intelliagent 0.1.1
Intelligent Agent for Dynamic Decision Making
Requires Python: >=3.8

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<div align="center">
  <h1>🧩 IntelliAgent</h1>
  <h3>Intelligent Agent for Dynamic Decision Making</h3>
  <h3>CA: soon</h3>
  <p>
    IntelliAgent is an AI agent designed to make real-time, context-aware
    decisions based on evolving data streams. By integrating advanced reasoning
    capabilities with adaptive learning, IntelliAgent continuously refines its
    decision-making processes to deliver efficient, personalized solutions.
  </p>
  <a href="https://badge.fury.io/py/intelliagent"
    ><img
      src="https://img.shields.io/pypi/v/intelliagent?logo=pypi&logoColor=white&style=flat"
      alt="PyPI version"
  /></a>
  <a href="https://opensource.org/license/apache-2-0"
    ><img
      src="https://img.shields.io/badge/License-Apache-yellow.svg"
      alt="License: Apache 2"
  /></a>
</div>

## 🤔 Why IntelliAgent?

<p>
  Traditional AI agents often rely on static algorithms or predefined rules,
  making them limited in adapting to real-world complexity. IntelliAgent overcomes
  this limitation by incorporating an adaptive learning model that evolves with
  new inputs. Whether it's a business application, a personal assistant, or a
  complex problem-solving tool, IntelliAgent learns from each interaction and
  continuously optimizes its decision-making process.
</p>
<p>
  By making use of dynamic, real-time data and personalized experiences,
  IntelliAgent can effectively respond to a wide range of use cases. It doesn't
  just perform tasks—it learns, adapts, and evolves, making it an indispensable
  tool in various domains.
</p>

## 🚀 Quick Start 
1. Install IntelliAgent:

```bash
pip install intelliagent
````

2. Use it in your project:

```python
from intelliagent import DecisionMaker

# Set up the agent with a context and learning model
agent = DecisionMaker(api_key="provider-api-key",
                      model="gpt-4",
                      domain="financial advisor",
                      continuous_learning=True)
```

3. Provide real-time data and get dynamic decisions:

```python
user_id = "user456"
situation = "The stock market has shown a significant drop today."

# Get decision recommendation based on current context
decision = agent.make_decision(user_id=user_id, input_data=situation)
print(decision)  # Output: "It is advisable to diversify your investments to reduce risk."
```

## 🧩 Adaptive Learning

<p>IntelliAgent doesn't just make decisions based on pre-programmed rules—it learns from each interaction. Over time, it evolves its decision-making process, taking into account user-specific patterns and preferences.</p>

### Dynamic Learning Cycle:

1. **Data Input**: The agent receives real-time, context-rich input (e.g., user actions, environmental data).
2. **Context Analysis**: The agent analyzes the input and evaluates it against its existing knowledge base.
3. **Decision Generation**: Based on the analysis, the agent generates a decision, recommendation, or action.
4. **Feedback Loop**: The agent receives feedback on the decision and incorporates it into its learning model, continuously refining its approach.

## 🌟 Features

<table>
  <thead>
    <tr>
      <th>Feature</th>
      <th>Status</th>
      <th>Description</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>🧠 Adaptive Decision Making</td>
      <td>✅</td>
      <td>Makes dynamic decisions based on evolving context and user-specific data</td>
    </tr>
    <tr>
      <td>📊 Real-Time Data Processing</td>
      <td>✅</td>
      <td>Integrates with real-time data streams for actionable insights and timely recommendations</td>
    </tr>
    <tr>
      <td>🔄 Continuous Learning</td>
      <td>✅</td>
      <td>Learns from feedback and adapts its decision-making process with each interaction</td>
    </tr>
    <tr>
      <td>🤖 Intelligent Context Analysis</td>
      <td>✅</td>
      <td>Understands complex user situations and makes informed, personalized decisions</td>
    </tr>
    <tr>
      <td>🔗 Easy Integration</td>
      <td>✅</td>
      <td>Simple API integration for various applications such as personal assistants, business advisors, and more</td>
    </tr>
    <tr>
      <td>📈 Performance Optimization</td>
      <td>✅</td>
      <td>Optimizes decisions based on historical data, usage patterns, and feedback</td>
    </tr>
    <tr>
      <td>🔒 Secure and Private</td>
      <td>✅</td>
      <td>Ensures user privacy and data security in all decision-making processes</td>
    </tr>
    <tr>
      <td>🌐 Multi-Domain Support</td>
      <td>✅</td>
      <td>Capable of handling different domains like healthcare, finance, marketing, and more</td>
    </tr>
    <tr>
      <td>🌍 Global Context Awareness</td>
      <td>✅</td>
      <td>Makes decisions that account for global events and local trends</td>
    </tr>
    <tr>
      <td>🧠 Customizable Decision Models</td>
      <td>🔜</td>
      <td>Allows for user-defined models and decision parameters to suit specific needs</td>
    </tr>
  </tbody>
</table>

## 🛠️ API Reference

### DecisionMaker

- `make_decision(user_id: str, input_data: str) -> JSON`: Make a decision based on user data and context
- `update_model(user_id: str, feedback: str) -> JSON`: Update the decision model with user feedback
- `get_decision_context(user_id: str) -> str`: Retrieve the decision-making context for a user
- `batch_process(user_id: str, inputs: List[str]) -> JSON`: Process multiple pieces of input data at once

### Continuous Learning

<p>IntelliAgent evolves its decision-making model by learning from feedback provided after each decision. This feedback can be positive or negative, influencing the agent's learning process for better future decisions.</p>

```python
feedback = "The investment suggestion was great, I made a 10% profit."
agent.update_model(user_id="user456", feedback=feedback)
```

### Sync vs Async Updates

- **Asynchronous Updates (`AsyncDecisionMaker`)**: Perfect for high-performance applications or those that require real-time responsiveness. This method processes input and updates in a non-blocking fashion, enhancing overall performance.
- **Synchronous Updates (`DecisionMaker`)**: Ideal for applications that require immediate decision generation and feedback handling in a sequential order.

## 🤝 Contributing

<p>We welcome contributions! Whether you've found a bug, have a feature request, or want to improve the documentation, we appreciate your help in making IntelliAgent even better!</p>
<p>Open an issue or submit a pull request. Let's build smarter, adaptive AI systems together! 💪</p>

## 📄 License

<p>IntelliAgent is Apache-2.0 licensed. See the <a href="LICENSE">LICENSE</a> file for details.</p>

---

<p>Ready to elevate your AI agent with adaptive decision-making? Start using IntelliAgent today and let it transform your applications! 🚀 If you find it useful, give us a star! ⭐</p>

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