🧠 LangGraph Lab

Building intelligence, one graph at a time.

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Graphs

1 — Tool Agent

An agent that decides whether to use a tool or answer directly.

You: "what is 2+3?"
        │
        ▼
   ┌─────────┐        "I need to call add(2,3)"
   │  Agent  │ ──────────────────────────────────► ┌──────────┐
   └─────────┘                                      │   Tool   │ → runs add() → returns 5
        ▲                                           └────┬─────┘
        │                 "2 + 3 = 5"                    │
        └───────────────────────────────────────────────┘
        │
        ▼
      Answer

No tool needed? Agent skips straight to the answer.

→ tool_agent.ipynb

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2 — Drafting Agent with Human Input

A human-in-the-loop agent that keeps revising a document until you're happy. Can also edit files on disk directly via the fix_txt_file tool.

You: "Draft a project proposal"
        │
        ▼
   ┌─────────┐    needs fix_txt_file?
   │  Agent  │ ──────────────────────► ┌──────────────┐
   └─────────┘                         │ fix_txt_file │ → reads file → edits → saves
        │                              └──────┬───────┘
        │  "Here's your draft..."             │
        ▼ ◄──────────────────────────────────┘
   ┌─────────┐
   │  Human  │ → "Make it more formal" → loops back to Agent
   └─────────┘
        │
      exit → done

The loop only breaks when you type exit. Every iteration the agent sees the full conversation history.

→ drafting_agent_with_human_input.py — run via CLI

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3 — RAG Agent

"Don't hallucinate. Go read."

A Retrieval-Augmented Generation agent that grounds every answer in a real PDF document. Instead of relying on the LLM's training data, it retrieves the most relevant chunks from a vector database before forming a response.

You: "Do they stock ibuprofen?"
        │
        ▼
   ┌───────────┐   "I need to search the PDF"
   │   Agent   │ ──────────────────────────────► ┌──────────────────────┐
   └───────────┘                                  │  retrieve_pdf_info   │
        ▲                                         │  ┌────────────────┐  │
        │                                         │  │  ChromaDB      │  │
        │   Top 5 matching chunks returned        │  │  (vector store)│  │
        │ ◄───────────────────────────────────    │  └────────────────┘  │
        │                                         └──────────────────────┘
        ▼
   "Ibuprofen is a major interaction with warfarin — avoid, use paracetamol."

How it works under the hood:

Step	What happens
1. Ingest	PDF is loaded, split into 1000-char chunks with 200-char overlap
2. Embed	Each chunk is embedded via text-embedding-3-small and stored in ChromaDB
3. Cache	If the vector DB already exists on disk, embedding is skipped entirely
4. Query	User question → similarity search → top 5 chunks retrieved
5. Answer	LLM reads the chunks and answers grounded in the actual document

Why RAG? The LLM has no knowledge of your specific PDF. RAG bridges that gap — the agent only answers from what's actually in the document, making it accurate, auditable, and hallucination-resistant.

→ Rag_Agent.py — run via CLI, place your PDF in data/example.pdf

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What's This?

An active workspace for experimenting with LangGraph — stateful, multi-agent workflows built as graphs.

Each node is a step. Each edge is a decision. Every run is a new path through the graph.

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Stack

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Setup

pip install langgraph langchain
cp .env.example .env   # add your API keys

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Graphs are just thoughts with structure.