feat(examples): add fake review model, agent wiring and llm review step

Author: DNKYr

examples/skills_code_review_agent/agent/__init__.py

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+# Tencent is pleased to support the open source community by making tRPC-Agent-Python available.
+#
+# Copyright (C) 2026 Tencent. All rights reserved.
+#
+# tRPC-Agent-Python is licensed under Apache-2.0.
+"""Agent wiring — fake model, prompts, config, and LlmAgent factory."""

examples/skills_code_review_agent/agent/agent.py

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+# Tencent is pleased to support the open source community by making tRPC-Agent-Python available.
+#
+# Copyright (C) 2026 Tencent. All rights reserved.
+#
+# tRPC-Agent-Python is licensed under Apache-2.0.
+"""LlmAgent wiring for the code review example."""
+from trpc_agent_sdk.agents import LlmAgent
+from trpc_agent_sdk.models import OpenAIModel
+from trpc_agent_sdk.skills import SkillToolSet
+
+from .config import get_model_config
+from .fake_model import FakeReviewModel
+from .prompts import INSTRUCTION
+
+
+def create_review_agent(repository, dry_run: bool, tool_filters: list) -> LlmAgent:
+    """Create the review agent. tool_filters guard skill_run via governance."""
+    if dry_run:
+        model = FakeReviewModel()
+    else:
+        api_key, url, model_name = get_model_config()
+        model = OpenAIModel(model_name=model_name, api_key=api_key, base_url=url)
+    toolset = SkillToolSet(repository=repository, filters=list(tool_filters))
+    return LlmAgent(
+        name="code_review_agent",
+        description="Automated code review agent combining skill scripts and LLM analysis.",
+        model=model,
+        instruction=INSTRUCTION,
+        tools=[toolset],
+        skill_repository=repository,
+    )

examples/skills_code_review_agent/agent/config.py

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+# Tencent is pleased to support the open source community by making tRPC-Agent-Python available.
+#
+# Copyright (C) 2026 Tencent. All rights reserved.
+#
+# tRPC-Agent-Python is licensed under Apache-2.0.
+"""Model configuration from environment variables."""
+import os
+
+
+def get_model_config() -> tuple[str, str, str]:
+    """Return (api_key, base_url, model_name) or raise when unset."""
+    api_key = os.getenv("TRPC_AGENT_API_KEY", "")
+    url = os.getenv("TRPC_AGENT_BASE_URL", "")
+    model_name = os.getenv("TRPC_AGENT_MODEL_NAME", "")
+    if not api_key or not url or not model_name:
+        raise ValueError("TRPC_AGENT_API_KEY, TRPC_AGENT_BASE_URL and "
+                         "TRPC_AGENT_MODEL_NAME must be set for real-model mode")
+    return api_key, url, model_name
+
+
+def is_dry_run(explicit: bool) -> bool:
+    """Dry-run when requested explicitly or when no API key is configured."""
+    return explicit or not os.getenv("TRPC_AGENT_API_KEY", "")

examples/skills_code_review_agent/agent/fake_model.py

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+# Tencent is pleased to support the open source community by making tRPC-Agent-Python available.
+#
+# Copyright (C) 2026 Tencent. All rights reserved.
+#
+# tRPC-Agent-Python is licensed under Apache-2.0.
+"""Deterministic fake model so the full pipeline runs without any API key."""
+import json
+from typing import List
+
+from trpc_agent_sdk.models import LLMModel, LlmResponse
+from trpc_agent_sdk.types import Content, Part
+
+_FAKE_PAYLOAD = {
+    "summary": "Dry-run review complete. Static findings are authoritative.",
+    "findings": [],
+}
+
+
+class FakeReviewModel(LLMModel):
+    """LLMModel returning one canned JSON review response."""
+
+    def __init__(self, model_name: str = "fake-review-model", **kwargs):
+        super().__init__(model_name=model_name, **kwargs)
+
+    @classmethod
+    def supported_models(cls) -> List[str]:
+        return [r"fake-review-.*"]
+
+    def validate_request(self, request) -> None:
+        return None
+
+    async def _generate_async_impl(self, request, stream=False, ctx=None):
+        text = json.dumps(_FAKE_PAYLOAD)
+        yield LlmResponse(content=Content(role="model", parts=[Part.from_text(text=text)]))

examples/skills_code_review_agent/agent/prompts.py

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+# Tencent is pleased to support the open source community by making tRPC-Agent-Python available.
+#
+# Copyright (C) 2026 Tencent. All rights reserved.
+#
+# tRPC-Agent-Python is licensed under Apache-2.0.
+"""Prompts for the code review agent."""
+
+INSTRUCTION = """You are an automated code reviewer.
+
+You receive a unified diff plus baseline findings produced by static rule
+scripts from the code-review skill. Confirm the baseline findings and add any
+extra issues you can justify from the diff alone.
+
+Reply with a single JSON object and nothing else:
+{"summary": "<one paragraph>", "findings": [{"severity": "critical|high|medium|low|info",
+"category": "security|async_resource_leak|db_lifecycle|missing_test|secret_leak",
+"file": "<path>", "line": <int>, "title": "<short>", "evidence": "<code excerpt>",
+"recommendation": "<fix>", "confidence": <0.0-1.0>}]}
+
+Only report issues visible in the diff. Do not repeat baseline findings.
+"""
+
+REVIEW_REQUEST_TEMPLATE = """Review this change.
+
+Baseline static findings (JSON):
+{findings_json}
+
+Unified diff (secrets already redacted):
+{diff}
+"""

examples/skills_code_review_agent/review/llm_review.py

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+# Tencent is pleased to support the open source community by making tRPC-Agent-Python available.
+#
+# Copyright (C) 2026 Tencent. All rights reserved.
+#
+# tRPC-Agent-Python is licensed under Apache-2.0.
+"""LLM enrichment step: run the agent once over the diff + static findings."""
+import json
+import re
+import uuid
+
+from trpc_agent_sdk.runners import Runner
+from trpc_agent_sdk.sessions import InMemorySessionService
+from trpc_agent_sdk.types import Content, Part
+
+from agent.prompts import REVIEW_REQUEST_TEMPLATE
+
+from .findings import Finding
+from .redaction import redact_text
+
+_FENCE_RE = re.compile(r"```(?:json)?\s*(\{.*?\})\s*```", re.DOTALL)
+
+
+def parse_llm_output(text: str):
+    """Extract (findings_dicts, summary) from model text; ([], "") on failure."""
+    candidates = [text.strip()]
+    fence = _FENCE_RE.search(text)
+    if fence:
+        candidates.insert(0, fence.group(1))
+    for candidate in candidates:
+        try:
+            payload = json.loads(candidate)
+        except (json.JSONDecodeError, ValueError):
+            continue
+        if isinstance(payload, dict):
+            return list(payload.get("findings") or []), str(payload.get("summary") or "")
+    return [], ""
+
+
+async def run_llm_review(agent, diff_text: str, static_findings):
+    """Run one review turn. Returns (llm_findings, summary, warnings)."""
+    warnings = []
+    runner = Runner(app_name="code_review_agent", agent=agent,
+                    session_service=InMemorySessionService())
+    prompt = REVIEW_REQUEST_TEMPLATE.format(
+        findings_json=json.dumps([f.model_dump() for f in static_findings]),
+        diff=redact_text(diff_text))
+    message = Content(role="user", parts=[Part.from_text(text=prompt)])
+    final_text = ""
+    async for event in runner.run_async(user_id="cr_user",
+                                        session_id=uuid.uuid4().hex,
+                                        new_message=message):
+        if event.partial or not event.content or not event.content.parts:
+            continue
+        for part in event.content.parts:
+            if getattr(part, "text", None) and not getattr(part, "thought", None):
+                final_text += part.text
+    raw_findings, summary = parse_llm_output(final_text)
+    if not summary and final_text:
+        warnings.append("llm output was not valid JSON; ignored")
+    findings = []
+    for raw in raw_findings:
+        try:
+            findings.append(Finding(
+                severity=str(raw.get("severity", "info")),
+                category=str(raw.get("category", "security")),
+                file=str(raw.get("file", "")),
+                line=int(raw.get("line", 0)),
+                title=str(raw.get("title", "")),
+                evidence=str(raw.get("evidence", "")),
+                recommendation=str(raw.get("recommendation", "")),
+                confidence=float(raw.get("confidence", 0.5)),
+                source="llm"))
+        except (TypeError, ValueError):
+            warnings.append(f"skipped malformed llm finding: {raw!r}")
+    return findings, summary, warnings

examples/skills_code_review_agent/tests/test_llm_review.py

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+# Tencent is pleased to support the open source community by making tRPC-Agent-Python available.
+#
+# Copyright (C) 2026 Tencent. All rights reserved.
+#
+# tRPC-Agent-Python is licensed under Apache-2.0.
+"""Tests for the fake model and the LLM review step."""
+from pathlib import Path
+
+from trpc_agent_sdk.skills import create_default_skill_repository
+
+from agent.agent import create_review_agent
+from agent.fake_model import FakeReviewModel
+from review.findings import Finding
+from review.llm_review import parse_llm_output, run_llm_review
+
+EXAMPLE_ROOT = Path(__file__).resolve().parents[1]
+SKILLS_DIR = EXAMPLE_ROOT / "skills"
+
+
+def test_parse_llm_output_plain_json():
+    findings, summary = parse_llm_output(
+        '{"summary": "ok", "findings": [{"severity": "high", "category": "security",'
+        ' "file": "a.py", "line": 3, "title": "x", "confidence": 0.8}]}')
+    assert summary == "ok"
+    assert findings[0]["line"] == 3
+
+
+def test_parse_llm_output_fenced_json():
+    text = 'Here you go:\n```json\n{"summary": "s", "findings": []}\n```\n'
+    findings, summary = parse_llm_output(text)
+    assert summary == "s" and findings == []
+
+
+def test_parse_llm_output_garbage_returns_empty():
+    findings, summary = parse_llm_output("I could not review this.")
+    assert findings == [] and summary == ""
+
+
+def test_fake_model_supported_models():
+    assert FakeReviewModel.supported_models() == [r"fake-review-.*"]
+
+
+async def test_run_llm_review_with_fake_model():
+    repository = create_default_skill_repository(str(SKILLS_DIR))
+    agent = create_review_agent(repository, dry_run=True, tool_filters=[])
+    static = [Finding(severity="high", category="security", file="a.py", line=1,
+                      title="eval", confidence=0.9)]
+    llm_findings, summary, warnings = await run_llm_review(agent, "diff text", static)
+    assert llm_findings == []
+    assert "Dry-run review complete" in summary
+    assert warnings == []