Providers and models · AI Engineering

Every provider id docker-agent 1.149.0 knows, the credential it reads, the provider/model reference form, the DMR endpoint order, the auto rule, and the keys that tune a model.

docker-agent doctor printed 20 providers with their credential variables on the capture machine, every one not set (our source record). The docs name 31 ids, and the schema description of provider lists the built-in aliases {{schema ProviderConfig}}. The table below is the union, in the order of the doctor output and then of the docs. The doctor prints canonical ids such as fireworks-ai, and the legacy ids stay accepted as aliases (conflict C58).

The provider ids

Provider id	Legacy id	Credential
`anthropic`	none	`ANTHROPIC_API_KEY`, or `auth.type: workload_identity_federation`
`openai`	none	`OPENAI_API_KEY`
`chatgpt`	none	none. A browser sign-in through `docker agent setup`, shown as `CHATGPT_OAUTH_TOKEN`
`github-copilot`	none	`GITHUB_TOKEN` or `GH_TOKEN`, a PAT with the `copilot` scope
`google`	none	`GOOGLE_API_KEY` or `GEMINI_API_KEY`. Vertex AI uses `GOOGLE_GENAI_USE_VERTEXAI`, `GOOGLE_CLOUD_PROJECT`, `GOOGLE_CLOUD_LOCATION`
`mistral`	none	`MISTRAL_API_KEY`
`openrouter`	none	`OPENROUTER_API_KEY`
`baseten`	none	`BASETEN_API_KEY`
`ovhcloud`	none	`OVH_AI_ENDPOINTS_ACCESS_TOKEN`
`groq`	none	`GROQ_API_KEY`
`fireworks-ai`	`fireworks`	`FIREWORKS_API_KEY`
`deepseek`	none	`DEEPSEEK_API_KEY`
`cerebras`	none	`CEREBRAS_API_KEY`
`togetherai`	`together`	`TOGETHER_API_KEY`
`huggingface`	none	`HF_TOKEN`
`moonshotai`	`moonshot`	`MOONSHOT_API_KEY`
`vercel`	none	`AI_GATEWAY_API_KEY`
`amazon-bedrock`	none	`AWS_BEARER_TOKEN_BEDROCK`, or the AWS credential chain, which the doctor counts as three more variables
`opencode`	`opencode-zen`	`OPENCODE_API_KEY`
`opencode-go`	none	`OPENCODE_API_KEY`
`dmr`	none	none. A local Docker Model Runner
`ollama`	none	none. An optional `base_url`
`xai`	none	`XAI_API_KEY`
`nebius`	none	`NEBIUS_API_KEY`
`nvidia`	none	`NVIDIA_API_KEY`
`minimax`	none	`MINIMAX_API_KEY`
`cloudflare-workers-ai`	none	`CLOUDFLARE_API_TOKEN` and `CLOUDFLARE_ACCOUNT_ID`
`cloudflare-ai-gateway`	none	`CLOUDFLARE_API_TOKEN`, `CLOUDFLARE_ACCOUNT_ID`, and `CLOUDFLARE_GATEWAY_ID`
`requesty`	none	`REQUESTY_API_KEY`
`azure`	none	`AZURE_API_KEY` and a `base_url`
a name under `providers`	none	the variable in `token_key`

The doctor lists eleven ids that the docs tables do not count as providers, and the docs list ten that the doctor did not print. A credential can also come from ~/.config/cagent/.env, from a credential_helper command in the user config, from Docker Desktop, or from a 1Password op:// reference {{docs-agent Secrets}}.

The model reference

Form	Example	Meaning	Source
`provider/model`	`dmr/ai/qwen3`, `anthropic/claude-sonnet-4-5`	An inline model on a known provider	{{docs-agent Models}}
a name from `models`	`model: local`	A named model with its own keys	{{docs-agent Models}}
`name/model`	`my_gateway/gpt-4o`	A model on a provider defined under `providers`	{{docs-agent Provider Definitions}}
`a,b`	`anthropic/claude-sonnet-4-5,openai/gpt-5`	An alloy. The runtime alternates between the models in one conversation	{{docs-agent Models}}
`auto`	`model: auto`	The first cloud provider with a credential, else a pulled DMR model	{{docs-agent Set up a model}}
`first_available: [...]`	a list of references	The first candidate whose credentials are configured, resolved at load time	{{docs-agent Models}}
`--model [agent=]provider/model`	`--model root=dmr/ai/qwen3`	A CLI override, repeatable per agent	{{help-agent docker-agent run}}

The auto rule

auto picks the first cloud provider with a configured credential and then a locally pulled Docker Model Runner model {{docs-agent Set up a model}}. On the DMR side it prefers the model named in model: when that model is already pulled. Otherwise it takes the first available non-embedding model, instead of asking to pull ai/qwen3:latest {{docs-agent Docker Model Runner}}. It never picks a provider defined under providers {{docs-agent Provider Definitions}}. DOCKER_AGENT_DEFAULT_MODEL sets the model used when none is given {{docs-agent User settings}}. On the capture machine, with no credential and the runner unreachable, the doctor still printed auto -> dmr/ai/qwen3:latest and reported one issue (our source record).

The DMR endpoint

Step	Endpoint
1	`base_url` on the model or the provider, when set
2	the endpoint the `docker model` plugin reports. The doctor runs `docker model status --json` through the Desktop context
3	the default `http://127.0.0.1:12434/engines/llama.cpp/v1` when the plugin is not found
in a container	`http://model-runner.docker.internal/engines/v1`, with `unload_api: /engines/_unload` on the provider
unload	the `base_url` with the trailing `/v1` replaced by `_unload`, called by the `unload` builtin on `on_agent_switch`

The runner needs no API key, and the API is not authenticated (conflict C84). The docs state only that the default URL is used when the plugin is not found. The order above puts that default third, after base_url and the plugin, as the research file reads it.

The models gateway

--models-gateway and DOCKER_AGENT_MODELS_GATEWAY route model traffic through one address, and bypass_models_gateway: true or a custom base_url sends a model directly to its provider {{docs-agent Model Configuration}}. docker agent models asks the gateway /v1/models first and uses the providers with credentials when the gateway answers nothing usable {{docs-agent features/cli}}. The Docker gateway needs a Docker token. Desktop hands out one that lasts 15 minutes. When Desktop has none, docker-agent exchanges the docker login access token for a fresh one over HTTPS {{docs-agent Secrets}}. The exchange is cached under the cache directory, DOCKER_AGENT_NO_TOKEN_EXCHANGE=1 turns it off, and docker agent debug auth shows the token in use.

Keys that tune a model

Key	Applies to	Values	Source
`temperature`, `top_p`, `frequency_penalty`, `presence_penalty`	every provider	sampling parameters, sent per request	{{schema ModelConfig}}
`max_tokens`	every provider	output tokens per response, not the context window	{{schema ModelConfig}}
`thinking_budget`	OpenAI	`none`, `minimal`, `low`, `medium`, `high`, `xhigh`, `max`. `xhigh` needs gpt-5.2 or later, `none` and `max` need gpt-5.6 or later	{{schema ModelConfig}}
`thinking_budget`	Anthropic	an integer from 1024 to 32768, `adaptive`, `adaptive/<effort>`, or an effort level	{{schema ModelConfig}}
`thinking_budget`	Amazon Bedrock with Claude	an integer, or `low`, `medium`, `high`	{{schema ModelConfig}}
`thinking_budget`	Gemini 2.5	an integer, `-1` dynamic, `0` off, 24576 at most	{{schema ModelConfig}}
`thinking_budget`	Gemini 3	`minimal` on Flash only, `low`, `medium`, `high`	{{schema ModelConfig}}
`thinking_budget`	DMR on llama.cpp	sent as `llamacpp.reasoning-budget` through `_configure`. On vLLM as `thinking_token_budget` per request. Ignored on MLX and SGLang	{{docs-agent Docker Model Runner}}
`task_budget`	Anthropic	an integer or `{type: tokens, total: N}`, sent as `output_config.task_budget`	{{schema ModelConfig}}
`provider_opts.context_size`	DMR	the context window, sent through `_configure`. `max_tokens` is never the window	{{docs-agent Docker Model Runner}}
`provider_opts.runtime_flags`, `raw_runtime_flags`	DMR	flags for the inference runtime, as a list or one string. Exclusive with each other	{{docs-agent Docker Model Runner}}
`provider_opts.keep_alive`	DMR	a Go duration, `0` unloads at once, `-1` never unloads	{{docs-agent Docker Model Runner}}
`provider_opts.mode`	DMR	`completion`, `embedding`, `reranking`, `image-generation`	{{docs-agent Docker Model Runner}}
`provider_opts.speculative_draft_model`, `speculative_num_tokens`, `speculative_acceptance_rate`	DMR	speculative decoding with a draft model	{{docs-agent Docker Model Runner}}
`provider_opts.gpu_memory_utilization`, `hf_overrides`	DMR on vLLM	engine settings sent through `_configure`	{{docs-agent Docker Model Runner}}
`provider_opts.supports_images`, `supports_pdf`	DMR	declare attachment types, because DMR models are not in the models.dev catalogue	{{schema ModelConfig}}
`provider_opts.http_headers`	OpenAI-compatible providers	headers on every request, such as the Copilot integration id	{{schema ModelConfig}}
`fallback.models`, `retries` (`2`), `cooldown` (`1m`)	every provider	models tried after a failure, with backoff	{{schema FallbackConfig}}
`capabilities`, `output_capabilities`, `cost`	every provider	attachment flags, image output, and USD prices that override the catalogue	{{schema ModelConfig}}
`title_model`, `compaction_model`, `compaction_threshold` (`0.9`)	every provider	cheaper models for titles and summaries, and the compaction point	{{schema ModelConfig}}
`providers.<name>` defaults	every provider	`temperature`, `max_tokens`, `thinking_budget`, `task_budget`, and the other defaults a model inherits	{{docs-agent Provider Definitions}}

The docs give a default reasoning effort per provider {{docs-agent Models}}. It is medium on OpenAI always-reasoning models, off on Anthropic, -1 on Gemini 2.5, and model-dependent on Gemini 3.

Sources: research/sources/probes/docker-agent-doctor.txt, docker-agent-models-list.txt; research/sources/agent-schema.json (ModelConfig, ProviderConfig, FallbackConfig); research/sources/docs-docker-agent.md (pages concepts/models, configuration/models, providers/overview, providers/custom, providers/dmr, getting-started/set-up-a-model, guides/secrets, configuration/user-settings, features/cli); research/sources/help-docker-agent.md (run, models, doctor); /Users/rohitghumare/.cache/aiefs-manuals-wip/docker-research/03-stack.md note 14; research/conflicts-register.md rows C58, C84