The Prompt Library supports two prompt structures:

- [**Text prompts**](#text-prompts) — Simple string templates with variable substitution. Good for one-shot generations.
- [**Chat prompts**](#chat-prompts) — Structured message lists in OpenAI format with system, user, and assistant roles. Good for multi-turn or system+user agents, and supports multimodal content (text, images, videos).

:::callout{intent="note"}
The prompt structure is fixed at creation time. A prompt created with `create_prompt` (text)
cannot later be turned into a chat prompt, and vice versa — attempting it raises
`PromptTemplateStructureMismatch`.
:::

## Text prompts

A text prompt is a single string template with `{{variable}}` substitution. They are ideal for
single-turn interactions or when you need to generate a single piece of text.

### Creating a text prompt

:::::tabs
:::tab{title="Python"}
```python
import opik

client = opik.Opik()

prompt = client.create_prompt(
    name="prompt-summary",
    prompt="Write a summary of the following text: {{text}}",
    metadata={"environment": "development"},
    project_name="my-agent",
)

# Render the template
print(prompt.format(text="Hello, world!"))
```
:::

:::tab{title="TypeScript"}
```typescript
import { Opik } from "opik";

const client = new Opik();

const prompt = await client.createPrompt({
  name: "prompt-summary",
  prompt: "Write a summary of the following text: {{text}}",
  metadata: { environment: "development" },
  projectName: "my-agent",
});

// Render the template
console.log(prompt?.format({ text: "Hello, world!" }));
```
:::

::::tab{title="Using the UI"}
You can create a prompt in the UI by navigating to the Prompt library and clicking
**Create new prompt**. This opens a dialog where you can enter the prompt name, the prompt
text, and an optional description:

:::frame
<img src="../img/apps/opik-documentation/documentation/fern/img/v2/prompt-library/create-prompt-text.png" alt="">
:::

You can edit a prompt later by clicking on its name in the library and choosing **Edit prompt**.
::::
:::::

Each call with new content creates a new version, which is visible in the library:

:::frame
<img src="../img/apps/opik-documentation/documentation/fern/img/v2/prompt-library/prompt-versions.png" alt="">
:::

### Fetching a text prompt

::::tabs
:::tab{title="Python"}
```python
import opik

client = opik.Opik()

prompt = client.get_prompt(name="prompt-summary", project_name="my-agent")

# Render the template
print(prompt.format(text="Hello, world!"))
```
:::

:::tab{title="TypeScript"}
```typescript
import { Opik } from "opik";

const client = new Opik();

const prompt = await client.getPrompt({
  name: "prompt-summary",
  projectName: "my-agent",
});

if (prompt) {
  console.log(prompt.format({ text: "Hello, world!" }));
}
```
:::
::::

If you are not using the SDK, you can also fetch a prompt through the
[REST API](https://www.comet.com/reference/rest-api/overview).

## Chat prompts

A chat prompt is a structured list of messages with roles (`system`, `user`, `assistant`), in
the same shape that OpenAI-compatible chat completion APIs accept. They are the right choice
when your agent has a multi-turn message structure.

### Key features

- **Structured messages** — Organize prompts as a list of messages with roles (system, user, assistant)
- **Multimodal support** — Include images, videos, and text in the same prompt
- **Variable substitution** — Use Mustache (`{{variable}}`) or Jinja2 syntax
- **Version control** — Automatic versioning when messages change

### Creating a chat prompt

:::::tabs
:::tab{title="Python"}
```python
import opik

client = opik.Opik()

messages = [
    {"role": "system", "content": "You are a helpful assistant specializing in {{domain}}."},
    {"role": "user", "content": "Explain {{topic}} in simple terms."},
]

chat_prompt = client.create_chat_prompt(
    name="educational-assistant",
    messages=messages,
    metadata={"category": "education"},
    project_name="my-agent",
)

# Render the messages with variables
formatted_messages = chat_prompt.format(
    variables={
        "domain": "physics",
        "topic": "quantum entanglement",
    }
)

print(formatted_messages)
# [
#     {"role": "system", "content": "You are a helpful assistant specializing in physics."},
#     {"role": "user", "content": "Explain quantum entanglement in simple terms."},
# ]
```
:::

:::tab{title="TypeScript"}
```typescript
import { Opik } from "opik";

const client = new Opik();

const messages = [
  {
    role: "system",
    content: "You are a helpful assistant specializing in {{domain}}.",
  },
  { role: "user", content: "Explain {{topic}} in simple terms." },
];

const chatPrompt = await client.createChatPrompt({
  name: "educational-assistant",
  messages,
  metadata: { category: "education" },
  projectName: "my-agent",
});

// Render the messages with variables
const formattedMessages = chatPrompt?.format({
  domain: "physics",
  topic: "quantum entanglement",
});

console.log(formattedMessages);
// [
//   { role: "system", content: "You are a helpful assistant specializing in physics." },
//   { role: "user", content: "Explain quantum entanglement in simple terms." },
// ]
```
:::

::::tab{title="Using the UI"}
To create a chat prompt in the UI, navigate to the Prompt Library and click **Create new
prompt**. Select **Chat prompt** as the prompt type, then add your messages with their
roles (system, user, assistant):

:::frame
<img src="../img/apps/opik-documentation/documentation/fern/img/v2/prompt-library/create-prompt-chat.png" alt="">
:::
::::
:::::

Once saved, a chat prompt has its own view in the Prompt Library:

:::frame
<img src="../img/apps/opik-documentation/documentation/fern/img/v2/prompt-library/chat-prompt-versions.png" alt="">
:::

### Using chat prompts with the OpenAI API

The output of `chat_prompt.format()` is already in the shape the OpenAI chat completion API
expects:

::::tabs
:::tab{title="Python"}
```python
import opik
from openai import OpenAI

client = opik.Opik()
openai_client = OpenAI()

chat_prompt = client.get_chat_prompt(
    name="educational-assistant",
    project_name="my-agent",
)

formatted_messages = chat_prompt.format(
    variables={"domain": "physics", "topic": "quantum entanglement"},
)

response = openai_client.chat.completions.create(
    model="gpt-4o-mini",
    messages=formatted_messages,
)

print(response.choices[0].message.content)
```
:::

:::tab{title="TypeScript"}
```typescript
import { Opik } from "opik";
import OpenAI from "openai";

const client = new Opik();
const openai = new OpenAI();

const chatPrompt = await client.getChatPrompt({
  name: "educational-assistant",
  projectName: "my-agent",
});

const formattedMessages = chatPrompt?.format({
  domain: "physics",
  topic: "quantum entanglement",
});

const response = await openai.chat.completions.create({
  model: "gpt-4o-mini",
  messages: formattedMessages,
});

console.log(response.choices[0].message.content);
```
:::
::::

### Multi-turn templates

Chat prompts can capture a multi-turn flow, with assistant turns inline and variables anywhere
in the conversation:

::::tabs
:::tab{title="Python"}
```python
import opik

client = opik.Opik()

messages = [
    {"role": "system", "content": "You are a customer support agent for {{company}}."},
    {"role": "user", "content": "I have an issue with {{product}}."},
    {"role": "assistant", "content": "I'd be happy to help with your {{product}}. Can you describe the issue?"},
    {"role": "user", "content": "{{issue_description}}"},
]

chat_prompt = client.create_chat_prompt(
    name="customer-support-flow",
    messages=messages,
    project_name="my-agent",
)

formatted = chat_prompt.format(
    variables={
        "company": "Acme Corp",
        "product": "Widget Pro",
        "issue_description": "It won't turn on",
    }
)
```
:::

:::tab{title="TypeScript"}
```typescript
import { Opik } from "opik";

const client = new Opik();

const messages = [
  {
    role: "system",
    content: "You are a customer support agent for {{company}}.",
  },
  { role: "user", content: "I have an issue with {{product}}." },
  {
    role: "assistant",
    content:
      "I'd be happy to help with your {{product}}. Can you describe the issue?",
  },
  { role: "user", content: "{{issue_description}}" },
];

const chatPrompt = await client.createChatPrompt({
  name: "customer-support-flow",
  messages,
  projectName: "my-agent",
});

const formatted = chatPrompt?.format({
  company: "Acme Corp",
  product: "Widget Pro",
  issue_description: "It won't turn on",
});
```
:::
::::

### Multimodal content

Chat prompts can include images and videos alongside text — useful for vision-enabled models.

#### Image analysis

::::tabs
:::tab{title="Python"}
```python
import opik

client = opik.Opik()

messages = [
    {"role": "system", "content": "You analyze images and provide detailed descriptions."},
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image of {{subject}}?"},
            {
                "type": "image_url",
                "image_url": {
                    "url": "{{image_url}}",
                    "detail": "high",
                },
            },
        ],
    },
]

chat_prompt = client.create_chat_prompt(
    name="image-analyzer",
    messages=messages,
    project_name="my-agent",
)

formatted = chat_prompt.format(
    variables={
        "subject": "a sunset",
        "image_url": "https://example.com/sunset.jpg",
    },
    supported_modalities={"vision": True},
)
```
:::

:::tab{title="TypeScript"}
```typescript
import { Opik } from "opik";

const client = new Opik();

const messages = [
  {
    role: "system",
    content: "You analyze images and provide detailed descriptions.",
  },
  {
    role: "user",
    content: [
      { type: "text", text: "What's in this image of {{subject}}?" },
      {
        type: "image_url",
        image_url: {
          url: "{{image_url}}",
          detail: "high",
        },
      },
    ],
  },
];

const chatPrompt = await client.createChatPrompt({
  name: "image-analyzer",
  messages,
  projectName: "my-agent",
});

const formatted = chatPrompt?.format(
  {
    subject: "a sunset",
    image_url: "https://example.com/sunset.jpg",
  },
  { vision: true },
);
```
:::
::::

#### Video analysis

::::tabs
:::tab{title="Python"}
```python
import opik

client = opik.Opik()

messages = [
    {"role": "system", "content": "You analyze videos and provide insights."},
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Analyze this video: {{description}}"},
            {
                "type": "video_url",
                "video_url": {
                    "url": "{{video_url}}",
                    "mime_type": "video/mp4",
                },
            },
        ],
    },
]

chat_prompt = client.create_chat_prompt(
    name="video-analyzer",
    messages=messages,
    project_name="my-agent",
)

formatted = chat_prompt.format(
    variables={
        "description": "traffic analysis",
        "video_url": "https://example.com/traffic.mp4",
    },
    supported_modalities={"vision": True},
)
```
:::

:::tab{title="TypeScript"}
```typescript
import { Opik } from "opik";

const client = new Opik();

const messages = [
  {
    role: "system",
    content: "You analyze videos and provide insights.",
  },
  {
    role: "user",
    content: [
      { type: "text", text: "Analyze this video: {{description}}" },
      {
        type: "video_url",
        video_url: {
          url: "{{video_url}}",
          mime_type: "video/mp4",
        },
      },
    ],
  },
];

const chatPrompt = await client.createChatPrompt({
  name: "video-analyzer",
  messages,
  projectName: "my-agent",
});

const formatted = chatPrompt?.format(
  {
    description: "traffic analysis",
    video_url: "https://example.com/traffic.mp4",
  },
  { vision: true, video: true },
);
```
:::
::::

#### Mixed content

You can combine multiple content blocks in a single message — e.g. two images with text around
them:

::::tabs
:::tab{title="Python"}
```python
import opik

client = opik.Opik()

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Compare these two images:"},
            {"type": "image_url", "image_url": {"url": "{{image1_url}}"}},
            {"type": "text", "text": "and"},
            {"type": "image_url", "image_url": {"url": "{{image2_url}}"}},
            {"type": "text", "text": "What are the main differences?"},
        ],
    },
]

chat_prompt = client.create_chat_prompt(
    name="image-comparison",
    messages=messages,
    project_name="my-agent",
)

formatted = chat_prompt.format(
    variables={
        "image1_url": "https://example.com/before.jpg",
        "image2_url": "https://example.com/after.jpg",
    },
    supported_modalities={"vision": True},
)
```
:::

:::tab{title="TypeScript"}
```typescript
import { Opik } from "opik";

const client = new Opik();

const messages = [
  {
    role: "user",
    content: [
      { type: "text", text: "Compare these two images:" },
      { type: "image_url", image_url: { url: "{{image1_url}}" } },
      { type: "text", text: "and" },
      { type: "image_url", image_url: { url: "{{image2_url}}" } },
      { type: "text", text: "What are the main differences?" },
    ],
  },
];

const chatPrompt = await client.createChatPrompt({
  name: "image-comparison",
  messages,
  projectName: "my-agent",
});

const formatted = chatPrompt?.format(
  {
    image1_url: "https://example.com/before.jpg",
    image2_url: "https://example.com/after.jpg",
  },
  { vision: true },
);
```
:::
::::

:::callout{intent="note"}
When formatting multimodal prompts, you can specify `supported_modalities` to control how
content is rendered:

- If a modality is supported (e.g. `{"vision": True}`), the structured content is preserved.
- If a modality is not supported, it's replaced with text placeholders (e.g. `<<<image>>><<</image>>>`).

This lets you reuse the same template with different models that may or may not support
certain modalities.
:::

## Template engines

Both text and chat prompts support two template engines for variable substitution:

### Mustache (default)

Mustache is simple, portable, and covers the common case of substituting variables into a
template.

::::tabs
:::tab{title="Python"}
```python
import opik
from opik.api_objects.prompt import PromptType

client = opik.Opik()

chat_prompt = client.create_chat_prompt(
    name="mustache-example",
    messages=[
        {"role": "user", "content": "Hello {{name}}, you live in {{city}}."},
    ],
    type=PromptType.MUSTACHE,  # Default
    project_name="my-agent",
)

formatted = chat_prompt.format(variables={"name": "Alice", "city": "Paris"})
# [{"role": "user", "content": "Hello Alice, you live in Paris."}]
```
:::

:::tab{title="TypeScript"}
```typescript
import { Opik, PromptType } from "opik";

const client = new Opik();

const chatPrompt = await client.createChatPrompt({
  name: "mustache-example",
  messages: [
    { role: "user", content: "Hello {{name}}, you live in {{city}}." },
  ],
  type: PromptType.MUSTACHE, // Default
  projectName: "my-agent",
});

const formatted = chatPrompt?.format({ name: "Alice", city: "Paris" });
// [{ role: "user", content: "Hello Alice, you live in Paris." }]
```
:::
::::

### Jinja2

Jinja2 supports conditionals, loops, and filters, which makes it a better fit when your prompt
needs to branch on input values.

::::tabs
:::tab{title="Python"}
```python
import opik
from opik.api_objects.prompt import PromptType

client = opik.Opik()

chat_prompt = client.create_chat_prompt(
    name="jinja-example",
    messages=[
        {
            "role": "user",
            "content": """
            {% if is_premium %}
            Hello {{ name }}, welcome to our premium service!
            {% else %}
            Hello {{ name }}, welcome!
            {% endif %}
            """,
        },
    ],
    type=PromptType.JINJA2,
    project_name="my-agent",
)

# Premium user
chat_prompt.format(variables={"name": "Alice", "is_premium": True})
# Regular user
chat_prompt.format(variables={"name": "Bob", "is_premium": False})
```
:::

:::tab{title="TypeScript"}
```typescript
import { Opik, PromptType } from "opik";

const client = new Opik();

const chatPrompt = await client.createChatPrompt({
  name: "jinja-example",
  messages: [
    {
      role: "user",
      content: `
        {% if is_premium %}
        Hello {{ name }}, welcome to our premium service!
        {% else %}
        Hello {{ name }}, welcome!
        {% endif %}
      `,
    },
  ],
  type: PromptType.JINJA2,
  projectName: "my-agent",
});

// Premium user
chatPrompt?.format({ name: "Alice", is_premium: true });
// Regular user
chatPrompt?.format({ name: "Bob", is_premium: false });
```
:::
::::

:::callout{intent="note"}
Jinja2 is more powerful for branching prompt logic; Mustache is simpler and more portable
across other tools that consume the same template. Pick the one that fits your prompt.
:::

## Searching prompts

To discover prompts by name substring or filter expression, use `search_prompts` /
`searchPrompts`. Filters use Opik Query Language (OQL), the same syntax used elsewhere in Opik:

::::tabs
:::tab{title="Python"}
```python
import opik

client = opik.Opik()

# Search by name substring
summaries = client.search_prompts(
    filter_string='name contains "summary"'
)

# Combine name + tags
filtered = client.search_prompts(
    filter_string='name contains "summary" AND tags contains "alpha" AND tags contains "beta"',
)

# Only text prompts
text_prompts = client.search_prompts(
    filter_string='template_structure = "text"'
)

# Only chat prompts
chat_prompts = client.search_prompts(
    filter_string='template_structure = "chat" AND name contains "assistant"'
)

for prompt in filtered:
    print(prompt.name, prompt.version, prompt.prompt)
```
:::

:::tab{title="TypeScript"}
```typescript
import { Opik } from "opik";

const client = new Opik();

// Search by name substring
const summaries = await client.searchPrompts(
  'name contains "summary"'
);

// Combine name + tags
const filtered = await client.searchPrompts(
  'name contains "summary" AND tags contains "alpha" AND tags contains "beta"'
);

// Only text prompts
const textPrompts = await client.searchPrompts(
  'template_structure = "text"'
);

// Only chat prompts
const chatPrompts = await client.searchPrompts(
  'template_structure = "chat" AND name contains "assistant"'
);

for (const prompt of filtered) {
  console.log(prompt.name, prompt.version, prompt.prompt);
}
```
:::
::::

`search_prompts` returns the **latest** version for each matching prompt. To explore the full
version history of a single prompt, see [Version control](https://www.comet.com/development/prompt-library/version-control).

### Filter syntax

The `filter_string` parameter takes one or more `<column> <operator> <value>` clauses joined
with `AND`:

| Column               | Type   | Operators                                                                   |
| -------------------- | ------ | --------------------------------------------------------------------------- |
| `id`                 | String | `=`, `!=`, `contains`, `not_contains`, `starts_with`, `ends_with`, `>`, `<` |
| `name`               | String | `=`, `!=`, `contains`, `not_contains`, `starts_with`, `ends_with`, `>`, `<` |
| `created_by`         | String | `=`, `!=`, `contains`, `not_contains`, `starts_with`, `ends_with`, `>`, `<` |
| `tags`               | List   | `contains`                                                                  |
| `template_structure` | String | `=`, `!=` (values: `"text"`, `"chat"`)                                      |

**Examples:**

- `tags contains "production"` — Filter by tag
- `name contains "summary"` — Filter by name substring
- `created_by = "user@example.com"` — Filter by creator
- `tags contains "alpha" AND tags contains "beta"` — Multiple tag filtering (AND)
- `template_structure = "text"` — Only text prompts
- `template_structure = "chat"` — Only chat prompts

## Related pages

- [Prompt Library Overview](./development-prompt-engineering-overview.md)
- [Getting started with the Prompt Library](./development-prompt-engineering-getting-started.md)
- [Version control](./development-prompt-engineering-version-control.md)

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