Agentic ai Projects using Langgraph
— Multi-Agent Systems & LLM AgentsAgentic AI goes beyond chatbots: agents plan multi-step goals, call tools (search, code, APIs, databases), maintain memory, reflect, and collaborate with other agents. Final-year projects that demonstrate planning, tool use, multi-agent coordination and evaluation produce the strongest IEEE-style results.
Below are 60+ carefully selected topics organised by domain, with frameworks used at international universities (Stanford, Berkeley, MIT, Oxford, CMU and others).
Agentic ai Projects for Beginners
Agent Frameworks Used at International UniversitiesLangChain / LangGraph
Composable agents, stateful graphs, tool calling, human-in-the-loop. Dominant in academic prototyping and courses.
Stanford · Berkeley · many CS departments
Microsoft AutoGen
Multi-agent conversation frameworks, group chat, code execution agents. Strong research + industry alignment.
Microsoft Research partners · CMU-related work
CrewAI
Role-based multi-agent crews, process orchestration, sequential and hierarchical workflows.
Popular in applied AI & startup-oriented courses
LlamaIndex
Data agents, RAG pipelines, query engines and agentic retrieval over documents and databases.
Widely used for RAG + agent hybrid projects
Semantic Kernel
Microsoft’s SDK for planners, skills/plugins and multi-language agent orchestration (.NET / Python).
Enterprise & .NET-focused university tracks
DSPy / Haystack
Programmatic prompt optimisation (DSPy) and production NLP pipelines with agent components (Haystack).
Research labs · Stanford-adjacent DSPy work
Multi-Agent Systems (1–12)
Collaboration · Debate · Hierarchical · Role-based · Consensus
| # | IEEE 2026 Agentic AI Project Topic | Domain | Tools Used |
|---|---|---|---|
| 01 | Multi-Agent Debate System for Fact-Checking and Claim VerificationMulti-Agent | Multi-Agent | LangGraph / AutoGen |
| 02 | Hierarchical Multi-Agent Team for Software Requirements AnalysisMulti-Agent | Multi-Agent | CrewAI / LangGraph |
| 03 | Role-Based Agent Crew for End-to-End Business Report GenerationMulti-Agent | Multi-Agent | CrewAI |
| 04 | Consensus-Seeking Multi-Agent System for Collaborative Decision SupportMulti-Agent | Multi-Agent | AutoGen / LangGraph |
| 05 | Multi-Agent Simulation of Supply-Chain Negotiation and BiddingMulti-Agent | Multi-Agent | AutoGen / Python |
| 06 | Supervisor–Worker Agent Architecture for Parallel Task DecompositionMulti-Agent | Multi-Agent | LangGraph |
| 07 | Multi-Agent Peer Review System for Academic Paper CritiqueMulti-Agent | Multi-Agent | CrewAI / AutoGen |
| 08 | Dynamic Agent Team Formation based on Task Complexity and SkillsMulti-Agent | Multi-Agent | LangGraph / Custom |
| 09 | Multi-Agent Game-Theoretic Negotiation for Resource AllocationMulti-Agent | Multi-Agent | Python / AutoGen |
| 10 | Cross-Domain Multi-Agent Collaboration for Healthcare Triage SupportMulti-Agent | Multi-Agent | LangGraph / LlamaIndex |
| 11 | Multi-Agent Swarm for Distributed Information GatheringMulti-Agent | Multi-Agent | AutoGen / LangGraph |
| 12 | Memory-Shared Multi-Agent System with Vector Store CoordinationMulti-Agent | Multi-Agent | LangGraph + Chroma / FAISS |
Tool-Using & ReAct Agents (13–22)
ReAct · Tool calling · API agents · Browser · Code interpreter
| # | IEEE 2026 Agentic AI Project Topic | Domain | Tools Used |
|---|---|---|---|
| 13 | ReAct Agent with Web Search, Calculator and Weather ToolsTool-Use | Tool-Using | LangChain / LangGraph |
| 14 | API-Orchestrating Agent for Multi-Service Workflow (REST + GraphQL)Tool-Use | Tool-Using | LangGraph / Semantic Kernel |
| 15 | Browser-Using Agent for Automated Form Filling and Data ExtractionTool-Use | Tool-Using | LangGraph + Playwright |
| 16 | SQL Database Agent with Schema-Aware Query Generation and Safety GuardsTool-Use | Tool-Using | LangChain / LlamaIndex |
| 17 | Multi-Tool Agent for Personal Productivity (Email, Calendar, Notes)Tool-Use | Tool-Using | LangGraph / Semantic Kernel |
| 18 | Code Interpreter Agent with Sandboxed Execution and Self-DebuggingTool-Use | Tool-Using | AutoGen / LangGraph |
| 19 | Plan-and-Execute Agent with Dynamic Tool SelectionTool-Use | Tool-Using | LangGraph |
| 20 | Agent with External Knowledge Graph Tool for Structured ReasoningTool-Use | Tool-Using | LangChain + Neo4j |
| 21 | Self-Reflective Tool-Using Agent with Error Recovery LoopsTool-Use | Tool-Using | LangGraph |
| 22 | Function-Calling Agent Benchmarked on ToolBench / API-Bank Style TasksTool-Use | Tool-Using | LangChain / Custom |
RAG & Knowledge Agents (23–32)
Agentic RAG · Query planning · Multi-hop · Document agents
| # | IEEE 2026 Agentic AI Project Topic | Domain | Tools Used |
|---|---|---|---|
| 23 | Agentic RAG with Query Decomposition and Multi-Hop RetrievalRAG Agent | RAG Agents | LlamaIndex / LangGraph |
| 24 | Self-Correcting RAG Agent with Retrieval Quality ReflectionRAG Agent | RAG Agents | LangGraph + Vector DB |
| 25 | Multi-Document Research Agent over Enterprise PDF Knowledge BaseRAG Agent | RAG Agents | LlamaIndex / CrewAI |
| 26 | Adaptive RAG Router: Keyword vs Semantic vs Hybrid Retrieval AgentRAG Agent | RAG Agents | LangChain / LlamaIndex |
| 27 | Conversational Knowledge Agent with Long-Term Memory and CitationsRAG Agent | RAG Agents | LangGraph + Chroma |
| 28 | Legal Document Q&A Agent with Clause-Level GroundingRAG Agent | RAG Agents | LlamaIndex |
| 29 | Medical Literature Agent with PubMed-Style Retrieval and SummarisationRAG Agent | RAG Agents | LangGraph / LlamaIndex |
| 30 | Graph-RAG Agent combining Knowledge Graphs and Vector RetrievalRAG Agent | RAG Agents | LlamaIndex + Neo4j |
| 31 | Multi-Modal RAG Agent for Text + Image Document CollectionsRAG Agent | RAG Agents | LlamaIndex / LangChain |
| 32 | Evaluation of Agentic vs Naive RAG on Multi-Hop QA BenchmarksRAG Agent | RAG Agents | LangGraph / Ragas |
Coding & Software Engineering Agents (33–40)
Code generation · Debugging · Repo agents · Test generation
| # | IEEE 2026 Agentic AI Project Topic | Domain | Tools Used |
|---|---|---|---|
| 33 | Autonomous Coding Agent with Plan–Code–Test–Debug LoopCoding Agent | Coding Agents | AutoGen / LangGraph |
| 34 | Multi-Agent Software Development Crew (Architect, Coder, Reviewer, Tester)Coding Agent | Coding Agents | CrewAI / AutoGen |
| 35 | Repository-Level Agent for Bug Localisation and Patch ProposalCoding Agent | Coding Agents | LangGraph + Git tools |
| 36 | Unit Test Generation Agent with Coverage-Guided RefinementCoding Agent | Coding Agents | AutoGen / LangChain |
| 37 | Legacy Code Modernisation Agent (Python 2→3 / Framework Migration)Coding Agent | Coding Agents | LangGraph |
| 38 | API Documentation and Client SDK Generation AgentCoding Agent | Coding Agents | CrewAI / LangChain |
| 39 | Competitive Programming Agent with Self-Play and Solution RankingCoding Agent | Coding Agents | AutoGen / Custom |
| 40 | CI/CD Pipeline Agent for Automated PR Review and Merge SuggestionsCoding Agent | Coding Agents | LangGraph + GitHub API |
Autonomous Research Agents (41–48)
Literature review · Hypothesis · Experiment design · Survey agents
| # | IEEE 2026 Agentic AI Project Topic | Domain | Tools Used |
|---|---|---|---|
| 41 | Autonomous Literature Review Agent with Citation Graph ConstructionResearch | Research Agents | LangGraph + Semantic Scholar API |
| 42 | Scientific Hypothesis Generation and Experiment Planning AgentResearch | Research Agents | AutoGen / LangGraph |
| 43 | Survey Paper Writing Agent with Multi-Source SynthesisResearch | Research Agents | CrewAI / LlamaIndex |
| 44 | Comparative Study Agent for Algorithm Benchmarking and ReportingResearch | Research Agents | LangGraph + Code tools |
| 45 | Patent Landscape Analysis Agent with Claim MappingResearch | Research Agents | LlamaIndex / LangGraph |
| 46 | Open-Source Project Trend Analysis Agent (GitHub + Papers)Research | Research Agents | LangGraph + APIs |
| 47 | Multi-Agent Peer-Review Simulation for Conference Paper EvaluationResearch | Research Agents | AutoGen / CrewAI |
| 48 | Dataset Discovery and Curation Agent for ML Research PipelinesResearch | Research Agents | LangGraph / LlamaIndex |
Workflow & Business Process Agents (49–54)
Automation · Customer support · Ops · Scheduling
| # | IEEE 2026 Agentic AI Project Topic | Domain | Tools Used |
|---|---|---|---|
| 49 | Customer Support Multi-Agent System with Escalation and Knowledge BaseAutomation | Workflow | CrewAI / LangGraph |
| 50 | IT Operations Agent for Log Analysis, Alert Triage and Runbook ExecutionAutomation | Workflow | LangGraph + Tools |
| 51 | HR Onboarding Workflow Agent with Document Collection and ChecksAutomation | Workflow | CrewAI / Semantic Kernel |
| 52 | Meeting Scheduling and Minutes Generation Multi-Agent PipelineAutomation | Workflow | LangGraph |
| 53 | Invoice Processing and Exception Handling AgentAutomation | Workflow | LangGraph + OCR tools |
| 54 | E-commerce Returns and Refund Decision Support AgentAutomation | Workflow | CrewAI / LangChain |
Embodied & Robotics Agents (55–58)
LLM + Robotics · Simulation · Task planning
| # | IEEE 2026 Agentic AI Project Topic | Domain | Tools Used |
|---|---|---|---|
| 55 | LLM Task Planner for Mobile Robot Navigation and Manipulation (Sim)Embodied | Embodied | LangGraph + ROS2 / Gazebo |
| 56 | Vision-Language Agent for Household Task Execution in SimulationEmbodied | Embodied | LangGraph + Habitat / AI2-THOR concepts |
| 57 | Multi-Robot Coordination Agent for Warehouse Task AllocationEmbodied | Embodied | AutoGen / Custom + ROS2 |
| 58 | Human–Robot Collaboration Agent with Natural Language Task SpecificationEmbodied | Embodied | LangGraph + ROS2 |
Evaluation, Safety & Alignment (59–65)
Benchmarks · Guardrails · Human-in-the-loop · Observability
| # | IEEE 2026 Agentic AI Project Topic | Domain | Tools Used |
|---|---|---|---|
| 59 | Agent Evaluation Framework with Task Success, Cost and Latency MetricsEvaluation | Evaluation | LangSmith / Custom + Ragas |
| 60 | Guardrail-Enforced Agent with Input/Output Policy ChecksSafety | Safety | LangGraph + Guardrails AI |
| 61 | Human-in-the-Loop Agentic Workflow with Approval and Override NodesHITL | Evaluation | LangGraph |
| 62 | Observability and Tracing Dashboard for Multi-Agent SystemsObservability | Evaluation | LangSmith / OpenTelemetry |
| 63 | Red-Teaming Agent System for Jailbreak and Tool-Misuse DetectionSafety | Safety | AutoGen / Custom |
| 64 | Cost-Aware Agent Routing: Small vs Large Model Selection PolicyEvaluation | Evaluation | LangGraph |
| 65 | Comparative Benchmark of LangGraph vs AutoGen vs CrewAI on Shared TasksEvaluation | Evaluation | All three frameworks |
Additional topics available: multi-agent reinforcement learning hybrids, agent memory architectures (vector + episodic), DSPy-optimised agent pipelines, and domain-specific agents for finance, education and legal. Contact us with your preferred framework and application area.
Need a Personalised Agentic AI Project?
Share your preferred domain (multi-agent, tool-use, RAG, coding, research…), framework (LangGraph, AutoGen, CrewAI, LlamaIndex) and deadline. We deliver complete Python code, agent graphs, evaluation scripts, IEEE base paper, report, PPT and viva support.
Frequently Asked Questions — Agentic AI Projects
What are the best Agentic AI project topics for final year students in 2026?
Top topics include multi-agent debate and research assistants, AutoGen/CrewAI coding crews, agentic RAG with multi-hop retrieval, tool-using ReAct agents, autonomous literature review agents, workflow automation crews, and evaluation/safety frameworks. LangGraph, AutoGen and CrewAI are the most requested stacks.
Which agent frameworks are used at international universities?
LangChain/LangGraph are widely taught at Stanford, Berkeley and many CS departments. AutoGen is used in research and courses linked to Microsoft Research. CrewAI is popular for role-based multi-agent projects. LlamaIndex dominates RAG+agent work. Semantic Kernel appears in enterprise/.NET tracks; DSPy is rising in research labs.
Do you provide complete Agentic AI project packages?
Yes. Packages include Python source, agent graphs or crew definitions, tool integrations, evaluation scripts, sample traces, IEEE 2026 base paper, university-format report, PPT and viva Q&A on planning, memory, tool use and multi-agent coordination.
What is the difference between an LLM chatbot and an Agentic AI system?
A chatbot mainly responds turn-by-turn. An Agentic AI system plans multi-step goals, selects and calls tools, maintains memory, reflects on intermediate results and can collaborate with other agents — enabling autonomous research, coding, workflow automation and decision support.