appkit-assistant
AI assistant component for Reflex applications with MCP server integration.
appkit-assistant provides a complete conversational AI interface built on Reflex, featuring OpenAI and Perplexity integrations, Model Context Protocol (MCP) server management, and secure credential handling. It includes both backend processing services and ready-to-use UI components for building AI-powered applications.
✨ Features
- Multi-Model Support - OpenAI Chat Completions, OpenAI Responses API, Perplexity, and fallback Lorem Ipsum processor
- MCP Server Integration - Manage and connect to Model Context Protocol servers as tools
- System Prompt Management - Versioned system prompts with admin editor interface
- Secure Credential Management - Encrypted storage and handling of API keys and server credentials
- Reflex UI Components - Pre-built assistant interface with composer, thread management, and message display
- Streaming Responses - Real-time streaming of AI responses with chunked content
- Thread Management - Persistent conversation threads with state management
🚀 Installation
As Part of AppKit Workspace
If you're using the full AppKit workspace:
git clone https://github.com/jenreh/appkit.git
cd appkit
uv sync
Standalone Installation
Install from PyPI:
pip install appkit-assistant
Or with uv:
uv add appkit-assistant
Dependencies
appkit-commons(shared utilities)openai>=2.3.0(OpenAI API client)
🏁 Quick Start
Basic Setup
- Configure your API keys in your application's configuration:
from appkit_assistant.configuration import AssistantConfig
# In your app configuration
assistant_config = AssistantConfig(
openai_api_key="your-openai-key",
perplexity_api_key="your-perplexity-key",
# Optional: custom OpenAI base URL
openai_base_url="https://api.openai.com/v1"
)
- Register processors with the ModelManager:
from appkit_assistant.backend.model_manager import ModelManager
from appkit_assistant.backend.processors.openai_chat_completion_processor import OpenAIChatCompletionProcessor
from appkit_assistant.backend.processors.perplexity_processor import PerplexityProcessor
manager = ModelManager()
manager.register_processor("openai", OpenAIChatCompletionProcessor(assistant_config))
manager.register_processor("perplexity", PerplexityProcessor(assistant_config))
- Use the assistant component in your Reflex app:
import reflex as rx
from appkit_assistant.components import Assistant
def assistant_page():
return rx.container(
Assistant(),
height="100vh"
)
📖 Usage
Model Management
The ModelManager singleton handles all AI processors and models:
from appkit_assistant.backend.model_manager import ModelManager
manager = ModelManager()
# Get all available models
models = manager.get_all_models()
# Get a specific model
model = manager.get_model("gpt-4")
# Set default model
manager.set_default_model("gpt-4")
Processing Messages
Process conversations using the registered processors:
from appkit_assistant.backend.models import Message, MessageType
messages = [
Message(role="user", content="Hello, how are you?", type=MessageType.TEXT)
]
async for chunk in manager.get_processor_for_model("gpt-4").process(messages, "gpt-4"):
print(f"Received: {chunk.content}")
MCP Server Management
Manage MCP servers for tool integration:
from appkit_assistant.backend.models import MCPServer
mcp_server = MCPServer(
name="my-server",
command="python",
args=["-m", "my_mcp_server"],
headers={"Authorization": "Bearer token"}
)
# Use in processing
async for chunk in processor.process(messages, "gpt-4", mcp_servers=[mcp_server]):
# Handle response with MCP tools
pass
UI Components
Assistant Interface
The main Assistant component provides a complete chat interface:
from appkit_assistant.components import Assistant
def chat_page():
return Assistant()
Individual Components
Use individual components for custom layouts:
import reflex as rx
from appkit_assistant.components import ThreadList, composer
def custom_assistant():
return mn.stack(
ThreadList(),
composer(),
spacing="4"
)
MCP Server Management UI
Display and manage MCP servers:
from appkit_assistant.components import mcp_servers_table
def servers_page():
return mcp_servers_table()
System Prompt Editor
Admin interface for managing versioned system prompts:
from appkit_assistant.components.system_prompt_editor import system_prompt_editor
def prompt_editor_page():
return system_prompt_editor()
🔧 Configuration
AssistantConfig
Configure API keys and settings:
from appkit_assistant.configuration import AssistantConfig
config = AssistantConfig(
openai_api_key="sk-...",
openai_base_url="https://custom.openai.endpoint/v1",
perplexity_api_key="pplx-...",
google_api_key="AIza..." # For future Google integrations
)
Processor Registration
Register processors based on available credentials:
from appkit_assistant.backend.processors import (
OpenAIChatCompletionProcessor,
PerplexityProcessor,
LoremIpsumProcessor
)
manager = ModelManager()
if config.openai_api_key:
manager.register_processor("openai", OpenAIChatCompletionProcessor(config))
if config.perplexity_api_key:
manager.register_processor("perplexity", PerplexityProcessor(config))
# Always available fallback
manager.register_processor("lorem", LoremIpsumProcessor())
📋 API Reference
Core Classes
ModelManager- Singleton model and processor registryProcessor- Abstract base for AI processorsAIModel- Model metadata and configurationMessage- Conversation message structureMCPServer- MCP server configuration
Component API
Assistant- Complete assistant interfacecomposer- Message input componentThreadList- Conversation thread listMessageComponent- Individual message displaymcp_servers_table- MCP server management tablesystem_prompt_editor- Admin interface for system prompts
State Management
ThreadState- Individual thread stateThreadListState- Thread list management
🔒 Security
- Use
SecretStrfor sensitive configuration values - Credentials are encrypted at rest when stored in the database
- MCP server headers support encrypted storage
🤝 Integration Examples
With AppKit User Management
Combine with appkit-user for authenticated assistants:
from appkit_user import authenticated, requires_role
@authenticated()
@requires_role("assistant_user")
def protected_assistant_page():
return assistant.Assistant()
Custom Processor Implementation
Implement your own AI processor:
from appkit_assistant.backend.processor import Processor
from appkit_assistant.backend.models import AIModel, Chunk, Message
class CustomProcessor(Processor):
def get_supported_models(self):
return {
"custom-model": AIModel(
id="custom-model",
text="Custom AI Model",
icon="🤖"
)
}
async def process(self, messages, model_id, files=None, mcp_servers=None):
# Your AI processing logic here
yield Chunk(content="Response from custom model", type="text")
📚 Related Components
- appkit-mantine - UI components used in the assistant interface
- appkit-user - User authentication and authorization
- appkit-commons - Shared utilities and configuration
1 maintainer
Metadata
1 maintainer
Release files for appkit-assistant 1.15.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| appkit_assistant-1.15.0.tar.gz | 1.9 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| appkit_assistant-1.15.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.1 MB
Release files / appkit_assistant-1.15.0.tar.gz
| Download URL | appkit_assistant-1.15.0.tar.gz |
|---|---|
| Size | 1.9 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / appkit_assistant-1.15.0-py3-none-any.whl
| Download URL | appkit_assistant-1.15.0-py3-none-any.whl |
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| Size | 212.3 kB |
| Tags | Python 3 |
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