Dependency Footprint¶

textfsm-ai supports 18 LLM providers, but no single install needs all of their SDKs at once. Since v0.6.0, pip install textfsm-ai installs only the core CLI/API — every provider SDK is an opt-in extra, either via pip install textfsm-ai[<provider>] or a matching pip install -r requirements/requirements-<provider>.txt file (see Installation for both). This page documents exactly what each extra pulls in, verified with real, clean-venv installs — useful when you're sizing a container image or just curious where the weight goes.

Bare install: 5 packages¶

pip install textfsm-ai

Installs only textfsm_ai itself plus its 4 core dependencies: PyYAML, requests, click, textfsm (tomli also lists as a dependency, but only actually installs on Python <3.11).

This is fully functional on its own — import textfsm_ai, the CLI, and textfsm-ai providers list all work. Only using a provider requires its extra:

>>> import textfsm_ai
>>> textfsm_ai.generate("sample", provider="anthropic", api_key="x", model="y")
ImportError: Provider 'anthropic' requires additional dependencies that
are not installed. Install with: pip install textfsm-ai[anthropic]

Per-provider package counts¶

Extra	Total packages	What makes up the difference
`[azure]`	14	Lightest — reuses `requests` directly, no `httpx`/`pydantic` stack
`[bedrock]`	16	`botocore` is the single largest file (~15MB, bundles every AWS service's API definitions), but pulls few extra packages
`[oci]`	22	Per-request cryptographic signing needs `cryptography` + `pyOpenSSL` + `PyJWT` (no bearer API key at all)
`[openai]` (+ 9 aliases below)	24	`httpx` (sync+async client) + `pydantic` (typed request/response models)
`[anthropic]`	24	Same `httpx` + `pydantic` stack as `openai`, plus `jiter`/`distro`/`docstring_parser`
`[gemini]` / `[vertexai]`	31	`httpx`+`pydantic`, plus `google-auth` → `cryptography` + `pyasn1` (for Application Default Credentials)
`[cohere]`	31	Unexpectedly pulls in `tokenizers` + all of `huggingface_hub` (local token counting)
`[mistral]`	34	Heaviest — a full OpenTelemetry SDK (`opentelemetry-api`/`-sdk`/exporters) plus `protobuf`, `googleapis-common-protos`, and `invoke` (a task-runner library)

[openai] also covers deepseek, groq, xai, together, fireworks, cerebras, perplexity, openrouter, and moonshot at no extra package cost — all nine subclass the same OpenAI-compatible chat- completions client and need nothing beyond the openai package itself.

Package count and disk size are two different axes. oci downloads the single largest file (~36MB) but installs fewer total packages (22) than cohere or mistral (31/34) — a heavy SDK author can produce either a few large files or many small ones.

Why some SDKs are heavier than others¶

The differences trace back to each provider's own upstream SDK design, not anything this package controls:

HTTP client choice: httpx (async-capable, used by openai, anthropic, gemini, cohere, mistral) pulls in httpcore, h11, anyio, and sniffio on top of itself. azure and bedrock reuse the plain, sync-only requests this package already depends on core.
Request/response validation: pydantic (used by most of the httpx-based SDKs) brings its own compiled-Rust pydantic_core plus annotated-types/typing-inspection.
Credential machinery: gemini/vertexai need google-auth for Application Default Credentials, which needs cryptography to verify signed service-account JWTs; oci needs cryptography+pyOpenSSL+ PyJWT because it signs every request itself rather than sending a bearer token.
Unrelated SDK features: cohere's tokenizers dependency (for local token counting) and mistral's OpenTelemetry stack (for built-in tracing) have nothing to do with making a chat-completions call, but ship as hard requirements of those SDKs regardless.

How this was verified¶

Every number above comes from an actual pip install into a fresh, empty virtual environment (not from reading pyproject.toml alone), followed by pip list and a functional check that the installed provider resolves via get_provider_by_name() while every other provider correctly raises the pip install textfsm-ai[...] error. See textfsm_ai/providers/registry.py for the lazy-loading mechanism that makes this possible.