Merge pull request #662 from lizekai-hash/feat/langchain-code-reviewer

feat(agents): add LangChain code review agent with A2A protocol

agents/langchain-code-reviewer/.gitignore

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+__pycache__/

agents/langchain-code-reviewer/agent.py

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+"""
+LangChain Code Review Agent — core review logic.
+
+Supports OpenAI, Ollama, and any LangChain-compatible LLM.
+"""
+
+import os
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+
+SYSTEM_PROMPT = """\
+You are a principal-level code reviewer with 15+ years of production experience \
+across multiple languages (Python, Rust, TypeScript, Java, Go, C/C++).
+You receive code snippets, diffs, or pull request descriptions and produce a \
+structured, actionable review report.
+
+You MUST respond in **中文**, but keep code snippets, variable names, and \
+technical terms in their original language.
+
+# ── 审核维度（按优先级排序） ──────────────────────────────
+
+## 1. 正确性 (Correctness)
+- 逻辑错误、off-by-one、边界条件
+- 空指针 / None / undefined 未处理
+- 错误处理不完整（吞异常、漏 catch、panic 路径）
+- 并发问题：竞态条件、死锁、数据竞争
+- 类型安全：隐式转换、溢出、精度丢失
+- 资源泄漏：未关闭的文件/连接/锁
+
+## 2. 安全性 (Security)
+- SQL / NoSQL / OS 命令注入
+- XSS、CSRF、SSRF
+- 硬编码密钥、token、密码
+- 不安全的反序列化
+- 路径穿越（Path Traversal）
+- 缺少输入校验 / 输出编码
+- 权限检查缺失或绕过
+- 敏感数据明文日志
+
+## 3. 性能 (Performance)
+- 算法复杂度不合理（O(n²) 可优化为 O(n)）
+- 不必要的内存分配 / 拷贝
+- N+1 查询、缺少批量操作
+- 阻塞 I/O 在异步上下文中
+- 缺少缓存 / 索引
+- 热路径上的正则编译 / 反射
+
+## 4. 可维护性 (Maintainability)
+- 命名不清晰、缩写歧义
+- 函数过长（>50行建议拆分）
+- 重复代码（DRY 违反）
+- 职责不单一（SRP 违反）
+- 缺少必要注释（复杂业务逻辑、非显而易见的决策）
+- 魔法数字 / 字符串
+- 耦合过紧、依赖方向不合理
+
+## 5. 测试 (Testing)
+- 关键路径缺少单元测试
+- 测试覆盖了 happy path 但遗漏了 edge case
+- 测试中有硬编码依赖（时间、文件路径、网络）
+- Mock 过度导致测试失去意义
+
+## 6. 风格 (Style)
+- 不符合语言惯例（Pythonic、Rust idiom 等）
+- 格式不一致（应由 formatter 处理的除外）
+- 不必要的复杂写法
+
+# ── 严重级别 ──────────────────────────────────────────
+
+| 级别 | 含义 | 是否阻塞合并 |
+|------|------|-------------|
+| 🔴 **[必须修复]** | 存在 bug、安全漏洞或数据丢失风险 | 是 |
+| 🟡 **[建议修复]** | 不影响功能但会影响可维护性或性能 | 否，但强烈建议 |
+| 🔵 **[小建议]** | 风格、命名等微小改进 | 否 |
+| 🟢 **[亮点]** | 写得好的地方，值得肯定 | — |
+
+# ── 输出格式 ──────────────────────────────────────────
+
+严格按以下 Markdown 格式输出：
+
+```
+## 📋 总结
+**结论**: [✅ 通过 / ⚠️ 需要修改 / 💬 仅评论]
+**概述**: [1-2 句话总体评价]
+**发现统计**: 🔴 X 个必须修复 | 🟡 X 个建议修复 | 🔵 X 个小建议 | 🟢 X 个亮点
+
+---
+
+## 🔍 详细发现
+
+### 🔴 [必须修复] 问题标题
+- **位置**: `文件名` 第 X-Y 行
+- **问题**: 具体描述
+- **原因**: 为什么这是个问题，可能造成什么后果
+- **修复建议**:
+（给出修复后的代码）
+
+### 🟡 [建议修复] 问题标题
+...
+
+### 🔵 [小建议] 问题标题
+...
+
+### 🟢 [亮点] 优点标题
+- **位置**: `文件名` 第 X-Y 行
+- **说明**: 为什么这段代码写得好
+
+---
+
+## 📊 评分
+| 维度 | 分数 | 说明 |
+|------|------|------|
+| 正确性 | X/10 | 一句话说明 |
+| 安全性 | X/10 | 一句话说明 |
+| 性能 | X/10 | 一句话说明 |
+| 可维护性 | X/10 | 一句话说明 |
+| 测试 | X/10 | 一句话说明 |
+| **综合** | **X/10** | 一句话总结 |
+```
+
+# ── 审核原则 ──────────────────────────────────────────
+
+1. **先肯定，再指出问题** — 不要只挑毛病，好的代码也要指出来
+2. **解释 WHY，不仅是 WHAT** — 每个问题都要说清楚「为什么不好」和「可能导致什么后果」
+3. **给出具体修复代码** — 不要只说"这里有问题"，要给出改好后的写法
+4. **区分严重级别** — 不要把小问题标成必须修复，也不要把严重 bug 标成小建议
+5. **尊重作者** — 用建设性的语气，避免 "这是错的" 这种措辞，用 "这里可以改进为..."
+6. **不纠结格式** — 如果项目有 formatter/linter，格式问题跳过
+7. **关注变更本身** — 如果是 diff，只审核变更的部分，不要评论未修改的代码
+8. **没有代码时** — 直接要求提交代码，不要编造审核结果"""
+
+
+def _build_llm():
+    """Build the LLM based on environment configuration."""
+    use_ollama = os.getenv("USE_OLLAMA", "").lower() in ("1", "true", "yes")
+
+    if use_ollama:
+        from langchain_ollama import ChatOllama
+        model = os.getenv("OLLAMA_MODEL", "qwen2.5")
+        base_url = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
+        return ChatOllama(model=model, base_url=base_url, temperature=0.2)
+
+    provider = os.getenv("LLM_PROVIDER", "openai").lower()
+
+    if provider == "deepseek":
+        from langchain_openai import ChatOpenAI
+        return ChatOpenAI(
+            model=os.getenv("DEEPSEEK_MODEL", "deepseek-chat"),
+            api_key=os.getenv("DEEPSEEK_API_KEY"),
+            base_url=os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com"),
+            temperature=0.2,
+            max_tokens=4096,
+        )
+
+    from langchain_openai import ChatOpenAI
+    return ChatOpenAI(
+        model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0.2,
+        max_tokens=4096,
+    )
+
+
+class CodeReviewAgent:
+    """LangChain-based code review agent."""
+
+    def __init__(self):
+        self.llm = _build_llm()
+        self.prompt = ChatPromptTemplate.from_messages([
+            ("system", SYSTEM_PROMPT),
+            ("human", "{input}"),
+        ])
+        self.chain = self.prompt | self.llm | StrOutputParser()
+
+    def review(self, code_or_diff: str) -> str:
+        """
+        Review the given code or diff.
+
+        Args:
+            code_or_diff: Source code, git diff, or PR description to review.
+
+        Returns:
+            Structured review report as markdown text.
+        """
+        if not code_or_diff.strip():
+            return "No code provided. Please submit code or a diff to review."
+
+        return self.chain.invoke({"input": code_or_diff})

agents/langchain-code-reviewer/config.example.toml

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+# Add this section to your ~/.openfang/config.toml
+# to register the LangChain code review agent.
+
+[a2a]
+enabled = true
+listen_path = "/a2a"
+
+[[a2a.external_agents]]
+name = "langchain-code-reviewer"
+url = "http://127.0.0.1:9100"

agents/langchain-code-reviewer/requirements.txt

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+langchain>=0.3
+langchain-openai>=0.3
+langchain-core>=0.3
+langchain-ollama>=0.3
+fastapi>=0.115
+uvicorn>=0.34

agents/langchain-code-reviewer/server.py

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+"""
+LangChain Code Review Agent — A2A-compatible server.
+
+Exposes a code review agent via Google's A2A protocol so that
+OpenFang workflows can call it as an external agent.
+
+Start:
+    OPENAI_API_KEY=sk-xxx python server.py
+    # or with Ollama (no key needed):
+    USE_OLLAMA=1 python server.py
+
+Endpoints:
+    GET  /.well-known/agent.json   — A2A Agent Card
+    POST /a2a                      — JSON-RPC task endpoint
+"""
+
+import os
+import uuid
+import asyncio
+from datetime import datetime, timezone
+
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
+import uvicorn
+
+from agent import CodeReviewAgent
+
+# ---------------------------------------------------------------------------
+# Config
+# ---------------------------------------------------------------------------
+
+HOST = os.getenv("HOST", "0.0.0.0")
+PORT = int(os.getenv("PORT", "9100"))
+BASE_URL = os.getenv("BASE_URL", f"http://127.0.0.1:{PORT}")
+
+app = FastAPI(title="LangChain Code Review Agent")
+agent = CodeReviewAgent()
+
+# In-memory task store
+tasks: dict[str, dict] = {}
+
+# ---------------------------------------------------------------------------
+# A2A Agent Card
+# ---------------------------------------------------------------------------
+
+AGENT_CARD = {
+    "name": "langchain-code-reviewer",
+    "description": (
+        "LangChain-powered code review agent. "
+        "Analyzes code for bugs, security issues, performance problems, "
+        "and style violations. Returns structured review with severity levels."
+    ),
+    "url": f"{BASE_URL}/a2a",
+    "version": "0.1.0",
+    "capabilities": {
+        "streaming": False,
+        "pushNotifications": False,
+        "stateTransitionHistory": True,
+    },
+    "skills": [
+        {
+            "id": "code-review",
+            "name": "Code Review",
+            "description": "Review code for correctness, security, performance, and style",
+            "tags": ["code", "review", "security", "quality"],
+            "examples": [
+                "Review this Python function for bugs",
+                "Check this Rust code for security issues",
+                "Analyze this PR diff for performance problems",
+            ],
+        },
+        {
+            "id": "pr-review",
+            "name": "Pull Request Review",
+            "description": "Review a git diff / pull request",
+            "tags": ["pr", "diff", "git"],
+            "examples": [
+                "Review this PR diff",
+                "Analyze these changes",
+            ],
+        },
+    ],
+    "defaultInputModes": ["text"],
+    "defaultOutputModes": ["text"],
+}
+
+
+@app.get("/.well-known/agent.json")
+async def agent_card():
+    return JSONResponse(content=AGENT_CARD)
+
+
+# ---------------------------------------------------------------------------
+# A2A JSON-RPC Endpoint
+# ---------------------------------------------------------------------------
+
+
+@app.post("/a2a")
+async def a2a_endpoint(request: Request):
+    body = await request.json()
+
+    jsonrpc = body.get("jsonrpc", "2.0")
+    req_id = body.get("id", 1)
+    method = body.get("method", "")
+    params = body.get("params", {})
+
+    if method == "tasks/send":
+        return await handle_tasks_send(jsonrpc, req_id, params)
+    elif method == "tasks/get":
+        return handle_tasks_get(jsonrpc, req_id, params)
+    elif method == "tasks/cancel":
+        return handle_tasks_cancel(jsonrpc, req_id, params)
+    else:
+        return JSONResponse(content={
+            "jsonrpc": jsonrpc,
+            "id": req_id,
+            "error": {"code": -32601, "message": f"Method not found: {method}"},
+        })
+
+
+async def handle_tasks_send(jsonrpc: str, req_id: int, params: dict):
+    message = params.get("message", {})
+    session_id = params.get("sessionId")
+    task_id = str(uuid.uuid4())
+
+    text_parts = [
+        p["text"] for p in message.get("parts", []) if p.get("type") == "text"
+    ]
+    user_input = "\n".join(text_parts)
+
+    task = {
+        "id": task_id,
+        "sessionId": session_id,
+        "status": {"state": "working", "message": None},
+        "messages": [message],
+        "artifacts": [],
+    }
+    tasks[task_id] = task
+
+    try:
+        review_result = await asyncio.to_thread(agent.review, user_input)
+
+        agent_message = {
+            "role": "agent",
+            "parts": [{"type": "text", "text": review_result}],
+        }
+        task["messages"].append(agent_message)
+        task["status"] = {"state": "completed", "message": None}
+        task["artifacts"] = [
+            {
+                "name": "code-review-report",
+                "description": "Structured code review report",
+                "parts": [{"type": "text", "text": review_result}],
+                "index": 0,
+                "lastChunk": True,
+            }
+        ]
+    except Exception as e:
+        task["status"] = {"state": "failed", "message": str(e)}
+        task["messages"].append({
+            "role": "agent",
+            "parts": [{"type": "text", "text": f"Review failed: {e}"}],
+        })
+
+    return JSONResponse(content={
+        "jsonrpc": jsonrpc,
+        "id": req_id,
+        "result": task,
+    })
+
+
+def handle_tasks_get(jsonrpc: str, req_id: int, params: dict):
+    task_id = params.get("id", "")
+    task = tasks.get(task_id)
+
+    if task is None:
+        return JSONResponse(content={
+            "jsonrpc": jsonrpc,
+            "id": req_id,
+            "error": {"code": -32000, "message": f"Task not found: {task_id}"},
+        })
+
+    return JSONResponse(content={
+        "jsonrpc": jsonrpc,
+        "id": req_id,
+        "result": task,
+    })
+
+
+def handle_tasks_cancel(jsonrpc: str, req_id: int, params: dict):
+    task_id = params.get("id", "")
+    task = tasks.get(task_id)
+
+    if task is None:
+        return JSONResponse(content={
+            "jsonrpc": jsonrpc,
+            "id": req_id,
+            "error": {"code": -32000, "message": f"Task not found: {task_id}"},
+        })
+
+    task["status"] = {"state": "cancelled", "message": None}
+    return JSONResponse(content={
+        "jsonrpc": jsonrpc,
+        "id": req_id,
+        "result": task,
+    })
+
+
+# ---------------------------------------------------------------------------
+# Health check
+# ---------------------------------------------------------------------------
+
+@app.get("/health")
+async def health():
+    return {"status": "ok", "agent": "langchain-code-reviewer", "tasks": len(tasks)}
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+if __name__ == "__main__":
+    print(f"Starting LangChain Code Review Agent on {HOST}:{PORT}")
+    print(f"Agent Card: {BASE_URL}/.well-known/agent.json")
+    print(f"A2A endpoint: {BASE_URL}/a2a")
+    uvicorn.run(app, host=HOST, port=PORT)

agents/langchain-code-reviewer/workflow.json

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+{
+  "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
+  "name": "langchain-code-review-pipeline",
+  "description": "Code review pipeline: uses LangChain external agent for deep review, then OpenFang Writer agent to format the final report.",
+  "created_at": "2026-03-16T00:00:00Z",
+  "steps": [
+    {
+      "name": "review-code",
+      "agent": { "name": "a2a-proxy" },
+      "prompt_template": "Use the a2a_send tool to send the following code to the external agent for code review. Set agent_name to langchain-code-reviewer and set message to the code below. Return the complete review result:\n\n{{input}}",
+      "mode": "sequential",
+      "timeout_secs": 300,
+      "output_var": "review_result"
+    },
+    {
+      "name": "format-report",
+      "agent": { "name": "Writer" },
+      "prompt_template": "Format the following code review into a clean, professional report. Preserve all severity levels and scores. Add a brief executive summary at the top:\n\n{{review_result}}",
+      "mode": "sequential",
+      "timeout_secs": 120
+    }
+  ]
+}