| Name | adaptiq JSON |
| Version |
0.12.2
JSON |
| download |
| home_page | None |
| Summary | An offline Q-learning framework for AI agent prompt optimization. |
| upload_time | 2025-07-21 13:57:39 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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|
| keywords |
ai
agent
reinforcement learning
q-learning
prompt engineering
llm
crewai
|
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|
# AdaptIQ — Adaptive Optimization Framework for AI Agents
[](https://www.python.org/)
[](https://pypi.org/project/adaptiq)
[](#benchmarks--methodology)
[](#benchmarks--methodology)
[](LICENSE)
[](https://benchmyagent.com)
**AdaptIQ — Adaptive Optimization Framework for AI Agents – Optimize behaviors, reduce resource usage, and accelerate learning with low-cognitive reinforcement tuning.**
---
## 🚀 Quick Overview
AdaptIQ uses reinforcement learning to automatically optimize your AI agents. Point it at your agent's logs, and it learns which actions work best in different situations, reducing costs by 30% while improving performance.
**Key Benefits:** Lower costs, better performance, data-driven optimization
**Current Support:** CrewAI + OpenAI (more coming soon)
---
## 📋 Table of Contents
1. [🤔 Why AdaptiQ?](#-why-adaptiq)
2. [⚡ Quick Start](#-quick-start)
3. [✨ Features](#-features)
4. [🧠 How It Works (RL + Q-table)](#-how-it-works-rl--q-table)
5. [🏗️ Architecture](#️-architecture)
6. [📊 Reporting Mode](#-reporting-mode)
7. [🔮 What's Next](#-whats-next)
8. [🏆 Leaderboard (agents)](#-leaderboard-agents)
9. [🎯 Bench my agent](#-bench-my-agent)
10. [☁️ Upgrade Path → AdaptiQ FinOps Cloud](#️-upgrade-path--adaptiq-finops-cloud)
11. [🗺️ Roadmap](#️-roadmap)
12. [🤝 Community & Contributing](#-community--contributing)
13. [📄 License](#-license)
---
## 🤔 Why AdaptiQ?
AdaptIQ addresses the critical challenge of optimizing AI agent performance through intelligent, data-driven approaches. Our framework transforms the traditionally manual and error-prone process of agent tuning into a systematic, reinforcement learning-powered optimization workflow that learns from execution patterns and continuously improves agent behavior while reducing costs and resource consumption.
| Pain point | Traditional workaround | **AdaptiQ advantage** |
|------------|-----------------------|-----------------------|
| Prompt/agent errors discovered **after** expensive runs | Manual review, trial‑and‑error | Detects & fixes issues **before** execution |
| GPU/LLM cost spikes | Spreadsheet audits | Predicts € & CO₂ inline |
| No common prompt style | Word/PDF guidelines | Enforced by lint rules, autofixable |
| Dev ↔ FinOps gap | Slack + e‑mails | Same CLI / dashboard for both teams |
---
## 🎬 Demo Video
[](https://www.youtube.com/watch?v=ymNvLe73EhI)
*Click the image above to watch the demo video*
---
## ⚡ Quick Start
### 📋 Prerequisites
Before installing AdaptIQ, ensure you have:
- **Python 3.12+** - Required for AdaptIQ framework
- **CrewAI framework** - Set up and configured for your agents
- **OpenAI API key** - For LLM provider access
- **Windows OS** - Linux and Mac support not tested yet
### 📦 Installation
First, install UV package manager:
```bash
# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
```
> ⚠️ **Note**: Linux and Mac support is not tested yet. We recommend using Windows for now.
Then activate your virtual environment and install AdaptIQ:
```bash
uv pip install adaptiq
```
For development mode:
```bash
uv pip install -e .
```
### 🪄 Quick Setup
**Non-interactive mode (recommended for first-time users):**
```bash
adaptiq wizard-headless --llm_provider openai --api_key your_api_key --prompt "wizard init <name_of_project>"
```
> 📝 **Note**: Only OpenAI provider is supported for the wizard assistant currently.
This will initialize a project with `adaptiq_config.yml` that you should configure.
### 🎮 Running AdaptIQ
**Interactive mode (DEV Environment):**
```bash
wizard validate config <path_of_config>
wizard start # For first optimization
```
**Non-interactive mode (PROD Environment):**
```bash
adaptiq wizard-headless --llm_provider openai --api_key your_api_key --prompt "wizard execute <path_config>"
```
> 📝 **Important**: AdaptIQ currently supports only **CrewAI** as the agentic framework, **OpenAI** as the provider, and **GPT-4.1-mini** as the LLM for the workflow. Other models and frameworks have not been tested yet.
---
## ✨ Features
| Category | Free | Cloud (SaaS) |
|----------|------|--------------|
| 🧙 YAML wizard & validation | ✅ | ✅ |
| 🔍 Prompt & agent lint rules | ✅ | ✅ |
| 💰 **Pre‑run cost** | ✅ | ✅ |
| 🤖 RL‑powered optimisation suggestions | ✅ | ✅ |
| 🏭 Automatic optimisation at scale | — | ✅ |
| 💚 GPU‑spot arbitrage, ESG ledger | — | ✅ |
| 📊 Multi‑tenant FinOps dashboard | — | ✅ |
---
## 🧠 How It Works (RL + Q-table)
### 🎯 ADAPTIQ - Agent Development & Prompt Tuning Iteratively with Q-Learning
ADAPTIQ is a framework designed for the iterative improvement of AI agent performance through offline Reinforcement Learning (RL). Its primary goal is to systematically enhance an agent's guiding Configuration, focusing mainly on its Task Description (Prompt), by learning from the agent's past execution behaviors and incorporating user validation through an interactive "Wizard" process. It provides a structured, data-driven alternative to purely manual prompt engineering.
### 🚀 Vision and Goal
Adaptiq's mission is to optimize agent behavior by refining its core instructions (prompts/task descriptions). It achieves this by analyzing what an agent intended to do (from its prompt), what it actually did (from execution logs), and how effective those actions were (via a multi-faceted reward system). It is especially suited for agents using frameworks like CrewAI, LangChain, etc., where direct, real-time RL control is often impractical.
### 🔧 Key Concepts in Adaptiq
#### 🧩 State (s)
Represents the agent's situation at a specific step, defined by features like:
- **Current_SubTask**: The immediate objective (validated via Wizard)
- **Last_Action_Taken**: The previous validated ARIC strategic action
- **Last_Outcome**: The validated result of the previous action
- **Key_Context**: Accumulated relevant information (validated flags/data)
States are transformed into consistent, hashable representations for Q-table storage, potentially using generalization techniques.
#### 🎯 Action (a)
A selection from a predefined menu of discrete, strategic actions (e.g., Use_Tool_X, Action_Write_Content). Adaptiq maps observed log events to these predefined actions.
#### 📊 Q-Table
The core knowledge base: `Q(state_representation, action) → value`. It stores the learned long-term value of taking an action in a specific state, refined through the Adaptiq loop.
#### 🏆 Reward (R)
Calculated offline during/after trace reconciliation, guided by the Wizard and predefined rules. It incorporates:
- **Plan Adherence**: How well the actual execution matched the intended plan from prompt parsing
- **Execution Success (R_execution/internal)**: Based on tool outcomes, task progress, constraint adherence, and output quality from the logs
- **External Feedback (R_external - Optional)**: Real-world impact metrics (e.g., email open rates, conversions). To be implemented soon (now as external feedback only human feedback of user's evaluation of the agent after adaptiq optimization)
### 🛠️ Trace Analysis & Reconciliation Strategy
Adaptiq employs a multi-stage approach:
1. **Prompt Parsing (default-run)**: An LLM-powered module analyzes the agent's task description to extract the intended sequence of sub-tasks and actions
2. **Hypothetical State Generation (default-run)**: Uses the prompt parser's output to define idealized states and actions for heuristic Q-table initialization
3. **Log Parsing (first step of the run)**: Module parses raw execution logs to identify actual agent thoughts, tool calls, and outcomes
4. **Reconciliation (second step of the run)**: A central facilitates the alignment of the intended plan with actual execution. It allows the user to:
- Validate/correct inferred states and actions
- Confirm/override calculated rewards
- Refine the understanding of the agent's behavior
This produces the mapping data.
**Lightweight Q‑table examples:**
| State | Action | Q‑value |
|-------|--------|---------|
| `('InformationRetrieval_Company', 'None', 'None', 'company info')` | FileReadTool | **0.6** |
| `('InformationRetrieval_Lead', 'FileReadTool', 'Success_DataFound', 'company info lead name')` | LeadNameTool | **0.7** |
| `('ActionExecution_SendEmail', 'Write_Email_Body', 'Success_ActionCompleted', 'email sent lead')` | SendEmailTool | **0.7** |
| `('ResultFinalization', 'SendEmailTool', 'Success_ActionCompleted', 'email content final answer')` | Formulate_Final_Answer | **0.8** |
---
## 🏗️ Architecture
---
## 📊 Reporting Mode
AdaptIQ offers flexible reporting options:
### 💾 Local Reporting
- Save optimization reports locally as Markdown
- Detailed performance metrics and recommendations
- Offline analysis capabilities
### 📧 Email Reports
- Send comprehensive reports to your email
- URL-based report sharing
- Real-time optimization insights (multiple)
> 📝 **Privacy Note**: When you provide your email in the YAML config, you acknowledge that we can process your data according to our privacy policy.
---
## 🔮 What's Next
### 🎯 Upcoming Features
- **🔄 Support for More Models and Providers**: Expanding compatibility beyond OpenAI to include other LLM providers and models
- **🔄 Context Engineering Optimization**: Advanced prompt and context management through Q-learning
- **📝 Prompt Optimization Workflow**: Implementing external rewards data type and tool tracking and evaluation
- **📚 Q-Table Strategy for RAG Systems**: Learn which effective chunks reduce cost and increase speed
- **🧠 Memory Layer Integration**: Q-table learns optimal context retention patterns - storing frequently accessed information states and reducing redundant retrievals through intelligent caching strategies
- **📊 Knowledge Graph Integration**: Dynamic relationship mapping between entities and concepts for contextually-aware agent decisions
- **🔌 External Context Integration APIs**: Seamless integration with CRM, databases, and third-party tools for enriched contextual understanding
- **🛡️ Governance & Constraints**:
- **🚧 Guardrails**: Q-learning enforced safety boundaries and compliance rules
- **🔐 Access Control**: Context-aware permission management
- **📋 Policy Enforcement**: Automated adherence to organizational guidelines and industry standards
- **📱 Q-Table for Edge Devices**: Optimizing AI models performance to work better on resource-constrained devices
---
## 🏆 Leaderboard (agents) - Coming Soon
A comprehensive evaluation system to benchmark your agents based on specific KPIs (Health Learning Index HLI). Agents working on the same tasks can anonymously compare their performance, fostering continuous improvement and healthy competition in the AI agent community. This system helps maintain agent quality in production environments through continuous monitoring and benchmarking.
---
## 🎯 Bench my agent
**🚀 Build better AI agents. Use AdaptiQ and see your Agent Learning Health Index**
| ⚙️ | Benefit | Description |
|-------|---------|-------------|
| 🏅 **Social proof** | Public badge increases repo trust |
| 💰 **FinOps insight** | Cost €/k-token & CO₂/tkn surfaced instantly |
| 🔒 **Security gate** | Evaluator flags jailbreaks & PII leaks before prod |
| ♻️ **Continuous learning** | LHI tracks the agent's health across versions |
### 🎬 See the leaderboard in action
---
## ☁️ Upgrade Path → AdaptiQ FinOps Cloud
Need hands‑free optimisation across hundreds of projects? 🏢
**AdaptiQ FinOps Cloud** adds:
* 🤖 Auto‑tuning RL in production
* 💎 GPU‑spot arbitrage
* 🌱 ESG & carbon ledger
* 👥 Role‑based dashboards (Dev / FinOps / C‑suite)
**🆓 30‑day free trial** — migrate in **one CLI command**.
**Contact us for more information via email**
---
## 🗺️ Roadmap
| Quarter | Milestone |
|---------|-----------|
| **Q3 2025** | 🔄 Support for More Models and Providers & Cost optimization via LLM routing |
| **Q4 2025** | 🔄 Context Engineering Optimization: Memory Layer, Knowledge Graphs, External API Integration |
| **2026** | 📱 Edge SDK (quantised Q‑table <16 MB), 🛡️ Governance & Constraints framework, GPU‑Spot optimiser |
Vote or propose features in [`discussions/`](https://github.com/adaptiq-ai/adaptiq/discussions). 🗳️
---
## 🤝 Community & Contributing
We ❤️ PRs: bug fixes, lint rules, language support.
See [`CONTRIBUTING.md`](./CONTRIBUTING.md).
* 💬 **Discord**: [**#adaptiq**](https://discord.com/invite/tZZUvcSY) (roadmap call 1st Tuesday)
* 🐦 **X/Twitter**: [@adaptiq_ai](https://x.com/adaptiq_ai)
---
## 🧪 Beta Version Notice
AdaptIQ is currently in **beta version**. We welcome any issues, bug reports, or contributions to improve the framework! Your feedback helps us build a better tool for the AI agent community. 🙏
Please feel free to:
- 🐛 Report bugs via GitHub Issues
- 💡 Suggest new features
- 🤝 Contribute code improvements
- 📝 Improve documentation
Together, we can make AdaptIQ the best optimization framework for AI agents! 🚀
## 📚 Citation
If you use AdaptIQ in your research, project, or commercial application, please cite us:
### 📖 BibTeX
```bibtex
@software{adaptiq2025,
title={AdaptIQ: Adaptive Optimization Framework for AI Agents},
author={AdaptIQ AI Team},
year={2025},
url={https://github.com/adaptiq-ai/adaptiq},
note={Adaptive Optimization Framework for AI Agents with Reinforcement Learning}
}
```
### 🔗 MLA Format
AdaptIQ AI Team. "AdaptIQ: Adaptive Optimization Framework for AI Agents." GitHub, 2025, https://github.com/adaptiq-ai/adaptiq.
### 📊 Research Papers
If you publish research using AdaptIQ, we'd love to hear about it! Please:
- 📧 Email us at research@getadaptiq.io
- 🐦 Tag us on Twitter [@adaptiq_ai](https://x.com/adaptiq_ai)
- 💬 Share in our Discord **#research** channel
---
## 📄 License
* **Code**: Apache 2.0 License 🆓
* **RL weights & FinOps Cloud components**: proprietary
© 2025 AdaptiQ AI. All trademarks belong to their respective owners.
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"description": "# AdaptIQ \u2014 Adaptive Optimization Framework for AI Agents\n\n[](https://www.python.org/)\n[](https://pypi.org/project/adaptiq)\n[](#benchmarks--methodology)\n[](#benchmarks--methodology)\n[](LICENSE)\n[](https://benchmyagent.com)\n\n**AdaptIQ \u2014 Adaptive Optimization Framework for AI Agents \u2013 Optimize behaviors, reduce resource usage, and accelerate learning with low-cognitive reinforcement tuning.**\n\n---\n\n## \ud83d\ude80 Quick Overview\n\nAdaptIQ uses reinforcement learning to automatically optimize your AI agents. Point it at your agent's logs, and it learns which actions work best in different situations, reducing costs by 30% while improving performance.\n\n**Key Benefits:** Lower costs, better performance, data-driven optimization \n**Current Support:** CrewAI + OpenAI (more coming soon)\n\n---\n\n## \ud83d\udccb Table of Contents\n1. [\ud83e\udd14 Why AdaptiQ?](#-why-adaptiq)\n2. [\u26a1 Quick Start](#-quick-start)\n3. [\u2728 Features](#-features)\n4. [\ud83e\udde0 How It Works (RL + Q-table)](#-how-it-works-rl--q-table)\n5. [\ud83c\udfd7\ufe0f Architecture](#\ufe0f-architecture)\n6. [\ud83d\udcca Reporting Mode](#-reporting-mode)\n7. [\ud83d\udd2e What's Next](#-whats-next)\n8. [\ud83c\udfc6 Leaderboard (agents)](#-leaderboard-agents)\n9. [\ud83c\udfaf Bench my agent](#-bench-my-agent)\n10. [\u2601\ufe0f Upgrade Path \u2192 AdaptiQ FinOps Cloud](#\ufe0f-upgrade-path--adaptiq-finops-cloud)\n11. [\ud83d\uddfa\ufe0f Roadmap](#\ufe0f-roadmap)\n12. [\ud83e\udd1d Community & Contributing](#-community--contributing)\n13. [\ud83d\udcc4 License](#-license)\n\n---\n\n## \ud83e\udd14 Why AdaptiQ?\n\nAdaptIQ addresses the critical challenge of optimizing AI agent performance through intelligent, data-driven approaches. Our framework transforms the traditionally manual and error-prone process of agent tuning into a systematic, reinforcement learning-powered optimization workflow that learns from execution patterns and continuously improves agent behavior while reducing costs and resource consumption.\n\n| Pain point | Traditional workaround | **AdaptiQ advantage** |\n|------------|-----------------------|-----------------------|\n| Prompt/agent errors discovered **after** expensive runs | Manual review, trial\u2011and\u2011error | Detects & fixes issues **before** execution |\n| GPU/LLM cost spikes | Spreadsheet audits | Predicts \u20ac & CO\u2082 inline |\n| No common prompt style | Word/PDF guidelines | Enforced by lint rules, autofixable |\n| Dev \u2194 FinOps gap | Slack + e\u2011mails | Same CLI / dashboard for both teams |\n\n---\n\n## \ud83c\udfac Demo Video\n\n[](https://www.youtube.com/watch?v=ymNvLe73EhI)\n\n*Click the image above to watch the demo video*\n\n---\n\n## \u26a1 Quick Start\n\n### \ud83d\udccb Prerequisites\n\nBefore installing AdaptIQ, ensure you have:\n\n- **Python 3.12+** - Required for AdaptIQ framework\n- **CrewAI framework** - Set up and configured for your agents\n- **OpenAI API key** - For LLM provider access\n- **Windows OS** - Linux and Mac support not tested yet\n\n### \ud83d\udce6 Installation\n\nFirst, install UV package manager:\n\n```bash\n# Windows (PowerShell)\npowershell -ExecutionPolicy ByPass -c \"irm https://astral.sh/uv/install.ps1 | iex\"\n```\n\n> \u26a0\ufe0f **Note**: Linux and Mac support is not tested yet. We recommend using Windows for now.\n\nThen activate your virtual environment and install AdaptIQ:\n\n```bash\nuv pip install adaptiq\n```\n\nFor development mode:\n```bash\nuv pip install -e .\n```\n\n### \ud83e\ude84 Quick Setup\n\n**Non-interactive mode (recommended for first-time users):**\n\n```bash\nadaptiq wizard-headless --llm_provider openai --api_key your_api_key --prompt \"wizard init <name_of_project>\"\n```\n\n> \ud83d\udcdd **Note**: Only OpenAI provider is supported for the wizard assistant currently.\n\nThis will initialize a project with `adaptiq_config.yml` that you should configure.\n\n### \ud83c\udfae Running AdaptIQ\n\n**Interactive mode (DEV Environment):**\n```bash\nwizard validate config <path_of_config>\nwizard start # For first optimization\n```\n\n**Non-interactive mode (PROD Environment):**\n```bash\nadaptiq wizard-headless --llm_provider openai --api_key your_api_key --prompt \"wizard execute <path_config>\"\n```\n\n> \ud83d\udcdd **Important**: AdaptIQ currently supports only **CrewAI** as the agentic framework, **OpenAI** as the provider, and **GPT-4.1-mini** as the LLM for the workflow. Other models and frameworks have not been tested yet.\n\n---\n\n## \u2728 Features\n\n| Category | Free | Cloud (SaaS) |\n|----------|------|--------------|\n| \ud83e\uddd9 YAML wizard & validation | \u2705 | \u2705 |\n| \ud83d\udd0d Prompt & agent lint rules | \u2705 | \u2705 |\n| \ud83d\udcb0 **Pre\u2011run cost** | \u2705 | \u2705 |\n| \ud83e\udd16 RL\u2011powered optimisation suggestions | \u2705 | \u2705 |\n| \ud83c\udfed Automatic optimisation at scale | \u2014 | \u2705 |\n| \ud83d\udc9a GPU\u2011spot arbitrage, ESG ledger | \u2014 | \u2705 |\n| \ud83d\udcca Multi\u2011tenant FinOps dashboard | \u2014 | \u2705 |\n\n---\n\n## \ud83e\udde0 How It Works (RL + Q-table)\n\n### \ud83c\udfaf ADAPTIQ - Agent Development & Prompt Tuning Iteratively with Q-Learning\n\nADAPTIQ is a framework designed for the iterative improvement of AI agent performance through offline Reinforcement Learning (RL). Its primary goal is to systematically enhance an agent's guiding Configuration, focusing mainly on its Task Description (Prompt), by learning from the agent's past execution behaviors and incorporating user validation through an interactive \"Wizard\" process. It provides a structured, data-driven alternative to purely manual prompt engineering.\n\n### \ud83d\ude80 Vision and Goal\n\nAdaptiq's mission is to optimize agent behavior by refining its core instructions (prompts/task descriptions). It achieves this by analyzing what an agent intended to do (from its prompt), what it actually did (from execution logs), and how effective those actions were (via a multi-faceted reward system). It is especially suited for agents using frameworks like CrewAI, LangChain, etc., where direct, real-time RL control is often impractical.\n\n### \ud83d\udd27 Key Concepts in Adaptiq\n\n#### \ud83e\udde9 State (s)\nRepresents the agent's situation at a specific step, defined by features like:\n\n- **Current_SubTask**: The immediate objective (validated via Wizard)\n- **Last_Action_Taken**: The previous validated ARIC strategic action\n- **Last_Outcome**: The validated result of the previous action\n- **Key_Context**: Accumulated relevant information (validated flags/data)\n\nStates are transformed into consistent, hashable representations for Q-table storage, potentially using generalization techniques.\n\n#### \ud83c\udfaf Action (a)\nA selection from a predefined menu of discrete, strategic actions (e.g., Use_Tool_X, Action_Write_Content). Adaptiq maps observed log events to these predefined actions.\n\n#### \ud83d\udcca Q-Table\nThe core knowledge base: `Q(state_representation, action) \u2192 value`. It stores the learned long-term value of taking an action in a specific state, refined through the Adaptiq loop.\n\n#### \ud83c\udfc6 Reward (R)\nCalculated offline during/after trace reconciliation, guided by the Wizard and predefined rules. It incorporates:\n\n- **Plan Adherence**: How well the actual execution matched the intended plan from prompt parsing\n- **Execution Success (R_execution/internal)**: Based on tool outcomes, task progress, constraint adherence, and output quality from the logs\n- **External Feedback (R_external - Optional)**: Real-world impact metrics (e.g., email open rates, conversions). To be implemented soon (now as external feedback only human feedback of user's evaluation of the agent after adaptiq optimization)\n\n### \ud83d\udee0\ufe0f Trace Analysis & Reconciliation Strategy\n\nAdaptiq employs a multi-stage approach:\n\n1. **Prompt Parsing (default-run)**: An LLM-powered module analyzes the agent's task description to extract the intended sequence of sub-tasks and actions\n\n2. **Hypothetical State Generation (default-run)**: Uses the prompt parser's output to define idealized states and actions for heuristic Q-table initialization\n\n3. **Log Parsing (first step of the run)**: Module parses raw execution logs to identify actual agent thoughts, tool calls, and outcomes\n\n4. **Reconciliation (second step of the run)**: A central facilitates the alignment of the intended plan with actual execution. It allows the user to:\n - Validate/correct inferred states and actions\n - Confirm/override calculated rewards\n - Refine the understanding of the agent's behavior\n \n This produces the mapping data.\n\n**Lightweight Q\u2011table examples:**\n\n| State | Action | Q\u2011value |\n|-------|--------|---------|\n| `('InformationRetrieval_Company', 'None', 'None', 'company info')` | FileReadTool | **0.6** |\n| `('InformationRetrieval_Lead', 'FileReadTool', 'Success_DataFound', 'company info lead name')` | LeadNameTool | **0.7** |\n| `('ActionExecution_SendEmail', 'Write_Email_Body', 'Success_ActionCompleted', 'email sent lead')` | SendEmailTool | **0.7** |\n| `('ResultFinalization', 'SendEmailTool', 'Success_ActionCompleted', 'email content final answer')` | Formulate_Final_Answer | **0.8** |\n\n---\n\n## \ud83c\udfd7\ufe0f Architecture\n\n\n\n---\n\n## \ud83d\udcca Reporting Mode\n\nAdaptIQ offers flexible reporting options:\n\n### \ud83d\udcbe Local Reporting\n- Save optimization reports locally as Markdown\n- Detailed performance metrics and recommendations\n- Offline analysis capabilities\n\n### \ud83d\udce7 Email Reports\n- Send comprehensive reports to your email\n- URL-based report sharing\n- Real-time optimization insights (multiple)\n\n> \ud83d\udcdd **Privacy Note**: When you provide your email in the YAML config, you acknowledge that we can process your data according to our privacy policy.\n\n\n\n---\n\n## \ud83d\udd2e What's Next\n\n### \ud83c\udfaf Upcoming Features\n\n- **\ud83d\udd04 Support for More Models and Providers**: Expanding compatibility beyond OpenAI to include other LLM providers and models\n- **\ud83d\udd04 Context Engineering Optimization**: Advanced prompt and context management through Q-learning\n - **\ud83d\udcdd Prompt Optimization Workflow**: Implementing external rewards data type and tool tracking and evaluation\n - **\ud83d\udcda Q-Table Strategy for RAG Systems**: Learn which effective chunks reduce cost and increase speed\n - **\ud83e\udde0 Memory Layer Integration**: Q-table learns optimal context retention patterns - storing frequently accessed information states and reducing redundant retrievals through intelligent caching strategies\n - **\ud83d\udcca Knowledge Graph Integration**: Dynamic relationship mapping between entities and concepts for contextually-aware agent decisions\n - **\ud83d\udd0c External Context Integration APIs**: Seamless integration with CRM, databases, and third-party tools for enriched contextual understanding\n - **\ud83d\udee1\ufe0f Governance & Constraints**: \n - **\ud83d\udea7 Guardrails**: Q-learning enforced safety boundaries and compliance rules\n - **\ud83d\udd10 Access Control**: Context-aware permission management\n - **\ud83d\udccb Policy Enforcement**: Automated adherence to organizational guidelines and industry standards\n- **\ud83d\udcf1 Q-Table for Edge Devices**: Optimizing AI models performance to work better on resource-constrained devices\n\n---\n\n## \ud83c\udfc6 Leaderboard (agents) - Coming Soon\n\nA comprehensive evaluation system to benchmark your agents based on specific KPIs (Health Learning Index HLI). Agents working on the same tasks can anonymously compare their performance, fostering continuous improvement and healthy competition in the AI agent community. This system helps maintain agent quality in production environments through continuous monitoring and benchmarking.\n\n---\n\n## \ud83c\udfaf Bench my agent\n\n**\ud83d\ude80 Build better AI agents. Use AdaptiQ and see your Agent Learning Health Index**\n\n| \u2699\ufe0f | Benefit | Description |\n|-------|---------|-------------|\n| \ud83c\udfc5 **Social proof** | Public badge increases repo trust |\n| \ud83d\udcb0 **FinOps insight** | Cost \u20ac/k-token & CO\u2082/tkn surfaced instantly |\n| \ud83d\udd12 **Security gate** | Evaluator flags jailbreaks & PII leaks before prod |\n| \u267b\ufe0f **Continuous learning** | LHI tracks the agent's health across versions |\n\n### \ud83c\udfac See the leaderboard in action\n\n\n\n---\n\n## \u2601\ufe0f Upgrade Path \u2192 AdaptiQ FinOps Cloud\n\nNeed hands\u2011free optimisation across hundreds of projects? \ud83c\udfe2 \n**AdaptiQ FinOps Cloud** adds:\n\n* \ud83e\udd16 Auto\u2011tuning RL in production \n* \ud83d\udc8e GPU\u2011spot arbitrage \n* \ud83c\udf31 ESG & carbon ledger \n* \ud83d\udc65 Role\u2011based dashboards (Dev / FinOps / C\u2011suite)\n\n**\ud83c\udd93 30\u2011day free trial** \u2014 migrate in **one CLI command**.\n\n**Contact us for more information via email**\n\n---\n\n## \ud83d\uddfa\ufe0f Roadmap\n\n| Quarter | Milestone |\n|---------|-----------|\n| **Q3 2025** | \ud83d\udd04 Support for More Models and Providers & Cost optimization via LLM routing |\n| **Q4 2025** | \ud83d\udd04 Context Engineering Optimization: Memory Layer, Knowledge Graphs, External API Integration |\n| **2026** | \ud83d\udcf1 Edge SDK (quantised Q\u2011table <16 MB), \ud83d\udee1\ufe0f Governance & Constraints framework, GPU\u2011Spot optimiser |\n\nVote or propose features in [`discussions/`](https://github.com/adaptiq-ai/adaptiq/discussions). \ud83d\uddf3\ufe0f\n\n---\n\n## \ud83e\udd1d Community & Contributing\n\nWe \u2764\ufe0f PRs: bug fixes, lint rules, language support. \nSee [`CONTRIBUTING.md`](./CONTRIBUTING.md).\n\n* \ud83d\udcac **Discord**: [**#adaptiq**](https://discord.com/invite/tZZUvcSY) (roadmap call 1st Tuesday) \n* \ud83d\udc26 **X/Twitter**: [@adaptiq_ai](https://x.com/adaptiq_ai)\n\n---\n\n## \ud83e\uddea Beta Version Notice\n\nAdaptIQ is currently in **beta version**. We welcome any issues, bug reports, or contributions to improve the framework! Your feedback helps us build a better tool for the AI agent community. \ud83d\ude4f\n\nPlease feel free to:\n- \ud83d\udc1b Report bugs via GitHub Issues\n- \ud83d\udca1 Suggest new features\n- \ud83e\udd1d Contribute code improvements\n- \ud83d\udcdd Improve documentation\n\nTogether, we can make AdaptIQ the best optimization framework for AI agents! \ud83d\ude80\n\n## \ud83d\udcda Citation\n\nIf you use AdaptIQ in your research, project, or commercial application, please cite us:\n\n### \ud83d\udcd6 BibTeX\n\n```bibtex\n@software{adaptiq2025,\n title={AdaptIQ: Adaptive Optimization Framework for AI Agents},\n author={AdaptIQ AI Team},\n year={2025},\n url={https://github.com/adaptiq-ai/adaptiq},\n note={Adaptive Optimization Framework for AI Agents with Reinforcement Learning}\n}\n```\n### \ud83d\udd17 MLA Format\n\nAdaptIQ AI Team. \"AdaptIQ: Adaptive Optimization Framework for AI Agents.\" GitHub, 2025, https://github.com/adaptiq-ai/adaptiq.\n\n### \ud83d\udcca Research Papers\n\nIf you publish research using AdaptIQ, we'd love to hear about it! Please:\n- \ud83d\udce7 Email us at research@getadaptiq.io\n- \ud83d\udc26 Tag us on Twitter [@adaptiq_ai](https://x.com/adaptiq_ai)\n- \ud83d\udcac Share in our Discord **#research** channel\n\n---\n\n## \ud83d\udcc4 License\n\n* **Code**: Apache 2.0 License \ud83c\udd93\n* **RL weights & FinOps Cloud components**: proprietary\n\n\u00a9 2025 AdaptiQ AI. All trademarks belong to their respective owners.\n",
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