CrewAI & AutoGen Framework Training Mumbai | Best

Best Training Mumbai | CrewAI & AutoGen Framework Training Mumbai

Build AI Teams That Work: CrewAI and AutoGen Frameworks Mumbai

Role-Based Crews & Conversational Agents — Both Mastered

Ship Real Multi-Agent Products From Day One

India’s Most Comprehensive CrewAI and AutoGen Training

Why Best Training Mumbai Provides Mumbai’s Deepest CrewAI and AutoGen Training

CrewAI and AutoGen frameworks are the two most widely adopted open-source multi-agent development tools in the industry — and mastering both is rapidly becoming a non-negotiable prerequisite for any AI developer building production multi-agent systems. Best Training Mumbai’s CrewAI and AutoGen Frameworks program provides the deepest, most production-oriented training in these tools available in Mumbai. We do not teach tutorials — we teach engineering mastery that makes you immediately productive in any team building with these frameworks.

CrewAI and AutoGen represent fundamentally different philosophical approaches to multi-agent coordination. CrewAI organises agents around professional roles and structured task assignments — a metaphor that maps naturally to how human organisations work. AutoGen organises agents around conversational dynamics — agents that communicate through structured dialogue, negotiate, and reach conclusions through discourse. Both approaches have distinct strengths, and the most effective multi-agent engineers know which to reach for in which context. Our program develops exactly this engineering judgment.

Faculty with Production CrewAI and AutoGen Deployments

Our instructors have shipped production multi-agent systems using both CrewAI and AutoGen for enterprise clients across BFSI, media, consulting, and software development domains. They bring live engineering experience — including hard-won knowledge of the failure modes, performance bottlenecks, and debugging approaches that only emerge from running these systems against real workloads. When you learn CrewAI and AutoGen from our faculty, you learn how these frameworks behave in production, not just in demos.

Placement Support for AI Multi-Agent Framework Developer Roles

The market for CrewAI and AutoGen expertise is growing rapidly as enterprises move from single-agent pilots to multi-agent production deployments. Our placement team maintains connections with AI product companies, digital transformation consulting firms, and technology MNCs that are actively building multi-agent teams and urgently hiring engineers with framework expertise. We provide framework-specific technical interview preparation — including live coding challenges, multi-agent architecture design sessions, and CrewAI and AutoGen debugging exercises.

Live Multi-Agent Framework Lab with Real Use Case Simulations

Best Training Mumbai’s multi-agent lab provides isolated environments for running crews and agent networks on real enterprise problem simulations. Students observe and diagnose complex multi-agent behaviours — emergent delegation patterns, context overflow events, quality gate failures — in a supervised environment where faculty can guide diagnosis and resolution. This experience develops the engineering intuition that makes a practitioner rather than just a framework user.

Certification Demonstrating CrewAI and AutoGen Framework Mastery

Graduates receive the Best Training Mumbai CrewAI and AutoGen Framework Mastery Certification, designed to communicate specific framework expertise to technical hiring managers. We also provide guidance for the DeepLearning.AI AI Agents in LangGraph and multi-agent specialisation certifications that complement this designation.

12 Core Learning Objectives — CrewAI and AutoGen Frameworks

• Understand the theoretical and philosophical differences between CrewAI’s role-based approach and AutoGen’s conversation-based approach, and develop judgment for selecting between them.
• Master CrewAI core components: Agent configuration (role, goal, backstory, tools), Task specification (description, expected output, context, agent assignment), and Crew assembly.
• Implement CrewAI process modes: sequential process for ordered execution and hierarchical process for manager-led delegation with automatic agent assignment.
• Design effective CrewAI crews for specific problem classes: research and analysis crews, content production crews, software development crews, and operational workflow crews.
• Configure AutoGen ConversableAgent with optimised system messages, human_input_mode settings, code execution configurations, and custom reply functions.
• Implement AutoGen GroupChat and GroupChatManager with all speaker selection strategies: auto, round_robin, random, and custom selection functions.
• Build AutoGen nested conversation patterns: an agent spawning a private sub-conversation with a group of specialist agents to solve a sub-problem before returning to the main conversation.
• Design AutoGen code-executing agent pipelines: AssistantAgent proposing code, UserProxyAgent executing and returning results, and iterative debugging loops.
• Implement quality control architectures across both frameworks: verifier-executor in AutoGen, critic role in CrewAI, and the optimal framework-specific patterns for accuracy improvement.
• Engineer shared context and memory systems for both frameworks: state passing in CrewAI task contexts and shared memory stores in AutoGen GroupChat configurations.
• Evaluate multi-agent system performance across both frameworks: benchmarking task completion quality, cost efficiency, and framework selection criteria for given problem types.
• Deploy production CrewAI and AutoGen systems: containerisation, API wrapping, observability integration, and production readiness patterns for both frameworks.

Program Highlights | Course Overview | Skills Covered | Tools Covered | Projects | Case Studies | Learning Path

Program Highlights

• Duration: 3 months | Weekend and weekday evening batches
• Live multi-agent lab with isolated crew and agent network environments
• 9 projects: mix of CrewAI-native, AutoGen-native, and hybrid framework applications
• Framework comparison sessions: benchmarking the same problem in CrewAI and AutoGen
• Deep-dive debugging workshops: diagnosing and fixing production multi-agent failures
• Framework selection curriculum: engineering judgment for choosing the right tool
• Placement connections for AI framework developer and multi-agent engineer roles

Course Overview

The CrewAI and AutoGen Frameworks Mastery program is structured to build genuine engineering depth in both frameworks simultaneously — not sequential coverage that risks students forgetting one while learning the other. The curriculum uses a deliberate comparison-and-contrast pedagogy: students learn a concept or pattern in CrewAI, immediately implement the equivalent in AutoGen, and then analyse the tradeoffs in a guided discussion. This comparative learning approach builds the framework selection judgment that distinguishes a truly capable multi-agent engineer.

The program is explicitly aligned to professional AI development work rather than academic AI research. Every technique, every pattern, and every optimisation strategy is evaluated against production readiness criteria: reliability, debuggability, cost efficiency, and maintainability. Students who complete this program are prepared to join any multi-agent development team and immediately contribute to production-quality CrewAI and AutoGen codebases.

Skills Covered

• CrewAI: Agent, Task, Crew classes; role/goal/backstory design; task context chaining
• CrewAI process modes: sequential, hierarchical, async execution
• CrewAI tools: BaseTool, @tool decorator, Serper, BrowserTool, custom tool integration
• CrewAI callbacks: step callbacks, task callbacks, crew-level event hooks
• AutoGen ConversableAgent: system_message, human_input_mode, llm_config, function_map
• AutoGen GroupChat: allowed_or_disallowed_speaker_transitions, speaker_selection_method
• AutoGen code execution: code_execution_config, DockerCommandLineCodeExecutor
• AutoGen 0.4 agentchat: AssistantAgent, UserProxyAgent, MessageContext, tool calls
• Quality control: verifier-executor (AutoGen), critic task (CrewAI), reflection patterns
• Shared memory: task context injection (CrewAI), initialised chat messages (AutoGen)

Tools Covered

• CrewAI (latest), crewai-tools, CrewAI+ (CrewAI cloud platform)
• AutoGen 0.4 (pyautogen), Autogen Studio for visual workflow design
• LangChain tools package for shared tool definitions
• E2B sandbox and Docker for AutoGen code execution environments
• Serper API, Brave Search, Exa for web search tool integration
• LangSmith and Langfuse for multi-agent observability
• OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, Google Gemini as backbone models
• FastAPI for wrapping CrewAI and AutoGen systems as production APIs
• Redis for shared state between agents in both frameworks

Projects

• Content Production Crew (CrewAI) — 5-agent crew: topic researcher, outline writer, content writer, SEO specialist, and editor agents producing a full blog post from a single keyword input.
• Code Review and Refactoring Pipeline (AutoGen) — AssistantAgent writing code, CriticAgent identifying issues, RefactoringAgent improving quality, and DocumentationAgent adding docstrings.
• Investment Research Crew (CrewAI) — 4-agent hierarchical crew: research manager coordinating a fundamentals analyst, technical analyst, and risk assessor to produce institutional reports.
• Automated Debate System (AutoGen) — Multi-agent group chat with proposition agent, opposition agent, evidence checker agent, and moderator producing structured debate transcripts.
• Software Development Crew (CrewAI) — Product manager, architect, backend developer, frontend developer, and QA engineer crew delivering complete feature specifications and code.
• Customer Service AutoGen Network — Triaging agent routing tickets to specialist agents (billing, technical, account), with quality reviewer verifying responses before delivery.
• Scientific Literature Crew (CrewAI) — Search agent, screening agent, analysis agent, synthesis agent, and citation checker producing structured literature review sections.
• AutoGen Data Analysis Pipeline — DataLoaderAgent, AnalysisAgent, VisualisationAgent, and InsightAgent collaborating to produce complete data analysis reports from CSV inputs.
• Hybrid CrewAI + AutoGen System — CrewAI high-level task orchestration calling AutoGen sub-conversations for tasks requiring iterative negotiation between specialist agents.

Case Studies

• Microsoft’s AutoGen research paper — the original 2023 architecture and how it has evolved across AutoGen 0.2, 0.3, and 0.4 releases.
• CrewAI’s adoption trajectory — from open-source release to 20,000+ GitHub stars in 6 months and enterprise adoption at Deloitte, IBM, and KPMG.
• How a Mumbai-based content agency deployed a 6-agent CrewAI crew to produce SEO articles at 10x the speed of their human team with editorial quality maintained.
• AutoGen at NVIDIA — how the company uses conversational multi-agent systems for internal research synthesis and competitive intelligence workflows.
• The MetaGPT experiment — what the role-based software development multi-agent system reveals about the current ceiling and floor of collaborative AI engineering.

Learning Path

• Phase 1 (Weeks 1–4): CrewAI Mastery — Agents, Tasks, Crews, Process Modes & Tool Integration
• Phase 2 (Weeks 5–8): AutoGen Mastery — ConversableAgent, GroupChat, Code Execution & Nested Patterns
• Phase 3 (Weeks 9–10): Advanced Patterns — Quality Control, Shared Memory & Framework Comparison
• Phase 4 (Weeks 11–12): Production Deployment — API Wrapping, Observability & Capstone Projects

Course Curriculum — Module Breakdown

Module 1: CrewAI Architecture and Core Components

Designed for: Python developers, AI engineers, automation specialists. Unlocks roles: CrewAI Developer, AI Team Architect, Multi-Agent Engineer.

CrewAI’s genius is in the naturalness of its role-based metaphor. When you define an agent with a role, goal, and backstory, you are leveraging the LLM’s extensive training on human professional behaviour to create agents that genuinely adopt the perspective and working style of their designated professional identity. This module goes deep on the art and science of this configuration — understanding how role design shapes agent output quality.

• Agent configuration deep-dive: how role, goal, backstory, and memory interact
• Task engineering: description precision, expected_output specification, context chaining
• Tool integration: built-in tools, BaseTool inheritance, @tool decorator patterns
• Sequential process: task ordering, output passing, workflow composition
• Crew configuration: verbose logging, process selection, memory and cache settings

Module 2: CrewAI Advanced — Hierarchical Process and Custom Patterns

Designed for: Intermediate CrewAI practitioners. Unlocks roles: Senior CrewAI Architect, AI Workflow Lead.

The hierarchical process mode is CrewAI’s most powerful feature — and its most misused. When correctly configured, a manager agent can intelligently decompose goals, assign tasks to appropriate specialists, and synthesise results in ways that significantly improve output quality compared to sequential execution. This module teaches hierarchical process design with the nuance that only comes from extensive production experience.

• Hierarchical process: manager agent role, dynamic task assignment, review loops
• Agent delegation: when to enable it, delegation guard patterns, delegation logging
• Async task execution: parallel crew running, result aggregation
• Callbacks: step_callback for monitoring, task_callback for post-processing
• Crew output handling: CrewOutput parsing, task output extraction, structured results

Module 3: AutoGen Core — Conversational Agent Patterns

Designed for: Python developers, AI researchers, conversational AI engineers. Unlocks roles: AutoGen Specialist, Conversational Multi-Agent Developer.

AutoGen’s conversational model requires thinking about multi-agent interaction differently from CrewAI. Rather than defining explicit task sequences, you define agents with system messages, configure how they respond to each other, and let conversation dynamics emerge. This module builds mastery of the AutoGen primitives and the engineering patterns that make its conversational approach reliable in production.

• ConversableAgent configuration: system message design for specialisation, human_input_mode
• Two-agent patterns: assistant + proxy, critic + executor, teacher + student
• Termination conditions: keyword triggers, max_consecutive_auto_reply, custom termination functions
• Code execution: code_execution_config, DockerCommandLineCodeExecutor for isolation
• Function calling: registered_functions, function_map, structured tool use in AutoGen

Module 4: AutoGen Advanced — GroupChat and Nested Conversations

Designed for: Senior AI engineers, multi-agent system architects. Unlocks roles: AutoGen Architect, Complex Multi-Agent System Lead.

GroupChat and nested conversations are where AutoGen’s full power becomes apparent. GroupChat enables dynamic many-to-many agent conversations with sophisticated speaker selection logic. Nested conversations allow an agent in one conversation to spawn an entirely separate private conversation with other agents to solve a sub-problem — creating recursive, hierarchical structures of extraordinary flexibility.

• GroupChat design: agent composition, speaker selection strategies, conversation flow control
• GroupChatManager: system message for moderation, speaker transition functions
• Nested conversation patterns: an agent spawning sub-conversations and integrating results
• AutoGen 0.4 agentchat: updated API, streaming support, CancellationToken usage
• Complex termination: multi-condition termination, voting-based completion detection

Module 5: Production Deployment and Framework Comparison

Designed for: All students preparing for production roles. Unlocks roles: AI Platform Engineer, Production Multi-Agent Developer.

This module addresses two critical outcomes: making both frameworks production-ready and developing the engineering judgment to select between them. The framework comparison methodology we teach is structured and repeatable — giving graduates a reliable decision process for any future multi-agent engineering challenge.

• API wrapping: FastAPI endpoints for CrewAI crews and AutoGen conversations
• Observability: Langfuse integration for both frameworks, cost attribution per agent
• Framework selection framework: structured evaluation criteria for CrewAI vs AutoGen
• Hybrid patterns: when and how to use both frameworks in the same system
• Production hardening: timeout management, error recovery, conversation state persistence

Why Best Training Mumbai for CrewAI and AutoGen Training?

• The only Mumbai program covering both CrewAI and AutoGen with full production depth — not a survey
• Comparative pedagogy: learning both frameworks simultaneously builds superior framework selection judgment
• AutoGen 0.4 agentchat coverage — most programs still teach the deprecated 0.2 API
• Hybrid system project: learn to combine CrewAI and AutoGen in the same architecture
• Framework comparison sessions develop engineering judgment, not just technical skill
• Alumni deployed in production CrewAI roles at consulting firms and AI-first companies

Frequently Asked Questions — CrewAI and AutoGen Frameworks Training

Q1. Should I learn CrewAI or AutoGen — or do I need both?

The most effective multi-agent engineers know both and select between them based on the problem structure. CrewAI excels for workflows that map to professional team structures with explicit roles and deliverable tasks. AutoGen excels for workflows requiring iterative refinement, code generation, and debate-style reasoning. Our program teaches you to make this selection with engineering rigour rather than personal preference or familiarity.

Q2. How current is the AutoGen content — is it covering AutoGen 0.4?

Yes. We cover AutoGen 0.4 (python-autogen) with the new agentchat API — including AssistantAgent, UserProxyAgent, the MessageContext pattern, and streaming support. We also cover AutoGen 0.2 concepts for context, since a large volume of production code and community examples still use the older API. Understanding both ensures you can contribute to existing codebases and build new systems with the current best practices.

Q3. What is the difference between CrewAI’s sequential and hierarchical process?

Sequential process executes tasks in a predefined order — Task 1 completes, its output becomes the context for Task 2, and so on. Hierarchical process introduces a manager agent that dynamically decides which agent should work on which task based on current context, can reassign tasks if results are insufficient, and synthesises the work of multiple agents. Hierarchical process produces higher quality outputs for complex problems but requires more careful agent configuration.

Q4. What salary does a CrewAI or AutoGen specialist earn?

In India, developers with demonstrated CrewAI and AutoGen expertise earn INR 16–28 LPA at junior-to-mid levels and INR 28–50 LPA at senior levels. These roles are titled AI Agent Developer, Multi-Agent Systems Engineer, or AI Platform Engineer. The skill is supply-constrained — companies know exactly what they want but cannot find engineers who have it at the depth required for production work.

Q5. Do I need to have completed a basic LangChain or AI agents course first?

Prior experience with LangChain or basic agent development (single-agent ReAct patterns) is strongly recommended. Completion of Best Training Mumbai’s Agentic AI Engineering Program or LangChain and LangGraph Mastery program is ideal preparation. Applicants without prior agent development experience are evaluated through our technical onboarding assessment to ensure they have the Python and LLM API foundations required.

Q6. How are the projects structured — are they individual or team-based?

All 9 projects are individual — each student builds and owns their complete project from architecture through deployment. This ensures every graduate has personally debugged real multi-agent systems rather than contributed a small piece to a shared project. However, we run weekly code review sessions where students present their in-progress projects for peer and instructor critique, simulating the collaborative code review process of professional AI engineering teams.

Q7. Does this program cover CrewAI’s latest enterprise features?

Yes. We cover the latest CrewAI releases including CrewAI+ (the cloud hosting and monitoring platform), the updated memory system with short-term, long-term, entity, and contextual memory, the new Flow API for event-driven crew orchestration, and the latest tool integrations. CrewAI is evolving rapidly and our curriculum team updates coverage within weeks of significant releases.

Q8. Can AutoGen agents write and execute real code?

Yes — and this is one of AutoGen’s most distinctive capabilities. We cover code execution configuration extensively in Module 3, including the DockerCommandLineCodeExecutor for sandboxed code execution (critical for any production deployment where code safety matters). You will build projects where AutoGen agents propose Python code solutions, execute them, observe the results, diagnose errors, and iterate — a capability that enables genuinely powerful software development automation.

Q9. Will I learn to integrate CrewAI and AutoGen with LangChain tools?

Yes. The LangChain tools ecosystem is the richest source of pre-built tools for both frameworks — web search, document loading, vector store integration, and API connectors. We cover LangChain tool integration in both CrewAI (through the langchain-tools package) and AutoGen (through function registration with the function_map pattern). This integration knowledge maximises your ability to equip agents with diverse capabilities quickly.

Q10. What is the batch size and schedule?

Batches are capped at 18 students for lab session quality. Weekend batches run Saturday and Sunday from 9 AM to 1 PM. Weekday evening batches run Monday to Thursday from 7:30 to 9:30 PM. The program runs for 3 months. Contact +91 7400019222 for the next available start date and to complete the technical onboarding assessment.

Master the Tools That Are Redefining What AI Can Do — Build With CrewAI and AutoGen

CrewAI and AutoGen are not just popular frameworks — they are the tools that are defining the multi-agent AI engineering discipline. The systems running these frameworks today are automating research, generating software, managing customer relationships, and executing complex analytical workflows that were out of reach for AI just two years ago. The engineers who master these tools are the architects of these transformations.

Best Training Mumbai’s CrewAI and AutoGen Frameworks Mastery program is your path to joining their ranks. Rigorous, production-focused, placement-supported, and always up to date — we give you everything you need to build the multi-agent systems that organisations are urgently deploying.

The next batch starts soon. Secure your spot today.

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