Langdock

https://docs.langdock.com
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Langdock is an AI platform offering various products like AI chat, custom assistants, API access,...
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### Use Examples in Prompts

Source: https://docs.langdock.com/en/using-langdock/workflows/nodes/agent-node

Include examples of desired input-output pairs to guide the agent, especially for complex tasks. This helps the agent learn the expected format and logic.

```text
Categorize these issues:
Example 1: "Can't log in, getting 500 error" → Urgent
Example 2: "How do I export data?" → Low

Now categorize: {{trigger.output.issue}}
```

--------------------------------

### System Instruction Example

Source: https://docs.langdock.com/en/developer/completion-api/google

Provides a system instruction to guide the model's behavior, can be a string or a Content object.

```json
{
  "systemInstruction": {
    "role": "system",
    "parts": [
      {
        "text": "You are a weather agent. Use the weather tool when asked about weather."
      }
    ]
  }
}
```

--------------------------------

### SKILL.md Format Example

Source: https://docs.langdock.com/api-endpoints/skills/import-skill

An example of a SKILL.md file, demonstrating the required YAML frontmatter and instructional content.

```mdx
--- 
name: Support Reply Style
slug: support-reply-style
description: Applies the support team's tone and escalation rules.
---

Write concise replies, include the next best action, and escalate billing issues to the account owner.
```

--------------------------------

### Example Success Response

Source: https://docs.langdock.com/en/developer/integrations-api/create-integration

An example of a successful response after creating an integration.

```json
{
  "integration": {
    "id": "550e8400-e29b-41d4-a716-446655440000",
    "name": "My Custom Integration",
    "description": "Connects to my internal company API for data retrieval",
    "createdAt": "2025-02-18T10:30:00.000Z"
  }
}

```

--------------------------------

### Example Tool Definition: Get Weather

Source: https://docs.langdock.com/en/developer/completion-api/anthropic

An example of defining a 'get_weather' tool with its input schema, including location and optional unit parameters.

```json
{
  "name": "get_weather",
  "description": "Get the current weather in a given location",
  "input_schema": {
    "properties": {
      "location": {
        "description": "The city and state, e.g. San Francisco, CA",
        "type": "string"
      },
      "unit": {
        "description": "Unit for the output - one of (celsius, fahrenheit)",
        "type": "string"
      }
    },
    "required": [
      "location"
    ],
    "type": "object"
  }
}
```

--------------------------------

### Example Success Response

Source: https://docs.langdock.com/api-endpoints/skills/list-skills

An example of a JSON response when successfully retrieving a list of skills. Shows a single skill object with all its properties.

```json
{
  "skills": [
    {
      "id": "550e8400-e29b-41d4-a716-446655440000",
      "name": "Support Reply Style",
      "slug": "support-reply-style",
      "description": "Applies the support team's tone and escalation rules.",
      "instructions": "Write concise replies, include the next best action, and escalate billing issues to the account owner.",
      "integrationIds": [],
      "createdAt": "2026-07-06T10:30:00.000Z",
      "updatedAt": "2026-07-06T10:30:00.000Z"
    }
  ],
  "nextCursor": "550e8400-e29b-41d4-a716-446655440000"
}
```

--------------------------------

### Example Dedicated Deployment Base URL

Source: https://docs.langdock.com/en/developer/overview/api-introduction

An example illustrating the structure of a base URL for a dedicated Langdock deployment, including the '/api/public' suffix.

```text
https://langdock.yourcompany.com/api/public
```

--------------------------------

### Input Variables Example

Source: https://docs.langdock.com/en/using-langdock/workflows/nodes/agent-node

Pass data from previous nodes to the agent for context. This example shows how to include customer, order ID, and issue details for support ticket analysis.

```text
Customer: {{trigger.output.customer_name}}
Order ID: {{trigger.output.order_id}}
Issue: {{trigger.output.description}}

Please analyze this support ticket and categorize it.


```

--------------------------------

### Example Success Response

Source: https://docs.langdock.com/en/developer/knowledge-folder-api/search-knowledge-folder

Illustrates an example of a successful response from the Search Knowledge Folder API, showing populated fields for search results.

```json
{
  "status": "success",
  "result": [
    {
      "id": "chunk_abc123",
      "text": "Q4 revenue projections indicate a 15% increase compared to Q3, driven primarily by strong enterprise sales in the EMEA region...",
      "similarity": 0.89,
      "subsource": "att_xyz789",
      "subname": "quarterly-report-2024.pdf",
      "url": "https://example.com/reports/q4-2024",
      "index": 0
    },
    {
      "id": "chunk_def456",
      "text": "The projected revenue for the fourth quarter takes into account seasonal trends and the impact of new product launches...",
      "similarity": 0.82,
      "subsource": "att_abc456",
      "subname": "financial-forecast.xlsx",
      "url": null,
      "index": 1
    }
  ]
}
```

--------------------------------

### Input Array Examples for Loop Node

Source: https://docs.langdock.com/en/using-langdock/workflows/nodes/loop-node

Examples of how to specify the input array for the Loop node. These typically reference outputs from previous nodes in the workflow.

```handlebars
{{ trigger.output.customers }}
```

```handlebars
{{ api_response.output.items }}
```

```handlebars
{{ google_sheets.output.rows }}
```

--------------------------------

### Variable Examples in Langdock

Source: https://docs.langdock.com/en/using-langdock/workflows/fundamentals/field-modes

Provides concrete examples of how to reference variables from different sources like triggers, agents, API responses, and loop items within Langdock workflows.

```handlebars
{{trigger.output.email}}              // Form field
{{agent.output.summary}}              // Agent output
{{http_request.output.data.userId}}   // API response
{{my_loop.output.currentItem.name}}   // Current loop item
```

--------------------------------

### Invite Users Response Examples (OpenAPI)

Source: https://docs.langdock.com/en/developer/user-management-api/invite

Examples of successful and partially successful responses when inviting users, including cases with all invitations succeeding and cases with invalid email addresses.

```yaml
status: success
message: Invitations processed
successfulInvites:
  - alice@example.com
  - bob@example.com
invalidEmails: []
```

```yaml
status: success
message: Invitations processed
successfulInvites:
  - alice@example.com
invalidEmails:
  - not-a-valid-email
```

--------------------------------

### Example Question for Company Knowledge

Source: https://docs.langdock.com/en/using-langdock/chat/tools/company-knowledge

Ask a natural language question to retrieve information from connected sources. This example queries Slack for information about API changes.

```text
What did Sarah say about the API changes in Slack?
```

--------------------------------

### Example Output Node Structure

Source: https://docs.langdock.com/en/using-langdock/workflows/nodes/output-node

This example shows how to structure the output of a workflow using Handlebars templating. It extracts priority, category, and a draft reply from the agent's output.

```handlebars
**Priority:** {{agent.output.structured.priority}}
**Category:** {{agent.output.structured.category}}
**Suggested response:**

{{agent.output.structured.draft_reply}}
```

--------------------------------

### Install and Run a Langdock Cookbook Recipe

Source: https://docs.langdock.com/en/developer/overview/cookbook

Clone the repository or copy a single recipe folder. Navigate into the recipe directory, install dependencies using pnpm, copy the environment file if applicable, and then run the development server.

```bash
cd mcp/authentication/okta-dcr   # any recipe folder
pnpm install
cp .env.example .env             # where applicable
pnpm dev                         # runs locally on http://localhost:3333
```

--------------------------------

### Multiple Files Output Example

Source: https://docs.langdock.com/en/using-langdock/guides/integrations/file-support-for-actions

Shows how to return multiple files from an action. The 'text' shortcut can be used for UTF-8 encoded text files.

```javascript
return {
  files: [
    {
      fileName: "data.csv",
      mimeType: "text/csv",
      text: "Name,Email\nJohn,john@example.com", // Text shortcut for UTF-8
    },
    {
      fileName: "chart.png",
      mimeType: "image/png",
      base64: "iVBORw0KGgoAAAANSUhEUgAA...",
    },
  ],
};
```

--------------------------------

### Few-shot Prompting Example

Source: https://docs.langdock.com/en/using-langdock/guides/prompt-engineering/advanced-techniques/prompting-techniques

Use few-shot prompting for complex or nuanced tasks where output format consistency is important. Provide 3-4 examples to guide the model's response.

```text
I was happy with the customer support today - satisfied
The product is horrible! - very unsatisfied
This is one of the best products I have ever used - very satisfied
This is such a great product! -
```

--------------------------------

### Chat with a Temporary Assistant Configuration

Source: https://docs.langdock.com/en/developer/assistants-api/assistant

This example demonstrates how to create and chat with a temporary assistant configuration on the fly. It allows defining assistant properties like name, instructions, and capabilities within the API call.

```javascript
const axios = require("axios");

async function chatWithNewAssistant() {
  const response = await axios.post(
    "https://api.langdock.com/assistant/v1/chat/completions",
    {
      assistant: {
        name: "Document Analyzer",
        instructions:
          "You are a helpful assistant who analyzes documents and answers questions about them",
        temperature: 0.7,
        model: "gpt-5",
        capabilities: {
          webSearch: true,
        },
        attachmentIds: ["550e8400-e29b-41d4-a716-446655440000"], // Obtain attachmentIds from upload attachment endpoint
      },
      messages: [
        {
          role: "user",
          content: "What are the key points in the document?",
        },
      ],
    },
    {
      headers: {
        Authorization: "Bearer YOUR_API_KEY",
      },
    }
  );

  console.log(response.data.result);
}
```

--------------------------------

### Tool Configuration Example

Source: https://docs.langdock.com/en/developer/completion-api/google

Configures function calling behavior, including mode and allowed function names.

```json
{
  "toolConfig": {
    "functionCallingConfig": {
      "mode": "ANY",
      "allowedFunctionNames": ["get_weather"]
    }
  }
}
```

--------------------------------

### Agent Instructions Referencing Form Fields

Source: https://docs.langdock.com/en/using-langdock/agents/form-fields

Example of how to reference form fields within the agent's instructions to guide its behavior. Ensure field names match those defined in the agent's form.

```text
You are a customer support specialist. Use the following information provided by the user:

- Customer Name: Use this to personalize your responses
- Issue Category: Focus your troubleshooting on this specific area
- Problem Description: Analyze this to understand the core issue
- Priority Level: Adjust your response urgency accordingly

Always address the customer by name and provide solutions relevant to their selected category.
```

--------------------------------

### Create a Basic Assistant

Source: https://docs.langdock.com/en/developer/assistants-api/assistant-create

Use this snippet to create a new assistant with basic configurations like name, description, and instructions. Ensure you replace 'YOUR_API_KEY' with your actual API key.

```javascript
const axios = require("axios");

async function createBasicAssistant() {
  const response = await axios.post(
    "https://api.langdock.com/assistant/v1/create",
    {
      name: "Document Analyzer",
      description: "Analyzes and summarizes documents",
      emoji: "📄",
      instruction: "You are a helpful Assistant that analyzes documents and provides clear summaries of key information.",
      creativity: 0.5,
      conversationStarters: [
        "Summarize this document",
        "What are the key points?",
        "Extract action items"
      ],
      webSearch: false
    },
    {
      headers: {
        Authorization: "Bearer YOUR_API_KEY",
        "Content-Type": "application/json"
      }
    }
  );

  console.log("Assistant created:", response.data.assistant.id);
}
```

--------------------------------

### Getting Enum-Based Output for Sentiment Analysis

Source: https://docs.langdock.com/en/developer/assistants-api/assistant

This example shows how to constrain the assistant's output to a predefined set of values using an enum. This is useful for categorizing text, such as determining sentiment (positive, neutral, negative).

```javascript
const axios = require("axios");

async function getSentimentAnalysis() {
  const response = await axios.post(
    "https://api.langdock.com/assistant/v1/chat/completions",
    {
      assistant: {
        name: "Sentiment Analyzer",
        instructions: "You analyze the sentiment of text",
      },
      messages: [
        {
          role: "user",
          content:
            "How would you rate this review: 'This product exceeded my expectations!'",
        },
      ],
      output: {
        type: "enum",
        enum: ["positive", "neutral", "negative"],
      },
    },
    {
      headers: {
        Authorization: "Bearer YOUR_API_KEY",
      },
    }
  );

  // Access the enum result directly from output
  console.log(response.data.output);
  // Output: "positive"
}
```

--------------------------------

### Streaming Text with Vercel AI SDK (Node.js)

Source: https://docs.langdock.com/en/developer/completion-api/openai

This example demonstrates how to stream text responses from the Langdock API using the Vercel AI SDK in a Node.js environment. It requires the AI SDK and OpenAI provider to be installed.

```typescript
import { streamText } from "ai";
import { createOpenAI } from "@ai-sdk/openai";

const langdockProvider = createOpenAI({
  baseURL: "https://api.langdock.com/openai/eu/v1",
  apiKey: "<YOUR_LANGDOCK_API_KEY>",
});

const result = await streamText({
  model: langdockProvider("gpt-5-mini"),
  prompt: "Write a short poem about cats",
});

for await (const textPart of result.textStream) {
  process.stdout.write(textPart);
}
```

--------------------------------

### API Request Options Example

Source: https://docs.langdock.com/en/using-langdock/guides/integrations/agent

This example demonstrates how to configure options for making an API request using `ld.request`. It includes setting the HTTP method, URL with dynamic parameters, and authorization headers.

```javascript
const options = {
  method: 'GET',
  url: `https://www.googleapis.com/drive/v3/files/${data.input.itemId}/export?mimeType=text/plain`,
  headers: {
    'Authorization': 'Bearer ' + data.auth.access_token,
    'Accept': 'application/json'
  }
};
```

--------------------------------

### Success Response Example

Source: https://docs.langdock.com/en/developer/integrations-api/delete-action

This TypeScript example shows the expected success response format when an action is successfully deleted.

```typescript
{
  message: "Action deleted";
  id: string;  // UUID of the deleted action
}

```

--------------------------------

### Generation Configuration Example

Source: https://docs.langdock.com/en/developer/completion-api/google

Configures text generation parameters such as temperature, topP, topK, and output tokens.

```json
{
  "generationConfig": {
    "temperature": 0.7,
    "topP": 0.9,
    "topK": 40,
    "maxOutputTokens": 1000,
    "stopSequences": ["END", "STOP"]
  }
}
```

--------------------------------

### Install python-dotenv Package

Source: https://docs.langdock.com/en/admin/ai-adoption-and-rollout/best-practices/api-key-best-practices

Install the python-dotenv library to load environment variables from a .env file into your Python application.

```bash
pip install python-dotenv
```

--------------------------------

### Update Parameter Names for Create, Get, Update Endpoints

Source: https://docs.langdock.com/en/developer/assistants-api/assistant-to-agent-migration

Replace 'assistantId' with 'agentId' for create, get, and update operations.

```javascript
// Before
{ assistantId: "asst_123" }

// After
{ agentId: "agent_123" }
```

--------------------------------

### Agent Instructions Example

Source: https://docs.langdock.com/en/using-langdock/workflows/nodes/agent-node

Provide clear, multi-part instructions for the agent to analyze feedback, including sentiment, category, urgency, and key issues. Uses templating for dynamic input.

```text
Analyze the customer feedback and determine:
1. Sentiment (positive, neutral, negative)
2. Main topic category (product, service, pricing, support)
3. Urgency level (low, medium, high)
4. Key issues mentioned

Feedback: {{trigger.output.feedback_text}}


```

--------------------------------

### Manual Search Query Example 2

Source: https://docs.langdock.com/en/using-langdock/workflows/nodes/file-search-node

Use this snippet to construct a search query that includes specific product information from a trigger output.

```text
Find information about {{trigger.output.product_name}} pricing and features
```

--------------------------------

### Langdock Model Selection Guide

Source: https://docs.langdock.com/en/using-langdock/models-and-limits/models

Use this guide to determine the best model for your task. Auto mode is recommended if unsure.

```text
Not sure where to start?
└─ Use Auto → Langdock picks the right model for you

Know what you need?
├─ Simple, quick task → Use a fast/mini/flash model
└─ Complex task
    ├─ Deep reasoning required → Use a flagship model with Thinking enabled
    └─ Otherwise → Use the standard flagship model
```

--------------------------------

### Invite Users Request Body Examples (OpenAPI)

Source: https://docs.langdock.com/en/developer/user-management-api/invite

Examples demonstrating how to structure the request body for inviting users, including single user invitations and multiple users with specified roles.

```yaml
users:
  - email: jane.doe@example.com
```

```yaml
users:
  - email: alice@example.com
    role: admin
  - email: bob@example.com
    role: member
  - email: carol@example.com
```

--------------------------------

### Message Object Example

Source: https://docs.langdock.com/en/developer/completion-api/anthropic

An example of a `Message` object returned by the API, including content, ID, model used, role, and stop reason.

```json
{
  "content": [
    {
      "text": ">-\nHere is a haiku about cats:\n\nFeline grace and charm,\nPurring softly by the fire,\nCats reign supreme.",
      "type": "text"
    }
  ],
  "id": "msg_013Zva2CMHLNnXjNJJKqJ2EF",
  "model": "claude-3-haiku-20240307",
  "role": "assistant",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "type": "message",
  "usage": {
    "input_tokens": 14,
    "output_tokens": 35
  }
}
```

--------------------------------

### Example Question for Company Knowledge - File Search

Source: https://docs.langdock.com/en/using-langdock/chat/tools/company-knowledge

Use natural language to find specific documents within connected sources. This example searches for a Q4 sales report in a marketing folder.

```text
Find the Q4 sales report from the marketing folder
```

--------------------------------

### Example Response for Listing Integrations

Source: https://docs.langdock.com/en/developer/integrations-api/list-integrations

This JSON object demonstrates a typical successful response when listing integrations, showcasing sample data for both Langdock-built and custom integrations.

```json
{
  "integrations": [
    {
      "id": "550e8400-e29b-41d4-a716-446655440000",
      "name": "Slack",
      "description": "Send messages and interact with Slack",
      "authType": "OAUTH",
      "buildByLangdock": true,
      "actions": [
        {
          "id": "660e8400-e29b-41d4-a716-446655440001",
          "name": "Send Message",
          "description": "Send a message to a Slack channel",
          "requiresConfirmation": true
        },
        {
          "id": "660e8400-e29b-41d4-a716-446655440002",
          "name": "Create Channel",
          "description": "Create a new Slack channel",
          "requiresConfirmation": true
        }
      ]
    },
    {
      "id": "770e8400-e29b-41d4-a716-446655440000",
      "name": "My Custom Integration",
      "description": "Internal API connector",
      "authType": "API_KEY",
      "buildByLangdock": false,
      "actions": [
        {
          "id": "880e8400-e29b-41d4-a716-446655440001",
          "name": "Get User Data",
          "description": "Retrieves user information",
          "requiresConfirmation": false
        }
      ]
    }
  ]
}

```

--------------------------------

### Single File Output Example

Source: https://docs.langdock.com/en/using-langdock/guides/integrations/file-support-for-actions

Demonstrates how to return a single file output from an action. Ensure the file content is Base64 encoded for binary files.

```javascript
return {
  files: {
    fileName: "report.pdf",
    mimeType: "application/pdf",
    base64: "JVBERi0xLjQK...", // Base64 encoded content
  },
};
```

--------------------------------

### Example message_delta event

Source: https://docs.langdock.com/en/developer/completion-api/google

An example of a 'message_delta' event received during a streaming response from the API. This event contains subsequent chunks of generated content.

```json
event: message_delta
data: {
  "candidates": [
    {
      "index": 0,
      "content": {
        "role": "model",
        "parts": [{ "text": "The ocean whispers..." }]
      }
    }
  ]
}
```

--------------------------------

### FIM Completion Response Example

Source: https://docs.langdock.com/en/developer/completion-api/mistral

Example of a successful response from the FIM completion endpoint. Includes usage statistics and the generated code completion.

```json
{
  "data": "asd",
  "id": "245c52bc936f53ba90327800c73d1c3e",
  "object": "chat.completion",
  "model": "codestral",
  "usage": {
    "prompt_tokens": 16,
    "completion_tokens": 102,
    "total_tokens": 118
  },
  "created": 1732902806,
  "choices": [
    {
      "index": 0,
      "message": {
        "content": "\n                          // Use a regular expression to match any non-alphanumeric character and replace it with an empty string\n                          return str.replace(/[^a-zA-Z0-9]/g, '');\n                        }\n\n\n                        // Test the function\n\n                        const inputString = \"Hello, World! 123\";\n\n                        const outputString =\n                        removeSpecialCharactersWithRegex(inputString);\n\n                        console.log(outputString); // Output: \"HelloWorld123\"
                      ",
        "prefix": false,
        "role": "assistant"
      },
      "finish_reason": "stop"
    }
  ]
}
```

--------------------------------

### Using a Temporary Agent Configuration

Source: https://docs.langdock.com/en/developer/agents-api/agent

This example shows how to use a temporary agent configuration directly in the request. This is useful for ad-hoc agent creation without needing to pre-register an agent.

```APIDOC
## POST /agent/v1/chat/completions

### Description
Initiates a chat completion using a temporary agent configuration defined within the request.

### Method
POST

### Endpoint
/agent/v1/chat/completions

### Request Body
- **agent** (object) - Optional - Configuration for a temporary agent.
  - **name** (string) - Required - The name of the agent.
  - **instructions** (string) - Required - The instructions for the agent.
  - **temperature** (number) - Optional - The sampling temperature for the agent's response.
  - **model** (string) - Required - The model to use for the agent.
  - **capabilities** (object) - Optional - Capabilities enabled for the agent.
    - **webSearch** (boolean) - Optional - Whether web search is enabled.
- **messages** (array) - Required - An array of message objects representing the conversation history.
  - **id** (string) - Required - Unique identifier for the message.
  - **role** (string) - Required - The role of the message sender (e.g., 'user', 'assistant').
  - **parts** (array) - Required - An array of message parts.
    - **type** (string) - Required - The type of message part (e.g., 'text').
    - **text** (string) - Required - The content of the text part.

### Request Example
```json
{
  "agent": {
    "name": "Document Analyzer",
    "instructions": "You are a helpful agent who analyzes documents and answers questions about them",
    "temperature": 0.7,
    "model": "gpt-5",
    "capabilities": {
      "webSearch": true
    }
  },
  "messages": [
    {
      "id": "msg_1",
      "role": "user",
      "parts": [
        {
          "type": "text",
          "text": "What are the key points in the document?"
        }
      ]
    }
  ]
}
```

### Response
#### Success Response (200)
- **messages** (array) - An array of message objects representing the agent's response.
  - **content** (string) - The text content of the agent's response.
```

--------------------------------

### Send Notification Node Message Example

Source: https://docs.langdock.com/en/using-langdock/workflows/guides/node-types

Example message content for the Send Notification Node, demonstrating the use of variables and markdown for dynamic notifications.

```handlebars
New high-priority support ticket received: Customer:
{{trigger.output.customer_name}}
Issue:
{{agent.output.structured.category}}
Summary:
{{agent.output.structured.summary}}

View ticket:
{{action.output.ticket_url}}
```

--------------------------------

### Hallucination Detection Guardrail Example

Source: https://docs.langdock.com/en/using-langdock/workflows/nodes/guardrails-node

Example configuration for the Hallucination Detection guardrail. This guardrail detects when AI-generated content contains false or unverifiable information.

```text
Input: {{agent.output.structured.generated_summary}}
Reference: {{http_request.output.original_data}}
Threshold: 0.7
Checks: Does summary accurately reflect source data?
```
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