Agents

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Introduction

​

Overview

• Role-based customization: Define the agent’s persona and task focus to generate contextually relevant responses.
• Multi-tool integration: Extend agent capabilities with custom tools like email sending, database querying, and more.
• Knowledge base connectivity: Equip agents with domain-specific knowledge for accurate and informed interactions.
• Safe and responsible AI: Built-in safety layers ensure ethical, secure, and reliable AI behavior.
• Testing & deployment: Easily test agents in real time before deploying them to production or the Agent Marketplace.

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Creating an Agent

1. Navigate to Agent Builder:
   From the Lyzr Agent Studio page, select Build Agent.
2. Provide Agent Details:
   Enter a unique name for your agent and add a detailed description outlining its purpose.
3. Select LLM Model:
   Choose an appropriate Large Language Model provider and specific model for your agent’s use case. Configure parameters like temperature and Top P as needed.
4. Define Agent Role and Instructions:
   Specify the agent’s role (e.g., Customer Support, Data Fetcher) and provide instructions to guide its behavior. Use the Improve feature to refine these details.
5. Add Tools (Optional):
   Enhance your agent’s capabilities by integrating tools such as email sending, database queries, or custom APIs.
6. Add Examples and Knowledge Bases (Optional):
   Provide example prompts and responses to train your agent, and link relevant knowledge bases to improve accuracy.
7. Select Features:
   Choose from available features to further customize the agent’s behavior and output.
8. Create and Test Agent:
   Click Create Agent to save. Test the agent with sample queries and fine-tune as needed.
9. Deploy Agent (Optional):
   Deploy your agent by publishing it to the Agent Marketplace or use the provided JSON and API key for integration into your applications.

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Updating an Agent

1. Access Agent List:
   In the Agent Lab, open the list of your created agents.
2. Select the Agent to Update:
   Click on the desired agent to open its configuration panel.
3. Modify Details:
   Update the agent’s name, description, role, instructions, model selection, tools, examples, knowledge bases, or features as required.
4. Save Changes:
   After modifications, click Save to apply the updates.
5. Retest the Agent:
   Run tests to verify that updates yield the desired improvements.

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Deleting an Agent

1. Navigate to Agent List:
   Open the list of all agents in the Agent Lab.
2. Locate the Agent:
   Find the agent you wish to delete.
3. Delete Action:
   Click the Delete option (usually a trash icon or button) next to the agent’s name.
4. Confirm Deletion:
   Confirm the action in the prompt to permanently remove the agent.

Note: Deleted agents cannot be recovered. Ensure you have backups or exports if necessary before deletion.

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Multimodal Chat Inference v3

• Chat naturally with an agent
• Provide documents or images as input (via asset IDs)
• Receive responses that incorporate those files

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🔁 Endpoint

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POST /v3/inference/chat/

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📥 Request

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Headers

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Body Parameters

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Sample Request

{
  "user_id": "default_user",
  "agent_id": "research_agent_001",
  "session_id": "abc123-session-xyz",
  "message": "Summarize the attached document.",
  "assets": ["6fefwfw84930dfe", "6fewfwf12023ab"]
}

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📤 Response

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200 OK

{
  "status": "success",
  "agent_response": "The attached document discusses...",
  "session_id": "abc123-session-xyz",
  "agent_id": "research_agent_001"
}

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🔍 Multimodal Use Case Examples

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1. Ask Questions About a PDF

{
  "message": "What are the key takeaways from this research paper?",
  "assets": ["asset_923fdf21"]
}

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2. Describe an Image

{
  "message": "What is in this image?",
  "assets": ["asset_image8921"]
}

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3. Reference Multiple Files

{
  "message": "Compare the information in these two reports.",
  "assets": ["asset_q1_report", "asset_q2_report"]
}

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📎 How to Get asset_ids

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POST /v3/assets/upload

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🧠 Tips

• Keep using the same session_id to maintain context in a chat.
• Agents can handle multiple file types in a single request.
• Files are securely stored and accessible only by the authenticated user.

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🧪 Python Example

import requests
import uuid

url = "https://agent-prod.studio.lyzr.ai/v3/inference/chat/"
headers = {
    "accept": "application/json",
    "Content-Type": "application/json"
}
payload = {
    "user_id": "default_user",
    "agent_id": "my_agent_01",
    "session_id": str(uuid.uuid4()),
    "message": "Summarize this document.",
    "assets": ["your_asset_id_here"]
}
response = requests.post(url, headers=headers, json=payload)
print(response.json())

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🚫 Errors