Chat App - Features & Capabilities Report

Template Version: 1.0.0 Generated: 2026-01-22 Package: @epicdm/flowstate-app-chat App ID: chat

Table of Contents

1. Overview
2. App Identity
3. Feature Categories
4. Capabilities
5. Data Models
6. User Workflows
7. Integration Points
8. Architecture Summary
9. Quality & Compliance

Overview

Purpose

The Chat App is a multi-provider LLM chat interface for FlowState. It enables users to have real-time streaming conversations with various AI models including Anthropic Claude, OpenAI GPT, and local models via LM Studio. Conversations are persistently stored in RxDB with organization and workspace scoping, enabling team collaboration and personal conversation management.

Target Users

Value Proposition

• Multi-Provider Support: Switch between Anthropic, OpenAI, and LM Studio seamlessly
• Real-Time Streaming: See responses token-by-token as they generate
• Persistent History: Never lose a conversation with RxDB-backed storage
• MCP Tool Integration: Execute tools directly from conversations
• Markdown Excellence: Full GFM support with syntax-highlighted code blocks
• Flexible Configuration: Per-conversation model and settings selection

App Identity

Entry Points

Feature Categories

Category 1: Conversation Management

Core conversation lifecycle and organization

Feature: Conversation CRUD Operations

Description: Create, view, update, and delete conversations with provider/model selection, custom settings, and metadata tracking.

User Story: As a user, I want to manage my conversations so that I can organize my chat history effectively.

Acceptance Criteria:

• [x] Create new conversations with default settings
• [x] View conversations in sidebar list sorted by most recent
• [x] Update conversation title inline
• [x] Delete conversations with cascade message deletion
• [x] Switch between conversations instantly
• [x] Track message count and last activity

Implementation:

Routes:

• /chat - Redirects to new conversation
• /chat/:conversationId - Active conversation view

Feature: Conversation Settings

Description: Per-conversation configuration including provider, model, temperature, max tokens, and system prompt.

User Story: As a user, I want to configure each conversation's settings so that I can customize AI behavior.

Acceptance Criteria:

• [x] Select provider (Anthropic, OpenAI, LM Studio)
• [x] Choose model within provider
• [x] Configure temperature setting
• [x] Set max tokens limit
• [x] Define custom system prompt
• [x] Persist settings per conversation

Implementation:

Category 2: Messaging

Core chat messaging with streaming

Feature: Message Sending

Description: Send messages to AI models with streaming response display and automatic scroll.

User Story: As a user, I want to send messages and see AI responses in real-time so that I can have natural conversations.

Acceptance Criteria:

• [x] Multi-line text input with auto-resize
• [x] Enter to send, Shift+Enter for new line
• [x] Disabled during streaming
• [x] Message persisted to database
• [x] Conversation metadata updated

Implementation:

Feature: Streaming Responses

Description: Real-time token-by-token response streaming with visual indicators and auto-scroll.

User Story: As a user, I want to see AI responses appear in real-time so that I don't have to wait for complete responses.

Acceptance Criteria:

• [x] Token-by-token content update
• [x] Streaming indicator visible during generation
• [x] Auto-scroll to latest content
• [x] Support for both API proxy and direct client modes
• [x] Graceful error handling

Implementation:

Feature: Message Display

Description: Rich message rendering with markdown support, syntax-highlighted code blocks, and metadata display.

User Story: As a user, I want messages to be beautifully formatted so that I can easily read code and structured content.

Acceptance Criteria:

• [x] Markdown rendering with GFM support
• [x] Syntax-highlighted code blocks
• [x] User/assistant message differentiation
• [x] Timestamp formatting (relative/absolute)
• [x] Token count and cost display (when available)
• [x] Latency metrics display

Implementation:

Dependencies:

• react-markdown - Markdown parsing
• remark-gfm - GitHub Flavored Markdown
• react-syntax-highlighter - Code syntax highlighting

Category 3: LLM Provider Integration

Multi-provider AI model support

Feature: Anthropic Claude Support

Description: Integration with Anthropic's Claude models including Sonnet, Opus, and Haiku variants.

User Story: As a user, I want to use Anthropic Claude models so that I can leverage their capabilities.

Acceptance Criteria:

• [x] Claude Sonnet 4.5 support
• [x] Claude Opus 4.1 support
• [x] Claude Haiku 4.5 support
• [x] Previous generation models (4.0, 4)
• [x] Streaming responses
• [x] Token usage tracking

Implementation:

Feature: OpenAI GPT Support

Description: Integration with OpenAI's GPT models including GPT-4o, GPT-4 Turbo, and GPT-3.5 Turbo.

User Story: As a user, I want to use OpenAI GPT models so that I can access their capabilities.

Acceptance Criteria:

• [x] GPT-4o support
• [x] GPT-4 Turbo support
• [x] GPT-3.5 Turbo support
• [x] Streaming responses
• [x] Token usage tracking

Implementation:

Feature: LM Studio Support

Description: Integration with local models via LM Studio's OpenAI-compatible API.

User Story: As a power user, I want to use local models so that I can run AI privately.

Acceptance Criteria:

• [x] Connect to local LM Studio server
• [x] Compatible with any loaded model
• [x] Streaming responses
• [x] Configurable base URL

Implementation:

Category 4: MCP Tool Integration

Model Context Protocol tool execution

Feature: Tool Discovery

Description: Automatic discovery of available MCP tools from the FlowState MCP Gateway.

User Story: As a user, I want tools to be automatically available so that the AI can perform actions on my behalf.

Acceptance Criteria:

• [x] Connect to MCP Gateway on initialization
• [x] Fetch available tools list
• [x] Track connection status
• [x] Support tool refresh
• [x] Graceful degradation when unavailable

Implementation:

Feature: Tool Execution Display

Description: Visual display of tool calls within messages showing arguments, results, and status.

User Story: As a user, I want to see what tools the AI is using so that I can understand its actions.

Acceptance Criteria:

• [x] Display tool name and status icon
• [x] Show pending/success/error states
• [x] Expandable argument display
• [x] Expandable result display
• [x] Color-coded status borders

Implementation:

Category 5: User Interface

Chat interface and navigation

Feature: Sidebar Navigation

Description: Sidebar with conversation list, new chat creation, and conversation switching.

User Story: As a user, I want a sidebar so that I can easily navigate between conversations.

Acceptance Criteria:

• [x] New Chat button at top
• [x] Scrollable conversation list
• [x] Active conversation highlighting
• [x] Relative timestamp display
• [x] Delete button on hover
• [x] Loading state indicator

Implementation:

Feature: Chat Header

Description: Header bar with conversation title editing, model selection, and metadata display.

User Story: As a user, I want a header so that I can see and edit conversation details.

Acceptance Criteria:

• [x] Editable conversation title
• [x] Inline edit with Enter/Escape keys
• [x] Model selector dropdown
• [x] Message count display
• [x] System prompt indicator

Implementation:

Capabilities

Core Capabilities

Technical Capabilities

Integration Capabilities

Data Models

Collections Used

Entity Relationships

Conversation
    |
    +-- Messages (hasMany)
    |       |
    |       +-- ToolCalls (embedded array)
    |
    +-- Settings (embedded)
    |
    +-- Metadata (embedded)

Key Data Flows

User Input --> ChatInput --> ChatContext --> ChatService --> LLM Provider
                                    |                |
                                    |                +--> RxDB (save message)
                                    |
                                    +--> Streaming Content --> MessageList

User Workflows

Workflow 1: New Conversation

Start a new chat conversation

Trigger: Click "New Chat" button

Steps:

Success Outcome: New conversation with first message Failure Handling: Error logged, user remains on page

Workflow 2: Send Message

Send a message and receive AI response

Trigger: Press Enter or click Send button

Steps:

Success Outcome: Complete AI response displayed Failure Handling: Error logged, streaming state reset

Workflow 3: Switch Conversation

Navigate between existing conversations

Trigger: Click conversation in sidebar

Steps:

Success Outcome: Selected conversation fully loaded Failure Handling: Error state displayed

Workflow 4: Delete Conversation

Remove a conversation and its messages

Trigger: Click delete button on conversation

Steps:

Success Outcome: Conversation fully removed Failure Handling: Error logged, no partial deletion

Integration Points

Internal Integrations

External Integrations

MCP Integration

Architecture Summary

Directory Structure

src/
├── components/          # UI components (8 files)
│   ├── ChatInput.tsx   # Message input
│   ├── ConversationItem.tsx  # Sidebar item
│   ├── Header.tsx      # Chat header
│   ├── MessageBubble.tsx  # Message display
│   ├── MessageList.tsx # Message container
│   ├── ModelSelector.tsx  # Provider/model select
│   ├── Sidebar.tsx     # Navigation sidebar
│   └── ToolCallIndicator.tsx  # Tool call display
├── contexts/           # React contexts (1 file)
│   └── ChatContext.tsx # Main chat state
├── hooks/              # Custom React hooks (2 files)
│   ├── useConversations.ts  # Conversation queries
│   └── useMessages.ts  # Message queries
├── pages/              # Page components (1 file)
│   └── ChatPage.tsx    # Main chat page
├── services/           # Business logic (1 file)
│   └── ChatService.ts  # LLM and MCP operations
├── types/              # TypeScript definitions (4 files)
│   ├── chat.ts         # Main types and config
│   ├── conversation.ts # Conversation types
│   ├── message.ts      # Message types
│   └── index.ts        # Type exports
├── utils/              # Utility functions (1 file)
│   └── index.ts        # Utilities
├── App.tsx             # Main app with routing
├── plugin.ts           # FlowState plugin
├── standalone.tsx      # Standalone entry
└── index.ts            # Package exports

Key Architectural Patterns

Services Overview

Hooks Overview

Quality & Compliance

Test Coverage

Accessibility

Security Considerations

FlowState Alignment

Spec-Driven Development

This app follows the FlowState Standard for spec-driven development:

• Requirements: .flowstate/specs/{{feature-name}}/requirements.md
• Design: .flowstate/specs/{{feature-name}}/design.md
• Wireframes: .flowstate/specs/{{feature-name}}/wireframes.html
• Tasks: .flowstate/specs/{{feature-name}}/tasks.md

Quality Gates

Appendix

Related Documentation

Version History