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Providers and models ​

Pythinker Code CLI supports connecting to multiple LLM platforms simultaneously — one-click login via the Pythinker Code managed service, connecting Claude with an Anthropic API key, or connecting third-party inference services via the OpenAI-compatible protocol. Each provider corresponds to a specific API protocol; models are declared on top of providers with their own name, context length, and capabilities. This page explains how to configure each type of provider in config.toml.

Supported provider types ​

The type field in the providers table determines which protocol implementation to use:

TypeProtocolTypical use
pythinkerOpenAI-compatiblePythinker Code managed service, Pythinker Platform API key
anthropicAnthropic MessagesClaude model family
openaiOpenAI Chat CompletionsOpenAI and compatible services, DeepSeek, Qwen, etc.
openai_responsesOpenAI Responses APIOpenAI's newer Responses interface
google-genaiGoogle GenAIGemini API
vertexaiGoogle GenAI on VertexGoogle Cloud Vertex AI

All providers communicate with models in streaming mode by default. Capabilities such as thinking, vision, and tool use are matched automatically by model name prefix — you typically do not need to declare them manually.

Credential priority: api_key direct field > the shell variable named by api_key_env_var > [providers.<name>.env] sub-table key > if all are absent, startup fails with an error. Shell variables are never guessed: a provider must name one with api_key_env_var. See Config overrides: provider credentials.

/provider — interactive provider management ​

Prefer not to edit TOML by hand? Type /provider in the TUI to open the provider manager, where you can interactively add or remove providers.

The manager displays providers as a list of entries grouped by source. Navigation:

  • ↑/↓ to move the cursor, ←/→ to page
  • d to delete the current provider (with [y/N] confirmation)
  • Press Enter on the [ Add New Platform ] row to add a new provider

Two paths when adding:

  • Known third-party provider: fetches the model catalog from models.dev, validates the catalog-declared credential variable from the shell, then lets you select a default model. Set the variable before starting the TUI; the token is not written to config.toml.
  • Custom registry (api.json): paste a custom registry URL and Bearer token; the CLI automatically creates the providers / models entries. On later startup, providers from the same registry URL are refreshed together, so upstream provider additions, removals, and model metadata changes are synced.

WARNING

Pythinker Code OAuth managed accounts logged in via /login do not appear in /provider. Use /login and /logout to manage them.

The same operations are also available in non-interactive environments via the shell command: pythinker provider.

Catalog-backed API and coding-plan providers ​

Catalog-backed setup uses live provider, endpoint, model, capability, and context-limit metadata from models.dev. Set the provider-issued API key or coding-plan token in the shell, then import the catalog entry:

sh
export DEEPSEEK_API_KEY=YOUR_DEEPSEEK_API_KEY
pythinker provider catalog add deepseek

export ZHIPU_API_KEY=YOUR_ZHIPU_API_KEY
pythinker provider catalog add zai-coding-plan

export MINIMAX_API_KEY=YOUR_MINIMAX_API_KEY
pythinker provider catalog add minimax-coding-plan

export KIMI_API_KEY=YOUR_KIMI_API_KEY
pythinker provider catalog add kimi-for-coding

The repository's .env.example lists these placeholder names, but Pythinker Code does not load .env files automatically. Export the values or provide them through your shell, CI, or secret manager before running the command or opening the TUI.

Catalog import stores only the environment-variable name. A DeepSeek import, for example, produces this provider shape plus live model aliases:

toml
[providers.deepseek]
type = "openai"
base_url = "https://api.deepseek.com"
api_key_env_var = "DEEPSEEK_API_KEY"
source = { kind = "modelsDev", url = "https://models.dev/api.json" }

DeepSeek and GLM Coding Plan use the existing OpenAI-compatible adapter. MiniMax Token Plan and Kimi For Coding use the existing Anthropic-compatible adapter. Catalog refresh updates catalog-derived model metadata while retaining api_key_env_var and user selections. Models, limits, and reasoning controls can change upstream, so pythinker provider catalog list <providerId> is authoritative at setup time. Plan quotas and rate limits remain enforced by the upstream provider; Pythinker Code does not predict or enforce subscription allowance locally.

How to add another catalog provider ​

  1. Run pythinker provider catalog list --filter <name> and inspect the provider with pythinker provider catalog list <providerId>.
  2. Set the first credential variable declared by that catalog entry. To use a different variable name, pass --api-key-env <name>.
  3. Run pythinker provider catalog add <providerId>, optionally with --default-model <modelId>.

Compatible providers need no provider-specific class: the catalog selects an existing wire adapter and supplies the endpoint and model metadata. Providers that require extra account fields or an unsupported transport are rejected instead of being guessed.

pythinker ​

For connecting to Pythoughts's OpenAI-compatible interface, including the Pythinker Code managed service and Pythinker Platform API keys.

  • Default base_url: https://api.pythoughts.ai/v1
  • Credential key names: PYTHINKER_API_KEY, PYTHINKER_BASE_URL
  • Additional capability: supports video upload
toml
[providers.pythinker]
type = "pythinker"
base_url = "https://api.pythoughts.ai/v1"
api_key = "sk-xxxxx"

When using the Pythinker Code managed service, running /login automatically configures base_url and credentials — no manual setup needed.

anthropic ​

For connecting to the Claude API. Standard Claude models automatically enable vision, tool use, and Thinking (where supported); custom or uncovered models need capabilities declared explicitly on [models.<alias>].

  • Default base_url: follows Anthropic SDK default
  • Credential key names: ANTHROPIC_API_KEY, ANTHROPIC_BASE_URL
  • Default max_tokens: inferred per model. To override, set max_output_size on the model alias
toml
[providers.anthropic]
type = "anthropic"
api_key = "sk-ant-xxxxx"

[models."claude-opus-4-7"]
provider = "anthropic"
model = "claude-opus-4-7"
max_context_size = 200000
# max_output_size = 32000  # optional; omit to use the model-inferred default

openai ​

For connecting to the OpenAI Chat Completions protocol, as well as any third-party service compatible with that protocol (override base_url as needed).

Third-party reasoning models (DeepSeek, Qwen, One API, etc.) work out of the box: the CLI automatically handles the reasoning_content field and reasoning_effort injection. If your gateway returns reasoning content under a non-standard field name, set reasoning_key on the model alias to override.

  • Default base_url: https://api.openai.com/v1
  • Credential key names: OPENAI_API_KEY, OPENAI_BASE_URL
toml
[providers.openai]
type = "openai"
base_url = "https://api.openai.com/v1"
api_key = "sk-xxxxx"

openai_responses ​

Corresponds to OpenAI's newer Responses API, always operating in streaming mode. Configuration is the same as openai.

  • Default base_url: https://api.openai.com/v1
  • Credential key names: OPENAI_API_KEY, OPENAI_BASE_URL
toml
[providers.openai-responses]
type = "openai_responses"
base_url = "https://api.openai.com/v1"
api_key = "sk-xxxxx"

google-genai ​

For connecting directly to the Google Gemini API. Thinking, vision, and multimodal capabilities are auto-detected by model name.

  • Credential key name: GOOGLE_API_KEY
toml
[providers.gemini]
type = "google-genai"
api_key = "xxxxx"

vertexai ​

Shares the same implementation as google-genai; setting type = "vertexai" switches to the Vertex AI access path.

Authentication follows the standard Google Cloud ADC flow (gcloud auth application-default login or a GOOGLE_APPLICATION_CREDENTIALS service account JSON) — this part is unrelated to Pythinker Code. The project ID and region must be written in the [providers.vertexai.env] sub-table — simply export GOOGLE_CLOUD_PROJECT in the shell will not be read by the CLI.

toml
[providers.vertexai]
type = "vertexai"

[providers.vertexai.env]
GOOGLE_CLOUD_PROJECT = "my-gcp-project"
GOOGLE_CLOUD_LOCATION = "us-central1"
sh
gcloud auth application-default login   # one-time authentication
pythinker

OAuth and credential injection ​

The Pythinker Code managed service uses OAuth rather than static API keys. After running /login, the built-in authentication toolchain automatically writes and refreshes credentials — no manual configuration is needed in config.toml for this.

Next steps ​