Agents Completions API

https://api.langdock.com/agent/v1/chat/completions

interface UIMessage {
  id: string;           // Eindeutige Kennung für diese Nachricht
  role: 'system' | 'user' | 'assistant';
  parts: MessagePart[]; // Array von Nachrichtenteilen
  metadata?: {          // Optionale Metadaten
    attachments?: string[];  // Array von Attachment-UUIDs
  };
}

{
  id: "msg_1",
  role: "user",
  parts: [
    {
      type: "text",
      text: "Hallo, wie geht es dir?"
    }
  ]
}

{
  id: "msg_2",
  role: "user",
  parts: [
    {
      type: "text",
      text: "Bitte analysiere dieses Dokument"
    }
  ],
  metadata: {
    attachments: ["550e8400-e29b-41d4-a716-446655440000"]
  }
}

{
  id: "msg_3",
  role: "assistant",
  parts: [
    {
      type: "tool-webSearch",
      toolCallId: "call_123",
      state: "output-available",
      input: {
        query: "latest news"
      },
      output: { /* Suchergebnisse */ }
    }
  ]
}

'use client';

import { useChat } from '@ai-sdk/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat({
    api: 'https://api.langdock.com/agent/v1/chat/completions',
    headers: {
      'Authorization': `Bearer ${process.env.NEXT_PUBLIC_LANGDOCK_API_KEY}`
    },
    body: {
      agentId: 'your-agent-id'
    }
  });

  return (
    <div>
      {messages.map(m => (
        <div key={m.id}>
          {m.role === 'user' ? 'User: ' : 'AI: '}
          {m.content}
        </div>
      ))}

      <form onSubmit={handleSubmit}>
        <input
          value={input}
          placeholder="Schreib etwas..."
          onChange={handleInputChange}
        />
      </form>
    </div>
  );
}

const response = await fetch('https://api.langdock.com/agent/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    agentId: 'agent_123',
    messages: [
      {
        id: 'msg_1',
        role: 'user',
        parts: [{ type: 'text', text: 'Hallo' }]
      }
    ],
    stream: true
  }),
});

const reader = response.body.getReader();
const decoder = new TextDecoder();

while (true) {
  const { done, value } = await reader.read();
  if (done) break;

  const chunk = decoder.decode(value);
  console.log(chunk);  // Streaming-Chunks verarbeiten
}

interface CompletionResponse {
  messages: Array<{
    id: string;
    role: "assistant";
    content: string;
  }>;

  // Strukturierte Ausgabe - enthalten wenn angefordert
  output?: object | array | string;
}

const response = await fetch(
  "https://api.langdock.com/agent/v1/chat/completions",
  {
    method: "POST",
    headers: {
      "Authorization": "Bearer YOUR_API_KEY",
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      agentId: "agent_123",
      messages: [
        {
          id: "msg_1",
          role: "user",
          parts: [
            {
              type: "text",
              text: "Kannst du dieses Dokument für mich analysieren?"
            }
          ],
          metadata: {
            attachments: ["550e8400-e29b-41d4-a716-446655440000"]
          }
        }
      ]
    })
  }
);

const data = await response.json();
const responseText = data.messages[0].content;
console.log(responseText);

const response = await fetch(
  "https://api.langdock.com/agent/v1/chat/completions",
  {
    method: "POST",
    headers: {
      "Authorization": "Bearer YOUR_API_KEY",
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      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: "Was sind die wichtigsten Punkte im Dokument?"
            }
          ]
        }
      ]
    })
  }
);

const data = await response.json();
console.log(data);

const response = await fetch(
  "https://api.langdock.com/agent/v1/chat/completions",
  {
    method: "POST",
    headers: {
      "Authorization": "Bearer YOUR_API_KEY",
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      agent: {
        name: "Weather Agent",
        instructions: "You are a helpful weather agent",
        model: "gpt-5",
        capabilities: {
          webSearch: true
        }
      },
      messages: [
        {
          id: "msg_1",
          role: "user",
          parts: [
            {
              type: "text",
              text: "Wie ist das Wetter heute in Paris, Berlin und London?"
            }
          ]
        }
      ],
      output: {
        type: "array",
        schema: {
          type: "object",
          properties: {
            weather: {
              type: "object",
              properties: {
                city: { type: "string" },
                tempInCelsius: { type: "number" },
                tempInFahrenheit: { type: "number" }
              },
              required: ["city", "tempInCelsius", "tempInFahrenheit"]
            }
          }
        }
      }
    })
  }
);

const data = await response.json();
console.log(data.output);
// Output:
// [
//   { "weather": { "city": "Paris", "tempInCelsius": 1, "tempInFahrenheit": 33 } },
//   { "weather": { "city": "Berlin", "tempInCelsius": 1, "tempInFahrenheit": 35 } },
//   { "weather": { "city": "London", "tempInCelsius": 7, "tempInFahrenheit": 45 } }
// ]

// app/actions.ts
'use server';

import { generateId } from 'ai';

export async function chatWithAgent(message: string) {
  const response = await fetch(
    'https://api.langdock.com/agent/v1/chat/completions',
    {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${process.env.LANGDOCK_API_KEY}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        agentId: process.env.AGENT_ID,
        messages: [
          {
            id: generateId(),
            role: 'user',
            parts: [
              {
                type: 'text',
                text: message
              }
            ]
          }
        ]
      })
    }
  );

  const data = await response.json();
  return data.messages[0].content;
}

try {
  const response = await fetch('https://api.langdock.com/agent/v1/chat/completions', options);

  if (!response.ok) {
    const error = await response.json();
    throw new Error(error.message || 'Request failed');
  }

  const data = await response.json();
  // Antwort verarbeiten
} catch (error) {
  console.error('Error:', error.message);
}