| Name | multi-agent-bot JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | https://github.com/jpalvarezb/multi-agent-bot |
| Summary | A multi-agent bot framework for research and automation |
| upload_time | 2025-08-18 22:09:29 |
| maintainer | None |
| docs_url | None |
| author | jpalvarezb |
| requires_python | >=3.11 |
| license | MIT License
Copyright (c) 2024 jpalvarezb
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
ai
agents
langchain
langgraph
chatbots
automation
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
fastapi
uvicorn
langchain
langchain-community
langchain-core
langchain-text-splitters
langgraph
langgraph-prebuilt
pydantic
python-dotenv
requests
databricks-sdk
databricks-ai-bridge
databricks-langchain
databricks-vectorsearch
numpy
pandas
scikit-learn
gunicorn
python-multipart
langchain-openai
langchain-tavily
botbuilder-core
botbuilder-schema
botframework-connector
aiohttp
PyJWT
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Multi-Agent Bot Framework
A modular AI-powered assistant framework featuring multiple specialized agents for market research, sales analysis, and web search capabilities.
## Features
- **Multi-Agent System**: Multiple specialized agents working together
- Market Study Agent (Experto Estudios)
- Sales Agent (Analista Ventas)
- Web Search Agent (Agente Web)
- Research Team coordination
- Response Synthesizer
- **FastAPI Backend**: RESTful API with automatic documentation
- **Vector Search**: Integration with vector databases for document retrieval
- **Web Search**: Integration with Tavily for real-time web search
- **Session Management**: User session tracking and context management
- **MLflow Integration**: Experiment tracking and cleanup utilities
## Architecture
The project follows a modular architecture with:
- `agents/`: Individual agent implementations
- `api/`: FastAPI application and engines
- `core/`: Base classes and abstractions
- `nodes/`: LangGraph node implementations
- `tools/`: External tool integrations
- `utils/`: Utility functions and helpers
## Requirements
- **Python 3.11+** (required for modern type annotation syntax like `Literal[*options]`)
- **direnv** (recommended for environment management)
- **pyenv** (recommended for Python version management)
## Installation
### 1. Set up Python Environment
**Option A: Using pyenv (recommended)**
```bash
# Install Python 3.11+ if not available
pyenv install 3.12.3 # or any Python 3.11+
pyenv local 3.12.3
# Verify version
python --version # Should show 3.11+
```
**Option B: System Python**
```bash
# Ensure you have Python 3.11+
python --version # Should show 3.11+
```
### 2. Install the Package
```bash
# Clone the repository
git clone https://github.com/jpalvarezb/multi-agent-bot.git
cd multi-agent-bot
# Install as editable package (enables clean imports)
pip install -e .
```
### 3. Configure Environment
**Option A: Using direnv (recommended)**
```bash
# Copy environment file
cp .env.example .env
# Edit .env with your API keys and configuration
# The .envrc file is already configured
direnv allow
```
**Option B: Manual environment setup**
```bash
# Copy and edit environment file
cp .env.example .env
# Edit .env with your API keys
# Load environment variables manually or use python-dotenv
export $(cat .env | xargs)
```
### 4. Run the Application
```bash
# Start the API server
uvicorn api.main:app --host 0.0.0.0 --port 8000 --reload
# Or run individual bots
python main/sales_bot.py "Show me sales data for 2024"
python main/market_study_bot.py "Analyze market trends"
```
## API Documentation
Once running, visit `http://localhost:8000/docs` for interactive API documentation.
## Usage
Send POST requests to `/api/query` with your questions about market research, competitors, and business intelligence.
## Configuration
The system uses environment variables for configuration. See `.env.example` for required variables.
Raw data
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"bugtrack_url": null,
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