> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Get a running Lyzr agent in under 5 minutes.

Get a running agent in under 5 minutes using the method that fits your workflow.

## Before you begin

* A Lyzr Studio account. Sign up at [studio.lyzr.ai](https://studio.lyzr.ai) via Google, GitHub, or email.
* For Option C or D: an API key from **Account > API Keys** in Agent Studio.

## Option A: Agent Studio (no code)

1. Sign in at [studio.lyzr.ai](https://studio.lyzr.ai).
2. On the home screen, describe what you want to build in plain English. The conversational builder asks follow-up questions and configures the agent for you. Alternatively, select **Agents** in the sidebar, then **New Agent** to use the manual builder.
3. Give your agent a name, select an LLM (GPT-4o Mini is a good starting point for cost), and write a role, goal, and instructions.
4. Optionally enable **Knowledge Base** and upload a PDF or enter a URL.
5. Select **Test** to open the chat interface. Send a message to verify the agent responds correctly.
6. Select **Deploy** and choose visibility: Private, Public, or Organization.

Your agent now has a live REST endpoint. Find it in **Agent > API tab**.

## Option B: Architect (natural language)

Architect builds a full-stack agentic application from a plain-English description. Use it when you want a working product with a UI, not just an API endpoint.

1. Go to [architect.new](https://www.architect.new/).
2. Type your prompt and hit enter. Architect will ask follow-up questions to confirm requirements, then generate the full application for you:

```
Build a Q&A agent that ingests my company documents, PDFs, and wiki pages,
then answers employee questions accurately with source references.
```

3. Architect configures the Knowledge Base, sets up the agent, and generates a frontend UI automatically.
4. Review the generated application in the preview, then deploy with one click.

Your application is live with a hosted URL, a multi-agent backend, and source-referenced answers out of the box.

## Option C: ADK (Python)

```bash theme={null}
pip install lyzr-adk
```

```python theme={null}
from lyzr_adk import Studio

studio = Studio(api_key="<YOUR_API_KEY>")  # get key from Agent Studio > Account

agent = studio.create_agent(
    name="Support Bot",
    provider="gpt-4o-mini",
    role="Customer support agent",
    goal="Resolve customer inquiries about billing and account issues",
    instructions="Be concise. Always ask for the account ID before looking up details."
)

response = agent.run("My invoice shows the wrong amount")
print(response.response)
```

## Option D: REST API

```bash theme={null}
# Create an agent
curl -X POST https://agent-prod.studio.lyzr.ai/v3/agent \
  -H "x-api-key: <YOUR_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Support Bot",
    "provider": "gpt-4o-mini",
    "role": "Customer support agent",
    "goal": "Resolve customer inquiries",
    "instructions": "Be concise and ask for account ID first"
  }'
```

```bash theme={null}
# Chat with the agent
curl -X POST https://agent-prod.studio.lyzr.ai/v3/agent/{agent_id}/chat \
  -H "x-api-key: <YOUR_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{"message": "My invoice shows the wrong amount", "session_id": "user-123"}'
```

## What's next

* Add a [Knowledge Base](../agent-studio/knowledgebase/studiokb) to ground your agent in your documents.
* Connect [Tools](../agent-studio/tools/overview) to let your agent take actions on external systems.
* Set up [Responsible AI](../agent-studio/responsible-safe-ai/Responsible) guardrails before going to production.
* [Evaluate your agent](../agent-studio/agent%20eval/agentsimulation) with automated test cases.