index_new5.html
../../../zaker_core/zaker_tpl_static/wap/tpl_guoji1.html
![]()
本教程演示了如何结合LangChain的结构化推理能力与AutoGen的协作代理系统，并利用Hugging Face的开源模型，构建一个完全无需付费API的智能多代理AI框架。教程从设置轻量级开源管道开始，逐步深入到结构化推理、多步工作流和协作代理交互。通过从LangChain的链式调用到模拟多代理系统，展示了推理、规划和执行如何无缝融合，形成完全可控的自主智能行为。这为开发者提供了一条无需昂贵API即可实现强大、自主AI系统的途径。
💡 **无API的开源框架构建**：教程核心在于展示如何利用LangChain、AutoGen和Hugging Face的开源模型，构建一个功能齐全的智能代理AI系统，完全避免了对付费API的依赖。通过加载Hugging Face的FLAN-T5模型作为本地语言模型，实现了成本效益和独立性。
🧠 **LangChain的推理与规划能力**：通过LangChain的基础链和多步推理演示，展示了如何构建智能提示模板，使模型能够分解复杂目标，并生成详细的、可操作的响应。这为代理系统的结构化思考和规划奠定了基础。
🤝 **协作式多代理系统**：教程设计了具有特定角色的轻量级代理（如研究员、开发者、审阅者），并模拟了它们在简单任务中的协作流程。此外，还介绍了AutoGen的核心概念，并通过模拟对话展示了其多代理通信和代码执行能力，强调了自动化协作的潜力。
🚀 **混合系统集成与实践**：最终，教程将LangChain的结构化推理与多代理系统相结合，创建了一个混合智能框架。LangChain负责问题分析，而代理则负责规划和执行，展示了如何通过集成开源工具构建适应性强的自主AI系统，验证了无需昂贵基础设施即可实现强大AI的可能性。
In this tutorial, we dive into the essence of Agentic AI by uniting LangChain, AutoGen, and Hugging Face into a single, fully functional framework that runs without paid APIs. We begin by setting up a lightweight open-source pipeline and then progress through structured reasoning, multi-step workflows, and collaborative agent interactions. As we move from LangChain chains to simulated multi-agent systems, we experience how reasoning, planning, and execution can seamlessly blend to form autonomous, intelligent behavior, entirely within our control and environment. Check out the FULL CODES here.
import warningswarnings.filterwarnings('ignore')from typing import List, Dictimport autogenfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChainfrom langchain_community.llms import HuggingFacePipelinefrom transformers import pipelineimport jsonprint(" Loading models...\n")pipe = pipeline( "text2text-generation", model="google/flan-t5-base", max_length=200, temperature=0.7)llm = HuggingFacePipeline(pipeline=pipe)print("✓ Models loaded!\n")
We start by setting up our environment and bringing in all the necessary libraries. We initialize a Hugging Face FLAN-T5 pipeline as our local language model, ensuring it can generate coherent, contextually rich text. We confirm that everything loads successfully, laying the groundwork for the agentic experiments that follow. Check out the FULL CODES here.
def demo_langchain_basics(): print("="*70) print("DEMO 1: LangChain - Intelligent Prompt Chains") print("="*70 + "\n") prompt = PromptTemplate( input_variables=["task"], template="Task: {task}\n\nProvide a detailed step-by-step solution:" ) chain = LLMChain(llm=llm, prompt=prompt) task = "Create a Python function to calculate fibonacci sequence" print(f"Task: {task}\n") result = chain.run(task=task) print(f"LangChain Response:\n{result}\n") print("✓ LangChain demo complete\n")def demo_langchain_multi_step(): print("="*70) print("DEMO 2: LangChain - Multi-Step Reasoning") print("="*70 + "\n") planner = PromptTemplate( input_variables=["goal"], template="Break down this goal into 3 steps: {goal}" ) executor = PromptTemplate( input_variables=["step"], template="Explain how to execute this step: {step}" ) plan_chain = LLMChain(llm=llm, prompt=planner) exec_chain = LLMChain(llm=llm, prompt=executor) goal = "Build a machine learning model" print(f"Goal: {goal}\n") plan = plan_chain.run(goal=goal) print(f"Plan:\n{plan}\n") print("Executing first step...") execution = exec_chain.run(step="Collect and prepare data") print(f"Execution:\n{execution}\n") print("✓ Multi-step reasoning complete\n")
We explore LangChain’s capabilities by constructing intelligent prompt templates that allow our model to reason through tasks. We build both a simple one-step chain and a multi-step reasoning flow that break complex goals into clear subtasks. We observe how LangChain enables structured thinking and turns plain instructions into detailed, actionable responses. Check out the FULL CODES here.
class SimpleAgent: def __init__(self, name: str, role: str, llm_pipeline): self.name = name self.role = role self.pipe = llm_pipeline self.memory = [] def process(self, message: str) -> str: prompt = f"You are a {self.role}.\nUser: {message}\nYour response:" response = self.pipe(prompt, max_length=150)[0]['generated_text'] self.memory.append({"user": message, "agent": response}) return response def __repr__(self): return f"Agent({self.name}, role={self.role})"def demo_simple_agents(): print("="*70) print("DEMO 3: Simple Multi-Agent System") print("="*70 + "\n") researcher = SimpleAgent("Researcher", "research specialist", pipe) coder = SimpleAgent("Coder", "Python developer", pipe) reviewer = SimpleAgent("Reviewer", "code reviewer", pipe) print("Agents created:", researcher, coder, reviewer, "\n") task = "Create a function to sort a list" print(f"Task: {task}\n") print(f"[{researcher.name}] Researching...") research = researcher.process(f"What's the best approach to: {task}") print(f"Research: {research[:100]}...\n") print(f"[{coder.name}] Coding...") code = coder.process(f"Write Python code to: {task}") print(f"Code: {code[:100]}...\n") print(f"[{reviewer.name}] Reviewing...") review = reviewer.process(f"Review this approach: {code[:50]}") print(f"Review: {review[:100]}...\n") print("✓ Multi-agent workflow complete\n")
We design lightweight agents powered by the same Hugging Face pipeline, each assigned a specific role, such as researcher, coder, or reviewer. We let these agents collaborate on a simple coding task, exchanging information and building upon each other’s outputs. We witness how a coordinated multi-agent workflow can emulate teamwork, creativity, and self-organization in an automated setting. Check out the FULL CODES here.
def demo_autogen_conceptual(): print("="*70) print("DEMO 4: AutoGen Concepts (Conceptual Demo)") print("="*70 + "\n") agent_config = { "agents": [ {"name": "UserProxy", "type": "user_proxy", "role": "Coordinates tasks"}, {"name": "Assistant", "type": "assistant", "role": "Solves problems"}, {"name": "Executor", "type": "executor", "role": "Runs code"} ], "workflow": [ "1. UserProxy receives task", "2. Assistant generates solution", "3. Executor tests solution", "4. Feedback loop until complete" ] } print(json.dumps(agent_config, indent=2)) print("\n AutoGen Key Features:") print(" • Automated agent chat conversations") print(" • Code execution capabilities") print(" • Human-in-the-loop support") print(" • Multi-agent collaboration") print(" • Tool/function calling\n") print("✓ AutoGen concepts explained\n")class MockLLM: def __init__(self): self.responses = { "code": "def fibonacci(n):\n if n <= 1:\n return n\n return fibonacci(n-1) + fibonacci(n-2)", "explain": "This is a recursive implementation of the Fibonacci sequence.", "review": "The code is correct but could be optimized with memoization.", "default": "I understand. Let me help with that task." } def generate(self, prompt: str) -> str: prompt_lower = prompt.lower() if "code" in prompt_lower or "function" in prompt_lower: return self.responses["code"] elif "explain" in prompt_lower: return self.responses["explain"] elif "review" in prompt_lower: return self.responses["review"] return self.responses["default"]def demo_autogen_with_mock(): print("="*70) print("DEMO 5: AutoGen with Custom LLM Backend") print("="*70 + "\n") mock_llm = MockLLM() conversation = [ ("User", "Create a fibonacci function"), ("CodeAgent", mock_llm.generate("write code for fibonacci")), ("ReviewAgent", mock_llm.generate("review this code")), ] print("Simulated AutoGen Multi-Agent Conversation:\n") for speaker, message in conversation: print(f"[{speaker}]") print(f"{message}\n") print("✓ AutoGen simulation complete\n")
We illustrate AutoGen’s core idea by defining a conceptual configuration of agents and their workflow. We then simulate an AutoGen-style conversation using a custom mock LLM that generates realistic yet controllable responses. We realize how this framework allows multiple agents to reason, test, and refine ideas collaboratively without relying on any external APIs. Check out the FULL CODES here.
def demo_hybrid_system(): print("="*70) print("DEMO 6: Hybrid LangChain + Multi-Agent System") print("="*70 + "\n") reasoning_prompt = PromptTemplate( input_variables=["problem"], template="Analyze this problem: {problem}\nWhat are the key steps?" ) reasoning_chain = LLMChain(llm=llm, prompt=reasoning_prompt) planner = SimpleAgent("Planner", "strategic planner", pipe) executor = SimpleAgent("Executor", "task executor", pipe) problem = "Optimize a slow database query" print(f"Problem: {problem}\n") print("[LangChain] Analyzing problem...") analysis = reasoning_chain.run(problem=problem) print(f"Analysis: {analysis[:120]}...\n") print(f"[{planner.name}] Creating plan...") plan = planner.process(f"Plan how to: {problem}") print(f"Plan: {plan[:120]}...\n") print(f"[{executor.name}] Executing...") result = executor.process(f"Execute: Add database indexes") print(f"Result: {result[:120]}...\n") print("✓ Hybrid system complete\n")if __name__ == "__main__": print("="*70) print(" ADVANCED AGENTIC AI TUTORIAL") print("AutoGen + LangChain + HuggingFace") print("="*70 + "\n") demo_langchain_basics() demo_langchain_multi_step() demo_simple_agents() demo_autogen_conceptual() demo_autogen_with_mock() demo_hybrid_system() print("="*70) print(" TUTORIAL COMPLETE!") print("="*70) print("\n What You Learned:") print(" ✓ LangChain prompt engineering and chains") print(" ✓ Multi-step reasoning with LangChain") print(" ✓ Building custom multi-agent systems") print(" ✓ AutoGen architecture and concepts") print(" ✓ Combining LangChain + agents") print(" ✓ Using HuggingFace models (no API needed!)") print("\n Key Takeaway:") print(" You can build powerful agentic AI systems without expensive APIs!") print(" Combine LangChain's chains with multi-agent architectures for") print(" intelligent, autonomous AI systems.") print("="*70 + "\n")
We combine LangChain’s structured reasoning with our simple agentic system to create a hybrid intelligent framework. We allow LangChain to analyze problems while the agents plan and execute corresponding actions in sequence. We conclude the demonstration by running all modules together, showcasing how open-source tools can integrate seamlessly to build adaptive, autonomous AI systems.
In conclusion, we witness how Agentic AI transforms from concept to reality through a simple, modular design. We combine the reasoning depth of LangChain with the cooperative power of agents to build adaptable systems that think, plan, and act independently. The result is a clear demonstration that powerful, autonomous AI systems can be built without expensive infrastructure, leveraging open-source tools, creative design, and a bit of experimentation.
Check out the FULL CODES here. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
The post How I Built an Intelligent Multi-Agent Systems with AutoGen, LangChain, and Hugging Face to Demonstrate Practical Agentic AI Workflows appeared first on MarkTechPost.
