If you're working with Large Language Models (LLMs), you've probably built a Retrieval-Augmented Generation (RAG) pipeline. It's a great first step: fetch a document, stuff it into a prompt, and get a context-aware answer. But what happens when the first document isn't right? Or when the user's question requires multiple steps and tools to answer?

That's when you move from simple pipelines to agentic systems. An "agent" is just an LLM wrapped in a control loop that allows it to use tools, reason about the results, and plan its next steps to achieve a goal.

At our company, we've gone deep into building agentic systems, especially for complex RAG. The goal isn't just to answer questions, but to create a system that can reliably find and synthesize information from our messy, real-world data sources. This post is a rundown of the frameworks we've worked with, why we chose what we did, and how you can think about them for your own projects.

The Two Foundational Tools: LangChain vs. LangGraph

LangChain

LangChain’s core idea is the LangChain Expression Language (LCEL), which lets you pipe components together. The flow is a Directed Acyclic Graph (DAG), meaning it goes one way, from start to finish.

chain = prompt | model | output_parser

It’s straightforward and clean.

Use it for:

Basic RAG: Retrieve a document, create a prompt, get an answer.
Summarization: Feed text into a summarization chain.
Data Extraction: Pull structured JSON from a block of text.

For our first internal documentation chatbot, this was perfect. It was predictable and easy to debug.

LangGraph

The problem with a simple chain is that it can't recover from errors. If the first document retrieval returns irrelevant junk, the chain fails. LangGraph, built on top of LangChain, solves this by letting you define your workflow as a graph with nodes and edges. The key difference? It allows for cycles (loops).

This means an agent can try something, check the result, and if it's not good enough, loop back to try again with a different tool or a refined query. It works by passing a state object between nodes, so the agent always knows what's been done and what the current goal is.

Use it for:

Self-Correcting RAG: If a document search fails, the agent can rephrase the query and search again.
Multi-agent Workflows: A supervisor agent can route tasks to a search or analysis agent, looping until the answer is ready.
Human-in-the-Loop: The graph can pause for approval before continuing.

Feature	LangChain	LangGraph
Core Abstraction	Chain (using LCEL)	Graph of Nodes
Workflow Type	Linear (Directed Acyclic Graph)	Cyclical (graphs with loops)
State Management	Generally stateless per run	Explicit, persistent state object
Primary Use	Simple, predictable sequences	Complex, dynamic, stateful agents

Our Takeaway: Start with LangChain. Once you find yourself wishing your chain could “try again” or “decide” what to do next based on an output, it's time to move to LangGraph.

How We Build Agentic RAG with LangGraph

Our support team must answer complex questions referencing technical docs, past tickets, and engineering wikis. A simple RAG system wasn't enough. Here’s the agentic workflow we built with LangGraph:

Node 1: Deconstruct Query. The initial question (e.g., “Customer X sees a timeout on API endpoint Y with version Z”) is turned into a structured plan with search terms.
Node 2: Parallel Retrieval. The agent searches multiple sources at once: vector DB for docs, Elasticsearch for tickets, and Confluence for wikis.
Conditional Edge: Validate Content. Documents are quickly scored for relevance.
- If scores are high → proceed to synthesis.
- If scores are low → loop back to Deconstruct Query with a hint to “think again” and re-generate terms.
Node 3: Synthesize Answer. Relevant documents are combined into a final prompt; the LLM outputs a step-by-step answer with links to sources.
Node 4: Final Output. The answer is presented to the support engineer.

Why this works: cyclical, stateful control makes the RAG system resilient to bad retrievals and partial failures.

Orchestration Frameworks: When You Need a Team of Agents

Sometimes, a single agent isn't enough. You need multiple specialized agents to collaborate. This is where higher-level orchestration frameworks come in.

Crew.AI: Define a “crew” with roles (role, goal, backstory). Great for workflows like content generation where a researcher (RAG) hands off to a writer.
Google’s ADK: An opinionated, production-focused framework with patterns like SequentialAgent/ParallelAgent. Feels like a factory for a fleet that already knows how to collaborate.

Framework	Core Idea	Best For
Microsoft AutoGen	Agents solve tasks by “chatting” with each other.	Dynamic problems with unclear solution paths.
LlamaIndex	Data framework for connecting LLMs to external data.	Data-heavy RAG, advanced retrieval and ingestion.
Haystack	Open-source framework for search & production RAG.	Enterprise-grade, scalable IR and RAG pipelines.
MetaGPT	Agents mimic company roles using SOPs.	Structured tasks like code generation or project plans.
SuperAGI	End-to-end platform to build, deploy, and monitor agents.	Teams wanting a full platform and GUI out-of-the-box.
Semantic Kernel	SDK to connect LLMs to conventional code (C#, Python).	Integrating LLM reasoning into existing applications.

Conclusion

The journey into agentic AI is a trade-off between control and convenience.

LangChain gives you simple, linear building blocks. It’s the place to start.
LangGraph gives you granular control over complex, looping logic for robust, self-correcting agents.
Crew.AI and Google’s ADK abstract orchestration for teams of agents.

For our team, the sweet spot for advanced RAG has been using powerful data frameworks like LlamaIndex for the retrieval part and LangGraph for the agent's core reasoning and tool-use logic. By choosing the right framework for the job, you can move beyond simple demos to build AI systems that can actually reason, plan, and solve real-world problems.

Beyond Simple Q&A: A Practical Look at Agentic Frameworks for RAG