# Query Parameters (/docs/features/url_query)

LibreChat supports dynamic configuration of chat conversations through URL query parameters. This feature allows you to initiate conversations with specific settings, models, and endpoints directly from the URL.

### Chat Paths

Query parameters must follow a valid chat path:
- For new conversations: `/c/new?`
- For existing conversations: `/c/[conversation-id]?` (where conversation-id is an existing one)

Examples:

```bash
https://your-domain.com/c/new?endpoint=ollama&model=llama3%3Alatest
https://your-domain.com/c/03debefd-6a50-438a-904d-1a806f82aad4?endpoint=openAI&model=o1-mini
```

## Basic Usage

The most common parameters to use are `endpoint` and `model`. Using both is recommended for the most predictable behavior:

```bash
https://your-domain.com/c/new?endpoint=azureOpenAI&model=o1-mini
```

### URL Encoding

Special characters in query params must be properly URL-encoded to work correctly. Common characters that need encoding:

- `:` → `%3A`
- `/` → `%2F`
- `?` → `%3F`
- `#` → `%23`
- `&` → `%26`
- `=` → `%3D`
- `+` → `%2B`
- Space → `%20` (or `+`)

Example with special characters:
```ts
Original: `Write a function: def hello()`
Encoded: `/c/new?prompt=Write%20a%20function%3A%20def%20hello()`
```

You can use JavaScript's built-in `encodeURIComponent()` function to properly encode prompts:
```javascript
const prompt = "Write a function: def hello()";
const encodedPrompt = encodeURIComponent(prompt);
const url = `/c/new?prompt=${encodedPrompt}`;
console.log(url);
```

Try running the code in your browser console to see the encoded URL (browser shortcut: `Ctrl+Shift+I`).

### Endpoint Selection

The `endpoint` parameter can be used alone:
```bash
https://your-domain.com/c/new?endpoint=google
```

When only `endpoint` is specified:
- It will use the last selected model from localStorage
- If no previous model exists, it will use the first available model in the endpoint's model list

#### Notes

- The `endpoint` value must be one of the following:
```bash
openAI, azureOpenAI, google, anthropic, assistants, azureAssistants, bedrock, agents
```
- If using a [custom endpoint](/docs/quick_start/custom_endpoints), you can use its name as the value (case-insensitive)

```bash
# using `endpoint=perplexity` for a custom endpoint named `Perplexity`
https://your-domain.com/c/new?endpoint=perplexity&model=llama-3.1-sonar-small-128k-online
```

### Model Selection

The `model` parameter can be used alone:
```bash
https://your-domain.com/c/new?model=gpt-4o
```

When only `model` is specified:
- It will only select the model if it's available in the current endpoint
- The current endpoint is either the default endpoint or the last selected endpoint

### Prompt Parameter

The `prompt` parameter allows you to pre-populate the chat input field:
```bash
https://your-domain.com/c/new?prompt=Explain quantum computing
```

You can also use `q` as a shorthand, which is interchangeable with `prompt`:
```bash
https://your-domain.com/c/new?q=Explain quantum computing
```

You can combine these with other parameters:
```bash
https://your-domain.com/c/new?endpoint=anthropic&model=claude-3-5-sonnet-20241022&prompt=Explain quantum computing
```

### Automatic Prompt Submission

The `submit` parameter allows you to automatically submit the prompt without manual intervention:
```bash
https://your-domain.com/c/new?prompt=Explain quantum computing&submit=true
```

This feature is particularly useful for:
- Creating automated workflows (e.g., Raycast, Alfred, Automater)
- Building external integrations

You can combine it with other parameters for complete automation:
```bash
https://your-domain.com/c/new?endpoint=openAI&model=gpt-4&prompt=Explain quantum computing&submit=true
```

### Special Endpoints

#### Model Specs
You can select a specific model spec by name:
```bash
https://your-domain.com/c/new?spec=meeting-notes-gpt4
```
This will load all the settings defined in the model spec. When using the `spec` parameter, other model parameters in the URL will be ignored.

#### Agents
You can directly load an agent using its ID without specifying the endpoint:
```bash
https://your-domain.com/c/new?agent_id=your-agent-id
```
This will automatically set the endpoint to `agents`.

#### Assistants
Similarly, you can load an assistant directly:
```bash
https://your-domain.com/c/new?assistant_id=your-assistant-id
```
This will automatically set the endpoint to `assistants`.

## Supported Parameters

LibreChat supports a wide range of parameters for fine-tuning your conversation settings:

### LibreChat Settings
- `maxContextTokens`: Override the system-defined context window
- `resendFiles`: Control file resubmission in subsequent messages
- `promptPrefix`: Set custom instructions/system message
- `imageDetail`: 'low', 'auto', or 'high' for image quality
  - Note: while this is a LibreChat-specific parameter, it only affects the following endpoints:
  - OpenAI, Custom Endpoints, which are OpenAI-like, and Azure OpenAI, for which this defaults to 'auto'
- `spec`: Select a specific LibreChat [Model Spec](/docs/configuration/librechat_yaml/object_structure/model_specs) by name
  - Must match the exact name of a configured model spec
  - When specified, other model parameters will not take effect, only those defined by the model spec
  - **Important:** If model specs are configured with `enforce: true`, using this parameter may be required for URL query params to work properly
- `fileTokenLimit`: Set maximum token limit for file processing to control costs and resource usage.
  - Note: Request value overrides YAML default.

### Model Parameters
Different endpoints support various parameters:

**OpenAI, Custom, Azure OpenAI:**
```bash
# Note: these should be valid values according to the provider's API
temperature, presence_penalty, frequency_penalty, stop, top_p, max_tokens,
reasoning_effort, reasoning_summary, verbosity, useResponsesApi, web_search, disableStreaming
```

**Google, Anthropic:**
```bash
# Note: these should be valid values according to the provider's API
topP, topK, maxOutputTokens, thinking, thinkingBudget, thinkingLevel, web_search
```

**Anthropic, Bedrock (Anthropic models):**

Set this to `true` or `false` to toggle the "prompt-caching":
```bash
promptCache
```

More info: https://www.anthropic.com/news/prompt-caching, https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html#prompt-caching-get-started

**Bedrock:**
```bash
# Bedrock region
region=us-west-2
# Bedrock equivalent of `max_tokens`
maxTokens=200
# Bedrock reasoning effort (for supported models like ZAI, MoonshotAI)
reasoning_effort=medium
```

**Assistants/Azure Assistants:**
```bash
# overrides existing assistant instructions for current run
instructions=your+instructions
```
```bash
# Adds the current date and time to `additional_instructions` for each run.
append_current_datetime=true
```

## More Info

For more information on any of the above, refer to [Model Spec Preset Fields](/docs/configuration/librechat_yaml/object_structure/model_specs), which shares most parameters.

**Example with multiple parameters:**
```bash
https://your-domain.com/c/new?endpoint=google&model=gemini-2.0-flash-exp&temperature=0.7&prompt=Oh hi mark
```

**Example with model spec:**
```bash
https://your-domain.com/c/new?spec=meeting-notes-gpt4&prompt=Here%20is%20the%20transcript...
```
Note: When using `spec`, other model parameters are ignored in favor of the model spec's configuration.

## ⚠️ Warning

Exercise caution when using query parameters:
- Misuse or exceeding provider limits may result in API errors
- If you encounter bad request errors, reset the conversation by clicking "New Chat"
- Some parameters may have no effect if they're not supported by the selected endpoint

## Best Practices

1. Always use both `endpoint` and `model` when possible
2. Verify parameter support for your chosen endpoint
3. Use reasonable values within provider limits
4. Test your parameter combinations before sharing URLs

## Parameter Validation

All parameters are validated against LibreChat's schema before being applied. Invalid parameters or values will be ignored, and valid settings will be applied to the conversation.

---

This feature enables powerful use cases like:
- Sharing specific conversation configurations
- Creating bookmarks for different chat settings
- Automating chat setup through URL parameters

---

#LibreChat #ChatConfiguration #AIParameters #OpenSource #URLQueryParameters