chore: consolidate Syslog Solution code into unified repository structure
- Moved scattered scripts, templates, and documentation into organized directories (applications/, scripts/, assets/). - Updated .gitignore to strictly exclude secrets, state files, and IDE configs. - Added comprehensive README.md outlining repository structure and best practices. - Preserved all existing documentation and technical architecture files. - Prepared infrastructure/ for AWS Org and Proxmox Terraform management.
This commit is contained in:
-70
@@ -1,70 +0,0 @@
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1|# 04 - Autonomous AI Agents — Home
|
||||
2|**Purpose:** Research and implementation of autonomous agent workflows.
|
||||
3|
|
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4|## Overview
|
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5|
|
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6|Autonomous AI agents can execute multi-step tasks independently, learn from experience, and make decisions based on context. This section covers advanced orchestration patterns for production-grade agent systems.
|
||||
7|
|
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8|## Key Capabilities
|
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9|
|
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10|### **Multi-Agent Collaboration**
|
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11|- Role-based agent delegation
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12|- Cross-agent memory sharing
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13|- Conflict resolution protocols
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14|- Distributed task execution
|
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15|
|
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16|### **Autonomous Behaviors**
|
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17|- Self-correction loops
|
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18|- Error recovery mechanisms
|
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19|- Performance optimization
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20|- Adaptive resource allocation
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21|
|
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22|### **Advanced Patterns**
|
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23|- Agent swarms (10+ coordinated agents)
|
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24|- Continuous learning cycles
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25|- Human-in-the-loop intervention points
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26|- Audit trails and observability
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27|
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28|## Implementation Strategies
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29|
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30|### **Sequential Workflows**
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31|```python
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32|agent1.run(task="Research market")
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33|agent2.run(task="Analyze data")
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34|agent3.run(task="Generate report")
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35|```
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36|
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37|### **Parallel Workflows**
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38|```python
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39|results = await asyncio.gather(
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40| agent1.run(task="A"),
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41| agent2.run(task="B"),
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42| agent3.run(task="C")
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43|)
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44|```
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45|
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46|### **Feedback Loops**
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47|```python
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48|while not agent.is_satisfied(result):
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49| agent.improve(result)
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50| result = agent.execute(prompt)
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51|```
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52|
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53|## Use Cases
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54|
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55|- Market research automation
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56|- Multi-step data analysis pipelines
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57|- Customer support orchestration
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58|- Content generation workflows
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59|- Codebase refactoring agents
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60|
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61|## Related Frameworks
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62|
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63|- **[OpenClaw](../02\ -\ OpenClaw\ Framework/)** — Agent orchestration platform
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64|- **[MCP](../03\ -\ MCP\ \(Model\ Context\ Protocol\)/)** — Tool integration layer
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65|- **[OpenMAIC](../06\ -\ OpenMAIC\ Education/)** — Educational resources
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66|
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67|---
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68|
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69|*Advanced agent patterns for Jerome & Theodore's complex automation projects.*
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70|
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1|# 01 - Claude Code & Opencode — Home
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2|**Purpose:** Agent framework tutorials and best practices for Claude Code and OpenCode CLI.
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3|
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4|## Overview
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5|
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6|This section provides practical guides for deploying and managing AI coding agents using Claude Code and OpenCode CLI tools.
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7|
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8|## Contents
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9|
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10|### **Claude Code**
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11|- Quick start and configuration
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12|- Integration with IDEs
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13|- Custom agent definitions
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14|- Security best practices
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15|
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16|### **OpenCode**
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17|- CLI setup and usage
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18|- Extending with custom commands
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19|- Multi-agent orchestration
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20|- Production deployment patterns
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21|
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22|### **Best Practices**
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23|- Context window management
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24|- Agent sandboxing rules
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25|- Error handling patterns
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26|- Performance optimization
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27|
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28|## Quick Start
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29|
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30|```bash
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31|# Install OpenCode CLI
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32|pip install opencode-cli
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33|
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34|# Configure API keys
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35|export OPENAI_API_KEY="***"
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36|
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37|# Run Claude Code
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38|opencode run "Analyze this codebase for security issues"
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39|
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40|# Create custom agent
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41|opencode agent create --name my-helper --template best-practices
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42|```
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43|
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44|## Related Frameworks
|
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45|
|
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46|- **[MCP (Model Context Protocol)](../03\ -\ MCP\ \(Model\ Context\ Protocol\)/)** — Tool integration
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47|- **[OpenClaw Framework](../02\ -\ OpenClaw\ Framework/)** — Multi-agent orchestration
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48|- **[Autonomous AI Agents](../04\ -\ Autonomous\ AI\ Agents/)** — Advanced workflows
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49|
|
||||
50|---
|
||||
51|
|
||||
52|*Practical guides for Jerome & Theodore to implement AI coding agents in their daily workflow.*
|
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53|
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@@ -1,87 +0,0 @@
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1|# 02 - MCP (Model Context Protocol) — Home
|
||||
2|**Purpose:** MCP integration guides and implementation examples.
|
||||
3|
|
||||
4|## What is MCP?
|
||||
5|Model Context Protocol (MCP) is a standardized interface for connecting LLMs to external tools, data sources, and services.
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6|
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7|## MCP Benefits
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8|- **Unified interface** for tool integration
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9|- **Hot-swappable** tool providers
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10|- **Standardized** input/output schemas
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11|- **Type-safe** API definitions
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12|
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13|## Core Concepts
|
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14|
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15|### Tools
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16|External capabilities exposed to the model:
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17|- File system access
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18|- Database queries
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19|- API endpoints
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20|- Custom business logic
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21|
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22|### Resources
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23|Data sources the model can read:
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24|- Documents
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25|- Configuration files
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26|- Real-time data feeds
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27|- Knowledge bases
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28|
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29|### Prompts
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30|Pre-defined interaction templates:
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31|- Task initiation patterns
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32|- System prompt variations
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33|- Role-definition templates
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34|
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35|## Quick Start
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36|
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37|```bash
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38|# Install MCP server
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39|pip install mcp-server-filesystem mcp-server-sqlite
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40|
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41|# Start a filesystem MCP server
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42|mcp-server-filesystem --path /path/to/allowed/directory
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43|
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44|# Connect via client
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45|from mcp import ClientSession, StdioServerParameters
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46|
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47|async with ClientSession(
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48| stdio_server_parameters=StdioServerParameters(
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49| command="mcp-server-filesystem",
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50| args=["--path", "/path/to/allowed/directory"]
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51| )
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52|) as session:
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53| # List available tools
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54| tools = await session.list_tools()
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55|```
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56|
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57|## Available MCP Servers
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58|
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59|- **Filesystem:** Access documents and configuration files
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60|- **SQL databases:** Query relational databases safely
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61|- **API Gateway:** Connect to REST/GraphQL endpoints
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62|- **Custom Business Logic:** Integrate internal tools and services
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63|
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64|## Security Considerations
|
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65|
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66|- **Path whitelisting:** Only allow specific directories
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67|- **Query rate limiting:** Prevent resource exhaustion
|
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68|- **Input sanitization:** Validate all user inputs
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69|- **Token-based access:** Require authentication for sensitive operations
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70|
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71|## Use Cases
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72|
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73|- **Document analysis:** Read and process large document collections
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74|- **Database queries:** Extract insights from operational databases
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75|- **API orchestration:** Coordinate across multiple external services
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76|- **Code generation:** Write and execute code safely
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77|
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78|## References
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79|
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80|- [MCP Specification](https://modelcontextprotocol.io)
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81|- [MCP Server Implementations](https://github.com/modelcontextprotocol/servers)
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82|- [Integration Examples](../03%20-%20AI%20AGENTS%20&%20LEARNING/02%20-%20OpenClaw%20Framework/)
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83|
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84|---
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||||
85|
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86|*MCP integrates seamlessly with OpenClaw agent orchestration for scalable AI applications.*
|
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87|
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-59
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1|# OpenClaw Framework — Home
|
||||
2|**Purpose:** Complete guide to setting up and using the OpenClaw agent orchestration framework.
|
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3|
|
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4|## What is OpenClaw?
|
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5|OpenClaw provides a powerful framework for orchestrating AI agents, enabling multi-agent collaboration, task delegation, and complex workflow automation.
|
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6|
|
||||
7|## Quick Start
|
||||
8|
|
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9|### Installation
|
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10|```bash
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11|# Clone the repository
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12|git clone https://github.com/openclaw/openclaw.git
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13|cd openclaw
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14|
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15|# Install dependencies
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16|pip install -r requirements.txt
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17|
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18|# Configure API keys
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19|export OPENCLAW_API_KEY="***"
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20|```
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21|
|
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22|### Basic Usage
|
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23|```python
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24|from openclaw import AgentOrchestrator
|
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25|
|
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26|# Create an orchestrator
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27|orchestrator = AgentOrchestrator()
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28|
|
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29|# Define agent roles
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30|agents = {
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31| "researcher": Agent(role="researcher"),
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32| "analyst": Agent(role="analyst"),
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33| "writer": Agent(role="writer")
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34|}
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35|
|
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36|# Run a multi-agent workflow
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37|result = orchestrator.run_task(
|
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38| task="Write market analysis report",
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39| agents=agents
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40|)
|
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41|```
|
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42|
|
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43|## Framework Components
|
||||
44|
|
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45|- **Agent Registry:** Pool of specialized agents
|
||||
46|- **Task Dispatcher:** Routes tasks to appropriate agents
|
||||
47|- **Collaboration Layer:** Enables agent-to-agent communication
|
||||
48|- **Orchestration Engine:** Coordinates complex workflows
|
||||
49|
|
||||
50|## Use Cases
|
||||
51|
|
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52|- Market research automation
|
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53|- Content generation pipelines
|
||||
54|- Multi-step data analysis
|
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55|- Autonomous decision making
|
||||
56|
|
||||
57|---
|
||||
58|
|
||||
59|*For advanced usage, refer to the Advanced Usage guide and API Reference documentation.*
|
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+92
-92
@@ -1,92 +1,92 @@
|
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1|# 06 - OpenMAIC Education — Home
|
||||
2|**Purpose:** OpenMAIC (Open Multi-Agent AI Collaboration) educational resources and curriculum.
|
||||
3|
|
||||
4|## What is OpenMAIC?
|
||||
5|
|
||||
6|OpenMAIC (Open Multi-Agent AI Collaboration) is an educational framework designed to help teams understand and implement multi-agent AI systems. It provides:
|
||||
7|
|
||||
8|- **Structured Curriculum:** From fundamentals to advanced patterns
|
||||
9|- **Hands-on Exercises:** Real-world practice through coding challenges
|
||||
10|- **Community Learning:** Collaborative problem-solving and knowledge sharing
|
||||
11|
|
||||
12|## Curriculum Structure
|
||||
13|
|
||||
14|### **Module 1: Fundamentals**
|
||||
15|- Introduction to multi-agent systems
|
||||
16|- Agent role definition and capabilities
|
||||
17|- Basic orchestration patterns
|
||||
18|- Simple agent collaboration examples
|
||||
19|
|
||||
20|### **Module 2: Advanced Collaboration**
|
||||
21|- Complex workflow design
|
||||
22|- Context sharing between agents
|
||||
23|- Error handling and recovery
|
||||
24|- Performance optimization
|
||||
25|
|
||||
26|### **Module 3: Production Patterns**
|
||||
27|- Scaling to 10+ agents
|
||||
28|- Monitoring and diagnostics
|
||||
29|- Security best practices
|
||||
30|- Cost optimization strategies
|
||||
31|
|
||||
32|### **Module 4: Real-World Applications**
|
||||
33|- Market analysis automation
|
||||
34|- Customer support orchestration
|
||||
35|- Content pipeline automation
|
||||
36|- Data processing workflows
|
||||
37|
|
||||
38|## Getting Started
|
||||
39|
|
||||
40|```bash
|
||||
41|# Step 1: Install OpenMAIC
|
||||
42|pip install openmaic-core openmaic-visualizer
|
||||
43|
|
||||
44|# Step 2: Clone learning resources
|
||||
45|git clone https://github.com/sysloggh/openmaic-learning.git
|
||||
46|cd openmaic-learning/modules
|
||||
47|
|
||||
48|# Step 3: Start with Module 1
|
||||
49|python module01_fundamentals.py
|
||||
50|```
|
||||
51|
|
||||
52|## Example Agent Collaboration
|
||||
53|
|
||||
54|```python
|
||||
55|from openmaic.core import Orchestrator, Agent
|
||||
56|
|
||||
57|# Initialize orchestrator
|
||||
58|orchestrator = Orchestrator(
|
||||
59| max_agents=3,
|
||||
60| collaboration_mode="sequential"
|
||||
61|)
|
||||
62|
|
||||
63|# Create agents
|
||||
64|researcher = Agent(name="researcher", role="Research Assistant")
|
||||
65|analyst = Agent(name="analyst", role="Data Analysis")
|
||||
66|writer = Agent(name="writer", role="Content Generator")
|
||||
67|
|
||||
68|# Execute workflow
|
||||
69|result = orchestrator.run([
|
||||
70| researcher.run(task="Research AI trends"),
|
||||
71| analyst.run(task="Analyze market data"),
|
||||
72| writer.run(task="Write market report")
|
||||
73|])
|
||||
74|```
|
||||
75|
|
||||
76|## Use Cases at Syslog GH/LLC
|
||||
77|
|
||||
78|- **Market Research Automation:** Automatically gather and analyze SMB AI adoption data
|
||||
79|- **Content Generation:** Create service descriptions and case studies
|
||||
80|- **Customer Support:** Multi-agent Q&A for client inquiries
|
||||
81|- **Code Review Agents:** Automated code quality checks
|
||||
82|
|
||||
83|## Related Resources
|
||||
84|
|
||||
85|- **[Autonomous AI Agents](../04\ -\ Autonomous\ AI\ Agents/)** — Advanced workflows
|
||||
86|- **[Claude Code & Opencode](../01\ -\ Claude\ Code\ \&\ Opencode/)** — Practical implementation
|
||||
87|- **[MCP (Model Context Protocol)](../03\ -\ MCP\ \(Model\ Context\ Protocol\)/)** — Tool integration
|
||||
88|
|
||||
89|---
|
||||
90|
|
||||
91|*Perfect for Jerome & Theodore's strategy sessions on AI agent frameworks and multi-agent collaboration patterns. This curriculum supports our mission to educate SMBs on AI adoption while building our own agent capabilities.*
|
||||
92|
|
||||
1|# 06 - OpenMAIC Education — Home
|
||||
2|**Purpose:** OpenMAIC (Open Multi-Agent AI Collaboration) educational resources and curriculum.
|
||||
3|
|
||||
4|## What is OpenMAIC?
|
||||
5|
|
||||
6|OpenMAIC (Open Multi-Agent AI Collaboration) is an educational framework designed to help teams understand and implement multi-agent AI systems. It provides:
|
||||
7|
|
||||
8|- **Structured Curriculum:** From fundamentals to advanced patterns
|
||||
9|- **Hands-on Exercises:** Real-world practice through coding challenges
|
||||
10|- **Community Learning:** Collaborative problem-solving and knowledge sharing
|
||||
11|
|
||||
12|## Curriculum Structure
|
||||
13|
|
||||
14|### **Module 1: Fundamentals**
|
||||
15|- Introduction to multi-agent systems
|
||||
16|- Agent role definition and capabilities
|
||||
17|- Basic orchestration patterns
|
||||
18|- Simple agent collaboration examples
|
||||
19|
|
||||
20|### **Module 2: Advanced Collaboration**
|
||||
21|- Complex workflow design
|
||||
22|- Context sharing between agents
|
||||
23|- Error handling and recovery
|
||||
24|- Performance optimization
|
||||
25|
|
||||
26|### **Module 3: Production Patterns**
|
||||
27|- Scaling to 10+ agents
|
||||
28|- Monitoring and diagnostics
|
||||
29|- Security best practices
|
||||
30|- Cost optimization strategies
|
||||
31|
|
||||
32|### **Module 4: Real-World Applications**
|
||||
33|- Market analysis automation
|
||||
34|- Customer support orchestration
|
||||
35|- Content pipeline automation
|
||||
36|- Data processing workflows
|
||||
37|
|
||||
38|## Getting Started
|
||||
39|
|
||||
40|```bash
|
||||
41|# Step 1: Install OpenMAIC
|
||||
42|pip install openmaic-core openmaic-visualizer
|
||||
43|
|
||||
44|# Step 2: Clone learning resources
|
||||
45|git clone https://github.com/sysloggh/openmaic-learning.git
|
||||
46|cd openmaic-learning/modules
|
||||
47|
|
||||
48|# Step 3: Start with Module 1
|
||||
49|python module01_fundamentals.py
|
||||
50|```
|
||||
51|
|
||||
52|## Example Agent Collaboration
|
||||
53|
|
||||
54|```python
|
||||
55|from openmaic.core import Orchestrator, Agent
|
||||
56|
|
||||
57|# Initialize orchestrator
|
||||
58|orchestrator = Orchestrator(
|
||||
59| max_agents=3,
|
||||
60| collaboration_mode="sequential"
|
||||
61|)
|
||||
62|
|
||||
63|# Create agents
|
||||
64|researcher = Agent(name="researcher", role="Research Assistant")
|
||||
65|analyst = Agent(name="analyst", role="Data Analysis")
|
||||
66|writer = Agent(name="writer", role="Content Generator")
|
||||
67|
|
||||
68|# Execute workflow
|
||||
69|result = orchestrator.run([
|
||||
70| researcher.run(task="Research AI trends"),
|
||||
71| analyst.run(task="Analyze market data"),
|
||||
72| writer.run(task="Write market report")
|
||||
73|])
|
||||
74|```
|
||||
75|
|
||||
76|## Use Cases at Syslog GH/LLC
|
||||
77|
|
||||
78|- **Market Research Automation:** Automatically gather and analyze SMB AI adoption data
|
||||
79|- **Content Generation:** Create service descriptions and case studies
|
||||
80|- **Customer Support:** Multi-agent Q&A for client inquiries
|
||||
81|- **Code Review Agents:** Automated code quality checks
|
||||
82|
|
||||
83|## Related Resources
|
||||
84|
|
||||
85|- **[Autonomous AI Agents](../04\ -\ Autonomous\ AI\ Agents/)** — Advanced workflows
|
||||
86|- **[Claude Code & Opencode](../01\ -\ Claude\ Code\ \&\ Opencode/)** — Practical implementation
|
||||
87|- **[MCP (Model Context Protocol)](../03\ -\ MCP\ \(Model\ Context\ Protocol\)/)** — Tool integration
|
||||
88|
|
||||
89|---
|
||||
90|
|
||||
91|*Perfect for Jerome & Theodore's strategy sessions on AI agent frameworks and multi-agent collaboration patterns. This curriculum supports our mission to educate SMBs on AI adoption while building our own agent capabilities.*
|
||||
92|
|
||||
-19
@@ -1,19 +0,0 @@
|
||||
1|# 03 - AI AGENTS & LEARNING — Home
|
||||
2|**Purpose:** AI agent frameworks, learning resources, and implementation guides.
|
||||
3|
|
||||
4|## Contents
|
||||
5|
|
||||
6|### **01 - Claude Code & Opencode** — Agent framework tutorials and best practices.
|
||||
7|
|
||||
8|### **02 - OpenClaw Framework** — Open-source agent orchestration platform setup and usage.
|
||||
9|
|
||||
10|### **03 - MCP (Model Context Protocol)** — Integration guides for connecting LLMs to external tools and data sources.
|
||||
11|
|
||||
12|### **04 - Autonomous AI Agents** — Research and implementation of autonomous agent workflows.
|
||||
13|
|
||||
14|### **05 - Agent Research & Learning** — Papers, YouTube tutorials, podcasts, and general learning resources.
|
||||
15|
|
||||
16|---
|
||||
17|
|
||||
18|*This section pairs conceptual guides with their implementation scripts for rapid prototyping and deployment.*
|
||||
19|
|
||||
-43
@@ -1,43 +0,0 @@
|
||||
1|# 04 - Qwen3.5 Model Setup — Home
|
||||
2|**Purpose:** Installation and configuration guides for Qwen3.5 model variants.
|
||||
3|
|
||||
4|## Available Models
|
||||
5|
|
||||
6|### **Qwen3.5-MoE (Mixture of Experts)** — Optimized for production inference with dynamic expert routing.
|
||||
7|
|
||||
8|### **Qwen3.5-Base** — Standard dense model variant.
|
||||
9|
|
||||
10|### **Qwen3.5-FineTuned** — Domain-specific fine-tuned variants.
|
||||
11|
|
||||
12|## GPU Compatibility
|
||||
13|
|
||||
14|- **AMD Strix Halo** — See [AMD GPU Passthrough Guide](../03\ -\ AMD\ GPU\ Passthrough/00\ -\ AMD\ GPU\ Passthrough.md)
|
||||
15|- **NVIDIA RTX 4090** — Standard CUDA setup
|
||||
16|- **Multi-GPU Clusters** — Distributed inference setup
|
||||
17|
|
||||
18|## Quick Start
|
||||
19|
|
||||
20|### Install Qwen3.5-MoE via Ollama
|
||||
21|```bash
|
||||
22|ollama pull qwen3.5:250b-moe
|
||||
23|ollama serve
|
||||
24|
|
||||
25|# Run locally at 192.168.68.8:8080
|
||||
26|ollama run qwen3.5:250b-moe "What are the top AI trends for 2026?"
|
||||
27|```
|
||||
28|
|
||||
29|### Advanced Configuration
|
||||
30|
|
||||
31|For production deployments with dynamic expert selection:
|
||||
32|- See `/60\ -\ Syslog/02\ -\ TECHNICAL\ INFRASTRUCTURE/04\ -\ Qwen3.5\ Model\ Setup/01\ -\ Qwen3.5-MoE\ Configuration.md`
|
||||
33|- GPU passthrough: `/60\ -\ Syslog/02\ -\ TECHNICAL\ INFRASTRUCTURE/03\ -\ AMD\ GPU\ Passthrough/00\ -\ AMD\ GPU\ Passthrough.md`
|
||||
34|
|
||||
35|## Scripts
|
||||
36|
|
||||
37|- `vulkan-qwen35-moe-setup.sh` — Complete setup script for Qwen3.5-MoE with Vulkan backend
|
||||
38|- `qwen35-moe-inference-config.sh` — Production inference configuration script
|
||||
39|
|
||||
40|---
|
||||
41|
|
||||
42|*For AMD GPU-specific setup, refer to the AMD GPU Passthrough guide.*
|
||||
43|
|
||||
-54
@@ -1,54 +0,0 @@
|
||||
1|# 05 - Agent Research & Learning — Home
|
||||
2|**Purpose:** Research papers, tutorials, and resources for AI agent development.
|
||||
3|
|
||||
4|## Overview
|
||||
5|
|
||||
6|Stay current with the latest developments in AI agent technology through curated research, tutorials, and community resources.
|
||||
7|
|
||||
8|## Learning Resources
|
||||
9|
|
||||
10|### **Academic Papers**
|
||||
11|- **Multi-Agent Systems:** Foundations and applications
|
||||
12|- **Reinforcement Learning for Agents:** Q-learning, PPO, GRPO
|
||||
13|- **Agent Communication Protocols:** FIPA, ACL, custom protocols
|
||||
14|- **Tool Learning:** In-context learning for tool selection
|
||||
15|
|
||||
16|### **Video Tutorials**
|
||||
17|- **YouTube Series:** Deep dives into agent architectures
|
||||
18|- **Conference Keynotes:** NeurIPS, ICML, ICLR talks
|
||||
19|- **Hands-on Labs:** Step-by-step implementation guides
|
||||
20|
|
||||
21|### **Podcasts**
|
||||
22|- **AI Agent Deep Dives:** Interviews with researchers
|
||||
23|- **Industry Applications:** Real-world use cases
|
||||
24|- **Future Trends:** Expert predictions and analysis
|
||||
25|
|
||||
26|### **Community Resources**
|
||||
27|- **GitHub Repositories:** Open-source agent frameworks
|
||||
28|- **Discord Servers:** Community support and collaboration
|
||||
29|- **Conferences:** Annual meetups and workshops
|
||||
30|
|
||||
31|## Recommended Reading
|
||||
32|
|
||||
33|1. **"Language Models as Zero-Shot Planners"** — arXiv:2106.04415
|
||||
34|2. **"ReAct: Synergizing Reasoning and Acting"** — arXiv:2210.03629
|
||||
35|3. **"Tool Learning: LLMs Learning to Call External APIs"** — arXiv:2302.04762
|
||||
36|4. **"Multi-Agent Collaboration: A Survey"** — arXiv:2310.08056
|
||||
37|
|
||||
38|## Stay Updated
|
||||
39|
|
||||
40|- **arXiv Alerts:** Set up daily/weekly digests for "agent" papers
|
||||
41|- **Twitter/X:** Follow key researchers and AI labs
|
||||
42|- **GitHub Topics:** #ai-agents, #multi-agent, #agent-orchestration
|
||||
43|- **Newsletter:** AI Weekly, Towards Data Science, ML News
|
||||
44|
|
||||
45|## Related Frameworks
|
||||
46|
|
||||
47|- **[Claude Code & Opencode](../01\ -\ Claude\ Code\ \&\ Opencode/)** — Practical implementation
|
||||
48|- **[OpenClaw Framework](../02\ -\ OpenClaw\ Framework/)** — Production orchestration
|
||||
49|- **[OpenMAIC Education](../06\ -\ OpenMAIC\ Education/)** — Structured learning
|
||||
50|
|
||||
51|---
|
||||
52|
|
||||
53|*Continuous learning resources for Jerome & Theodore to stay ahead in AI agent development.*
|
||||
54|
|
||||
Reference in New Issue
Block a user