{"id":574,"date":"2026-08-19T09:00:00","date_gmt":"2026-08-19T00:00:00","guid":{"rendered":"https:\/\/www.theagenticprotocol.com\/?p=574"},"modified":"2026-08-18T12:55:06","modified_gmt":"2026-08-18T03:55:06","slug":"crewai-tutorial-2026","status":"publish","type":"post","link":"https:\/\/www.theagenticprotocol.com\/index.php\/crewai-tutorial-2026\/","title":{"rendered":"CrewAI Tutorial 2026: Build Multi-Agent Systems in Python"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">CrewAI is the fastest path to a working multi-agent system in Python \u2014 and the clearest example of what role-based agent collaboration looks like when the workflow resembles an org chart rather than a flowchart.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.theagenticprotocol.com\/wp-content\/uploads\/2026\/08\/grok-image-3f636591-1a04-44e0-ba74-136e04aad9dd-1024x576.jpg\" alt=\"CrewAI tutorial 2026 multi-agent Python Claude step by step\" class=\"wp-image-575\" srcset=\"https:\/\/www.theagenticprotocol.com\/wp-content\/uploads\/2026\/08\/grok-image-3f636591-1a04-44e0-ba74-136e04aad9dd-1024x576.jpg 1024w, https:\/\/www.theagenticprotocol.com\/wp-content\/uploads\/2026\/08\/grok-image-3f636591-1a04-44e0-ba74-136e04aad9dd-300x169.jpg 300w, https:\/\/www.theagenticprotocol.com\/wp-content\/uploads\/2026\/08\/grok-image-3f636591-1a04-44e0-ba74-136e04aad9dd-768x432.jpg 768w, https:\/\/www.theagenticprotocol.com\/wp-content\/uploads\/2026\/08\/grok-image-3f636591-1a04-44e0-ba74-136e04aad9dd-1536x864.jpg 1536w, https:\/\/www.theagenticprotocol.com\/wp-content\/uploads\/2026\/08\/grok-image-3f636591-1a04-44e0-ba74-136e04aad9dd.jpg 1792w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/www.theagenticprotocol.com\/index.php\/langgraph-vs-crewai\/\">LangGraph vs CrewAI<\/a> post in this series established when to choose each framework: CrewAI wins when your workflow has clear roles, sequential handoffs, and you need a working prototype this week. LangGraph wins when you need durable state, conditional branching, or human-in-the-loop approval gates. This CrewAI tutorial is the implementation companion to that decision: if you chose CrewAI, here&#8217;s every line of code you need to go from installation to a production-ready multi-agent research and writing system.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_86 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.theagenticprotocol.com\/index.php\/crewai-tutorial-2026\/#Setup_Install_CrewAI_in_3_Minutes\" >Setup: Install CrewAI in 3 Minutes<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.theagenticprotocol.com\/index.php\/crewai-tutorial-2026\/#Part_1_Your_First_Crew_%E2%80%94_Research_and_Write_in_50_Lines\" >Part 1: Your First Crew \u2014 Research and Write in 50 Lines<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.theagenticprotocol.com\/index.php\/crewai-tutorial-2026\/#Part_2_Adding_Tools_%E2%80%94_Give_Agents_Real_Capabilities\" >Part 2: Adding Tools \u2014 Give Agents Real Capabilities<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.theagenticprotocol.com\/index.php\/crewai-tutorial-2026\/#Part_3_Cost_Optimization_%E2%80%94_Reduce_Costs_by_60%E2%80%9370\" >Part 3: Cost Optimization \u2014 Reduce Costs by 60\u201370%<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.theagenticprotocol.com\/index.php\/crewai-tutorial-2026\/#Production_Checklist_5_Common_CrewAI_Problems_and_Their_Fixes\" >Production Checklist: 5 Common CrewAI Problems and Their Fixes<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.theagenticprotocol.com\/index.php\/crewai-tutorial-2026\/#The_Builders_Takeaway\" >The Builder&#8217;s Takeaway<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.theagenticprotocol.com\/index.php\/crewai-tutorial-2026\/#Continue_in_This_Series\" >Continue in This Series<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Setup_Install_CrewAI_in_3_Minutes\"><\/span>Setup: Install CrewAI in 3 Minutes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">CrewAI requires Python 3.10 or higher (3.12 recommended). It enforces strict Python version bounds and depends on compiled C extensions \u2014 check your version first.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># Check your Python version (must be 3.10\u20133.13)\npython --version\n\n# Install CrewAI with the tools package\npip install \"crewai&#91;tools]\" python-dotenv<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code># .env \u2014 CrewAI uses LiteLLM internally, which wraps any provider\nANTHROPIC_API_KEY=sk-ant-your-key-here\n\n# CrewAI defaults to OpenAI if no model is specified.\n# We're using Claude \u2014 set this to override the default:\nMODEL=claude\/claude-sonnet-5<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">CrewAI uses LiteLLM internally, which means any provider supported by LiteLLM works \u2014 Claude, GPT-5.6, Gemini, or local Ollama models. The <code>MODEL=claude\/claude-sonnet-5<\/code> environment variable sets Claude as the default for all agents in the crew.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Part_1_Your_First_Crew_%E2%80%94_Research_and_Write_in_50_Lines\"><\/span>Part 1: Your First Crew \u2014 Research and Write in 50 Lines<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The minimal working CrewAI system: two agents (Researcher and Writer), two tasks (research and write), one crew that runs them sequentially. The Researcher gathers information; the Writer uses that output to produce a polished article.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import os\nfrom dotenv import load_dotenv\nfrom crewai import Agent, Task, Crew, Process\n\nload_dotenv()\n\n# \u2500\u2500\u2500 Define Your Agents \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n# Each agent has: role, goal, and backstory.\n# The backstory is surprisingly important \u2014 it shapes how Claude\n# approaches its role. Write it like a job description.\n\nresearcher = Agent(\n    role=\"Senior Research Analyst\",\n    goal=\"Find accurate, current information on {topic} \"\n         \"and produce a comprehensive research brief\",\n    backstory=\"\"\"You are an expert researcher with a talent for\n    finding relevant information quickly and synthesizing it into\n    clear, well-organized briefs. You prioritize accuracy over\n    comprehensiveness \u2014 if you're unsure, you say so.\"\"\",\n    verbose=True,\n    llm=\"claude\/claude-sonnet-5\",    # researcher uses Sonnet 5\n    allow_delegation=False            # prevents delegation loops\n)\n\nwriter = Agent(\n    role=\"Technical Content Writer\",\n    goal=\"Transform the research brief into a clear, \"\n         \"engaging article for a technical audience\",\n    backstory=\"\"\"You are a technical writer who excels at making\n    complex topics accessible without sacrificing accuracy. You\n    write in an active voice, avoid jargon unless necessary, and\n    always lead with the most important point.\"\"\",\n    verbose=True,\n    llm=\"claude\/claude-sonnet-5\",\n    allow_delegation=False\n)\n\n# \u2500\u2500\u2500 Define Your Tasks \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n# Tasks specify: what to do, who does it, and what the output looks like.\n# {topic} is a placeholder filled at crew.kickoff() time.\n\nresearch_task = Task(\n    description=\"\"\"Research the following topic thoroughly: {topic}\n\n    Your research brief must include:\n    1. A concise definition (2-3 sentences)\n    2. Why it matters in 2026 (3-4 key points)\n    3. The top 3 real-world use cases with specific examples\n    4. Common misconceptions or pitfalls to avoid\n    5. Recommended resources for deeper learning\n\n    Cite sources where possible. If information is uncertain, flag it.\"\"\",\n    expected_output=\"\"\"A structured research brief with all five\n    sections above, formatted with headers. Total length: 400-600 words.\"\"\",\n    agent=researcher\n)\n\nwrite_task = Task(\n    description=\"\"\"Using the research brief provided, write a complete\n    article on: {topic}\n\n    Requirements:\n    - Lead with the most compelling point (inverted pyramid structure)\n    - Technical audience: assume programming knowledge, not AI expertise\n    - Include 1-2 concrete code examples or real-world scenarios\n    - End with a clear \"what to do next\" recommendation\n    - Target length: 600-800 words\"\"\",\n    expected_output=\"A complete, publication-ready article in markdown format.\",\n    agent=writer,\n    context=&#91;research_task]    # Writer receives researcher's output as context\n)\n\n# \u2500\u2500\u2500 Assemble and Run the Crew \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\ncrew = Crew(\n    agents=&#91;researcher, writer],\n    tasks=&#91;research_task, write_task],\n    process=Process.sequential,    # tasks run in order\n    verbose=True\n)\n\n# kickoff() runs the crew. The inputs dict fills {topic} placeholders.\nresult = crew.kickoff(inputs={\"topic\": \"RAG (Retrieval-Augmented Generation)\"})\n\nprint(\"\\n\" + \"=\"*60)\nprint(\"FINAL ARTICLE\")\nprint(\"=\"*60)\nprint(result.raw)<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Run this and you&#8217;ll see both agents working in the terminal \u2014 the researcher gathering information, then the writer using that output to produce a polished article. The <code>verbose=True<\/code> setting shows each agent&#8217;s reasoning steps, which is invaluable for debugging unexpected outputs.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Part_2_Adding_Tools_%E2%80%94_Give_Agents_Real_Capabilities\"><\/span>Part 2: Adding Tools \u2014 Give Agents Real Capabilities<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The crew above uses only the agents&#8217; training knowledge. Add tools and agents can search the web, read files, scrape URLs, and call APIs. CrewAI&#8217;s tools package includes ready-built tools for the most common use cases.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code\">from crewai_tools import SerperDevTool, ScrapeWebsiteTool, FileReadTool\nfrom crewai.tools import tool\n# \u2500\u2500\u2500 Built-in tools from crewai-tools \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n# SerperDevTool: Google search via Serper API (free tier: 100 searches\/month)\n# Requires: SERPER_API_KEY in .env\nsearch_tool = SerperDevTool()\n# ScrapeWebsiteTool: Reads and returns a webpage's text content\nscraper_tool = ScrapeWebsiteTool()\n# \u2500\u2500\u2500 Custom tool with the @tool decorator \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n# Use this for any function you want to expose to agents\n@tool(\"Save to File\")\ndef save_to_file(filename: str, content: str) -> str:\n    \"\"\"\n    Save content to a file on disk.\n    Use when the user wants to save the final output.\n    Args:\n        filename: The name of the file to create (e.g., 'report.md')\n        content: The text content to write\n    \"\"\"\n    with open(filename, \"w\") as f:\n        f.write(content)\n    return f\"Successfully saved to {filename}\"\n# \u2500\u2500\u2500 Assign tools to specific agents \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n# Only the researcher needs search and scraping.\n# The writer only needs to save the final article.\nresearcher_with_tools = Agent(\n    role=\"Senior Research Analyst\",\n    goal=\"Find current, accurate information on {topic} using web search\",\n    backstory=\"\"\"Expert researcher who knows how to find reliable sources\n    quickly. You always verify information from at least two sources before\n    including it in the brief.\"\"\",\n    tools=[search_tool, scraper_tool],    # give tools here\n    verbose=True,\n    llm=\"claude\/claude-sonnet-5\",\n    allow_delegation=False\n)\nwriter_with_tools = Agent(\n    role=\"Technical Content Writer\",\n    goal=\"Write and save a polished article on {topic}\",\n    backstory=\"Expert technical writer who produces clear, accurate content.\",\n    tools=[save_to_file],    # only the writer saves the output\n    verbose=True,\n    llm=\"claude\/claude-sonnet-5\",\n    allow_delegation=False\n)\n# Tasks and crew assembly remain the same \u2014 just swap in the new agents\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Part_3_Cost_Optimization_%E2%80%94_Reduce_Costs_by_60%E2%80%9370\"><\/span>Part 3: Cost Optimization \u2014 Reduce Costs by 60\u201370%<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A two-agent research crew using Claude Sonnet 5 for all agents costs approximately $0.10 to $0.30 per run. For high-volume use cases \u2014 running the crew 100 times per day \u2014 this compounds quickly. The CrewAI cost optimization pattern: use the most capable model only where it produces meaningfully better output.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># Cost-optimized crew: Haiku does the heavy lifting,\n# Sonnet handles the final polished output.\n# Reduces cost by 60-70% for research-heavy workflows.\n\nimport os\nfrom crewai import Agent, Task, Crew, Process\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\n# Haiku for data gathering and initial analysis (cheap, fast)\nRESEARCH_MODEL = \"claude\/claude-haiku-4-5-20251001\"   # $0.80\/$4.00 per MTok\n\n# Sonnet for final synthesis and polished output (quality matters here)\nWRITING_MODEL = \"claude\/claude-sonnet-5\"               # $2.00\/$10.00 per MTok\n\ndata_collector = Agent(\n    role=\"Data Collection Specialist\",\n    goal=\"Quickly gather raw information on {topic} from multiple sources\",\n    backstory=\"Fast, efficient data gatherer. Quality over completeness.\",\n    llm=RESEARCH_MODEL,    # Haiku: cheap and fast for structured gathering\n    verbose=False,          # Turn off verbose for cheaper agents\n    allow_delegation=False\n)\n\nanalyst = Agent(\n    role=\"Research Analyst\",\n    goal=\"Synthesize the collected data into a clear research brief\",\n    backstory=\"Expert at turning raw data into structured insights.\",\n    llm=RESEARCH_MODEL,    # Haiku handles structured synthesis too\n    verbose=False,\n    allow_delegation=False\n)\n\nwriter = Agent(\n    role=\"Senior Technical Writer\",\n    goal=\"Write a polished, publication-ready article from the research brief\",\n    backstory=\"Expert writer who elevates good content into great content.\",\n    llm=WRITING_MODEL,    # Sonnet only for the final quality output\n    verbose=True,\n    allow_delegation=False\n)\n\n# Cost estimate per run:\n# Data collection (Haiku): ~$0.002\n# Analysis (Haiku): ~$0.003\n# Writing (Sonnet): ~$0.05\n# Total: ~$0.055 vs ~$0.20 with all-Sonnet crew\n# Savings: ~72% reduction\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Production_Checklist_5_Common_CrewAI_Problems_and_Their_Fixes\"><\/span>Production Checklist: 5 Common CrewAI Problems and Their Fixes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Delegation loops (agents keep passing tasks to each other):<\/strong> Set <code>allow_delegation=False<\/code> on all non-manager agents. Delegation loops happen when multiple agents have overlapping roles. Only the manager agent in a hierarchical crew should have delegation enabled.<\/li>\n\n\n\n<li><strong>Agents going off-script:<\/strong> Be more specific in the task description&#8217;s <code>expected_output<\/code> field. Vague expected outputs produce creative interpretations. &#8220;A 400-600 word research brief with exactly five sections&#8221; beats &#8220;a research brief.&#8221;<\/li>\n\n\n\n<li><strong>High costs on repeated runs:<\/strong> Enable agent caching to avoid paying for repeated tool call summaries: <code>cache=True<\/code> on the agent. This caches tool results so the same web search doesn&#8217;t get billed twice across runs.<\/li>\n\n\n\n<li><strong>Installation failures (<code>ModuleNotFoundError<\/code>):<\/strong> CrewAI requires Python 3.10 to 3.13. Python 3.9 and below are not supported. If you have multiple Python installations, use <code>python3.12 -m pip install \"crewai[tools]\"<\/code> explicitly.<\/li>\n\n\n\n<li><strong>Context window exceeded on long tasks:<\/strong> Break the task description into smaller, more specific tasks. A single task that asks an agent to &#8220;research everything about X&#8221; can generate context windows that overflow. Three focused tasks with specific deliverables stay within limits reliably.<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">For the complete CrewAI documentation including Flows for pipeline orchestration, see <a href=\"https:\/\/docs.crewai.com\/introduction\" target=\"_blank\" rel=\"noopener\">CrewAI&#8217;s official documentation<\/a>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Builders_Takeaway\"><\/span>The Builder&#8217;s Takeaway<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">CrewAI is the most intuitive multi-agent framework available in 2026 for workflows that map cleanly to team roles. Define the agents, describe their jobs, specify what each task should produce, and CrewAI handles the coordination. The role-backstory-goal structure takes five minutes to learn and produces working multi-agent systems in under 50 lines. The cost optimization pattern \u2014 Haiku for research, Sonnet for output \u2014 cuts per-run costs by 60 to 70 percent without meaningful quality loss on the final deliverable. The five production problems above cover 90 percent of the issues builders hit in the first week. Build the two-agent crew from Part 1 today, add tools from Part 2 this week, and apply the cost optimization before you run it at scale. The <a href=\"https:\/\/www.theagenticprotocol.com\/index.php\/langgraph-vs-crewai\/\">LangGraph vs CrewAI<\/a> decision framework tells you when to reach for each \u2014 this tutorial gives you the CrewAI implementation once you&#8217;ve made that call.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Continue_in_This_Series\"><\/span>Continue in This Series<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.theagenticprotocol.com\/index.php\/langgraph-vs-crewai\/\">LangGraph vs CrewAI<\/a> \u2014 the framework decision: when to use CrewAI vs LangGraph with working code for both<\/li>\n\n\n\n<li><a href=\"https:\/\/www.theagenticprotocol.com\/index.php\/langchain-tutorial-2026\/\">LangChain Tutorial 2026<\/a> \u2014 the higher-level chain and agent framework that CrewAI sits alongside<\/li>\n\n\n\n<li><a href=\"https:\/\/www.theagenticprotocol.com\/index.php\/how-to-build-ai-agent-python\/\">How to Build an AI Agent With Python<\/a> \u2014 the raw SDK agent loop that all frameworks build on<\/li>\n\n\n\n<li><a href=\"https:\/\/www.theagenticprotocol.com\/index.php\/rag-tutorial-python-2026\/\">RAG Tutorial Python 2026<\/a> \u2014 add a knowledge retrieval layer to your CrewAI researcher agent<\/li>\n\n\n\n<li><a href=\"https:\/\/www.theagenticprotocol.com\/index.php\/ai-agent-unit-economics\/\">AI Agent Unit Economics<\/a> \u2014 the T\/R ratio that determines whether the Haiku\/Sonnet cost split makes sense for your specific use case<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\"><em>This post is part of The Agentic Protocol&#8217;s Work series \u2014 the connective infrastructure layer beneath every autonomous pipeline. See also: <a href=\"https:\/\/www.theagenticprotocol.com\/index.php\/langgraph-vs-crewai\/\">LangGraph vs CrewAI<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>CrewAI is the fastest path to a working multi-agent system in Python \u2014 and the clearest example of what role-based agent collaboration looks like when the workflow resembles an org chart rather than a flowchart. The LangGraph vs CrewAI post in this series established when to choose each framework: CrewAI wins when your workflow has &#8230; <a title=\"CrewAI Tutorial 2026: Build Multi-Agent Systems in Python\" class=\"read-more\" href=\"https:\/\/www.theagenticprotocol.com\/index.php\/crewai-tutorial-2026\/\" aria-label=\"Read more about CrewAI Tutorial 2026: Build Multi-Agent Systems in Python\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":575,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[725,724,723,722],"class_list":["post-574","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-work-agentic-ai","tag-crewai-claude-tutorial","tag-crewai-python-2026","tag-crewai-tutorial","tag-multi-agent-crewai-example"],"_links":{"self":[{"href":"https:\/\/www.theagenticprotocol.com\/index.php\/wp-json\/wp\/v2\/posts\/574","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.theagenticprotocol.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.theagenticprotocol.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.theagenticprotocol.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.theagenticprotocol.com\/index.php\/wp-json\/wp\/v2\/comments?post=574"}],"version-history":[{"count":1,"href":"https:\/\/www.theagenticprotocol.com\/index.php\/wp-json\/wp\/v2\/posts\/574\/revisions"}],"predecessor-version":[{"id":576,"href":"https:\/\/www.theagenticprotocol.com\/index.php\/wp-json\/wp\/v2\/posts\/574\/revisions\/576"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.theagenticprotocol.com\/index.php\/wp-json\/wp\/v2\/media\/575"}],"wp:attachment":[{"href":"https:\/\/www.theagenticprotocol.com\/index.php\/wp-json\/wp\/v2\/media?parent=574"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.theagenticprotocol.com\/index.php\/wp-json\/wp\/v2\/categories?post=574"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.theagenticprotocol.com\/index.php\/wp-json\/wp\/v2\/tags?post=574"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}