Providers

Using any LLM provider in codeflow.

codeflow uses the AI SDK and Models.dev to support for 75+ LLM providers and it supports running local models.

To add a provider you need to:

Add the API keys for the provider using codeflow auth login.
Configure the provider in your codeflow config.

Credentials

When you add a provider’s API keys with codeflow auth login, they are stored in ~/.local/share/codeflow/auth.json.

Config

You can customize the providers through the provider section in your codeflow config.

Base URL

You can customize the base URL for any provider by setting the baseURL option. This is useful when using proxy services or custom endpoints.

{
  "$schema": "https://codeflow.ai/config.json",
  "provider": {
    "anthropic": {
      "options": {
        "baseURL": "https://api.anthropic.com/v1"
      }
    }
  }
}

Custom provider

To add any OpenAI-compatible provider that’s not listed in codeflow auth login:

Run codeflow auth login and scroll down to Other.

$ codeflow auth login

┌  Add credential
│
◆  Select provider
│  ...
│  ● Other
└

Enter a unique ID for the provider.
Terminal window
```
$ codeflow auth login

┌  Add credential
│
◇  Enter provider id
│  myprovider
└
```
Choose a memorable ID, you’ll use this in your config file.

Enter your API key for the provider.

$ codeflow auth login

┌  Add credential
│
▲  This only stores a credential for myprovider - you will need configure it in codeflow.json, check the docs for examples.
│
◇  Enter your API key
│  sk-...
└

Create or update your codeflow.json file in your project directory:

{
  "$schema": "https://codeflow.ai/config.json",
  "provider": {
    "myprovider": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "My AI ProviderDisplay Name",
      "options": {
        "baseURL": "https://api.myprovider.com/v1"
      },
      "models": {
        "my-model-name": {
          "name": "My Model Display Name"
        }
      }
    }
  }
}

Here are the configuration options:

npm: AI SDK package to use, @ai-sdk/openai-compatible for OpenAI-compatible providers
name: Display name in UI.
models: Available models.
options.baseURL: API endpoint URL.
options.apiKey: Optionally set the API key, if not using auth.
options.headers: Optionally set custom headers.

More on the advanced options in the example below.

Run the /models command and your custom provider and models will appear in the selection list.

Example

Here’s an example setting the apiKey and headers options.

{
  "$schema": "https://codeflow.ai/config.json",
  "provider": {
    "myprovider": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "My AI ProviderDisplay Name",
      "options": {
        "baseURL": "https://api.myprovider.com/v1",
        "apiKey": "{env:ANTHROPIC_API_KEY}",
        "headers": {
          "Authorization": "Bearer custom-token"
        }
      },
      "models": {
        "my-model-name": {
          "name": "My Model Display Name"
        }
      }
    }
  }
}

We are setting the apiKey using the env variable syntax, learn more.

Directory

Let’s look at some of the providers in detail. If you’d like to add a provider to the list, feel free to open a PR.

Amazon Bedrock

To use Amazon Bedrock with codeflow:

Head over to the Model catalog in the Amazon Bedrock console and request access to the models you want.

You need to have access to the model you want in Amazon Bedrock.
You’ll need either to set one of the following environment variables:
- AWS_ACCESS_KEY_ID: You can get this by creating an IAM user and generating an access key for it.
- AWS_PROFILE: First login through AWS IAM Identity Center (or AWS SSO) using aws sso login. Then get the name of the profile you want to use.
- AWS_BEARER_TOKEN_BEDROCK: You can generate a long-term API key from the Amazon Bedrock console.
Once you have one of the above, set it while running codeflow.
Terminal window
```
AWS_ACCESS_KEY_ID=XXX codeflow
```
Or add it to a .env file in the project root.
.env
```
AWS_ACCESS_KEY_ID=XXX
```
Or add it to your bash profile.
~/.bash_profile
```
export AWS_ACCESS_KEY_ID=XXX
```
Run the /models command to select the model you want.

Anthropic

We recommend signing up for Claude Pro or Max, it’s the most cost-effective way to use codeflow.

Once you’ve singed up, run codeflow auth login and select Anthropic.

$ codeflow auth login

┌  Add credential
│
◆  Select provider
│  ● Anthropic (recommended)
│  ○ OpenAI
│  ○ Google
│  ...
└

Here you can select the Claude Pro/Max option and it’ll open your browser and ask you to authenticate.

$ codeflow auth login
┌  Add credential
│
◇  Select provider
│  Anthropic
│
◆  Login method
│  ● Claude Pro/Max
│  ○ Create API Key
│  ○ Manually enter API Key
└

Now all the the Anthropic models should be available when you use the /models command.

Using API keys

You can also select Create API Key if you don’t have a Pro/Max subscription. It’ll also open your browser and ask you to login to Anthropic and give you a code you can paste in your terminal.

Or if you already have an API key, you can select Manually enter API Key and paste it in your terminal.

Azure OpenAI

Head over to the Azure portal and create an Azure OpenAI resource. You’ll need:
- Resource name: This becomes part of your API endpoint (https://RESOURCE_NAME.openai.azure.com/)
- API key: Either KEY 1 or KEY 2 from your resource
Go to Azure AI Foundry and deploy a model.

The deployment name must match the model name for codeflow to work properly.

Run codeflow auth login and select Azure.

$ codeflow auth login

┌  Add credential
│
◆  Select provider
│  ● Azure
│  ...
└

Enter your API key.

$ codeflow auth login

┌  Add credential
│
◇  Select provider
│  Azure
│
◇  Enter your API key
│  _
└

Set your resource name as an environment variable:
Terminal window
```
AZURE_RESOURCE_NAME=XXX codeflow
```
Or add it to a .env file in the project root:
.env
```
AZURE_RESOURCE_NAME=XXX
```
Or add it to your bash profile:
~/.bash_profile
```
export AZURE_RESOURCE_NAME=XXX
```
Run the /models command to select your deployed model.

Cerebras

Head over to the Cerebras console, create an account, and generate an API key.

Run codeflow auth login and select Cerebras.

$ codeflow auth login

┌  Add credential
│
◆  Select provider
│  ● Cerebras
│  ...
└

Enter your Cerebras API key.

$ codeflow auth login

┌  Add credential
│
◇  Select provider
│  Cerebras
│
◇  Enter your API key
│  _
└

Run the /models command to select a model like Qwen 3 Coder 480B.

DeepSeek

Head over to the DeepSeek console, create an account, and click Create new API key.

Run codeflow auth login and select DeepSeek.

$ codeflow auth login

┌  Add credential
│
◆  Select provider
│  ● DeepSeek
│  ...
└

Enter your DeepSeek API key.

$ codeflow auth login

┌  Add credential
│
◇  Select provider
│  DeepSeek
│
◇  Enter your API key
│  _
└

Run the /models command to select a DeepSeek model like DeepSeek Reasoner.

Fireworks AI

Head over to the Fireworks AI console, create an account, and click Create API Key.

Run codeflow auth login and select Fireworks AI.

$ codeflow auth login

┌  Add credential
│
◆  Select provider
│  ● Fireworks AI
│  ...
└

Enter your Fireworks AI API key.

$ codeflow auth login

┌  Add credential
│
◇  Select provider
│  Fireworks AI
│
◇  Enter your API key
│  _
└

Run the /models command to select a model like Kimi K2 Instruct.

GitHub Copilot

To use your GitHub Copilot subscription with codeflow:

Run codeflow auth login and select GitHub Copilot.

$ codeflow auth login
┌  Add credential

│
◇  Select provider
│  GitHub Copilot
│
◇   ──────────────────────────────────────────────╮
│                                                 │
│  Please visit: https://github.com/login/device  │
│  Enter code: 8F43-6FCF                          │
│                                                 │
├─────────────────────────────────────────────────╯
│
◓  Waiting for authorization...

Navigate to github.com/login/device and enter the code.
Now run the /models command to select the model you want.

Groq

Head over to the Groq console, click Create API Key, and copy the key.

Run codeflow auth login and select Groq.

$ codeflow auth login

┌  Add credential
│
◆  Select provider
│  ● Groq
│  ...
└

Enter the API key for the provider.

$ codeflow auth login

┌  Add credential
│
◇  Select provider
│  Groq
│
◇  Enter your API key
│  _
└

Run the /models command to select the one you want.

LM Studio

You can configure codeflow to use local models through LM Studio.

{
  "$schema": "https://codeflow.ai/config.json",
  "provider": {
    "lmstudio": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "LM Studio (local)",
      "options": {
        "baseURL": "http://127.0.0.1:1234/v1"
      },
      "models": {
        "google/gemma-3n-e4b": {
          "name": "Gemma 3n-e4b (local)"
        }
      }
    }
  }
}

In this example:

lmstudio is the custom provider ID. This can be any string you want.
npm specifies the package to use for this provider. Here, @ai-sdk/openai-compatible is used for any OpenAI-compatible API.
name is the display name for the provider in the UI.
options.baseURL is the endpoint for the local server.
models is a map of model IDs to their configurations. The model name will be displayed in the model selection list.

Moonshot AI

To use Kimi K2 from Moonshot AI:

Head over to the Moonshot AI console, create an account, and click Create API key.

Run codeflow auth login and select Moonshot AI.

$ codeflow auth login

┌  Add credential
│
◆  Select provider
│  ...
│  ● Moonshot AI
└

Enter your Moonshot API key.

$ codeflow auth login

┌  Add credential
│
◇  Select provider
│  Moonshot AI
│
◇  Enter your API key
│  _
└

Run the /models command to select Kimi K2.

Ollama

You can configure codeflow to use local models through Ollama.

{
  "$schema": "https://codeflow.ai/config.json",
  "provider": {
    "ollama": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Ollama (local)",
      "options": {
        "baseURL": "http://localhost:11434/v1"
      },
      "models": {
        "llama2": {
          "name": "Llama 2"
        }
      }
    }
  }
}

In this example:

ollama is the custom provider ID. This can be any string you want.
npm specifies the package to use for this provider. Here, @ai-sdk/openai-compatible is used for any OpenAI-compatible API.
name is the display name for the provider in the UI.
options.baseURL is the endpoint for the local server.
models is a map of model IDs to their configurations. The model name will be displayed in the model selection list.

OpenAI

Head over to the OpenAI Platform console, click Create new secret key, and copy the key.

Run codeflow auth login and select OpenAI.

$ codeflow auth login

┌  Add credential
│
◆  Select provider
│  ● OpenAI
│  ...
└

Enter the API key for the provider.

$ codeflow auth login

┌  Add credential
│
◇  Select provider
│  OpenAI
│
◇  Enter your API key
│  _
└

Run the /models command to select the one you want.

OpenRouter

Head over to the OpenRouter dashboard, click Create API Key, and copy the key.

Run codeflow auth login and select OpenRouter.

$ codeflow auth login

┌  Add credential
│
◆  Select provider
│  ● OpenRouter
│  ○ Anthropic
│  ○ Google
│  ...
└

Enter the API key for the provider.

$ codeflow auth login

┌  Add credential
│
◇  Select provider
│  OpenRouter
│
◇  Enter your API key
│  _
└

Many OpenRouter models are preloaded by default, run the /models command to select the one you want.

You can also add additional models through your codeflow config.

{
  "$schema": "https://codeflow.ai/config.json",
  "provider": {
    "openrouter": {
      "models": {
        "somecoolnewmodel": {}
      }
    }
  }
}

You can also customize them through your codeflow config. Here’s an example of specifying a provider

{
  "$schema": "https://codeflow.ai/config.json",
  "provider": {
    "openrouter": {
      "models": {
        "moonshotai/kimi-k2": {
          "options": {
            "provider": {
              "order": ["baseten"],
              "allow_fallbacks": false
            }
          }
        }
      }
    }
  }
}

Together AI

Head over to the Together AI console, create an account, and click Add Key.

Run codeflow auth login and select Together AI.

$ codeflow auth login

┌  Add credential
│
◆  Select provider
│  ● Together AI
│  ...
└

Enter your Together AI API key.

$ codeflow auth login

┌  Add credential
│
◇  Select provider
│  Together AI
│
◇  Enter your API key
│  _
└

Run the /models command to select a model like Kimi K2 Instruct.

xAI

For a limited time, you can use xAI’s Grok Code for free with codeflow.

Make sure you are on the latest version of codeflow.
Run the /models command and select Grok Code Free.

As a part of the trial period, the xAI team will be using the request logs to monitor and improve Grok Code.

Z.AI

Head over to the Z.AI API console, create an account, and click Create a new API key.

Run codeflow auth login and select Z.AI.

$ codeflow auth login

┌  Add credential
│
◆  Select provider
│  ● Z.AI
│  ...
└

Enter your Z.AI API key.

$ codeflow auth login

┌  Add credential
│
◇  Select provider
│  Z.AI
│
◇  Enter your API key
│  _
└

Run the /models command to select a model like GLM-4.5.

Troubleshooting

If you are having trouble with configuring a provider, check the following:

Check the auth setup: Run codeflow auth list to see if the credentials for the provider are added to your config.

This doesn’t apply to providers like Amazon Bedrock, that rely on environment variables for their auth.
For custom providers, check the codeflow config and:
- Make sure the provider ID used in codeflow auth login matches the ID in your codeflow config.
- The right npm package is used for the provider. For example, use @ai-sdk/cerebras for Cerebras. And for all other OpenAI-compatible providers, use @ai-sdk/openai-compatible.
- Check correct API endpoint is used in the options.baseURL field.