missing is either switched off with a log message or reports a clear error that names the extra to install.

Not every extra can be installed next to every Airflow version: mcp does not resolve with Airflow 3.1. See Airflow compatibility for the matrix.

Installing extras

Extras go in square brackets. Quote the requirement so the shell does not expand the brackets.

# pip
pip install 'autoair[standalone,format,git,mcp,collab,postgres]'

# uv, in a project
uv add 'autoair[standalone,format,git,mcp,collab,postgres]'

# uv, one-off tool run
uvx --from 'autoair[standalone,airflow]' autoair local

# a local wheel
pip install "dist/autoair-0.1.0-py3-none-any.whl[standalone,format]"

Inside an Airflow image, install AutoAir with the extras in the same pip install as your providers, and pin apache-airflow to the image's version so pip does not change it (the demo Dockerfile does pip install "apache-airflow==${AIRFLOW_VERSION}" "<wheel>[standalone,format,git,ai,ai-openai,ai-anthropic,collab,collab-redis,postgres,otel,yaml]" apache-airflow-providers-standard apache-airflow-providers-git).

Which extras for which mode

Mode	Recommended extras
Local mode	standalone,airflow (add format for formatted output)
Airflow plugin	format,git,mcp plus ai and a provider extra such as ai-anthropic for AI (add collab, postgres, otel, shell as needed); never airflow (Airflow is already installed) and not standalone (Airflow's API server serves AutoAir). On Airflow 3.1 leave out mcp and run the MCP server separately
Standalone server	standalone,format,git,mcp,collab plus postgres for several replicas and ai,ai-<provider> for AI
Separate AI service	standalone,mcp,ai plus the ai-* provider extras you use (the main server then needs no ai extra)

Reference

Runtime and server

Extra	Installs	Enables	Turned on by
airflow	apache-airflow>=3.0,<4, apache-airflow-providers-standard	A standard operator palette (the operators and sensors of apache-airflow-providers-standard, such as BashOperator, PythonOperator and EmptyOperator) and the DagBag import check when AutoAir runs without an Airflow installation, mainly for autoair local	Installed packages are picked up automatically. In local mode import_check_on_deploy is turned on only when Airflow is importable.
standalone	uvicorn[standard]	The HTTP server behind autoair serve, autoair local, autoair mcp-serve --http and autoair ai-serve. Without it these commands exit with code 1 and a message naming the extra to install (autoair local also suggests airflow while Airflow is not installed).	Running one of those commands. Not needed in plugin mode.
format	ruff	ruff format of generated DAG files and live ruff lint of Python task code in the editor	Automatic when ruff is found on PATH or importable. Tune with codegen.line_length, codegen.format_timeout, editor.python_lint_select. Without ruff, generated code is emitted unformatted.
yaml	pyyaml	YAML configuration files (AUTOAIR_CONFIG_FILE=/etc/autoair/autoair.yaml)	PyYAML is also a core dependency; the extra is a stable install name. A missing PyYAML is reported as "YAML configuration files need PyYAML: pip install 'autoair[yaml]'".
otel	opentelemetry-instrumentation-fastapi, opentelemetry-sdk, opentelemetry-exporter-otlp-proto-http	OpenTelemetry tracing of the API	AUTOAIR_OTEL_ENABLED=true. Exporter settings use the standard OTEL_* variables (OTEL_EXPORTER_OTLP_ENDPOINT, OTEL_SERVICE_NAME); inside Airflow an existing tracer provider is reused. server.otel_excluded_urls skips health, ready and metrics.

Deploy targets and state

Extra	Installs	Enables	Turned on by
git	nothing	Stable install name for the Git deploy target	AUTOAIR_STORAGE__MODE=git, AUTOAIR_STORAGE__GIT__PROVIDER, AUTOAIR_STORAGE__GIT__REPO and credentials (AUTOAIR_STORAGE__GIT__CONN_ID or AUTOAIR_STORAGE__GIT__TOKEN); every option is listed under storage.git. The Git target talks to GitHub, GitLab, Bitbucket and Azure DevOps through their REST APIs; storage.git.provider=local (any remote) and gitea (Gitea or Forgejo) run the git command-line tool, which must be on PATH.
sqlite	sqlalchemy (1.4.49 or newer; Airflow 3.0 installs 1.4)	AutoAir state (saved templates, the audit trail, reviews and deploy proposals, the deploy target chosen in the UI, custom icons, AI memory, the AI audit log, AI knowledge overrides, per-user AI model settings, collaboration room snapshots, users seen and replica heartbeats) in one SQLite file	AUTOAIR_DATABASE__BACKEND=sqlite, optionally AUTOAIR_DATABASE__URL=sqlite:////var/lib/autoair/autoair.db (default <state>/autoair.db). Suits several workers on one host.
postgres	sqlalchemy, psycopg[binary]	AutoAir state in PostgreSQL, shared by every replica	AUTOAIR_DATABASE__BACKEND=postgresql, AUTOAIR_DATABASE__URL=postgresql+psycopg://autoair@db:5432/autoair, AUTOAIR_DATABASE__PASSWORD. Missing driver: "cannot load the postgresql driver (...): pip install 'autoair[postgres]'". On Airflow 3.0 (SQLAlchemy 1.4) the driver is psycopg2: see PostgreSQL on Airflow 3.0.

note

The git extra is empty: every Git provider uses either the host's REST API (over httpx2, a core dependency) or the git CLI, so AutoAir needs no Python Git library. The extra only keeps autoair[git] (and autoair[all]) installable under a stable name; a Git deploy target works without it as long as the git binary is installed where the local or gitea provider runs. (Airflow's GitDagBundle needs apache-airflow-providers-git on the Airflow side, which brings its own dependencies.)

Object storage targets (AUTOAIR_STORAGE__MODE=object) use fsspec, which is a core dependency; install the fsspec implementation for your store yourself (s3fs, gcsfs, adlfs). See Object storage and Database.

Collaboration

Extra	Installs	Enables	Turned on by
collab	pycrdt, pycrdt-websocket	Live co-editing (Yjs over WebSockets at /collab/<dag_id>)	AUTOAIR_FEATURES__COLLAB=true (default). Without the packages the /collab WebSocket endpoint is not mounted, so co-editing is unavailable. Several standalone replicas also need AUTOAIR_COLLAB_SECRET (or a shared database) and collab.sync.
collab-redis	redis	Fan-out of collaboration rooms through Redis pub/sub	AUTOAIR_COLLAB__SYNC=redis and AUTOAIR_COLLAB__REDIS_URL=redis://redis:6379/0. collab.sync=auto (default) picks Redis when collab.redis_url is set.

Agents and AI

Extra	Installs	Enables	Turned on by
mcp	fastmcp, mcp	The MCP endpoint at /mcp (plugin: /autoair/mcp/), autoair mcp (stdio) and autoair mcp-serve	AUTOAIR_FEATURES__MCP=true (default). Without the packages the server logs "MCP endpoint disabled: ... (install autoair[mcp])", /api/meta reports mcp_unavailable_reason and the admin Features tab shows mcp as Not installed. Writes are controlled by ai.mcp.allow_writes. Cannot be installed next to Airflow 3.1 (why).
ai	pydantic-ai-slim	The AI runtime (pydantic-ai, as used by Airflow's common.ai provider; compatible with it in one environment). Without it AI is off: /api/meta reports ai_unavailable_reason, the admin panel shows the hint, only GET /api/ai/status answers, the AI MCP tools are hidden and the admin Features tab shows ai as Not installed and autoair ai-serve exits with the extra to install.	Install it with a provider extra, then configure AUTOAIR_AI__PROVIDER, AUTOAIR_AI__MODEL, AUTOAIR_AI__API_KEY (or AUTOAIR_AI__LLM_CONN_ID, a pydanticai connection).
ai-anthropic	pydantic-ai-slim[anthropic] (the anthropic SDK)	Claude	AUTOAIR_AI__PROVIDER=anthropic
ai-openai	pydantic-ai-slim[openai] (the openai SDK)	OpenAI, Azure OpenAI and OpenAI-compatible servers (vLLM, LM Studio, LiteLLM)	AUTOAIR_AI__PROVIDER=openai / azure; AUTOAIR_AI__BASE_URL for compatible servers
ai-google	pydantic-ai-slim[google] (google-genai)	Gemini and Vertex AI	AUTOAIR_AI__PROVIDER=gemini / google-cloud
ai-ollama	pydantic-ai-slim[openai]	Ollama (its OpenAI-compatible API)	AUTOAIR_AI__PROVIDER=ollama, AUTOAIR_AI__BASE_URL=http://ollama:11434
ai-bedrock, ai-groq, ai-mistral, ai-cohere	pydantic-ai-slim[bedrock] / [groq] / [mistral] / [cohere]	Amazon Bedrock, Groq, Mistral, Cohere	AUTOAIR_AI__PROVIDER=bedrock / groq / mistral / cohere and AUTOAIR_AI__MODEL
ai-all	every provider extra above	All providers
ai-rerank	sentence-transformers (pulls PyTorch; large)	Local cross-encoder reranking for AI retrieval	AUTOAIR_AI__RETRIEVAL__RERANKER=cross_encoder, optional AUTOAIR_AI__RETRIEVAL__RERANK_MODEL. Without it the reranker is skipped with a warning.
ai-guardrails	llm-guard	The LLM Guard input scanners as an extra guardrail	AUTOAIR_AI__GUARDRAILS__EXTERNAL='["llm_guard"]'. NeMo Guardrails (nemo:<config dir>) and Guardrails AI (guardrails_ai) need nemoguardrails or guardrails-ai installed separately.

See AI providers, Guardrails, AI service and MCP.

Documentation knowledge base

The knowledge base works without these extras on its default local store (SQLite, no dependencies).

Extra	Installs	Enables	Turned on by
kb-pgvector	sqlalchemy (1.4.49 or newer), psycopg[binary] (psycopg2 on SQLAlchemy 1.4)	The pgvector knowledge-base store in PostgreSQL	AUTOAIR_KB__STORE=pgvector, optionally AUTOAIR_KB__PGVECTOR__URL (default: AutoAir's own database when database.backend=postgresql)
kb-elasticsearch	nothing	Stable install name for the elasticsearch store, which uses the REST API over httpx2	AUTOAIR_KB__STORE=elasticsearch, AUTOAIR_KB__ELASTICSEARCH__URL

See Knowledge base.

Editors

Extra	Installs	Enables	Turned on by
shell	shellcheck-py (the ShellCheck binary)	ShellCheck diagnostics for bash_command, @task.bash and .sh script files, in the editor and as validation warnings	Automatic when installed. editor.shellcheck_in_validation, editor.shellcheck_exclude, editor.shellcheck_exclude_extra, editor.shellcheck_timeout.
lsp	python-lsp-server, python-lsp-ruff	IDE-grade Python IntelliSense through pylsp	AUTOAIR_FEATURES__IDE_LANGUAGE_SERVER=true and AUTOAIR_LSP__PYTHON_SERVER=pylsp
lsp-pyright	basedpyright (bundles Node through nodejs-wheel)	IDE-grade Python IntelliSense through basedpyright	AUTOAIR_FEATURES__IDE_LANGUAGE_SERVER=true (lsp.python_server defaults to basedpyright)

Without a language server the editors keep the built-in jedi completion, hover and signature help. Bash IntelliSense through a language server needs npm i -g bash-language-server (lsp.bash_command); without it shell fields keep the built-in completion and ShellCheck. Limits: lsp.max_sessions (16), lsp.max_sessions_per_user (4), lsp.idle_timeout (600 s), lsp.start_timeout (30 s), lsp.memory_limit_mb (2048), lsp.cpu_limit_seconds (3600) and lsp.max_message_bytes (8 MiB); see lsp in the configuration reference.

Astronomer Blueprint

Extra	Installs	Enables	Turned on by
blueprint	airflow-blueprint>=0.3,<1	Config schemas, task previews and config validation of Blueprint steps (templates are imported in a child process). Airflow needs the package too, to parse the generated DAGs	features.blueprints (default on) and templates from entry points, blueprints.template_dirs or blueprints.modules

all

autoair[all] is a shortcut for standalone,format,git,ai,ai-anthropic,ai-openai,otel,collab,shell,yaml: everything that installs next to every supported Airflow. It does not include mcp (it cannot be installed with Airflow 3.1), airflow, sqlite, postgres, collab-redis, kb-pgvector, kb-elasticsearch, the other ai-* extras (ai-google, ai-bedrock, ..., ai-rerank, ai-guardrails), lsp or lsp-pyright; add those explicitly.

pip install 'autoair[all,mcp,postgres]'   # Airflow 3.0, 3.2 and newer, or standalone
pip install 'autoair[all,postgres]'       # Airflow 3.1

Checking what is active

• autoair config show --changed prints the settings that differ from the defaults and where each came from.
• The admin panel shows the AutoAir version, the state backend and its schema version, and the operator catalog statistics. See Admin panel.
• Server logs name each optional feature that was switched off because its extra is missing.