Multi-Agent AI System using LangGraph

Single-prompt chatbots are 2023. The 2026 skill employers ask about is agentic AI — teams of specialized AI agents that plan, research, critique, and refine work autonomously. This project builds one with LangGraph: a Researcher agent gathers information with web search and RAG, a Planner agent structures it into a plan, and a Critic agent challenges weaknesses until the output passes quality gates — all visible in a Streamlit control room showing the live agent graph, message flow, and intermediate artifacts. "AI agent final year project" is the fastest-growing keyword on our SEO list with essentially zero kit-seller competition. First-mover pages win.

How it works:

1. The user submits a mission (e.g., "validate this startup idea") in the control room.
2. LangGraph routes the mission through the agent graph: Researcher gathers evidence via search and RAG, Planner structures findings, Critic evaluates against quality rubrics.
3. Failed critiques loop back with specific feedback; the graph iterates until quality gates pass or max iterations hit.
4. Every agent step, tool call, and message is logged and visualized live in the control room.
5. The final artifact (validation report / literature review) is exported with its full trace — the evidence trail examiners love.

Features:

• Three cooperating agents — Researcher, Planner, Critic — orchestrated as a LangGraph state machine
• Researcher uses web search + RAG over uploaded documents for grounded information gathering
• Planner decomposes goals into structured task plans with dependencies
• Critic scores drafts against rubrics and sends them back for revision loops
• Streamlit control room: live agent graph, inter-agent messages, token usage, and artifacts
• Two demo missions included: startup idea validation and literature-review assistant
• Human-in-the-loop checkpoints: approve or redirect agents mid-run
• Run history with full trace logs for the report and viva walkthrough

What's included:

• Complete source code (agent definitions, graph, control room)
• Two ready demo missions with sample outputs
• Project report PDF (synopsis, agentic-AI literature, architecture, evaluation, conclusion)
• Presentation PPT for review/demo day
• Viva Q&A sheet covering agents vs chains, LangGraph concepts, RAG, and evaluation of agent systems
• Setup guide (API keys, local-LLM option, running missions)

Limitations:

• Agent runs consume LLM API tokens; the guide includes cost-control settings and a local-LLM (Ollama) option to run free
• Output quality depends on the underlying LLM — the architecture, not any single model, is the project
• Multi-agent loops can be slow (minutes per mission); the control room shows progress live
• Web-search grounding needs internet; offline mode restricts the researcher to uploaded documents
• Evaluation of agent systems is an open research area — the report uses task-completion rubrics honestly