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from hermes.interface import DiscordInterface, SlackInterface, CLIInterface

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from hermes.orchestrator import AgentOrchestrator
from hermes.agent import HermesAgent

orchestrator = AgentOrchestrator()

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from hermes.agent import HermesAgent
from hermes.memory import MemoryConfig
from hermes.skills import SkillRegistry

agent = HermesAgent(
    name="my_assistant",
    model="gpt-4-turbo",
    temperature=0.7,
    memory_config=MemoryConfig(
        session_memory=True,
        persistent_memory=True,
        vector_store="chromadb"
    ),
    skill_registry=SkillRegistry.load_default(),
    system_prompt="""You are a helpful AI assistant with access to 
    various tools and a persistent memory system."""
)

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from hermes.tools import (
    WebSearchTool, FileSystemTool, GitHubTool,
    DatabaseTool, CodeExecutionTool, APIRequestTool
)

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from hermes.mcp import MCPClient

mcp = MCPClient()

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from hermes.memory import SessionMemory

session = SessionMemory(
    max_tokens=4096,
    summarization_threshold=3000
)

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from hermes.memory import PersistentMemory

persistent = PersistentMemory(
    backend="chromadb",
    collection_name="hermes_memory",
    embedding_model="text-embedding-3-small"
)

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from hermes.skills import Skill

class ProjectManagementSkill(Skill):
    def __init__(self):
        super().__init__(name="project_management")
        self.memory = self.get_skill_memory()
    
    async def track_project(self, project_name: str, status: str):
        await self.memory.store({
            "project": project_name,
            "status": status,
            "timestamp": datetime.now()
        })
    
    async def get_active_projects(self):
        return await self.memory.query(
            "status:active",
            filter_type="metadata"
        )

from hermes.skills import SkillRegistry

registry = SkillRegistry()

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from hermes.skills import Skill, skill_action

class CustomResearchSkill(Skill):
    """Advanced research skill with citation tracking"""
    
    name = "advanced_research"
    description = "Perform deep research with source tracking"
    
    def __init__(self):
        super().__init__()
        self.sources = []
    
    @skill_action(
        description="Search and summarize academic papers",
        parameters={
            "query": {"type": "string", "required": True},
            "max_results": {"type": "integer", "default": 10}
        }
    )
    async def search_papers(self, query: str, max_results: int = 10):
        # Implementation
        results = await self.tools.web_search(
            f"{query} site:arxiv.org OR site:scholar.google.com",
            max_results=max_results
        )
        
        # Track sources
        for result in results:
            self.sources.append({
                "title": result.title,
                "url": result.url,
                "timestamp": datetime.now()
            })
        
        summary = await self.summarize(results)
        return {
            "summary": summary,
            "sources": self.sources
        }
    
    @skill_action(description="Generate bibliography from tracked sources")
    async def generate_bibliography(self):
        return "\n".join([
            f"- {s['title']}: {s['url']}"
            for s in self.sources
        ])

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from hermes.skills import SkillsHub

hub = SkillsHub(api_key=os.getenv("SKILLS_HUB_API_KEY"))

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from hermes.tools import WebSearchTool, WebScrapingTool, APIRequestTool

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from hermes.tools import FileSystemTool

fs = FileSystemTool(
    base_path="/workspace",
    allowed_operations=["read", "write", "list"]
)

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from hermes.tools import CodeExecutionTool

executor = CodeExecutionTool(
    sandbox_mode=True,
    timeout=30,
    allowed_imports=["requests", "pandas", "numpy"]
)

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from hermes.tools import DatabaseTool

db = DatabaseTool(
    connection_string=os.getenv("DATABASE_URL"),
    read_only=False
)

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from hermes.tools import GitHubTool

github = GitHubTool(token=os.getenv("GITHUB_TOKEN"))

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from hermes.tools import EmailTool, SlackTool

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from hermes.tools import DataAnalysisTool, ImageProcessingTool

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from hermes.platforms import DiscordPlatform

discord = DiscordPlatform(
    token=os.getenv("DISCORD_BOT_TOKEN"),
    command_prefix="!hermes"
)

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from hermes.platforms import SlackPlatform

slack = SlackPlatform(
    token=os.getenv("SLACK_BOT_TOKEN"),
    signing_secret=os.getenv("SLACK_SIGNING_SECRET")
)

slack.register_agent(agent)

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from hermes.platforms import WeChatPlatform

wechat = WeChatPlatform(
    app_id=os.getenv("WECHAT_APP_ID"),
    app_secret=os.getenv("WECHAT_APP_SECRET")
)

wechat.register_agent(agent)

@wechat.message_handler()
async def handle_message(message):
    response = await agent.chat(message.content)
    return response

wechat.run()

from hermes.mcp import MCPClient, MCPServer

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from hermes.mcp import MCPServer, mcp_tool

class CustomMCPServer(MCPServer):
    name = "custom_analytics"
    
    @mcp_tool(
        name="analyze_metrics",
        description="Analyze custom business metrics"
    )
    async def analyze_metrics(self, metric_type: str, date_range: str):
        # Your implementation
        data = await self.fetch_metrics(metric_type, date_range)
        analysis = self.perform_analysis(data)
        return analysis
    
    @mcp_tool(name="generate_report")
    async def generate_report(self, template: str):
        # Implementation
        pass

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from hermes.automation import CronScheduler

scheduler = CronScheduler(agent)

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from hermes.automation import EventTrigger

triggers = EventTrigger(agent)

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from hermes.orchestrator import SequentialWorkflow

workflow = SequentialWorkflow()

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from hermes.orchestrator import ParallelWorkflow

workflow = ParallelWorkflow()

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from hermes.orchestrator import HierarchicalOrchestrator

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from hermes.utils import retry_with_backoff

@retry_with_backoff(max_retries=3, backoff_factor=2)
async def execute_task_with_retry(agent, task):
    try:
        result = await agent.chat(task)
        return result
    except Exception as e:
        agent.logger.error(f"Task failed: {e}")
        raise

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async def stream_agent_response(agent, query):
    async for chunk in agent.chat_stream(query):
        print(chunk, end="", flush=True)
        # Or send to UI
        await websocket.send(chunk)

from hermes.context import ContextManager

async def task_with_context(agent, user_id):
    context = ContextManager(agent)
    
    # Load user context
    await context.load_user_context(user_id)
    
    # Add temporary context
    with context.temporary({
        "project": "current_project",
        "mode": "development"
    }):
        result = await agent.chat("Review the latest code changes")
    
    # Context automatically cleaned up
    return result

from hermes.monitoring import AgentMonitor

monitor = AgentMonitor(agent)

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**2. Memory issues**
```python

**3. Tool execution failures**
```python

**4. MCP connection issues**
```python

**5. High token usage**
```python

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