# How to integrate Benzinga MCP with Autogen

```json
{
  "title": "How to integrate Benzinga MCP with Autogen",
  "toolkit": "Benzinga",
  "toolkit_slug": "benzinga",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/benzinga/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/benzinga/framework/autogen.md",
  "updated_at": "2026-05-06T08:02:31.170Z"
}
```

## Introduction

This guide walks you through connecting Benzinga to AutoGen using the Composio tool router. By the end, you'll have a working Benzinga agent that can stream real-time news about tesla today, list this week's upcoming earnings reports, show latest analyst ratings for apple through natural language commands.
This guide will help you understand how to give your AutoGen agent real control over a Benzinga account through Composio's Benzinga MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Benzinga with

- [OpenAI Agents SDK](https://composio.dev/toolkits/benzinga/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/benzinga/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/benzinga/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/benzinga/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/benzinga/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/benzinga/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/benzinga/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/benzinga/framework/cli)
- [Google ADK](https://composio.dev/toolkits/benzinga/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/benzinga/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/benzinga/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/benzinga/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/benzinga/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/benzinga/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Install the required dependencies for Autogen and Composio
- Initialize Composio and create a Tool Router session for Benzinga
- Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
- Configure an Autogen AssistantAgent that can call Benzinga tools
- Run a live chat loop where you ask the agent to perform Benzinga operations

## What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.
Key features include:
- Multi-Agent Systems: Build collaborative agent workflows
- MCP Workbench: Native support for Model Context Protocol tools
- Streaming HTTP: Connect to external services through streamable HTTP
- AssistantAgent: Pre-built agent class for tool-using assistants

## What is the Benzinga MCP server, and what's possible with it?

The Benzinga MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Benzinga account. It provides structured and secure access to real-time financial news and market data, so your agent can track earnings, monitor analyst ratings, stream news, and analyze economic events on your behalf.
- Live financial news streaming: Instantly stream real-time news updates, market-moving events, and breaking headlines as they happen so your agent always stays informed.
- Earnings and conference call tracking: Automatically retrieve upcoming earnings dates, actuals, estimates, and conference call details for any ticker or date range.
- Analyst sentiment and ratings insights: Fetch consensus analyst ratings, price targets, and detailed rating calendars to help evaluate stock sentiment and trends.
- Economic event analysis: Access comprehensive economic calendar events, including values, consensus, and importance filters to understand macroeconomic impacts.
- Audit and manage removed items: Identify and review deleted news articles or cancelled calendar events for full transparency and compliance.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `BENZINGA_GET_CALENDAR_EARNINGS_STREAM` | Get Calendar Earnings Stream | Tool to subscribe to real-time earnings calendar events via websocket. use when you need immediate updates as reports are announced. |
| `BENZINGA_GET_CONFERENCE_CALLS` | Get Conference Calls | Tool to retrieve upcoming and historical conference call details such as times, tickers, and webcast urls. use when filtering conference calls by date range or ticker symbols. |
| `BENZINGA_GET_CONSENSUS_RATINGS` | Get Consensus Ratings | Tool to get consensus analyst ratings and price targets for a specified ticker over a date range. use when you need to gauge analyst sentiment and target price consensus for investment decisions. |
| `BENZINGA_GET_EARNINGS_V21` | Get Earnings Calendar V2.1 | Tool to retrieve earnings calendar data (v2.1). use when you need dates, estimates, and actuals for upcoming earnings events. |
| `BENZINGA_GET_ECONOMICS` | Get Economics | Tool to retrieve economic calendar events including actual, consensus, and prior values and importance. use when filtering by date, country, importance, or updated timestamp. |
| `BENZINGA_GET_NEWSFEED_STREAM` | Get Newsfeed Stream | Tool to stream real-time news events via websocket. use when you need immediate updates to news as they are created, updated, or removed. |
| `BENZINGA_GET_RATINGS` | Get Analyst Ratings | Tool to fetch analyst ratings calendar data including rating actions, price targets, and analyst details. use when querying upcoming or historical analyst ratings. |
| `BENZINGA_GET_REMOVED` | Get Removed | Tool to retrieve removed or cancelled calendar events. use when you need to identify calendar items removed for specific event types or filtered by removal timestamp. |
| `BENZINGA_GET_REMOVED_NEWS` | Get Removed News | Tool to retrieve news articles that have been deleted from the database. use when you need to fetch removed articles with pagination and timestamp filters. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Benzinga MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Benzinga. Instead of manually wiring Benzinga APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

You will need:
- A Composio API key
- An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
- A Benzinga account you can connect to Composio
- Some basic familiarity with Autogen and Python async

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider.
- Keep the API key safe.
Composio API Key
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

Install Composio, Autogen extensions, and dotenv.
What's happening:
- composio connects your agent to Benzinga via MCP
- autogen-agentchat provides the AssistantAgent class
- autogen-ext-openai provides the OpenAI model client
- autogen-ext-tools provides MCP workbench support
```bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools
```

### 3. Set up environment variables

Create a .env file in your project folder.
What's happening:
- COMPOSIO_API_KEY is required to talk to Composio
- OPENAI_API_KEY is used by Autogen's OpenAI client
- USER_ID is how Composio identifies which user's Benzinga connections to use
```bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com
```

### 4. Import dependencies and create Tool Router session

What's happening:
- load_dotenv() reads your .env file
- Composio(api_key=...) initializes the SDK
- create(...) creates a Tool Router session that exposes Benzinga tools
- session.mcp.url is the MCP endpoint that Autogen will connect to
```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Benzinga session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["benzinga"]
    )
    url = session.mcp.url
```

### 5. Configure MCP parameters for Autogen

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.
What's happening:
- url points to the Tool Router MCP endpoint from Composio
- timeout is the HTTP timeout for requests
- sse_read_timeout controls how long to wait when streaming responses
- terminate_on_close=True cleans up the MCP server process when the workbench is closed
```python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)
```

### 6. Create the model client and agent

What's happening:
- OpenAIChatCompletionClient wraps the OpenAI model for Autogen
- McpWorkbench connects the agent to the MCP tools
- AssistantAgent is configured with the Benzinga tools from the workbench
```python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Benzinga assistant agent with MCP tools
    agent = AssistantAgent(
        name="benzinga_assistant",
        description="An AI assistant that helps with Benzinga operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )
```

### 7. Run the interactive chat loop

What's happening:
- The script prompts you in a loop with You:
- Autogen passes your input to the model, which decides which Benzinga tools to call via MCP
- agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
- Typing exit, quit, or bye ends the loop
```python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Benzinga related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Benzinga session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["benzinga"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Benzinga assistant agent with MCP tools
        agent = AssistantAgent(
            name="benzinga_assistant",
            description="An AI assistant that helps with Benzinga operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Benzinga related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())
```

## Conclusion

You now have an Autogen assistant wired into Benzinga through Composio's Tool Router and MCP. From here you can:
- Add more toolkits to the toolkits list, for example notion or hubspot
- Refine the agent description to point it at specific workflows
- Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Benzinga, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

## How to build Benzinga MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/benzinga/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/benzinga/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/benzinga/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/benzinga/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/benzinga/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/benzinga/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/benzinga/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/benzinga/framework/cli)
- [Google ADK](https://composio.dev/toolkits/benzinga/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/benzinga/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/benzinga/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/benzinga/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/benzinga/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/benzinga/framework/crew-ai)

## Related Toolkits

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- [Daffy](https://composio.dev/toolkits/daffy) - Daffy is a modern charitable giving platform with a donor-advised fund. Easily set aside funds, grow them tax-free, and donate to over 1.7 million U.S. charities.
- [Eagle doc](https://composio.dev/toolkits/eagle_doc) - Eagle doc is an AI-powered OCR API for invoices and receipts. It delivers fast, reliable, and accurate document data extraction for seamless automation.
- [Elorus](https://composio.dev/toolkits/elorus) - Elorus is an online invoicing and time-tracking software for freelancers and small businesses. Easily manage finances, bill clients, and track work in one place.
- [Eodhd apis](https://composio.dev/toolkits/eodhd_apis) - Eodhd apis delivers comprehensive financial data, including live and historical stock prices, via robust APIs. Easily access reliable, up-to-date market insights to power your apps, dashboards, and analytics.
- [Fidel api](https://composio.dev/toolkits/fidel_api) - Fidel api is a secure platform for linking payment cards to web and mobile apps. It enables real-time card transaction monitoring and event-based automation for businesses.
- [Finage](https://composio.dev/toolkits/finage) - Finage is a secure API platform delivering real-time and historical financial data for stocks, forex, crypto, indices, and commodities. It empowers developers and businesses to access, analyze, and act on market data instantly.
- [Finmei](https://composio.dev/toolkits/finmei) - Finmei is an invoicing tool that simplifies billing, invoice management, and expense tracking. Ideal for automating and organizing your business finances in one place.
- [Fixer](https://composio.dev/toolkits/fixer) - Fixer is a currency data API offering real-time and historical exchange rates for 170 currencies. Instantly access accurate, up-to-date forex data for your applications and workflows.
- [Fixer io](https://composio.dev/toolkits/fixer_io) - Fixer.io is a lightweight API for real-time and historical foreign exchange rates. It makes global currency conversion fast, accurate, and hassle-free.
- [Flutterwave](https://composio.dev/toolkits/flutterwave) - Flutterwave is a global payments platform enabling businesses to accept and send payments across Africa and beyond. Its robust APIs simplify cross-border transactions and financial operations.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Benzinga MCP?

With a standalone Benzinga MCP server, the agents and LLMs can only access a fixed set of Benzinga tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Benzinga and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with Autogen?

Yes, you can. Autogen fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Benzinga tools.

### Can I manage the permissions and scopes for Benzinga while using Tool Router?

Yes, absolutely. You can configure which Benzinga scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Benzinga data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)
