README.md

metadata

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

🤖 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

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🗂️ 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

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🧩 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.

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🔧 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

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🚀 Setup

1. Clone & install dependencies

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:

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

python app.py

Then open http://localhost:7860 in your browser.

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🖥️ 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

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🔍 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

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📝 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

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⚠️ 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.

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🛠️ 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.).

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