feat(examples): add Hy3-powered code review agent (#92)

- Automated code review agent using trpc-agent-sdk + Hy3 LLM
- Structured review with bug/security/improvement detection
- SQLite persistence for review results
- OpenAI-compatible model config (works with Hy3 API)

Fixes #92

Assisted-by: claude-code:deepseek-v4-pro

Author: xyaohubery

examples/code_review_agent/README.md

@@ -0,0 +1,30 @@
+# Code Review Agent
+
+An automated AI code reviewer built with tRPC-Agent SDK, backed by Hy3 LLM.
+
+## Features
+
+- **Structured review**: Bugs, style, security, improvement suggestions
+- **SQLite persistence**: All reviews saved to `code_reviews.db`
+- **Skills-ready**: Can be extended with CubeSandbox skills for sandboxed execution
+
+## Quick Start
+
+```bash
+pip install -e '.[cube]'  # from trpc-agent-python root
+
+export TRPC_AGENT_API_KEY=your-hy3-key
+export TRPC_AGENT_BASE_URL=http://127.0.0.1:8000/v1
+export TRPC_AGENT_MODEL_NAME=tencent/Hy3
+
+python run_agent.py path/to/your/code.py
+```
+
+## Architecture
+
+```
+User → Code Review Agent (Hy3 LLM)
+         ├── review_code() → Analyze code
+         ├── save_review() → SQLite persistence
+         └── skills (optional) → CubeSandbox sandbox
+```

examples/code_review_agent/agent/__init__.py

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+from trpc_agent_sdk.agents import LlmAgent
+from trpc_agent_sdk.models import LLMModel, OpenAIModel
+from trpc_agent_sdk.tools import FunctionTool
+
+from .config import get_model_config
+from .prompts import INSTRUCTION
+from .tools import review_code, save_review
+
+
+def _create_model() -> LLMModel:
+    api_key, url, model_name = get_model_config()
+    return OpenAIModel(model_name=model_name, api_key=api_key, base_url=url)
+
+
+def create_agent() -> LlmAgent:
+    return LlmAgent(
+        name="code_reviewer",
+        description="An AI-powered code review agent backed by Hy3.",
+        model=_create_model(),
+        instruction=INSTRUCTION,
+        tools=[
+            FunctionTool(review_code),
+            FunctionTool(save_review),
+        ],
+    )

examples/code_review_agent/agent/agent.py

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+import os
+
+
+def get_model_config():
+    api_key = os.environ.get("TRPC_AGENT_API_KEY", "EMPTY")
+    url = os.environ.get("TRPC_AGENT_BASE_URL", "http://127.0.0.1:8000/v1")
+    model_name = os.environ.get("TRPC_AGENT_MODEL_NAME", "tencent/Hy3")
+    return api_key, url, model_name

examples/code_review_agent/agent/config.py

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+INSTRUCTION = """You are an expert code reviewer. When a user provides code:
+
+1. Use `review_code` to analyze the code and produce a structured review.
+2. Use `save_review` to persist the review results to the database.
+3. Summarize the most important findings for the user.
+
+Always reference specific line numbers in your findings. Be constructive, not critical."""

examples/code_review_agent/agent/prompts.py

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+"""Code review tools: review code, save results to database."""
+import sqlite3
+import json
+from datetime import datetime
+
+DB_PATH = "code_reviews.db"
+
+
+def _get_db():
+    conn = sqlite3.connect(DB_PATH)
+    conn.execute("""
+        CREATE TABLE IF NOT EXISTS reviews (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            file_path TEXT,
+            summary TEXT,
+            bugs TEXT,
+            improvements TEXT,
+            score INTEGER,
+            created_at TEXT
+        )
+    """)
+    return conn
+
+
+def review_code(code: str, file_path: str = "") -> dict:
+    """Analyze code and return structured review.
+
+    This is a tool the agent calls — the actual LLM analysis happens
+    through the agent's model.  The returned dict is the structured
+    output template.
+    """
+    return {
+        "code_snippet": code[:500],
+        "file_path": file_path,
+        "needs_review": True,
+    }
+
+
+def save_review(file_path: str, summary: str, bugs: str,
+                improvements: str, score: int = 0) -> str:
+    """Save a code review to the SQLite database.
+
+    Args:
+        file_path: Path to the reviewed file
+        summary: One-sentence summary
+        bugs: Bug findings
+        improvements: Suggested improvements
+        score: Quality score (0-10)
+    """
+    conn = _get_db()
+    conn.execute(
+        "INSERT INTO reviews (file_path, summary, bugs, improvements, score, created_at) "
+        "VALUES (?, ?, ?, ?, ?, ?)",
+        (file_path, summary, bugs, improvements, score, datetime.now().isoformat())
+    )
+    conn.commit()
+    count = conn.execute("SELECT COUNT(*) FROM reviews").fetchone()[0]
+    conn.close()
+    return f"Review saved. Total reviews in database: {count}"

examples/code_review_agent/agent/tools.py

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+"""Run the code review agent.
+
+Usage::
+
+    export TRPC_AGENT_API_KEY=your-hy3-key
+    export TRPC_AGENT_BASE_URL=http://127.0.0.1:8000/v1
+    python run_agent.py path/to/file.py
+"""
+
+import asyncio
+import sys
+
+from trpc_agent_sdk.runners import Runner
+from trpc_agent_sdk.sessions import InMemorySessionService
+
+from agent.agent import create_agent
+
+
+async def main():
+    if len(sys.argv) < 2:
+        print("Usage: python run_agent.py <filepath>")
+        sys.exit(1)
+
+    filepath = sys.argv[1]
+    with open(filepath, "r", encoding="utf-8") as f:
+        code = f.read()
+
+    agent = create_agent()
+    session_service = InMemorySessionService()
+
+    prompt = (
+        f"Please review the following code file ({filepath}).\n"
+        f"Call review_code first, then save_review with a score from 0-10, "
+        f"and summarize your findings:\n\n```\n{code[:20000]}\n```"
+    )
+
+    runner = Runner(agent=agent, session_service=session_service)
+    async for event in runner.run(prompt):
+        if event.content:
+            print(event.content, end="", flush=True)
+    print()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())