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

# Chat agents API overview

The [SambaNova Agent](/en/build/agents) provides access to specialized AI agents for different tasks. Each agent has both a fire-and-forget endpoint (single call) and an interactive endpoint (multi-turn conversation).

## Available agents

| Agent              | Endpoint                      | Capabilities                                           |
| :----------------- | :---------------------------- | :----------------------------------------------------- |
| Main Agent         | `api/agent/mainagent`         | General-purpose assistant with access to all subagents |
| Financial Analysis | `api/agent/financialanalysis` | Stock analysis, financial metrics, market data         |
| Deep Research      | `api/agent/deepresearch`      | Multi-iteration research with comprehensive reports    |
| Data Science       | `api/agent/datascience`       | Dataset analysis, visualizations, ML workflows         |
| Coding Agent       | `api/agent/coding`            | Code execution, debugging, sandboxed Python            |

## Key features

* **Artifacts System:** Files (charts, PDFs, scripts) returned as downloadable IDs
* **Multi-turn Conversations:** Interactive endpoints with thread\_id continuity
* **Auto-approval:** Deep research and data science interrupts handled automatically
* **File Upload:** Data science accepts dataset uploads
* **Bearer Auth:** Secure API key authentication

## Common patterns

### Fire-and-forget pattern

Single API call that processes the request and returns the complete result.

**Use case:** Quick queries, one-shot tasks, automated workflows

<CodeGroup>
  ```bash Request theme={null}
  curl -X POST "https://<base-url>/agent/mainagent" \
    -H "Content-Type: application/json" \
    -d '{
      "prompt": "Create a bar chart for Q1 sales"
    }'
  ```

  ```json Response theme={null}
  {
    "status": "success",
    "result": "I've created the bar chart...",
    "artifacts": ["file-id-123"],
    "thread_id": "thread-abc"
  }
  ```
</CodeGroup>

***

### Interactive pattern

Multi-turn conversation with persistent context using thread\_id.

**Use case:** Follow-up questions, iterative development, complex workflows

#### Step 1: Initial request

<CodeGroup>
  ```bash Request theme={null}
  curl -X POST "https://<base-url>/agent/mainagent/interactive" \
    -H "Content-Type: application/json" \
    -d '{
      "prompt": "Create a bar chart for Q1 sales",
      "resume": false
    }'
  ```

  ```json Response theme={null}
  {
    "status": "success",
    "result": "I've created the bar chart...",
    "artifacts": ["file-id-123"],
    "thread_id": "thread-abc"
  }
  ```
</CodeGroup>

#### Step 2: Follow-up request

<CodeGroup>
  ```bash Request theme={null}
  curl -X POST "https://<base-url>/agent/mainagent/interactive" \
    -H "Content-Type: application/json" \
    -d '{
      "prompt": "Now create a pie chart showing the distribution",
      "thread_id": "thread-abc",
      "resume": true
    }'
  ```

  ```json Response theme={null}
  {
    "status": "success",
    "result": "I've created the pie chart...",
    "artifacts": ["file-id-456"],
    "thread_id": "thread-abc"
  }
  ```
</CodeGroup>