A Streamlit-based playground that lets you chat with large language models and seamlessly plug in external Multi-Server Command Protocol (MCP) tools. Spin up multiple FastMCP servers (Weather & Currency) alongside a Streamlit client, all orchestrated with Docker Compose. The client is provider-agnostic (OpenAI • Amazon Bedrock • Anthropic • Google Gemini) thanks to LangChain + LangGraph.
Want a deep dive into how it all works? Check out the detailed walkthrough in this Medium article: https://medium.com/@elkhan.alizada/your-own-ai-agent-playground-build-it-with-streamlit-langgraph-and-docker-4caeb6fe0ac4
| Feature | Description |
|---|---|
| 🔌 Multi-Server MCP | Register any number of MCP servers; the agent auto-detects available tools & routes calls. |
| 🖥️ Streamlit Chat UI | Rich chat experience with history, sidebar controls and live tool execution output. |
| 🧩 Provider-Agnostic | One LangChain interface for OpenAI, Bedrock, Anthropic, Google, Groq. Switch on the fly. |
| 🤖 React Agent via LangGraph | create_react_agent enables dynamic tool selection and reasoning. |
| 🐳 Docker-First | Separate Dockerfiles for client & each server + a single docker-compose.yaml. |
| 📦 Extensible | Drop-in new MCP servers or providers without touching UI code. |
mcp-playground/
├─ docker-compose.yaml # One-command orchestration
├─ client/ # Streamlit UI
│ ├─ app.py # Main entry-point
│ ├─ config.py # Typed settings & defaults
│ ├─ servers_config.json # MCP endpoint catalogue
│ ├─ ui_components/ # Streamlit widgets
│ └─ ...
└─ servers/
├─ server1/ # Weather Service MCP
│ └─ main.py
└─ server2/ # Currency Exchange MCP
└─ main.py
- Docker ≥ 24 & Docker Compose
- At least one LLM provider key (e.g.
OPENAI_API_KEY) or AWS creds for Bedrock.
git clone https://github.com/your-org/mcp-playground.git
cd mcp-playground
docker compose up --build| Service | URL | Default Port |
|---|---|---|
| Streamlit Client | http://localhost:8501 | 8501 |
| Weather MCP | http://localhost:8000 | 8000 |
| Currency MCP | http://localhost:8001 | 8001 |
All runtime settings are concentrated in client/config.py and environment variables.
| Variable | Purpose |
|---|---|
MODEL_ID |
Provider selector (OpenAI, Bedrock, Anthropic, Google, Groq). |
TEMPERATURE |
Sampling temperature (sidebar slider). |
MAX_TOKENS |
Token limit (sidebar). |
MODEL_OPTIONS = {
'OpenAI': 'gpt-4o',
'Antropic': 'claude-3-5-sonnet-20240620',
'Google': 'gemini-2.0-flash-001',
'Bedrock': 'us.anthropic.claude-3-7-sonnet-20250219-v1:0',
'Groq': 'meta-llama/llama-4-scout-17b-16e-instruct'
}MCP endpoints live in servers_config.json – edit to add/remove servers without code changes.
- Select Provider · Pick your LLM in the sidebar and paste the corresponding credentials.
- Connect MCP Servers · Toggle connections; available tools appear in the MCP Tools list.
- Chat · Type a question.
- If connected, the React agent decides whether to call an MCP tool (e.g. get_current_weather).
- Otherwise it falls back to plain LLM chat.
- Inspect Tool Calls · Tool invocations are streamed back as YAML blocks with inputs & outputs.
Try:
"What will the weather be in Baku tomorrow and how much is 100 USD in AZN?"
mcp = FastMCP("Weather Service", host="0.0.0.0", port=8000)
@mcp.tool()
async def get_current_weather(location: str) -> dict: ...
@mcp.tool()
async def get_forecast(location: str, days: int = 3) -> dict: ...mcp = FastMCP("Currency Exchange", host="0.0.0.0", port=8001)
@mcp.tool()
async def get_currency_rates(date: str = None) -> dict: ...
@mcp.tool()
async def convert_currency(amount: float, from_currency: str, to_currency: str, date: str = None) -> dict: ...