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@@ -12,498 +12,197 @@ short_description: Chatbot
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-# Multi-User Memory Chatbot with LangChain, Tools, and Gradio
-A conversational AI chatbot built with **LangChain**, **Gradio**, and multiple custom tools.
-This project supports:
-- **multi-user chat sessions**
-- **multiple threads per user**
-- **memory-aware conversations**
-- **tool calling**
-- optional **tool activity display in the UI**
-It is useful for experimenting with AI agents that can search, calculate, scrape websites, fetch weather, read files, and more.
 ---
-## Features
-- 👤 **Multiple users**
-- 🧵 **Multiple chat threads per user**
-- 🧠 **Conversation memory using `thread_id` and `user_id`**
-- 🛠 **Tool-enabled chatbot**
-- 🌐 **Web search using Tavily**
-- 📚 **Wikipedia search**
-- ☁️ **Weather lookup**
-- 🧮 **Calculator**
-- 🐍 **Python code execution**
-- 📄 **Read local files**
-- 🔎 **Scrape webpage text**
-- 🧾 **Pretty-print JSON**
-- 💾 **Save user preferences**
-- 🖥 **System info tool**
-- 🎨 **Gradio web interface**
-- ✅ Optional **tool activity panel** to show which tools were called
----
-## Project Structure
-A recommended project structure:
-```text
-project/
-│
-├── tools.py               # All tool definitions
-├── memory_chatbot.py      # Chatbot / LangChain agent backend
-├── app.py                 # Gradio UI
-├── .env                   # Environment variables
-├── requirements.txt       # Python dependencies
-└── README.md
 ```
-> **Important:**
-> If your UI file imports `chatbot` like this:
-```python
-from memory_chatbot import chatbot
 ```
-then your UI file should **not** also be named `memory_chatbot.py`, otherwise you may get a **circular import error**.
-Use a separate file name such as `app.py` for the Gradio interface.
 ---
-## Tools Included
-The project currently includes the following tools:
-### 1. `time_date`
-Returns today’s date in `yyyy-mm-dd` format.
-### 2. `calculator`
-Evaluates mathematical expressions.
-### 3. `python_exec`
-Executes Python code and returns local variables.
-### 4. `get_weather`
-Fetches current weather for a city using `wttr.in`.
-### 5. `wikipedia_search`
-Searches Wikipedia and returns a short summary.
-### 6. `scrape_website`
-Extracts text content from a webpage.
-### 7. `read_file`
-Reads the contents of a local file.
-### 8. `format_json`
-Formats and prettifies a JSON string.
-### 9. `generate_sql`
-Converts natural language into a placeholder SQL query.
-### 10. `system_info`
-Returns system/platform information.
-### 11. `save_user_preference`
-Stores user preference text (currently mocked).
-### 12. `tool_tavily`
-Performs web search using Tavily.
 ---
-## Installation
-## 1. Clone the repository
-```bash
-git clone https://github.com/your-username/your-repo-name.git
-cd your-repo-name
-```
-## 2. Create a virtual environment
-### Windows
-```bash
-python -m venv venv
-venv\Scripts\activate
-```
-### macOS / Linux
-```bash
-python3 -m venv venv
-source venv/bin/activate
-```
-## 3. Install dependencies
 ```bash
 pip install -r requirements.txt
 ```
-If you do not already have a `requirements.txt`, you can use something like this:
-```txt
-gradio
 langchain
-langchain-core
 langchain-tavily
-python-dotenv
-requests
 wikipedia
 beautifulsoup4
 ```
-Depending on your chatbot backend, you may also need your model provider package, such as:
-- `langchain-openai`
-- `langchain-google-genai`
-- `langchain-groq`
-- or another LLM integration
----
-## Environment Variables
-Create a `.env` file in the root directory.
-Example:
 ```env
 TAVILY_API_KEY=your_tavily_api_key
-OPENAI_API_KEY=your_openai_api_key
 ```
-If you are using another model provider, replace `OPENAI_API_KEY` with the correct key for your setup.
----
-## Running the App
-Start the Gradio interface with:
 ```bash
 python app.py
 ```
-Then open the local Gradio URL shown in the terminal, usually:
-```text
-http://127.0.0.1:7860
-```
 ---
-## How It Works
-### User and Thread Management
-Each conversation is identified using:
-- `user_id`
-- `thread_id`
-This allows the chatbot to maintain separate memory for:
-- different users
-- different chat sessions for the same user
-### Chat Flow
-When the user sends a message:
-1. the message is wrapped as a `HumanMessage`
-2. the chatbot is invoked with `thread_id` and `user_id`
-3. the agent may call one or more tools
-4. the final AI response is displayed in the chat UI
-5. the conversation is stored under that user/thread pair
----
-## Gradio UI Features
-The Gradio app includes:
-- **User selector**
-- **New user button**
-- **Thread selector**
-- **New chat button**
-- **Chat area**
-- **Message input box**
-This makes it easy to simulate multiple users and sessions in one interface.
----
-## Showing Tool Calls in the UI
-Yes — this project can be extended to show:
-- which tool was called
-- tool arguments
-- tool output
-For example, if the chatbot uses `get_weather("Delhi")`, the UI can display something like:
-```text
-Calling tool: get_weather
-Arguments: {"city": "Delhi"}
-Output: Delhi: +34°C, Sunny
 ```
-This is useful for:
-- debugging
-- understanding agent behavior
-- teaching/demo purposes
-A good way to display this is by adding a `gr.Markdown()` or `gr.Textbox()` panel in the UI and parsing:
-- `AIMessage.tool_calls`
-- `ToolMessage.content`
----
-## Example Use Cases
-- Ask today’s date
-- Calculate expressions like `25 * 12`
-- Execute small Python snippets
-- Look up weather in a city
-- Search Wikipedia topics
-- Scrape a webpage summary
-- Read a local file
-- Format JSON data
-- Save user preferences
----
-## Example Prompts
-Try asking:
-```text
-What is today's date?
 ```
-```text
-Calculate 125 * 48
-```
-```text
-What is the weather in London?
-```
-```text
-Search Wikipedia for Artificial Intelligence
-```
-```text
-Read the contents of sample.txt
-```
-```text
-Format this JSON: {"name":"John","age":25}
-```
 ---
-## Security Warning
-This project includes some tools that are **unsafe for public deployment** without restrictions.
-### Risky tools
-- `calculator` uses `eval()`
-- `python_exec` uses `exec()`
-- `read_file` can access local files
-- `scrape_website` can fetch arbitrary URLs
-### Why this matters
-If exposed publicly, users may be able to:
-- run arbitrary Python code
-- read sensitive local files
-- access internal resources
-- misuse the server
-### Recommendation
-Use this project:
-- for **local development**
-- for **learning**
-- for **internal testing only**
-Before deploying publicly, you should:
-- remove `eval()` and `exec()`
-- restrict file access
-- validate URLs
-- sandbox execution
-- add authentication and rate limiting
-Tiny chatbot, big chaos potential. Better to keep the chaos leashed.
----
-## Known Notes
-### 1. Circular Import Risk
-If your UI file is named `memory_chatbot.py` and also contains:
-```python
-from memory_chatbot import chatbot
 ```
-you will likely get an import issue.
-### Fix
-Rename the Gradio UI file to something like:
-```text
-app.py
 ```
-and keep the chatbot backend in:
-```text
-memory_chatbot.py
-```
----
-### 2. Tool Results May Differ
-Some tools depend on external services:
-- `wttr.in`
-- `Wikipedia`
-- `Tavily`
-- websites being scraped
-If those services are unavailable, the tool may fail or return unexpected results.
----
-## Future Improvements
-Possible enhancements for this project:
-- real memory store integration
-- persistent database for chat history
-- streaming responses
-- live tool-call updates in the UI
-- better error handling
-- authentication
-- safer code execution sandbox
-- SQL generation using LLM
-- file upload support
-- improved styling for the chat interface
----
-## Sample `tools.py`
-This file contains all your custom tools used by the chatbot agent.
-It includes tools for:
-- date/time
-- calculator
-- Python execution
-- weather
-- Wikipedia
-- web scraping
-- file reading
-- JSON formatting
-- SQL generation
-- system information
-- user preference saving
-- Tavily search
----
-## Sample Workflow
-1. Start app
-2. Select or create a user
-3. Create a new thread
-4. Ask a question
-5. Agent decides whether to use tools
-6. Tool result is returned
-7. Final AI answer is shown
-8. Conversation history is stored per user/thread
----
-## Requirements Summary
-Main libraries used:
-- `gradio`
-- `langchain`
-- `langchain-core`
-- `langchain-tavily`
-- `requests`
-- `wikipedia`
-- `beautifulsoup4`
-- `python-dotenv`
 ---
-## License
-This project is for educational and development use.
-Add your preferred license here, for example:
-```text
-MIT License
-```
 ---
-## Author
-Built with LangChain, Gradio, and a healthy amount of agent curiosity.
-If you want, you can customize this section with your name, GitHub profile, or portfolio link.
 ---
-## Contributing
-Contributions are welcome.
-You can improve this project by:
-- adding safer tool implementations
-- improving UI design
-- adding persistent memory
-- improving tool logging
-- integrating streaming
-- adding tests
 ---
-## Final Notes
-This project is a great starting point if you want to learn:
-- LangChain tools
-- agent-based workflows
-- memory-aware chat apps
-- multi-thread chat management
-- Gradio interfaces
-If you are planning to deploy it publicly, please review the security concerns carefully before doing so.

 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🤖 Memory Chatbot
+A multi-user, multi-thread AI chatbot with **short-term and long-term memory**, powered by LangGraph, LangChain, Groq (Qwen3-32B), and a Gradio UI. The chatbot can call a variety of tools and displays which tools were used directly in the chat interface.
 ---
+## ✨ Features
+| Feature | Description |
+|---|---|
+| 🧠 Short-term memory | Remembers conversation context within a thread using `InMemorySaver` |
+| 💾 Long-term memory | Stores user preferences, profile, interests, and project info using `InMemoryStore` |
+| 👥 Multi-user support | Each user has isolated memory and conversation threads |
+| 🧵 Multi-thread support | Each user can have multiple independent chat sessions |
+| 🔧 Tool use | 12 built-in tools (web search, weather, calculator, Python exec, and more) |
+| 🏷️ Tool call badges | The UI shows which tools were called for each AI response |
+| ⚡ Fast inference | Powered by Groq's ultra-fast LLM API |
+---
+## 🗂️ Project Structure
 ```
+.
+├── memory_chatbot.py   # LangGraph graph: memory nodes + agent chat node
+├── app.py              # Gradio UI with multi-user / multi-thread support
+├── tool.py             # All tool definitions (12 tools)
+├── .env                # API keys (not committed)
+└── README.md
 ```
 ---
+## 🧩 Architecture
+```
+User Message
+     │
+     ▼
+┌─────────────────┐
+│ memory_analyzer │  ← Detects if message contains info worth storing long-term
+└────────┬────────┘
+         │
+         ▼
+┌──────────────────┐
+│ memory_retrieval │  ← Searches long-term store and injects relevant memories
+└────────┬─────────┘
+         │
+         ▼
+┌───────────────┐
+│   chat_node   │  ← LangChain agent with 12 tools; returns answer + tool log
+└───────────────┘
+         │
+         ▼
+    AI Response
+  + Tool Call Badges
+```
+### Memory Types
+- **Short-term (checkpointer):** Full conversation history per `thread_id`, managed automatically by LangGraph's `InMemorySaver`.
+- **Long-term (store):** Key-value memories namespaced by `(user_id, category)`. Categories: `preferences`, `profile`, `interests`, `project`.
 ---
+## 🔧 Tools
+| Tool | Description |
+|---|---|
+| `tool_tavily` | Web search via Tavily API |
+| `time_date` | Returns today's date |
+| `calculator` | Evaluates math expressions |
+| `python_exec` | Executes arbitrary Python code |
+| `get_weather` | Current weather for any city |
+| `wikipedia_search` | Wikipedia summary search |
+| `scrape_website` | Extracts text from a URL |
+| `read_file` | Reads a local file |
+| `format_json` | Pretty-prints JSON |
+| `generate_sql` | Converts natural language to SQL |
+| `system_info` | Returns OS/platform info |
+| `save_user_preference` | Saves a user preference to memory |
+---
+## 🚀 Setup
+### 1. Clone & install dependencies
 ```bash
+git clone <your-repo-url>
+cd memory-chatbot
 pip install -r requirements.txt
 ```
+**Key dependencies:**
+```
+langgraph
 langchain
+langchain-groq
 langchain-tavily
+gradio
 wikipedia
 beautifulsoup4
+requests
+python-dotenv
 ```
+### 2. Configure API keys
+Create a `.env` file in the project root:
 ```env
+GROQ_API_KEY=your_groq_api_key
 TAVILY_API_KEY=your_tavily_api_key
 ```
+Get your keys:
+- Groq: https://console.groq.com
+- Tavily: https://app.tavily.com
+### 3. Run
 ```bash
 python app.py
 ```
+Then open http://localhost:7860 in your browser.
 ---
+## 🖥️ UI Guide
 ```
+┌─────────────────┬──────────────────────────────────────┐
+│  👤 Users       │                                      │
+│  ┌──────────┐   │   [Chat history appears here]        │
+│  │ user_abc │   │                                      │
+│  └──────────┘   │   User: What's the weather in Paris? │
+│  [➕ New User]  │                                      │
+│                 │   AI: It's 18°C and sunny in Paris.  │
+│  🧵 Threads     │   🔧 get_weather                     │
+│  ○ thread_123   │                                      │
+│  ● thread_456   │                                      │
+│  [➕ New Chat]  │   ┌─────────────────────────────┐    │
+│                 │   │ Type message and press Enter │    │
+└─────────────────┴───┴─────────────────────────────┴────┘
 ```
+- **New User** — creates an isolated user with fresh memory and a new thread
+- **New Chat** — starts a new thread for the current user (long-term memory is preserved)
+- **Tool badges** — appear below each AI reply showing which tools were called
 ---
+## 🔍 How Tool Display Works
+When the agent calls tools, `chat_node` inspects the returned messages for `tool_calls` and `ToolMessage` entries and builds a `tool_log` list. The Gradio UI renders these as colored HTML badges inline in the chat (Gradio's `Chatbot` renders HTML by default).
+Example badge output:
 ```
+🔧 get_weather   🔧 wikipedia_search
 ```
 ---
+## 📝 Configuration
+| Parameter | Location | Default | Description |
+|---|---|---|---|
+| `model` | `memory_chatbot.py` | `qwen/qwen3-32b` | Groq model to use |
+| `temperature` | `memory_chatbot.py` | `0` | LLM temperature |
+| `max_results` | `tool.py` (Tavily) | `5` | Web search result count |
+| `memory categories` | `memory_chatbot.py` | preferences, profile, interests, project | Long-term memory namespaces |
 ---
+## ⚠️ Limitations
+- Memory is **in-process only** — all memory resets when the server restarts. For persistence, replace `InMemorySaver` and `InMemoryStore` with database-backed alternatives (e.g., `PostgresSaver`, `RedisStore`).
+- `python_exec` tool executes arbitrary code with no sandboxing — use with caution in production.
+- `scrape_website` does not handle JavaScript-rendered pages.
 ---
+## 🛠️ Extending
+**Add a new tool:** Define it with `@tool` in `tool.py`, then add it to the `tools` list in `memory_chatbot.py`.
+**Persist memory to a database:** Replace `InMemorySaver()` with a LangGraph-compatible checkpointer and `InMemoryStore()` with a persistent store.
+**Change the LLM:** Swap `ChatGroq` for any LangChain-compatible chat model (OpenAI, Anthropic, etc.).
 ---

README1.md ADDED Viewed

	@@ -0,0 +1,509 @@

+---
+title: Chatbot
+emoji: 💻
+colorFrom: yellow
+colorTo: green
+sdk: gradio
+sdk_version: 6.6.0
+app_file: app.py
+pinned: false
+short_description: Chatbot
+---
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Multi-User Memory Chatbot with LangChain, Tools, and Gradio
+A conversational AI chatbot built with **LangChain**, **Gradio**, and multiple custom tools.
+This project supports:
+- **multi-user chat sessions**
+- **multiple threads per user**
+- **memory-aware conversations**
+- **tool calling**
+- optional **tool activity display in the UI**
+It is useful for experimenting with AI agents that can search, calculate, scrape websites, fetch weather, read files, and more.
+---
+## Features
+- 👤 **Multiple users**
+- 🧵 **Multiple chat threads per user**
+- 🧠 **Conversation memory using `thread_id` and `user_id`**
+- 🛠 **Tool-enabled chatbot**
+- 🌐 **Web search using Tavily**
+- 📚 **Wikipedia search**
+- ☁️ **Weather lookup**
+- 🧮 **Calculator**
+- 🐍 **Python code execution**
+- 📄 **Read local files**
+- 🔎 **Scrape webpage text**
+- 🧾 **Pretty-print JSON**
+- 💾 **Save user preferences**
+- 🖥 **System info tool**
+- 🎨 **Gradio web interface**
+- ✅ Optional **tool activity panel** to show which tools were called
+---
+## Project Structure
+A recommended project structure:
+```text
+project/
+│
+├── tools.py               # All tool definitions
+├── memory_chatbot.py      # Chatbot / LangChain agent backend
+├── app.py                 # Gradio UI
+├── .env                   # Environment variables
+├── requirements.txt       # Python dependencies
+└── README.md
+```
+> **Important:**
+> If your UI file imports `chatbot` like this:
+```python
+from memory_chatbot import chatbot
+```
+then your UI file should **not** also be named `memory_chatbot.py`, otherwise you may get a **circular import error**.
+Use a separate file name such as `app.py` for the Gradio interface.
+---
+## Tools Included
+The project currently includes the following tools:
+### 1. `time_date`
+Returns today’s date in `yyyy-mm-dd` format.
+### 2. `calculator`
+Evaluates mathematical expressions.
+### 3. `python_exec`
+Executes Python code and returns local variables.
+### 4. `get_weather`
+Fetches current weather for a city using `wttr.in`.
+### 5. `wikipedia_search`
+Searches Wikipedia and returns a short summary.
+### 6. `scrape_website`
+Extracts text content from a webpage.
+### 7. `read_file`
+Reads the contents of a local file.
+### 8. `format_json`
+Formats and prettifies a JSON string.
+### 9. `generate_sql`
+Converts natural language into a placeholder SQL query.
+### 10. `system_info`
+Returns system/platform information.
+### 11. `save_user_preference`
+Stores user preference text (currently mocked).
+### 12. `tool_tavily`
+Performs web search using Tavily.
+---
+## Installation
+## 1. Clone the repository
+```bash
+git clone https://github.com/your-username/your-repo-name.git
+cd your-repo-name
+```
+## 2. Create a virtual environment
+### Windows
+```bash
+python -m venv venv
+venv\Scripts\activate
+```
+### macOS / Linux
+```bash
+python3 -m venv venv
+source venv/bin/activate
+```
+## 3. Install dependencies
+```bash
+pip install -r requirements.txt
+```
+If you do not already have a `requirements.txt`, you can use something like this:
+```txt
+gradio
+langchain
+langchain-core
+langchain-tavily
+python-dotenv
+requests
+wikipedia
+beautifulsoup4
+```
+Depending on your chatbot backend, you may also need your model provider package, such as:
+- `langchain-openai`
+- `langchain-google-genai`
+- `langchain-groq`
+- or another LLM integration
+---
+## Environment Variables
+Create a `.env` file in the root directory.
+Example:
+```env
+TAVILY_API_KEY=your_tavily_api_key
+OPENAI_API_KEY=your_openai_api_key
+```
+If you are using another model provider, replace `OPENAI_API_KEY` with the correct key for your setup.
+---
+## Running the App
+Start the Gradio interface with:
+```bash
+python app.py
+```
+Then open the local Gradio URL shown in the terminal, usually:
+```text
+http://127.0.0.1:7860
+```
+---
+## How It Works
+### User and Thread Management
+Each conversation is identified using:
+- `user_id`
+- `thread_id`
+This allows the chatbot to maintain separate memory for:
+- different users
+- different chat sessions for the same user
+### Chat Flow
+When the user sends a message:
+1. the message is wrapped as a `HumanMessage`
+2. the chatbot is invoked with `thread_id` and `user_id`
+3. the agent may call one or more tools
+4. the final AI response is displayed in the chat UI
+5. the conversation is stored under that user/thread pair
+---
+## Gradio UI Features
+The Gradio app includes:
+- **User selector**
+- **New user button**
+- **Thread selector**
+- **New chat button**
+- **Chat area**
+- **Message input box**
+This makes it easy to simulate multiple users and sessions in one interface.
+---
+## Showing Tool Calls in the UI
+Yes — this project can be extended to show:
+- which tool was called
+- tool arguments
+- tool output
+For example, if the chatbot uses `get_weather("Delhi")`, the UI can display something like:
+```text
+Calling tool: get_weather
+Arguments: {"city": "Delhi"}
+Output: Delhi: +34°C, Sunny
+```
+This is useful for:
+- debugging
+- understanding agent behavior
+- teaching/demo purposes
+A good way to display this is by adding a `gr.Markdown()` or `gr.Textbox()` panel in the UI and parsing:
+- `AIMessage.tool_calls`
+- `ToolMessage.content`
+---
+## Example Use Cases
+- Ask today’s date
+- Calculate expressions like `25 * 12`
+- Execute small Python snippets
+- Look up weather in a city
+- Search Wikipedia topics
+- Scrape a webpage summary
+- Read a local file
+- Format JSON data
+- Save user preferences
+---
+## Example Prompts
+Try asking:
+```text
+What is today's date?
+```
+```text
+Calculate 125 * 48
+```
+```text
+What is the weather in London?
+```
+```text
+Search Wikipedia for Artificial Intelligence
+```
+```text
+Read the contents of sample.txt
+```
+```text
+Format this JSON: {"name":"John","age":25}
+```
+---
+## Security Warning
+This project includes some tools that are **unsafe for public deployment** without restrictions.
+### Risky tools
+- `calculator` uses `eval()`
+- `python_exec` uses `exec()`
+- `read_file` can access local files
+- `scrape_website` can fetch arbitrary URLs
+### Why this matters
+If exposed publicly, users may be able to:
+- run arbitrary Python code
+- read sensitive local files
+- access internal resources
+- misuse the server
+### Recommendation
+Use this project:
+- for **local development**
+- for **learning**
+- for **internal testing only**
+Before deploying publicly, you should:
+- remove `eval()` and `exec()`
+- restrict file access
+- validate URLs
+- sandbox execution
+- add authentication and rate limiting
+Tiny chatbot, big chaos potential. Better to keep the chaos leashed.
+---
+## Known Notes
+### 1. Circular Import Risk
+If your UI file is named `memory_chatbot.py` and also contains:
+```python
+from memory_chatbot import chatbot
+```
+you will likely get an import issue.
+### Fix
+Rename the Gradio UI file to something like:
+```text
+app.py
+```
+and keep the chatbot backend in:
+```text
+memory_chatbot.py
+```
+---
+### 2. Tool Results May Differ
+Some tools depend on external services:
+- `wttr.in`
+- `Wikipedia`
+- `Tavily`
+- websites being scraped
+If those services are unavailable, the tool may fail or return unexpected results.
+---
+## Future Improvements
+Possible enhancements for this project:
+- real memory store integration
+- persistent database for chat history
+- streaming responses
+- live tool-call updates in the UI
+- better error handling
+- authentication
+- safer code execution sandbox
+- SQL generation using LLM
+- file upload support
+- improved styling for the chat interface
+---
+## Sample `tools.py`
+This file contains all your custom tools used by the chatbot agent.
+It includes tools for:
+- date/time
+- calculator
+- Python execution
+- weather
+- Wikipedia
+- web scraping
+- file reading
+- JSON formatting
+- SQL generation
+- system information
+- user preference saving
+- Tavily search
+---
+## Sample Workflow
+1. Start app
+2. Select or create a user
+3. Create a new thread
+4. Ask a question
+5. Agent decides whether to use tools
+6. Tool result is returned
+7. Final AI answer is shown
+8. Conversation history is stored per user/thread
+---
+## Requirements Summary
+Main libraries used:
+- `gradio`
+- `langchain`
+- `langchain-core`
+- `langchain-tavily`
+- `requests`
+- `wikipedia`
+- `beautifulsoup4`
+- `python-dotenv`
+---
+## License
+This project is for educational and development use.
+Add your preferred license here, for example:
+```text
+MIT License
+```
+---
+## Author
+Built with LangChain, Gradio, and a healthy amount of agent curiosity.
+If you want, you can customize this section with your name, GitHub profile, or portfolio link.
+---
+## Contributing
+Contributions are welcome.
+You can improve this project by:
+- adding safer tool implementations
+- improving UI design
+- adding persistent memory
+- improving tool logging
+- integrating streaming
+- adding tests
+---
+## Final Notes
+This project is a great starting point if you want to learn:
+- LangChain tools
+- agent-based workflows
+- memory-aware chat apps
+- multi-thread chat management
+- Gradio interfaces
+If you are planning to deploy it publicly, please review the security concerns carefully before doing so.