Agents Overview

This page covers several ways to build multi-agent systems. It starts with Make It Heavy, a small project that emulates Grok Heavy by running parallel research agents. For that project you get its features, component table, built-in tools, setup steps, and an honest list of pros and cons. A short entry then points to Awesome LLM Apps as a source of example applications. CrewAI gets the longest treatment, explaining roles, crews, processes, and the CrewManager, then weighing its strengths against the lack of agent conversation and long-term memory. Suitability notes follow. The page ends with a list of general agent frameworks such as Suna, VoltAgent, OpenAI Agents Python, Google ADK, OWL, and PraisonAI. Read the CrewAI section when choosing between frameworks.

Make It Heavy

https://github.com/Doriandarko/make-it-heavy

Key Features

• Grok Heavy emulation: A multi-agent system that recreates the deep, multi-perspective analysis mode of Grok Heavy.
• Parallel agents: Runs 4 (or more) specialized agents at the same time to ensure broad coverage of information and viewpoints.
• Dynamic question generation: The AI automatically generates 4 in-depth research questions from the user's query so that each aspect is explored independently.
• Execution status display: The console interface shows per-agent progress in real time.
• Flexible tool integration: The tool system automatically discovers libraries and tool functions added to the tools/ directory, allowing "hot-plugging".
• Intelligent synthesis: Merges results from the agents' different perspectives into one unified, insightful answer.
• Single-agent mode: Can run with just one agent (for simpler problems).

Main Components

Built-in Tools

AI Model Integration

• Supports choosing OpenRouter models (Claude, GPT-4.1, Gemini, Llama, and so on)
• Customizable number of parallel agents (orchestrator configuration)
• Tools can be added quickly by simply adding a Python file that inherits from BaseTool

Installation & Basic Usage

• Requires Python 3.8+, the uv package manager, and an OpenRouter API key
• Launch single-agent mode: uv run main.py
• Launch Grok Heavy (multi-agent): uv run make_it_heavy.py
• Customize the bot, tools, and config in config.yaml

Pros and Cons

Pros

• Multi-dimensional, in-depth analysis: Each agent approaches a different angle → deep synthesis with fewer gaps in information.
• Workflow automation: No effort spent generating questions or coordinating; just enter the query.
• Easy tool extension: Just add a new tool file to the right directory.
• Easy to configure: Customize through a YAML file; supports many models, flexible in performance and cost.
• Simple and advanced modes: Fits many different problems and use cases.

Cons

• Depends on the OpenRouter API and quota: Each agent consumes its own requests, limited by your service plan.
• Overhead for simple tasks: The orchestrator/agent process can be redundant if the problem is short and simple.
• Requires initial setup (API key, Python environment); newcomers must go through several setup steps.
• The default tools are basic; more advanced ones must be developed yourself.

Potential Use Cases

• Comprehensive research: Requests that need in-depth analysis from many aspects (e.g., the impact of AI on programming, technology trends).
• Technical consulting/diagnosis: Identifying, comparing, and validating multiple options or counterarguments (code advice, framework comparison, data validation).
• Creative scenarios/risk analysis: Startup planning, market assessment, financial analysis, risk assessment.
• Synthesizing large documents: Splitting the work by specialty (like teamwork), replacing small-group brainstorming.

Awesome LLM Apps

• Repository: https://github.com/Shubhamsaboo/awesome-llm-apps
• Description: Curated collection of awesome LLM applications with AI Agents and RAG using OpenAI, Anthropic, Gemini, and open-source models
• Categories:
  • AI Agents
  • RAG (Retrieval-Augmented Generation) applications
  • LLM-powered applications
  • Open-source model implementations
• Use Cases: Finding LLM application examples, learning RAG implementations, exploring AI agent architectures

CrewAI

https://github.com/crewAIInc/crewAI

CrewAI is a lightweight, high-speed Python framework for orchestrating multi-agent systems built on large language models (LLM Multi-Agent Orchestration Framework).

Main Features of CrewAI

• CrewAI organizes agents (representing roles such as Researcher, Writer, Critic) to work together as a "crew" to handle specific tasks.
• Each agent has a clear role, context, and goal, and can use default or dedicated tools for each task.
• Supports flexible workflows (processes): executing tasks sequentially or in parallel, with or without interaction within the agent group.
• Everything is coordinated by the CrewManager, which manages initialization, activation, and the logic for dividing tasks and returning the final result.
• Lets you assign dedicated tools to each agent, supports LLMs such as OpenAI GPT, Claude, Mistral, and Cohere, and is easily configured through langchain.llms.
• Provides a simple, easy-to-understand API that works well for real-world AI workflows of the form Research → Write → Critique → Synthesize → Publish.

Pros

• A mental model like real teamwork: easy to visualize and easy to add new roles to the system.
• Fits real-world multi-task AI workflows with clear stages.
• Supports many kinds of LLMs and a variety of tool configurations.
• Clear, simple programming interface, easy for newcomers.
• Lets you assign dedicated tools to each agent, increasing flexibility for specialized work.

Cons

• No direct conversation between agents (agents do not interact in two-way exchanges as in some other frameworks such as AutoGen).
• Tasks are linear, with no automatic feedback loops or multi-round interaction, meaning tasks run in one direction and do not repeat or self-adjust while running.
• No long-term memory or multi-turn conversation built in by default.

Suitable Use Cases

• Multi-task AI workflows that need clearly divided roles such as research, writing, critique, synthesis, and content publishing.
• Complex automation systems with many sequential or parallel processing steps, for example:
  • Automated content creation with multiple steps combining logic processing and evaluation.
  • AI project management with many virtual specialists playing different roles.
  • Systems that need clear task-oriented interaction for each agent, such as multi-agent chatbots and complex decision-support systems.

CrewAI can be used as a standalone framework, independent of LangChain or other agent frameworks, giving flexibility and speed when building LLM-based multi-agent orchestration.

AI Agents Frameworks

• Suna (Kortix): Framework for building, managing, and training AI agents - https://github.com/kortix-ai/suna #AI #agents #framework
• VoltAgent: AI Agent framework - https://github.com/VoltAgent/voltagent #AI #agents
• OpenAI Agents Python: Lightweight yet powerful framework for building multi-agent workflows in Python - GitHub #AI #agents #openai
• Google ADK: Open-source development kit (Python, TypeScript, Go, Java) for building, evaluating, and deploying sophisticated AI agents with flexibility and control - GitHub Python - Documentation #AI #agents #google
• OWL (Optimized Workforce Learning): Multi-agent support system for automating real-world tasks - GitHub #AI #agents #multi-agent
• PraisonAI: Open-source framework for developing AI agent applications, supporting the building of chatbots and interactive tools based on LLMs - GitHub #AI #agents #framework

See also: Multi Agent Systems · Agent Frameworks · Coding Agents