- Introduction
- What is Agentic AI?
- Why LangGraph for Agentic AI?
- End-to-End Agentic AI Roadmap
- Project Ideas
- Key 2026 Trends
- Top Resources
- Sample Code
- Contributing
- License
This roadmap provides a structured, hands-on path for mastering Agentic AI development using LangGraph. It's designed for developers who want to build production-grade autonomous AI systems—agents that plan, reason, use tools, persist memory, and collaborate in multi-agent architectures.
Agentic AI refers to systems that can autonomously reason, plan, and take actions to accomplish goals without explicit step-by-step instructions. Key characteristics:
- Reasoning: Deliberative problem solving
- Self-improvement: Reflexion/self-critique loops
- Tool Use: Integrating APIs, databases, web search
- Multi-Agent Collaboration: Teams of specialized agents
- Persistent Memory: Long-term, context-aware knowledge
- Governed Execution: Safe, compliant tool invocation
LangGraph is a stateful orchestration framework designed for building agentic workflows. It extends LangChain with:
- Graph-based workflows: Model complex agent behaviors as state machines
- Multi-agent support: Compose specialized agents for complex tasks
- Persistent memory graphs: Store and recall experiences atomically
- Reflexion loops: Built-in support for agent self-improvement
- Tool/API integration: Fine-grained, governed tool access
- Production features: Checkpointing, tracing, error handling
January 2026: LangGraph now supports adaptive deliberation, Zettelkasten-style memory graphs, and advanced multi-agent orchestration—making it the go-to framework for next-generation agents. [1] [2]
Goal: Build a rock-solid base in AI, LLMs, and LangChain.
| Topic | Key Concepts |
|---|---|
| LLM Fundamentals | Tokenization, prompting, inference, temperature |
| Prompt Engineering | Zero/Few-shot, Chain of Thought, Self-consistency |
| LangChain Basics | Chains, Agents, Memory, Tools, LCEL |
| Python Async & State Mgmt | asyncio, state machines, event-driven programming |
Mini-Projects:
- Build a simple Q&A chatbot with LangChain
- Implement a retrieval-augmented generator (RAG)
Goal: Deeply understand LangGraph's architecture and API.
| Topic | Key Concepts |
|---|---|
| Graph Architecture | Nodes, edges, state, transitions, conditional logic |
| Building State Machines | Designing, running, debugging agent graphs |
| Memory in LangGraph | Checkpointing, episodic memory, persistent state |
| Tool Integration | Connecting LLMs to APIs, DBs, external services |
| Human-in-the-Loop | Adding human review checkpoints |
Mini-Projects:
- Create a customer support agent using state graphs
- Add memory and tool calling to an agent
Goal: Design, build, and test advanced agentic systems.
| Topic | Key Concepts |
|---|---|
| Multi-Agent Orchestration | Planners, executors, routers, orchestrators |
| ReAct Agents | Reasoning + Acting loops, self-correction |
| Plan-and-Execute Pattern | Decompose & conquer complex tasks |
| Reflexion Loops | Self-review, introspection, iterative improvement |
| Agentic Memory Graphs | Zettelkasten-style linking, context-aware retrieval |
Mini-Projects:
- Build a plan-and-execute document analyst
- Multi-agent workflow: planner + researcher + summarizer
Goal: Master enterprise-grade and cutting-edge agent patterns.
| Topic | Key Concepts |
|---|---|
| Adaptive Deliberation | Fast/slow reasoning, context-aware switching |
| Self-Improving Agents | Learning from past errors, memory-enhanced agents |
| Governed Tool Use | Permissions, logging, safety constraints |
| Complex Branching & Cycles | Non-linear graphs, parallel/sequential flows |
| Multimodal Agents | Integrating text, images, audio in agent workflows |
| Enterprise Compliance | Audit trails, data privacy, compliance layers |
Mini-Projects:
- Self-improving code review agent with reflexion
- Governed multi-tool workflow for regulated domains (e.g., healthcare, finance)
Goal: Deploy, monitor, and scale agentic AI systems.
| Topic | Key Concepts |
|---|---|
| Checkpointing & Reliability | Save/restore state, error handling, retries |
| Logging & Monitoring | Tracing, observability, LLM monitoring tools |
| Deployment | APIs (FastAPI, Cloud Run), containerization |
| Scaling Multi-Agent Systems | Distributed agents, load balancing |
| Continuous Improvement | User feedback loops, A/B testing, agent updates |
Mini-Projects:
- Deploy a production-ready agent API with monitoring
- Build a feedback loop pipeline for agent improvement
| Project | Description |
|---|---|
| Autonomous Research Assistant | Researches, summarizes, and compiles information from web and documents |
| Multi-Agent Customer Support | Router, retriever, and responder agents for support tickets |
| Self-Healing Code Agent | Detects, diagnoses, and fixes code bugs autonomously |
| Personal Knowledge Manager | Memory graph-powered assistant with long-term recall |
| Agentic Workflow Automation | Multi-step business process automation (e.g., invoice processing) |
- Multi-Agent Systems: Enterprises are shifting from single-agent to orchestrated teams of specialized agents ("microservices for AI")
- Agentic Memory Graphs: Persistent, context-aware memory is now standard for production agents
- Reflexion & Self-Improvement: Agents that learn from their mistakes and improve over time
- Governed, Safe Tooling: Permissioned tool use with full audit trails for regulated industries
- Adaptive Reasoning: Fast vs deep deliberation based on context and task complexity
"Over 40% of enterprise software now embeds agentic AI functionalities." [2]
- DeepLearning.AI - LangGraph for Agentic AI
- Udemy: Build AI Agents 10x Faster 2026
- AICurator: LangGraph Conversational AI Guide 2026
- How to Design Agentic AI with LangGraph (MarkTechPost)
- 7 Agentic AI Trends to Watch in 2026
- Agentic AI Frameworks: Enterprise Guide 2026
- ReAct: Synergizing Reasoning and Acting
- Reflexion: Language Agents with Verbal Reinforcement Learning
- Tree of Thoughts: Deliberate Problem Solving
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain. tools import tool
# Define a simple tool
@tool
def web_search(query: str) -> str:
"""Search the web for information."""
# Integrate with your preferred search API
return f"[Simulated search result for: {query}]"
# Define agent state
class AgentState(dict):
messages: list
tool_calls: list
result: str
# Define nodes
def call_llm(state):
llm = ChatOpenAI(model="gpt-4o", temperature=0)
response = llm.invoke(state["messages"])
state["messages"].append(response)
return state
def invoke_tool(state):
# Execute tool calls here
for call in state["tool_calls"]:
result = web_search. invoke(call["args"])
state["result"] = result
return state
# Build graph
graph = StateGraph(AgentState)
graph.add_node("llm", call_llm)
graph.add_node("tool", invoke_tool)
graph.add_edge("llm", "tool")
graph.add_edge("tool", END)
graph.set_entry_point("llm")
# Compile and run
agent = graph.compile()
result = agent.invoke({"messages": [{"role": "user", "content": "What is LangGraph? "}], "tool_calls": [], "result": ""})
print(result["result"])from langgraph.graph import StateGraph, END
class WorkflowState(dict):
plan: list
step: int
results: list
def planner(state):
# Generate a plan (list of steps)
state["plan"] = ["Research topic", "Summarize findings", "Generate report"]
state["step"] = 0
return state
def executor(state):
# Execute the current step
current = state["plan"][state["step"]]
state["results"].append(f"Completed: {current}")
state["step"] += 1
return state
def should_continue(state):
if state["step"] < len(state["plan"]):
return "executor"
return END
graph = StateGraph(WorkflowState)
graph.add_node("planner", planner)
graph.add_node("executor", executor)
graph.add_conditional_edges("executor", should_continue)
graph.add_edge("planner", "executor")
graph.set_entry_point("planner")
workflow = graph.compile()
result = workflow.invoke({"plan": [], "step": 0, "results": []})
print(result["results"])Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
This project is licensed under the MIT License - see the LICENSE file for details.
- Website: Adil Shamim
- GitHub: Adil Shamim
- Create an issue in this repository for questions or suggestions
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