LangGraph Multi-Agent Study Backend
A multi-agent backend is a graph of roles, tools, state, and handoffs, not just a pile of prompts.
Site connection
Lykke describes chat, study, weekly-plan, and orchestrator agents for routing course tasks across tools and LMS context.
Visual model
Routing and handoff as graph structure
Toggle between a tool-routing architecture and an agent-handoff architecture.
Interactive
Agent systems are graphs of state, routing, and tool access
A study backend has multiple kinds of work: answer from documents, generate quizzes, plan the week, inspect due dates, calculate formulas, and decide when web search is allowed. A graph architecture makes those roles explicit.
When Multi-Agent Helps
Multi-agent design helps when different subtasks need different context, tools, or evaluation criteria. A quiz generator, calendar planner, and document-grounded tutor should not all carry the same prompt and memory.
It does not help when a single well-tooled agent can do the job. Extra agents add latency, debugging surface, and routing failure modes.
Study Workflow Example
A user asks, 'What should I study before Friday?' The orchestrator retrieves Canvas deadlines, asks a study agent to identify relevant topics, asks a planning agent to allocate time blocks, and returns a source-linked plan.
The output should expose what evidence was used: due dates, lecture titles, assignment rubrics, and weak spots from previous quizzes.
| Agent | Responsibility |
|---|---|
| Chat agent | Answer source-grounded questions |
| Study agent | Create flashcards, quizzes, and review guides |
| Weekly plan agent | Map due dates to study sessions |
| Orchestrator | Route requests and enforce tool policy |
| Verifier | Check citations, freshness, and missing evidence |
Common Pitfalls
- Adding agents before the workflow actually needs them.
- Letting agents pass unstructured prose instead of typed state.
- Forgetting source-grounding when handing work between agents.
- Hiding routing decisions from debugging views.
Quick check
Quiz
What is the main advantage of graph-based agent design?
- Explicit state and routing
- No need for tools
- Guaranteed correctness
- No latency
Graphs make the workflow structure, state transitions, and routing rules visible.