llm-anthropic-general

Register Anthropic-protocol-compatible providers as first-class llm models.

Why

llm-anthropic hardcodes its model list in register_models and reads no configuration of its own. Its base_url is only settable as a Python attribute, and needs_key is fixed at anthropic. The single available override is the process-wide ANTHROPIC_BASE_URL environment variable, which redirects every Anthropic model at once and leaves Anthropic's model names in your logs.

This plugin is the per-model equivalent of llm's built-in extra-openai-models.yaml, for hosts that speak the Anthropic protocol rather than the OpenAI one:

• Each model gets its own namespace (zai/glm-5.3-flash), instead of every model being forced under anthropic/.
• Each provider gets its own key entry, so zai and anthropic keys stay separate.
• Real Anthropic models are untouched.

Requirements

• Python 3.13 or newer
• uv
• llm 0.34 or newer, with llm-anthropic 0.28 or newer

Install

Into the llm environment:

llm install llm-anthropic-general

From a local checkout:

llm install -e .

Development setup:

uv sync

Quick start

llm keys set zai            # paste the key when prompted
llm -m zai/glm-5.3-flash "hello"   # or the alias: llm -m glm "hello"
llm models list | grep glm

Built-in models

Registered with no configuration file present. Any of them can be replaced by a configuration entry using the same model_id.

Both base URLs were verified against provider documentation on 2026-09-06.

The Qwen host is the QwenCloud Token Plan endpoint for ap-southeast-1, not the generic dashscope-intl host. A QwenCloud key is bound to both a plan and its base URL, so a Token Plan key (prefix sk-sp-) authenticates only against this host, and a pay-as-you-go key (prefix sk-) will not. On a different plan or region, override base_url in the configuration file.

Providers move endpoints and retire models, so check before assuming a failure is a bug in this plugin.

DashScope publishes many more Anthropic-protocol model ids than the one shipped here, including the qwen3.8-max, qwen3.6-plus, qwen3.8-flash and qwen3-coder-plus families. Add whichever you use as configuration entries.

Do not append /v1 to a base URL. The Anthropic client adds its own path segment, and a base URL ending in /v1 produces a duplicated /v1/v1/... request that returns HTTP 404.

Configuration

Create extra-anthropic-models.yaml in llm's user directory. Find it with:

dirname "$(llm logs path)"

The file is a list of model entries:

- model_id: glm-5.3-flash
  prefix: zai
  base_url: https://api.z.ai/api/anthropic
  needs_key: zai
  key_env_var: ZAI_API_KEY
  aliases:
    - glm
  supports_thinking: true
  default_max_tokens: 8192

- model_id: my-proxy-sonnet
  claude_model_id: claude-sonnet-4-5
  base_url: https://gateway.internal.example/anthropic
  needs_key: gateway

Entry fields

Capability fields

All optional, all passed through to llm-anthropic. An unrecognized field is reported as an error rather than silently ignored.

Model id namespacing

Every provider plugin namespaces its models: llm-openrouter registers openrouter/..., llm-gemini registers gemini/gemini-2.5-flash, llm-anthropic registers anthropic/.... This plugin follows that convention, with the prefix chosen per entry rather than fixed.

It is not cosmetic. llm resolves models through a flat dictionary keyed by id (llm.get_model_aliases), so two models registering the same id silently overwrite one another - no warning, and the survivor depends on registration order. A prefix is what lets the same model name served by two hosts coexist.

Configuration-file entries are unprefixed unless they set prefix, matching llm's own extra-openai-models.yaml, which registers model_id verbatim. The shipped built-ins set it explicitly.

The prefix affects only the id llm exposes. The provider still receives claude_model_id (defaulting to model_id), so zai/glm-5.3-flash is sent over the wire as glm-5.3-flash.

How it works

model_id, needs_key and key_env_var are llm's documented configuration surface for a model. model_id is declared on llm.models._BaseModel, and needs_key (required) and key_env_var (optional) are the attributes the plugin documentation tells authors to set on an llm.KeyModel subclass - see "Models that accept API keys" in llm's docs/plugins/advanced-model-plugins.md.

llm_anthropic._Shared.__init__ already accepts base_url, but it also forces self.model_id = "anthropic/" + model_id and takes needs_key / key_env_var from class attributes. So this plugin subclasses ClaudeMessages and AsyncClaudeMessages and sets the three attributes in the subclass constructor, after super().__init__(). That is the same thing llm-gemini does to build gemini/{id} and llm-openrouter to build openrouter/{id}; the only difference is that the assignment must follow the parent constructor rather than sit in the class body.

A malformed entry is logged and skipped rather than raised, so one bad line cannot stop llm from starting or take the other models down with it.

Development

uv sync                      # install dependencies
uv run pytest                # run tests
uv run pytest --cov          # run tests with coverage
uv run ruff check --fix .    # lint and autofix
uv run ruff format .         # format
uv run mypy                  # type check

License

MIT