How To Build Your Own Ai Research Agent With One Groq Api Call

How to Build Your Own AI Research Agent with One Groq API Call | Groq is fast, low cost inference. Simplifying the Complexity of AI Agents with Server-Side Tool Use Large Language Models (LLMs) are powerful but constrained by static training data, lacking the ability to access real-time information or interact dynamically with external environments. We need real-time data, not snapshots from 2023. This limitation has driven the adoption of tool use (also known as function calling), which equips LLMs with tools or functions to fetch live data, execute code, and navigate complex tasks (including playing Pokémon ). This evolution has led us to AI agents , which are systems that leverage LLMs and tools to autonomously interact with external environments. Neon recently reported that over 80% of their databases were created by AI agents, outpacing human contributions by 4x. There are even AI agent marketplaces now where we can list and recruit agents for jobs. Building AI agents, however, introduces significant complexity to orchestrate tool pipelines and ensure reliable results. If you’ve built AI agents before, you’re familiar with the challenges of having to spend more time debugging tool call failures than building features. Further, agentic workflows – where multiple AI agents autonomously orchestrate multiple tool calls in the backend – demand more than capability. They require speed. Latency in tool execution can bottleneck real-time applications, degrade user experience, and limit scalability, especially when agents iterate through web searches, computations, or various API integrations. Image source: ChatGPT (generating this took forever - can’t wait until we get image generation models on Groq!) Enter compound-beta and compound-beta-mini , built by Ben Klieger (give him a follow!) and available in preview on Groq API. These systems leverage server-side tool orchestration to abstract tool use complexity, enabling us to build powerful, real-time AI agents with minimal configuration. compound-beta  is a compound AI system that integrates multiple open-source models to handle complex, multi-step tasks. It supports simultaneous tool calls, such as web searches and Python code execution, making it ideal for iterative workflows.  compound-beta-mini , a lightweight variant, focuses on single-tool tasks, prioritizing low latency for real-time applications. By combining open-source models ( Llama 4 Scout and Llama 3.3 70B ),  compound-beta  delivers robust, multi-tool workflows, while  compound-beta-mini  optimizes for single-tool tasks with even lower latency. Both models are accessible via Groq API by specifying  compound-beta  or  compound-beta-mini  as the model parameter within a standard chat completion request, requiring no additional code. Technical Specifications Inference Speed I could just say that  compound-beta  achieves ~350 tokens/second while compound-beta-mini  achieves ~275 tokens/second, but this doesn’t illustrate how much time and effort these systems save us. As an example, I ran the following experiment on OpenAI’s gpt-4o with web search enabled,  compound-beta-mini , OpenAI’s 3o, and  compound-beta : Query: Write an academic report about what Groq was in the news for this past week focusing on impact and implications. Model Number of Sources Used Time Taken for Results(s) Output Tokens (Length of Generated Report) gpt-4o 3 4.26 312

compound-beta-mini 17 3.14 756

o3 Cited 6 sources ranging from 2023-2025 (not current) 54.04 1,926

compound-beta 17 10.54 2,419

Each tool call, whether fetching live news or executing code, adds latency. Groq hardware ensures these operations are near-instantaneous so we don’t have to get bored watching loading icons. As shown above (and you can experiment yourself on Groq Console Playground ), compound systems on Groq deliver dramatically more comprehensive research! While  gpt-4o  accessed 3 sources from web search,  compound-beta-mini  leveraged 17 different sources – that’s nearly 6x more research depth – while running faster than  gpt-4o  and delivering a more comprehensive, detailed report that was twice as long. Compared to o3,  compound-beta  outperforms by searching 3x more sources (all current), generating a more detailed analysis, and delivering results 5x faster. As a side note, in my experimentation, I kept getting hallucinated and outdated sources from o3, which was interesting. Let’s think of  compound-beta-mini ’s results of a report with information cited from 17 sources produced in 3.14 second. For us humans, that effectively means compressing a full day’s research work into the time it takes to read this sentence. This represents not just minutes saved, but a fundamental transformation in how research can be conducted. Quality Speed is crucial for AI agents, but so is quality. Thanks to a lack of evaluations for live data fetching, we open-sourced the RealtimeEval benchmark that can be automatically updated with information from the past 24 hours designed to evaluate tool use for current events. On this benchmark,  compound-beta  outperformed  GPT-4o-search-preview  and  GPT-4o-mini-search-preview  significantly with performance on par with Perplexity Sonar. Ease of Integration If you’re migrating from existing agent frameworks, both  compound-beta and  compound-beta-mini  integrate naturally with your current stack – all you need to do is set the model parameter in your standard Groq API call to one of them. If you’re building from scratch, start with the tutorial below and see more code examples in our documentation. Building a Foreign Policy Research AI Agent (Python) Now that we’ve learned about how powerful  compound-beta and  compound-beta-mini  are, let’s see how simple it is to build a research agent that fetches today’s foreign policy news involving President Trump, restricts sources to cnn.com and foxnews.com domains, and generates a tweet summarizing key updates using  compound-beta ’s fast real-time web search and reasoning via a single Groq API call. Step 1: Environment Setup Obtain a free Groq API key from Groq Console and install the Python SDK (we have a generous free tier with thousands of tokens per minute for you to play with across all available models): 1 pip install groq Copy

Step 2: Initialize the Client Set your Groq API key as an environment variable and initialize the Groq Client: import os from groq import Groq client = Groq(api_key=os.environ.get("GROQ_API_KEY")) 1 import os 2...