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By leveraging the power of autonomous AI agents, this framework enables efficient and effective performance of complex, multi-step tasks. \n## Goal\nDesigning effective AI agents and organize team of AI agents them to perform complex, multi-step tasks.\n\n## Why AI Agents better than LLMs\n#### LLMs \nProvide human feedback iteratively to fine-tune response\n\n#### AI Agents\nWhen LLMs operate autonomously, they become agents. AI Agents ask and answer questions on its own.\n\nLLMs + Cognition = AI Agents.\n\n\n\nSource: deeplearning.ai\n\n## crewAI\nFramework for building multi-agent systems (that are autonomous, role-playing and collaborate)\n
\ncrew : Team of AI agents working together, each with a specific role.\n\n## Why Multi AI Agents rather single agent \n\n\n - Assign specific role and specific task to each agent and improved output. Eg. One agent does exhaustive research and other does professional writing.
\n - Use different LLMs for specific tasks
\n
\n\n\n\n\nSource: deeplearning.ai\n\n## Applications of multi-agent systems.\n\n - Resume Strategist : Tailor resumes and interview prep
\n - Design, build and test website
\n - Research, write and fact-check technical papers
\n - Automate customer support inquiries
\n - Conduct social media campaigns
\n - Perform financial analysis
\n
\n\n\n## What is Agentic Automation\nNew way to write software. Provide fizzy inputs, apply fuzzy tranformations and get fuzzy outputs.\n\nReason why people love chatGPT: Probablistic nature\n\n\n\nSource: deeplearning.ai\n\n## How Agentic Automation improves regular automation\n\n### Regular Automation (Regular Data Collection and Analysis)\n\n- Capture information about the company\n- Use classification to generate scores for company\n- Prioritise for sales\n \n\n\nSource: deeplearning.ai\n\n### Agentic Automation (Data Collection and Analysis using crew)\n\n- AI agent research about company (via Google, internal database)\n- AI agent compares companies (new ones, old ones)\n- AI agent scores companies (based on parameters)\n- AI agent provides intelligent questions to ask based on scores \n\n\n\nSource: deeplearning.ai\n\n## Key Components of AI Agent\n\n - Role: Assign specialized role to agents
\n - Memory: Provide agents with short-term, long-term and entity memory
\n - Tools: Assign pre-built and custom tools to each agent (eg. for web search)
\n - Focus: Break down task, goals and tools and assign multiple AI agents for better performance
\n - Guardrails: Effectively handle errors, hallucinations and infinite loops.
\n - Cooperation: Perform tasks in series, in parallel and hierarchical fashion
\n
\n\n\n\n\n\nSource: deeplearning.ai\n\n### Role Playing \nMore specific role = Better response. Gives clear idea about agent's function in the crew.\n\n**Example:** You are a financial analyst v/s you are FINRA approved financial analyst.\n\n```python\nfrom crewai import Agent\n\nagent = Agent(\n role='Data Analyst',\n goal='Extract actionable insights',\n backstory=\"\"\"You're a data analyst at a large company.\n You're responsible for analyzing data and providing insights\n to the business.\"\"\"\n)\n\n```\n\n### Focus \nAssinging too many tasks, tools, context to a single agent, cause losing essential information and hallucinate.\n\nTherefore, break down task, goals and tools and assign to multiple AI agents for better performance\n\n```python\n\nresearch_ai_task = Task(\n description='Find and summarize the latest AI news',\n expected_output='A bullet list summary of the top 5 most important AI news',\n agent=research_agent,\n tools=[search_tool]\n)\n\nresearch_ops_task = Task(\n description='Find and summarize the latest AI Ops news',\n expected_output='A bullet list summary of the top 5 most important AI Ops news',\n agent=research_agent,\n tools=[search_tool]\n)\n\nwrite_blog_task = Task(\n description=\"Write a full blog post about the importance of AI and its latest news\",\n expected_output='Full blog post that is 4 paragraphs long',\n agent=writer_agent,\n context=[research_ai_task, research_ops_task]\n)\n\n```\n\n### Tools \nAssign tools to AI Agents and Tasks for improving execution and performance.\n\n```python\nfrom crewai import Agent\n\nresearcher = Agent(\n role='Market Research Analyst',\n goal='Provide up-to-date market analysis of the AI industry',\n backstory='An expert analyst with a keen eye for market trends.',\n tools=[search_tool, web_rag_tool]\n)\n```\n\n**Note:** Tasks specific tools override an agent's default tools.\n\n```python\ntask = Task(\n description='Find and summarize the latest AI news',\n expected_output='A bullet list summary of the top 5 most important AI news',\n agent=research_agent,\n tools=[search_tool]\n)\n\n```\n\n### Collaboration \nAgents collobrate to combine skills, share information, delegate tasks to each other.\n\n#### Sequential Collaboration\n\nIdeal for projects requiring tasks to be completed in a specific order.\n\n```python\nreport_crew = Crew(\n agents=[researcher, analyst, writer],\n tasks=[research_task, analysis_task, writing_task], # tasks executed in the order of listing, with output of one task serving as context for the next\n process=Process.sequential\n)\n```\n\n#### Hierarchical Collaboration\n\n\n - CrewAI automatically creates a manager agent, requiring the specification of a manager language model (manager_llm) for the manager agent.
\n - THe manager allocates tasks among crew members based on their roles, tools and capabilities.
\n - The manager evaluates outcomes to ensure they meet the required standards.
\n - set Process attribute to Process.hierarchical for Crew object
\n - set manager_llm for Crew Object. Mandatory for hierarchical process
\n
\n\n```python\nfrom crewai import Crew\nfrom crewai.process import Process\nfrom langchain_openai import ChatOpenAI\n\n# Example: Creating a crew with a hierarchical process\n# Ensure to provide a manager_llm\ncrew = Crew(\n agents=my_agents,\n tasks=my_tasks,\n process=Process.hierarchical,\n manager_llm=ChatOpenAI(model=\"gpt-4\")\n)\n```\n\n#### Parallel Collaboration\nTasks can now be executed asynchronously, allowing for parallel processing and efficiency improvements\n\n```python\nlist_ideas = Task(\n description=\"List of 5 interesting ideas to explore for an article about AI.\",\n expected_output=\"Bullet point list of 5 ideas for an article.\",\n agent=researcher,\n async_execution=True # Will be executed asynchronously\n)\n\nlist_important_history = Task(\n description=\"Research the history of AI and give me the 5 most important events.\",\n expected_output=\"Bullet point list of 5 important events.\",\n agent=researcher,\n async_execution=True # Will be executed asynchronously\n)\n\nwrite_article = Task(\n description=\"Write an article about AI, its history, and interesting ideas.\",\n expected_output=\"A 4 paragraph article about AI.\",\n agent=writer,\n context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed\n)\n```\n\n### Gaurdrails\nImplemented at Framework level to prevrnt hallucinations, errors and infintite loops. \n\n### Memory\nCrewAI provides short-term memory, long-term memory, entity memory, and newly identified contextual memory to help AI agents to remember, reason, and learn from past interactions.\n\nAdvantages of Memory\n- **More contexual awareness**, leading to more coherent and relevant responses\n- **Experience Accumulation**, learning from past actions to improve future decision-making and problem-solving.\n- **Entity Understanding**, agents can recognize and remember key entities, enhancing understanding.\n\n\n\nSource: deeplearning.ai\n\nEnable memory by setting memory=True in the Crew objects arguments.\n\n```python\nfrom crewai import Crew, Agent, Task, Process\n\n# Assemble your crew with memory capabilities\nmy_crew = Crew(\n agents=[...],\n tasks=[...],\n process=Process.sequential,\n memory=True,\n verbose=True\n)\n```\n\n\nSource: deeplearning.ai\n\n## Mental Framework for Agent creations\n\nThink of yourself as a **Manager**\n\nAnswer 3 questions: \n\n - What is the Goal ?
\n - What is the Process ?
\n - What kind of people I would like to hire, to get the work done
\n
\n\nThis will help to create agents (roles, goals, backstory)\n\n\n\nSource: deeplearning.ai\n\n## What makes a great Tool ?\n\n- **Versatile:** Hndle Fuzzy inputs and provide strongly typed outputs\n- **Caching Mechanism:** Reuse previous results. Caching layer prevent unnecessary requests, stay within rate limits, speed up execution time\n- **Error Handling:** Gracefully handle erors & exceptions. How ? Sending error message to agent and ask agent to retry\n\n **NOTE:** CrewAI supports both crewAI Toolkit and LangChain Tools\n\n## Mental Framework for Task creations\n\nThink of yourself as a **Manager**\n\nAsk what kind of process and tasks I expect individuals on my team to do.\n\nTask requires min. 3 things: \n\n - description
\n - expected_output
\n - agent that will perform the task
\n
\n\n\n\nSource: deeplearning.ai\n\n## Multi-agent Collaboration\n\n### Problem with Sequential Collaboration\n\nInitial context fades away as tasks flows from agent to agent.\n\n\n\nSource: deeplearning.ai\n\n### Advantages with Hierarchical Collaboration\n\n- Manager always remeber initial goal\n- Automatically delegates tasks\n- Asks agents for further improvement, if required.\n\n \n","readmeExcerpt":"Multi-AI-Agent-Systems-with-crewAI This project is dedicated to automating business workflows using multi-agent AI systems. By leveraging the power of autonomous AI agents, this framework enables efficient and effective performance of complex, multi-step tasks. Goal Designing effective AI agents and organize team of AI agents them to perform complex, multi-step tasks. 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This framework leverages autonomous, role-specific AI agents to collaboratively perform multi-step tasks, enhancing efficiency and accuracy across various domains. Ideal for applications in resume tailoring, website design, research, customer support, and more. Multi-AI-Agent-Systems-with-crewAI This project is dedicated to automating business workflows using multi-agent AI systems. By leveraging the power of autonomous AI agents, this framework enables efficient and effective performance of complex, multi-step tasks. Goal Designing effective AI agents and organize team of AI agents them to perform complex, multi-step tasks. 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