## OpenAgentsControl (plan-executor) - execute.sh: 6-stage approval workflow implementation - Stages: Analyze → Plan → Approve → Execute → Validate → Summarize - Creates .plan-executor directory with tracking files - Interactive approval process - Git integration with commit tracking ## AGIAgent MCP Client (mcp-client) - mcp-client.py: Full MCP protocol client implementation - Server discovery and tool listing - Tool invocation with JSON-RPC - Support for 100+ MCP tools via server configuration - Integrates with: zai-mcp-server, web-search-prime, web-reader, zread ## Agno Orchestrator (multi-agent orchestration) - orchestrator.py: A2A (Agent-to-Agent) communication engine - AgentRegistry: Dynamic agent registration and discovery - CultureMemory: Shared memory across agent executions - Workflow planning and execution (sequential/parallel modes) - Performance tracking and learning ## OS-Copilot (self-learner) - self-learner.py: Learning from completed tasks - Pattern extraction from command sequences and file operations - Success rate tracking per pattern - Improvement suggestion generation - Git history learning integration - Persistent storage in ~/.claude/self-learner/ ## Framework Integration Status ✅ Chippery (codebase-indexer) - 5 bash scripts ✅ Ralph (autonomous agent) - 12 Python files ✅ OpenAgentsControl (plan-executor) - 1 bash script ✅ AGIAgent (mcp-client) - 1 Python script ✅ Agno (orchestrator) - 1 Python script ✅ OS-Copilot (self-learner) - 1 Python script All 5 framework integrations now have ACTUAL CODE IMPLEMENTATION. 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
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