Building Agents with LangGraph and Needle
Learn how to build a powerful RAG agent that combines Needle's document processing with LangGraph's workflow orchestration
Key Takeaways
- Build a complete RAG agent in 4 steps: upload, index, search, and respond - using just 3 dependencies.
- Needle handles document ingestion, chunking, embedding, and semantic search with zero infrastructure management.
- LangGraph provides stateful multi-step workflows that track context between each step automatically.
- The agent waits 30 seconds for Needle to index documents before querying - fully automated, no manual timing needed.
- The architecture is easily extensible: add new workflow steps without modifying existing ones.
In this tutorial, we'll build a powerful RAG agent that performs four key tasks:
- Uploads documents to a Needle collection.
- Waits for the documents to finish indexing.
- Searches those documents based on a user's query.
- Responds with an answer using a Large Language Model (LLM).
We'll build this agent using two tools:
- Needle, a “RAG API and Knowledge Threading” platform for document ingestion, chunking, embedding, and semantic search.
- LangGraph, a library that lets you create stateful, multi-step LLM workflows (or “agents“) that can maintain state between steps.
Why Needle and LangGraph?
- Needle handles the heavy lifting of storing and indexing your documents. Its built-in chunking, embedding, and semantic search features make it easy to build RAG applications without managing complex infrastructure.
- LangGraph specializes in creating stateful workflows for LLM-powered agents. It helps you chain together steps, like uploading files, waiting for indexing, searching, and more, while keeping track of important context along the way.
Together, these tools provide a clean, modular approach to building powerful RAG workflows.
Needle + LangGraph vs. Building RAG from Scratch
| Aspect | DIY RAG Agent | Needle + LangGraph |
|---|---|---|
| Dependencies | 10+ packages (vector DB, embeddings, orchestration) | 3 packages (needle, langgraph, openai) |
| Document Processing | Manual chunking & embedding pipeline | Automatic via Needle API |
| State Management | Custom state tracking code | Built-in via LangGraph |
| Infrastructure | Self-managed vector DB + API layer | Fully managed by Needle |
| Extensibility | Requires refactoring | Add steps modularly |
Prerequisites
Before you begin, make sure you have:
- Python 3.9+ installed.
- A Needle account and API key.
- An OpenAI account and API key.
- Dependencies installed:
needle langgraph langchain_community openai - A Needle collection already created.
How It Works: 5-Step Workflow
- Initialize: Specify your API keys, collection ID, and create a loader/retriever.
- Add a File: The agent calls
add_file_to_collectionto upload a document. - Indexing Delay: Wait 30 seconds for Needle to automatically chunk, embed, and index the file.
- Search: The agent queries the newly indexed file for the user's question using semantic search.
- Multi-Step Tracking: LangGraph tracks these steps so each one executes at the right time with full context.
Summary
Combining Needle and LangGraph lets you build RAG agents that gracefully handle multi-step processes - uploading documents, waiting for indexing, searching, and responding - with just 3 dependencies and zero infrastructure management. The architecture is modular and easily extensible: add new workflow steps without altering existing ones. This tutorial provides a starting point; customize the wait time, build more elaborate logic, or add new tools to your workflow.
Check out the Needle documentation for more details on advanced indexing options and LangGraph on GitHub for building more sophisticated multi-step LLM workflows.
