Multi-Agents with LangChain & LangGraph: Build 4 Projects

Configure multi-agent systems to emit structured outputs with a Pydantic schema and LangChain tool strategy, delivering concise summaries, details, action items, warnings, and confidence levels for deterministic results.

Add memory to an agent with a check pointer (e-memory saver) to maintain conversation context across turns, enabling session memory with a thread like Alice; production may use Postgres saver.

Explore the hand-off pattern to coordinate agents in LangChain and LangGraph, enabling seamless task delegation and state transfer across multiple agents.

Define the agent state by building a state schema that tracks conversation history and skills loaded with progressive disclosure, using typed dicts with LandGraph and an append-only messages list.

Define a middleware to inject each skill's front matter into the system prompt and build a base-plus-skills system prompt, exporting load_skill and get_tools functions for multi-agent workflows.

Define and assemble the agent graph for a web troubleshooting multi-agent system by creating the agent node, tool node, and conditional edges, binding the LLM to tools and managing state.

Define state variables for a multi-agent system. Create a state.py to manage intake, assessment, and decision data for HR, technical assessor, and hiring manager handoffs.

Create a tool to record technical assessments, score candidates from 1 to 10, capture assessment notes, and hand off results to the IRA manager, while updating conversation history for traceability.

Build two hiring manager tools for the decision stage: a make decision tool recording hire, reject, or hold with feedback, and a reassessment tool returning candidates to assessment.

Define the end-to-end handoff configuration for multi-agent interviews by building step configs with intake, assessment, and decision agents, each with prompts and tools, enabling swaps via state changes.

Build the flights sub-agent to search flights and compare prices using two tools, handling natural language queries, destination, budget, and stop preferences to surface best deals.

Discover how to build an activity sub-agent within a multi-agent setup using LangChain and LangGraph to complete four projects.

Learn to build a supervisor agent that coordinates four sub-agents—flights, hotels, activities, and itinerary—wrapped as tools, with a shared model and an in-memory store for short-term memory.

Orchestrate a memory-enabled, command-line, chat-based multi-agent system with a supervisor guiding sub-agents, using UUID thread IDs for persistent context in a smart travel planner.