---
title: Anthropic compatibility
---

Ollama provides compatibility with the [Anthropic Messages API](https://docs.anthropic.com/en/api/messages) to help connect existing applications to Ollama, including tools like Claude Code.

## Usage

### Environment variables

To use Ollama with tools that expect the Anthropic API (like Claude Code), set these environment variables:

```shell
export ANTHROPIC_AUTH_TOKEN=ollama  # required but ignored
export ANTHROPIC_BASE_URL=http://localhost:11434
```

### Simple `/v1/messages` example

<CodeGroup dropdown>

```python basic.py
import anthropic

client = anthropic.Anthropic(
    base_url='http://localhost:11434',
    api_key='ollama',  # required but ignored
)

message = client.messages.create(
    model='qwen3-coder',
    max_tokens=1024,
    messages=[
        {'role': 'user', 'content': 'Hello, how are you?'}
    ]
)
print(message.content[0].text)
```

```javascript basic.js
import Anthropic from "@anthropic-ai/sdk";

const anthropic = new Anthropic({
  baseURL: "http://localhost:11434",
  apiKey: "ollama", // required but ignored
});

const message = await anthropic.messages.create({
  model: "qwen3-coder",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Hello, how are you?" }],
});

console.log(message.content[0].text);
```

```shell basic.sh
curl -X POST http://localhost:11434/v1/messages \
-H "Content-Type: application/json" \
-H "x-api-key: ollama" \
-H "anthropic-version: 2023-06-01" \
-d '{
  "model": "qwen3-coder",
  "max_tokens": 1024,
  "messages": [{ "role": "user", "content": "Hello, how are you?" }]
}'
```

</CodeGroup>

### Streaming example

<CodeGroup dropdown>

```python streaming.py
import anthropic

client = anthropic.Anthropic(
    base_url='http://localhost:11434',
    api_key='ollama',
)

with client.messages.stream(
    model='qwen3-coder',
    max_tokens=1024,
    messages=[{'role': 'user', 'content': 'Count from 1 to 10'}]
) as stream:
    for text in stream.text_stream:
        print(text, end='', flush=True)
```

```javascript streaming.js
import Anthropic from "@anthropic-ai/sdk";

const anthropic = new Anthropic({
  baseURL: "http://localhost:11434",
  apiKey: "ollama",
});

const stream = await anthropic.messages.stream({
  model: "qwen3-coder",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Count from 1 to 10" }],
});

for await (const event of stream) {
  if (
    event.type === "content_block_delta" &&
    event.delta.type === "text_delta"
  ) {
    process.stdout.write(event.delta.text);
  }
}
```

```shell streaming.sh
curl -X POST http://localhost:11434/v1/messages \
-H "Content-Type: application/json" \
-d '{
  "model": "qwen3-coder",
  "max_tokens": 1024,
  "stream": true,
  "messages": [{ "role": "user", "content": "Count from 1 to 10" }]
}'
```

</CodeGroup>

### Tool calling example

<CodeGroup dropdown>

```python tools.py
import anthropic

client = anthropic.Anthropic(
    base_url='http://localhost:11434',
    api_key='ollama',
)

message = client.messages.create(
    model='qwen3-coder',
    max_tokens=1024,
    tools=[
        {
            'name': 'get_weather',
            'description': 'Get the current weather in a location',
            'input_schema': {
                'type': 'object',
                'properties': {
                    'location': {
                        'type': 'string',
                        'description': 'The city and state, e.g. San Francisco, CA'
                    }
                },
                'required': ['location']
            }
        }
    ],
    messages=[{'role': 'user', 'content': "What's the weather in San Francisco?"}]
)

for block in message.content:
    if block.type == 'tool_use':
        print(f'Tool: {block.name}')
        print(f'Input: {block.input}')
```

```javascript tools.js
import Anthropic from "@anthropic-ai/sdk";

const anthropic = new Anthropic({
  baseURL: "http://localhost:11434",
  apiKey: "ollama",
});

const message = await anthropic.messages.create({
  model: "qwen3-coder",
  max_tokens: 1024,
  tools: [
    {
      name: "get_weather",
      description: "Get the current weather in a location",
      input_schema: {
        type: "object",
        properties: {
          location: {
            type: "string",
            description: "The city and state, e.g. San Francisco, CA",
          },
        },
        required: ["location"],
      },
    },
  ],
  messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
});

for (const block of message.content) {
  if (block.type === "tool_use") {
    console.log("Tool:", block.name);
    console.log("Input:", block.input);
  }
}
```

```shell tools.sh
curl -X POST http://localhost:11434/v1/messages \
-H "Content-Type: application/json" \
-d '{
  "model": "qwen3-coder",
  "max_tokens": 1024,
  "tools": [
    {
      "name": "get_weather",
      "description": "Get the current weather in a location",
      "input_schema": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city and state"
          }
        },
        "required": ["location"]
      }
    }
  ],
  "messages": [{ "role": "user", "content": "What is the weather in San Francisco?" }]
}'
```

</CodeGroup>

## Using with Claude Code

[Claude Code](https://code.claude.com/docs/en/overview) can be configured to use Ollama as its backend. 

### Recommended models

For coding use cases, models like `glm-4.7`, `minimax-m2.1`, and `qwen3-coder` are recommended.

Download a model before use:

```shell
ollama pull qwen3-coder
```
> Note: Qwen 3 coder is a 30B parameter model requiring at least 24GB of VRAM to run smoothly. More is required for longer context lengths. 

```shell
ollama pull glm-4.7:cloud
```

### Quick setup

```shell
ollama launch claude
```

This will prompt you to select a model, configure Claude Code automatically, and launch it. To configure without launching:

```shell
ollama launch claude --config
```

### Manual setup

Set the environment variables and run Claude Code:

```shell
ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 claude --model qwen3-coder
```

Or set the environment variables in your shell profile:

```shell
export ANTHROPIC_AUTH_TOKEN=ollama
export ANTHROPIC_BASE_URL=http://localhost:11434
```

Then run Claude Code with any Ollama model:

```shell
claude --model qwen3-coder
```

## Endpoints

### `/v1/messages`

#### Supported features

- [x] Messages
- [x] Streaming
- [x] System prompts
- [x] Multi-turn conversations
- [x] Vision (images)
- [x] Tools (function calling)
- [x] Tool results
- [x] Thinking/extended thinking

#### Supported request fields

- [x] `model`
- [x] `max_tokens`
- [x] `messages`
  - [x] Text `content`
  - [x] Image `content` (base64)
  - [x] Array of content blocks
  - [x] `tool_use` blocks
  - [x] `tool_result` blocks
  - [x] `thinking` blocks
- [x] `system` (string or array)
- [x] `stream`
- [x] `temperature`
- [x] `top_p`
- [x] `top_k`
- [x] `stop_sequences`
- [x] `tools`
- [x] `thinking`
- [ ] `tool_choice`
- [ ] `metadata`

#### Supported response fields

- [x] `id`
- [x] `type`
- [x] `role`
- [x] `model`
- [x] `content` (text, tool_use, thinking blocks)
- [x] `stop_reason` (end_turn, max_tokens, tool_use)
- [x] `usage` (input_tokens, output_tokens)

#### Streaming events

- [x] `message_start`
- [x] `content_block_start`
- [x] `content_block_delta` (text_delta, input_json_delta, thinking_delta)
- [x] `content_block_stop`
- [x] `message_delta`
- [x] `message_stop`
- [x] `ping`
- [x] `error`

## Models

Ollama supports both local and cloud models.

### Local models

Pull a local model before use:

```shell
ollama pull qwen3-coder
```

Recommended local models:
- `qwen3-coder` - Excellent for coding tasks
- `gpt-oss:20b` - Strong general-purpose model

### Cloud models

Cloud models are available immediately without pulling:

- `glm-4.7:cloud` - High-performance cloud model
- `minimax-m2.1:cloud` - Fast cloud model

### Default model names

For tooling that relies on default Anthropic model names such as `claude-3-5-sonnet`, use `ollama cp` to copy an existing model name:

```shell
ollama cp qwen3-coder claude-3-5-sonnet
```

Afterwards, this new model name can be specified in the `model` field:

```shell
curl http://localhost:11434/v1/messages \
    -H "Content-Type: application/json" \
    -d '{
        "model": "claude-3-5-sonnet",
        "max_tokens": 1024,
        "messages": [
            {
                "role": "user",
                "content": "Hello!"
            }
        ]
    }'
```

## Differences from the Anthropic API

### Behavior differences

- API key is accepted but not validated
- `anthropic-version` header is accepted but not used
- Token counts are approximations based on the underlying model's tokenizer

### Not supported

The following Anthropic API features are not currently supported:

| Feature | Description |
|---------|-------------|
| `/v1/messages/count_tokens` | Token counting endpoint |
| `tool_choice` | Forcing specific tool use or disabling tools |
| `metadata` | Request metadata (user_id) |
| Prompt caching | `cache_control` blocks for caching prefixes |
| Batches API | `/v1/messages/batches` for async batch processing |
| Citations | `citations` content blocks |
| PDF support | `document` content blocks with PDF files |
| Server-sent errors | `error` events during streaming (errors return HTTP status) |

### Partial support

| Feature | Status |
|---------|--------|
| Image content | Base64 images supported; URL images not supported |
| Extended thinking | Basic support; `budget_tokens` accepted but not enforced |
