redesign fully scaffolded and web login works

gateway/knowledge/platform/agent-system.md

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

gateway/knowledge/platform/mcp-integration.md

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+# Model Context Protocol (MCP) Integration
+
+Dexorder uses the Model Context Protocol for user-specific tool execution and state management.
+
+## Container Architecture
+
+Each user has a dedicated Kubernetes pod running:
+- **Agent Container**: Python environment with conda packages
+- **Lifecycle Sidecar**: Manages container lifecycle and communication
+- **Persistent Storage**: User's git repository with indicators/strategies
+
+## Authentication Modes
+
+Three MCP authentication modes:
+
+### 1. Public Mode (Free Tier)
+- No authentication required
+- Container creates anonymous session
+- Limited to read-only resources
+- Session expires after timeout
+
+### 2. Gateway Auth Mode (Standard)
+- Gateway authenticates user
+- Passes verified user ID to container
+- Container trusts gateway's authentication
+- Full access to user's tools and data
+
+### 3. Direct Auth Mode (Enterprise)
+- User authenticates directly with container
+- Gateway forwards encrypted credentials
+- Container validates credentials independently
+- Highest security for sensitive operations
+
+## MCP Resources
+
+The container exposes standard resources:
+
+### context://user-profile
+User preferences and trading style
+
+### context://conversation-summary
+Recent conversation context and history
+
+### context://workspace-state
+Current chart, indicators, and analysis state
+
+### context://system-prompt
+User's custom agent instructions
+
+### indicators://list
+Available indicators with signatures
+
+### strategies://list
+User's trading strategies
+
+## Tool Execution Flow
+
+1. User sends message to gateway
+2. Gateway queries user's MCP resources for context
+3. LLM generates response with tool calls
+4. Gateway routes tool calls:
+   - Platform tools → handled by gateway
+   - User tools → proxied to MCP container
+5. Tool results returned to LLM
+6. Final response sent to user
+7. Conversation saved to MCP container
+
+## Container Lifecycle
+
+### Startup
+1. Gateway receives user connection
+2. Checks if container exists
+3. Creates pod if needed (cold start ~5-10s)
+4. Waits for container ready
+5. Establishes MCP connection
+
+### Active
+- Container stays alive during active session
+- Receives tool calls via MCP
+- Maintains workspace state
+- Saves files to persistent storage
+
+### Shutdown
+- Free users: timeout after 15 minutes idle
+- Paid users: longer timeout based on license
+- Graceful shutdown saves state
+- Persistent storage retained
+- Fast restart on next connection