Skill Guide

LangChain or LlamaIndex for orchestrating AI agent workflows

LangChain and LlamaIndex are Python-based frameworks for building, chaining, and managing stateful, multi-step applications that combine Large Language Models (LLMs), data sources, external tools, and logic.

This skill enables organizations to automate complex, knowledge-intensive workflows, significantly reducing operational overhead and accelerating decision-making. It directly impacts business outcomes by transforming LLMs from simple text generators into integrated, actionable business process agents.

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How to Learn LangChain or LlamaIndex for orchestrating AI agent workflows

Focus on core framework concepts: understanding LLMs, Prompt Templates, Chains, and basic Data Connectors. Practice with the official documentation and starter templates. Build a single, linear chain (e.g., a simple Q&A bot over a PDF).

Move to stateful, conditional logic with LangGraph or LlamaIndex workflows. Implement tool use (e.g., API calls, calculations), basic RAG pipelines with source attribution, and error handling. Understand memory types (conversation buffer, summary). Avoid creating overly complex, brittle chains; start simple.

Design and architect multi-agent systems with clear delegation and collaboration patterns. Focus on system observability (LangSmith, tracing), performance optimization (caching, batching), security guardrails, and production deployment (containerization, scaling). Mentor teams on framework best practices and anti-patterns.

Practice Projects

Beginner

Project

Build a Context-Aware Document Q&A Bot

Scenario

You need to build an internal tool that allows employees to ask natural language questions about a collection of company policy PDFs and get accurate, cited answers.

How to Execute

1. Use a document loader (PyPDFLoader) to ingest PDFs. 2. Create a vector store (e.g., FAISS, Chroma) with a text splitter and an embedding model. 3. Build a retrieval chain using LangChain's RetrievalQA or LlamaIndex's query engine. 4. Implement a simple conversational interface with conversation memory.

Intermediate

Project

Create an Autonomous Research Agent with Tool Use

Scenario

Build an agent that, given a research topic, can search the web, summarize findings, extract key data points, and generate a structured report with sources.

How to Execute

1. Define custom tools using @tool decorator for web search (via SerpAPI), text summarization, and file I/O. 2. Use an agent executor (e.g., LangChain Agent) with a reasoning framework like ReAct. 3. Implement a state graph or workflow to manage the research process stages. 4. Add logging and simple validation to handle tool errors and hallucination.

Advanced

Project

Design a Multi-Agent Customer Support System

Scenario

Architect a system where a primary triage agent classifies incoming support tickets (billing, technical, general), and delegates to specialized sub-agents (one for account lookup, one for troubleshooting, one for FAQs) that can collaborate to resolve complex issues.

How to Execute

1. Use LangGraph or LlamaIndex Workflows to define a state machine with nodes for each specialist agent and edges for delegation logic. 2. Implement shared state (ticket data, customer history) and communication protocols between agents. 3. Build monitoring dashboards for agent performance, conversation flow, and resolution rates. 4. Implement fallback mechanisms to human escalation and define clear success metrics.

Tools & Frameworks

Core Orchestration Frameworks

LangChainLlamaIndexLangGraph (LangChain)LlamaIndex Workflows

LangChain provides a broader set of integrations and agents. LlamaIndex excels at advanced data indexing and retrieval. LangGraph is the recommended tool for building complex, stateful agent graphs with cycles. Use based on project primary need: general orchestration vs. deep RAG vs. complex agent logic.

Observability & Evaluation

LangSmithPhoenix (Arize)RagasWeights & Biases

LangSmith is the integrated tracing and debugging platform for LangChain. Phoenix offers open-source LLM observability. Ragas provides metrics for evaluating RAG pipelines. Use these for debugging chain logic, evaluating answer quality, and monitoring production costs and latency.

Deployment & Infrastructure

LangServeFastAPIDockerAWS Lambda/Step Functions

LangServe deploys LangChain chains as REST APIs. FastAPI is a common choice for custom endpoints. Containerize with Docker for consistent environments. Use serverless (AWS Lambda) or orchestration services (Step Functions) for scalable, event-driven agent execution.

Interview Questions

Answer Strategy

Structure the answer around: 1) System Design (using LangGraph for stateful flow), 2) Tool Implementation (wrapping DB/API with clear error handling and validation), 3) Guardrails (input/output validation, confirmation steps for critical actions), and 4) Observability (tracing tool calls and agent reasoning). Sample: 'I'd design a stateful graph with a planner node and separate nodes for each tool. Each tool node would have validation schemas and retry logic. I'd implement a guardrail agent to review tool outputs before final response and use LangSmith to trace every step for debugging and audit.'

Answer Strategy

Tests practical experience with production systems and cost-awareness. The candidate should discuss specific metrics (latency, tokens/request, cost, accuracy) and technical solutions. Sample: 'In a RAG application, I tracked retrieval precision and answer latency. I optimized by implementing a reranker model to improve retrieval accuracy, which reduced the number of documents passed to the LLM. I also added a caching layer for frequent queries and used a smaller, fine-tuned model for the final answer generation, reducing token cost by 30% without sacrificing quality.'