diff --git a/README.md b/README.md
index 9077f5b..5a05b0a 100644
--- a/README.md
+++ b/README.md
@@ -1,79 +1,79 @@
-# RAG Agent with ChromaDB and Tavily
+# RAG Agent with Qdrant and Tavily
 
-This repository contains a lightweight RAG (Retrieval‑Augmented Generation) agent that:
+This repository implements an AI agent that can answer questions using a local knowledge base stored in **Qdrant** and up‑to‑date information fetched from the web via **Tavily**.  The agent is built with **LangChain 1.x** and **Ollama** for local LLM and embeddings.
 
-1. Stores local knowledge in **ChromaDB** using **Ollama** embeddings.
-2. Performs semantic search over the local store.
-3. Falls back to **Tavily** web search for up‑to‑date information.
-4. Decides automatically which source to use and indicates the source in the answer.
+## Features
 
-## Prerequisites
-
-* Python 3.10+ (recommended via `pyenv` or `conda`).
-* [Ollama](https://ollama.ai/) installed locally.
-* A Tavily API key – set it in a `.env` file.
-
-```bash
-# Pull the required models
-ollama pull llama3
-ollama pull nomic-embed-text
-```
+* **Local RAG** – Semantic search in Qdrant using Ollama embeddings.
+* **Web search** – Tavily integration for real‑time information.
+* **Automatic source selection** – The LLM decides whether to use the local KB or the web.
+* **Persistent vector store** – Data is saved in `./qdrant_db` and reused across runs.
+* **Interactive CLI** – Add documents, ask questions, and see the source.
 
 ## Installation
 
 ```bash
+# 1. Pull required Ollama models
+ollama pull llama3
+ollama pull nomic-embed-text
+
+# 2. Install Python dependencies
 pip install -r requirements.txt
+
+# 3. Run Qdrant (Docker recommended)
+#    If you prefer a local binary, download from https://qdrant.tech
+#    Docker command:
+#    docker run -p 6333:6333 qdrant/qdrant
 ```
 
 ## Usage
 
 ```bash
-# Create a .env file with your Tavily key
-# TAVILY_API_KEY=YOUR_KEY
-
-# Populate the vector store from the documents folder
-python main.py
+# Start the CLI
+python -m workspace.task-6a1864f78a94f887e50d46da.cli
 ```
 
-You will be presented with a prompt.  Type your question and press **Enter**.
-Type `exit` to quit.
+Commands:
+
+* `/add <directory>` – Load all `.txt` and `.md` files from the directory into Qdrant.
+* `/search <question>` – Ask the agent a question.
+* `/quit` – Exit.
+
+Example:
+
+```
+> /add ./documents
+Loaded 12 chunks into Qdrant.
+Documents added.
+> /search What is LangGraph?
+Answer:
+LangGraph is a framework for building ...
+Source: chromadb
+```
+
+## Environment Variables
+
+* `TAVILY_API_KEY` – Your Tavily API key.
+
+Create a `.env` file in the project root:
+
+```
+TAVILY_API_KEY=your_api_key_here
+```
 
 ## Project Structure
 
 ```
-├── agent.py          # Agent definition
-├── main.py           # CLI entry point
-├── tools.py          # Local KB and web search tools
-├── vectorstore.py    # ChromaDB helpers
-├── requirements.txt
-├── README.md
-└── documents/        # Folder with .txt/.md files to ingest
+workspace/
+├─ task-6a1864f78a94f887e50d46da/
+│  ├─ vector_store.py          # Qdrant vector store helpers
+│  ├─ tools.py                 # Local KB and web search tools
+│  ├─ agent.py                 # Agent definition
+│  ├─ cli.py                   # Interactive command line
+│  ├─ requirements.txt
+│  └─ README.md
 ```
 
-## How It Works
+## License
 
-1. **Vector Store** – `vectorstore.py` creates a ChromaDB instance backed by
-   `OllamaEmbeddings`.  Documents from `documents/` are chunked with
-   `RecursiveCharacterTextSplitter` and added to the store.
-
-2. **Tools** – `tools.py` exposes two LangChain tools:
-   * `search_local_kb` – semantic search in ChromaDB.
-   * `web_search` – web search via Tavily.
-
-3. **Agent** – `agent.py` builds an OpenAI‑functions‑style agent that
-   chooses between the two tools based on the user’s query.  The system prompt
-   instructs the LLM to use `search_local_kb` for knowledge‑base queries and
-   `web_search` for recent facts.  The answer always contains a source tag.
-
-4. **CLI** – `main.py` ties everything together: it loads the vector store,
-   creates the agent and runs an interactive chat loop.
-
-## Extending
-
-* Replace the LLM with any other LangChain‑compatible model.
-* Add more tools (e.g., database queries, file system access).
-* Persist the vector store across runs – it already does this via `persist_directory`.
-
----
-
-Happy experimenting!
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+MIT
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