2026开源AI智能体生态全景：OpenClaw 2.0与社区新力量

# OpenClaw 2.0一键安装
curl -fsSL https://openclaw.dev/install.sh | bash

# 或使用Docker快速部署
docker run -d \
  --name openclaw \
  -p 8080:8080 \
  -v openclaw_data:/data \
  -e OPENAI_API_KEY=$OPENAI_API_KEY \
  openclaw/openclaw:2.0

# 自定义配置
openclaw config set memory.backend postgresql
openclaw config set browser.engine chromium
openclaw config set plugins.auto_update true

# Aider使用示例：AI辅助重构
# 终端运行
# aider --model gpt-6-astra src/auth/service.py

# Continue.dev自定义命令示例（.continue/config.json）
{
  "models": {
    "default": {
      "provider": "openai",
      "model": "gpt-6-astra",
      "apiKey": "{{apiKey}}"
    }
  },
  "customCommands": {
    "security-audit": {
      "description": "对当前文件进行安全审计",
      "prompt": "分析以下代码的安全漏洞，按严重程度排序，并给出修复建议：{{{input}}}"
    },
    "refactor": {
      "description": "重构当前选中的代码",
      "prompt": "重构以下代码，提高可读性和可维护性，保持功能不变：{{{input}}}"
    }
  }
}

from crewai import Agent, Task, Crew, Process
from langchain_openai import ChatOpenAI

# 定义AI模型
llm = ChatOpenAI(model="gpt-6-astra", temperature=0.7)

# 创建安全分析师Agent
security_analyst = Agent(
    role='安全分析师',
    goal='发现代码中的安全漏洞并提供详细分析',
    backstory='你是一位拥有15年经验的网络安全专家，'
              '精通OWASP Top 10漏洞类型和修复策略。',
    llm=llm,
    verbose=True
)

# 创建修复工程师Agent
fix_engineer = Agent(
    role='修复工程师',
    goal='根据安全分析结果编写修复代码',
    backstory='你是一位资深全栈工程师，擅长编写安全、'
              '高效、可维护的代码。',
    llm=llm,
    verbose=True
)

# 创建审计员Agent
auditor = Agent(
    role='审计员',
    goal='验证修复是否完整且未引入新问题',
    backstory='你是严格的质量保证专家，确保每次修复'
              '都彻底解决问题且不产生副作用。',
    llm=llm,
    verbose=True
)

# 定义任务流水线
analysis_task = Task(
    description='对代码库进行安全审计，输出漏洞清单',
    agent=security_analyst,
    expected_output='JSON格式的漏洞分析报告'
)

fix_task = Task(
    description='根据安全分析结果修复所有发现的漏洞',
    agent=fix_engineer,
    expected_output='修复后的代码及变更说明'
)

audit_task = Task(
    description='验证修复效果，确保无回归问题',
    agent=auditor,
    expected_output='审计通过/失败报告'
)

# 组建并运行Crew
crew = Crew(
    agents=[security_analyst, fix_engineer, auditor],
    tasks=[analysis_task, fix_task, audit_task],
    process=Process.sequential
)

result = crew.kickoff()

# MCP Server示例：暴露文件系统工具
from mcp.server import Server
from mcp.types import Tool, TextContent

server = Server("filesystem-tools")

@server.list_tools()
async def list_tools():
    return [
        Tool(
            name="read_file",
            description="读取文件内容",
            inputSchema={
                "type": "object",
                "properties": {
                    "path": {"type": "string", "description": "文件路径"}
                },
                "required": ["path"]
            }
        ),
        Tool(
            name="list_directory",
            description="列出目录内容",
            inputSchema={
                "type": "object",
                "properties": {
                    "path": {"type": "string", "description": "目录路径"}
                },
                "required": ["path"]
            }
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "read_file":
        content = read_file_safely(arguments["path"])
        return [TextContent(type="text", text=content)]
    elif name == "list_directory":
        entries = list_dir_safely(arguments["path"])
        return [TextContent(type="text", text=json.dumps(entries))]

# Ollama安装与使用
# 安装
curl -fsSL https://ollama.com/install.sh | sh

# 拉取并运行模型
ollama run llama3.2:70b
ollama run qwen2.5:72b
ollama run deepseek-coder-v2:16b

# 结合OpenClaw使用本地模型
OPENCLAW_MODEL=ollama/llama3.2:70b openclaw start

# 性能监控
ollama ps  # 查看已加载模型
ollama show llama3.2:70b  # 查看模型详情