Metadata-Version: 2.4
Name: oraicle
Version: 3.2.0
Summary: Oraicle-Agent — Turn any Google-ADK Agent into a root_agent dynamically.
Author-email: Zack Mariano <zack.cmariano@oraicle.ai>
License: MIT License
        
        Copyright (c) 2026 Zack
        
        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.
        
Project-URL: Homepage, https://github.com/zackcmariano/oraicle-agent
Project-URL: Documentation, https://github.com/zackcmariano/oraicle-agent
Project-URL: Issues, https://github.com/zackcmariano/oraicle-agent/issues
Keywords: google-adk,agent,ai-agents,vertex-ai,multi-agent,a2a
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: google-adk>=1.16.0
Dynamic: license-file

# Oraicle-Agent

![Oraicle Agent](https://raw.githubusercontent.com/zackcmariano/oraicle-agent/refs/heads/master/assets/oraicle-agent-lib.png)


> **Turn any Google-ADK Agent into a Root Agent - instantly.**

Oraicle-Agent is a **groundbreaking Python library** that extends the Google Agent Development Kit (Google-ADK), enabling **any agent to become a `root_agent` dynamically**, without hacks, forks, or duplicated apps.

This unlocks a **new architectural paradigm** for multi-agent systems:
- Multiple conversational entrypoints
- Direct chat with sub-agents
- True Agent-to-Agent (A2A) orchestration
- Seamless deployment on **Vertex AI Agent Engine**

---

## 🚀 Why Oraicle-Agent exists

Google-ADK assumes:
- One `root_agent` per application
- Sub-agents can’t be direct entrypoints
- One conversational domain per deployment

**Oraicle-Agent breaks this limitation - intentionally and cleanly.**

With Oraicle-Agent, **any agent can become the root of a conversation**, while still participating in a larger A2A system.

> 🔥 This is not a hack.  
> 🔥 This is not a fork.  
> 🔥 This is a new abstraction layer.

---

## ✨ What makes Oraicle-Agent unique?

### ✅ Dynamic Root Agents
Choose *at runtime* which agent owns the conversation.

### ✅ Direct Sub-Agent Chat
Users can talk directly to a specialized agent (teacher, assistant, expert) **without losing context isolation**.

### ✅ A2A-First Architecture
Root agents orchestrate other agents, while still being callable as standalone conversational entities.

### ✅ Google-ADK & Vertex AI Compatible
Works with:
- `adk web`
- ADK loader
- Vertex AI Agent Engine

---

## 🧩 The core idea (one line)

> **Any `Agent` can be a `root_agent` if it is explicitly declared as one.**

Oraicle-Agent formalizes this idea.

---

## 📦 Installation

```bash
pip install oraicle
```

---

## ⚡ Quick Start

```python
from oraicle import autoagent
from google.adk.agents import Agent

history_teacher = Agent(
    name="history_teacher",
    model="gemini-2.0-flash",
    instruction="You are a history teacher."
)

autoagent(history_teacher)
```

---

## ☁️ Deploying to Vertex AI Agent Engine

```python
from app.sub_agents.history_teacher.agent import history_teacher
```

---

## 👤 Identifying the user in Agent Engine Sessions (User ID)

When your agent is deployed on **Vertex AI Agent Engine**, each chat creates a **Session**. In the Agent Engine UI, sessions have default fields like:

- Session ID
- Display name
- **User ID**
- Created
- Last active

By default, many deployments end up with a generic/opaque `User ID`. This makes it hard to audit who started each conversation in:

- Agent Engine **Playground**
- **Gemini Enterprise** chat

Oraicle provides an **optional** helper to resolve a human-readable `user_id` (usually an **email** or **name**) from:

- An explicit parameter you already have (e.g. `user`)
- Request headers (e.g. Google/IAP/forwarded identity headers)
- A JWT found in `Authorization: Bearer ...` (decoded only; no signature verification)

### ✅ How to activate (opt-in)

Coloque isso no seu `agent.py` (padrão de uso):

```python
from google.adk.agents import Agent
from oraicle.adk import get_agent_engine_user_id
from oraicle.adk.local import configure_local_adk_web

# (Opcional) recomendado para rodar `adk web` local sem ruídos no log
configure_local_adk_web()

root_agent = Agent(
    name="diretorescola",
    model="gemini-2.0-flash",
    instruction=AGENT_PROMPT,
)

# (Opcional) facilita importar os 2 símbolos do mesmo arquivo:
__all__ = ["root_agent", "get_agent_engine_user_id"]
```

### ✅ Using headers (when available)

Na hora de iniciar/rodar uma Session (runtime), use a função assim:

```python
user_id = get_agent_engine_user_id(
    request=request,          # lê request.headers (Cloud Run / IAP / proxies)
    default="anonymous",
)
```

### Notes

- **Backward compatible**: this feature is **100% opt-in** (it does nothing unless you import and use it).
- **Security**: JWT decoding here is for **display/identification only**. Do not use it for authorization decisions.

---

## ☎️ Calling an Agent Engine deployed agent (backend-to-agent)

If you need to call an agent deployed on **Vertex AI Agent Engine** from your backend (like the official template examples), Oraicle provides small helpers:

```python
import os
from oraicle.adk import get_adk_app_from_agent_engine, async_stream_query_text

async def call_agent(user_input: str, user_id: str) -> str:
    adk_app = get_adk_app_from_agent_engine(
        agent_display_name="genaiagent-sgen-base-cicd",
        project=os.environ["AGENT_ENGINE_PROJECT"],
        location=os.environ["LOCATION"],
    )
    return await async_stream_query_text(adk_app, user_id=user_id, message=user_input)
```

## 🔧 Using Tools with Oraicle-Agent

Oraicle-Agent provides **automatic runtime discovery of tools**, allowing sub-agents to use tools **without importing directly from** `app.tools`.

This ensures:
- No PYTHONPATH hacks
- No custom bootstrap commands
- Full compatibility with adk web ./sub_agents
- Clean, scalable architecture

## 📁 Recommended project structure
```bash
app/
├── tools/
│   ├── student_exam.py
│   ├── register_student_grades.py
│   └── any_new_tool.py
│
└── sub_agents/
    └── history_teacher/
        ├── agent.py
        └── prompt.py
```

Each tool should live inside `app/tools` and expose public functions
(optionally using `__all__`).

### ✅ Importing tools inside sub-agents (correct way)
Instead of importing tools directly from app.tools, always import them from oraicle.tools.

❌ **Do NOT do this**
```python
from app.tools.student_exam import student_exam
from app.tools.register_student_grades import register_student_grades
```
### ✅ Do this
```python
from oraicle.tools import student_exam, register_student_grades
```
Oraicle-Agent will **automatically discover and load all tools inside** `app/tools` **at runtime**.

### ⚡ Example: Sub-agent using tools
```python
from google.adk.agents import Agent
from oraicle import autoagent
from oraicle.tools import student_exam, register_student_grades
from .prompt import AGENT_PROMPT

history_teacher = Agent(
    name="history_teacher",
    model="gemini-2.0-flash-lite",
    instruction=AGENT_PROMPT,
    tools=[
        student_exam,
        register_student_grades,
    ],
)

autoagent(history_teacher)
```
No additional configuration is required.

## 🚀 Running locally with ADK
Oraicle-Agent works with the **default ADK command**, no bootstrap needed:
```bash
adk web ./sub_agents
```
All sub-agents will:
- Be discovered automatically
- Act as independent root_agents
- Have full access to shared tools
- Maintain isolated conversational contexts


## 🧠 Why this matters
This design turns `app/tools` into a **runtime tool registry**, not a static import dependency.
You get:
- True plug-and-play tools
- Zero coupling between app and sub-agents
- A clean A2A + Tooling architecture
- Production-safe execution (local & Vertex AI)


## 📄 License

MIT License © 2026  
Built with 🤖 for the GenAI community.
