AI Agents with CrewAI
Basic Information
This repository demonstrates how to simulate an agentic workflow for multiple AI agents using CrewAI. It is intended as an example and reference showing how to orchestrate autonomous agents to work together on complex tasks, implementing a simple multi-agent research team with CrewAI as the coordination backbone. The code configures agents to assume roles, define goals and tasks, and connect to large language model tools. The README highlights both a basic two-agent collaborative example and an alternate example that integrates a large LLM via Groq for high-speed inference. The repo is focused on illustrating collaborative intelligence patterns and agent orchestration rather than providing a production system or extensive tooling.
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App Details
Features
Configures AI agents with explicit roles, goals and task definitions to enable collaborative workflows under CrewAI orchestration. Integrates LLM tools from OpenAI for agent reasoning and responses. Includes a demonstrated example script named crewai_with_groq_llama70b.py that programs agents to use a Llama3 70b model served via the Groq API to enable faster inference. Shows multi-agent interactions and how agents can be composed into a small research-style team. The repository emphasizes clear examples of orchestration logic and LLM integration rather than wide-ranging infrastructure or deployment automation.
Use Cases
This repository is helpful for developers and researchers who want a compact example of multi-agent orchestration with CrewAI. It provides concrete patterns for assigning roles, specifying goals and tasks, and attaching LLM tools so teams of agents can collaborate. The included Groq+Llama3 70b example demonstrates how to plug in a high-performance inference backend for large models, which can be useful for experimenting with latency and local execution. Overall it serves as a learning and prototyping resource to study agent coordination, tool integration, and simple multi-agent research workflows.
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