> For the complete documentation index, see [llms.txt](https://docs.nexos.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.nexos.ai/gateway-api/integrations/streamlit.md).

# Streamlit

#### **What is Streamlit?** <a href="#what-is-streamlit" id="what-is-streamlit"></a>

Streamlit is an open-source Python library that allows you to create beautiful, interactive web applications for data science and machine learning projects without requiring front-end development skills (like HTML, CSS, or JavaScript).

In essence, it lets data scientists and ML engineers transform their data scripts, models, and analyses into shareable web apps very quickly and with minimal code.

**Sample implementation using nexos.ai**

* Install dependencies
* Define environments\
  `NEXOS_API_KEY=team-api-key`\
  `NEXOS_BASE_URL=https://api.nexos.ai/v1`.

```
import os
import streamlit as st

from openai import OpenAI
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()
# --- Configuration ---
NEXOS_BASE_URL = os.getenv("NEXOS_BASE_URL")
NEXOS_API_KEY = os.getenv("NEXOS_API_KEY")
if not NEXOS_BASE_URL or not NEXOS_API_KEY:
    raise ValueError("Please set NEXOS_BASE_URL and NEXOS_API_KEY in your .env file")

# Configure OpenAI client once, pointing to Nexos
client = OpenAI(
  api_key=NEXOS_API_KEY,
  base_url=NEXOS_BASE_URL # e.g. "https://api.nexos.ai/v1"
)

st.title("Chat via Nexos.ai")

user_input = st.text_input("Ask something:")

if user_input:
    try:
        response = client.chat.completions.create(
            model='Claude Haiku 4.5', # or any other OpenAI-compatible model ID available to you 
            messages=[{"role": "user", "content": user_input}]
        )
        st.write("Nexos Response:")
        st.write(response.choices[0].message.content)
    except Exception as e:
        st.error(f"Error calling Nexos: {e}")
```

You can use any open AI compatible model. To check what models are available for you, call [Gateway API | nexos.ai documentation](https://docs.nexos.ai/gateway-api#get-v1-models) You can use either `nexos_model_id`or `id` as model.

## FAQ

### Which other integrations are available in nexos.ai Gateway?

nexos.ai Gateway supports integrations with the following coding tools:

* [CrewAI](https://docs.nexos.ai/gateway-api/integrations/crewai)
* [Codex CLI](https://docs.nexos.ai/gateway-api/integrations/codex-cli)
* [OpenCode](https://docs.nexos.ai/gateway-api/integrations/opencode)
* [Claude Code](https://docs.nexos.ai/gateway-api/integrations/claude-code)
* [Claude Cowork](https://docs.nexos.ai/gateway-api/integrations/claude-cowork)
* [Bubble](https://docs.nexos.ai/gateway-api/integrations/bubble)
* [GitLab CI](https://docs.nexos.ai/gateway-api/integrations/gitlab-ci)
* [OpenAI Agents SDK](https://docs.nexos.ai/gateway-api/integrations/openai-agents-sdk)
* [Vercel AI SDK](https://docs.nexos.ai/gateway-api/integrations/vercel-ai-sdk)
* [ProxyAI](https://docs.nexos.ai/gateway-api/integrations/proxyai)
* [Langfuse](https://docs.nexos.ai/gateway-api/integrations/langfuse)
* [LangGraph](https://docs.nexos.ai/gateway-api/integrations/langgraph)
* [LangChain](https://docs.nexos.ai/gateway-api/integrations/langchain)
* [Roo Code](https://docs.nexos.ai/gateway-api/integrations/roo-code)
* [n8n](https://docs.nexos.ai/gateway-api/integrations/n8n)

&#x20;


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.nexos.ai/gateway-api/integrations/streamlit.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.