72 lines
2.0 KiB
Markdown
72 lines
2.0 KiB
Markdown
# Agent System Architecture
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The Dexorder AI platform uses a sophisticated agent harness that orchestrates between user interactions, LLM models, and user-specific tools.
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## Core Components
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### Gateway
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Multi-channel gateway supporting:
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- WebSocket connections for web/mobile
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- Telegram integration
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- Real-time event streaming
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### Agent Harness
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Stateless orchestrator that:
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1. Fetches context from user's MCP server
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2. Routes to appropriate LLM model based on license
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3. Calls LLM with embedded context
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4. Routes tool calls to user's MCP or platform tools
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5. Saves conversation history back to MCP
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### Memory Architecture
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Three-tier storage system:
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- **Redis**: Hot state for active sessions and checkpoints
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- **Qdrant**: Vector search for RAG and semantic memory
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- **Iceberg**: Cold storage for durable conversations and analytics
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### User Context
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Every interaction includes:
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- User ID and license information
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- Active channel (websocket, telegram, etc.)
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- Channel capabilities (markdown, images, buttons)
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- Conversation history
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- Relevant memories from RAG
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- Workspace state
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## Skills vs Subagents
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### Skills
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Self-contained capabilities for specific tasks:
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- Market analysis
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- Strategy validation
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- Indicator development
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- Defined in markdown + TypeScript
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- Use when task is well-defined and scoped
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### Subagents
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Specialized agents with dedicated memory:
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- Code reviewer with review guidelines
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- Risk analyzer with risk models
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- Multi-file knowledge base
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- Custom system prompts
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- Use when domain expertise is needed
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## Global vs User Memory
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### Global Memory (user_id="0")
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Platform-wide knowledge available to all users:
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- Trading concepts and terminology
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- Platform capabilities
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- Indicator documentation
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- Strategy patterns
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- Best practices
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### User Memory
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Personal context specific to each user:
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- Conversation history
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- Preferences and trading style
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- Custom indicators and strategies
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- Workspace state
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All RAG queries automatically search both global and user-specific memories.
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