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https://github.com/daltonnyx/swissknife

Chat application with multi-agents system supports multi-models and MCP
https://github.com/daltonnyx/swissknife

ai-agents ai-assistant anthropic-claude mcp-client model-context-protocol multi-agent-systems terminal-app tool-use

Last synced: 30 days ago
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Chat application with multi-agents system supports multi-models and MCP

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README 

        
# AI Multi-Agent Interactive System



A sophisticated chat application that provides an enhanced interactive AI

experience through a multi-agent architecture and support for multiple LLM

providers. It features specialized agents, intelligent transfers, file

integration, web capabilities, and a modular extension system.



## Screenshots



![main chat](./examples/images/chat.png)

![chat with code format](./examples/images/code_format.png)

![chat with image file](./examples/images/image_file.png)

![agent config](./examples/images/agent_config.png)

![MCP config](./examples/images/mcp_config.png)



## Installation



### Prerequisites



- Python 3.10 or higher.

- [uv](https://github.com/astral-sh/uv) for package manager.

- An Anthropic API key (for Claude AI models).

- A Groq API key (optional, for Groq LLM models).

- An OpenAI API key (optional, for GPT models).

- A Google Gemini API key (optional, for Gemini models).

- A Tavily API key (for web search features).



### Setup



1. Clone this repository:



   ```bash

   git clone https://github.com/daltonnyx/swissknife.git

   cd swissknife

   ```



2. Install the package:



   ```bash

   uv sync

   ```



3. Install the tool



   ```bash

   uv tool install .

   ```



4. Make sure you have one of `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`,

   `GOOGLE_API_KEY`, `GROQ_API_KEY`, `DEEPINFRA_API_KEY` in Environment

   variables. you can setup multi LLM api key for switching model in app



   ```

   ANTHROPIC_API_KEY=your_anthropic_api_key

   OPENAI_API_KEY=your_openai_api_key

   GROQ_API_KEY=your_groq_api_key

   GOOGLE_API_KEY=your_google_api_key

   TAVILY_API_KEY=your_tavily_api_key

   ```



   `TAVILY_API_KEY` is for web search capability. omit it if you don't need that

   features



## Usage



### Start an Interactive Chat



```bash

swissknife chat

```



### Start an Interactive Chat with TUI



```bash

swissknife chat --console

```



### Start a Chat with Different Providers



```bash

# Use Claude models from Anthropic

swissknife chat --provider claude



# Use LLM models from Groq

swissknife chat --provider groq



# Use GPT models from OpenAI

swissknife chat --provider openai



# Use Gemini models from Google

swissknife chat --provider gemini



# Use LLM models from Deepinfra

swissknife chat --provider deepinfra

```



### Start chat with different agent config



```bash

swissknife chat --agent-config path/to/custom_agents.toml

```



### Start Google A2A server for remote connection



```bash

uv run swissknife/main.py a2a-server --provider google --agent-config ~/.swissknife/agents.toml --port 41241

```



## Security & Responsibility Notice



Swissknife gives you powerful control over AI agents and external tools.



- You are responsible for the content of agent system prompts.

- You are responsible for which external tools (MCP, file, clipboard) you

  enable.

- Unsafe prompts or tools may result in data leaks, loss, or unwanted actions.

- Always review prompts and tool settings before use.

- Use safe defaults unless you understand the risks of advanced configurations.

  For maximum safety:

  - Limit tool permissions to only what is needed.

  - Never include passwords or sensitive data in prompts or config files.

  - Confirm before enabling tools with file system or network access.



## Core Architecture



### Multi-Agent System



This system implements a multi-agent architecture with specialized agents with

`toml` config file `agents.toml`:



```toml

[[agents]]

name = "Coding"

description = "Specialized in code implementation, debugging, programming assistance and aider prompt"

tools = ["clipboard", "memory", "code_analysis", "spec_validator"]

system_prompt = """You are Harvey, a focused code implementation expert. Your guiding principle: **SIMPLICITY IN IMPLEMENTATION** (Simple + Practical Implementation). Prioritize clean, maintainable code that aligns with best practices.



Today is {current_date}.

"""

```



Current supported dynamic template tag is:



- `current_date`: return current date in YYYY-MM-dd format



The system intelligently routes tasks to the appropriate agent and features

seamless **agent transfer** when a conversation requires different expertise.

This allows for:



- Domain-specialized reasoning and responses.

- Continuity of context during complex multi-domain tasks.

- Improved problem-solving through agent collaboration.



```

[User Input] --> [Agent Manager] --> [Selected Agent] --> [LLM Service] --> [Response]

```



### Agent Transfer Mechanism



The intelligent transfer system allows seamless transitions between specialized

agents:



1. The current agent identifies when a different expertise is required.

2. A transfer request is initiated with sharing context Mechanism.

3. The appropriate specialized agent is activated.

4. Conversation continues with maintained context but specialized capabilities.



```mermaid

sequenceDiagram

    participant agentA as Agent A

    participant agentManager as AgentManager

    participant agentB as Agent B



    Note over agentA: Has its own separate message history

    Note over agentB: Has its own separate message history



    agentA->>agentManager: Calls transfer(target_agent=Agent B, task, relevant_messages)

    activate agentManager

    Note over agentManager: Uses 'relevant_messages' indices to select messages from Agent A's history



    agentManager->>agentManager: Prepare new context (system prompt + selected messages from Agent A)

    agentManager->>agentB: Replace internal message history with new context

    agentManager->>agentB: Activate Agent B

    deactivate agentManager



    activate agentB

    agentB->>agentB: Process task using injected context

    deactivate agentB

```



## Key Features



- **Interactive Chat**: Rich conversation interface with various AI models.

- **File Integration**: Include PDFs and text files directly in conversations.

- **Web Capabilities**: Fetch content, perform searches, and extract information

  from the web.

- **Tool Ecosystem**: Extensible set of tools via MCP support.

- **Keyboard Shortcuts**: Efficient message editing and response management.

- **Persistent Memory**: Cross-conversation knowledge retention and retrieval.

- **Code Analysis**: Deep analysis of code structure in repositories.



## Agent System Details



### Agent Manager



The system employs a centralized Agent Manager that:



- Maintains the registry of available specialized agents.

- Handles selection of the appropriate agent for each task.

- Manages the transfer process between agents.

- Ensures context sharing during transitions.



### Agent Activation



Each agent features:



- A specialized system prompt optimized for its domain.

- Specific tools relevant to its expertise.

- Custom reasoning patterns suited to its specialty.



## LLM Provider Integration



The system implements a unified service layer that:



- Standardizes interactions across different LLM providers.

- Transforms messages between provider-specific formats.

- Handles provider-specific authentication and API interactions.

- Manages tool registration and execution for each provider.



### Provider-Specific Features



- **Claude (Anthropic)**: Extended thinking mode for complex reasoning.

- **GPT (OpenAI)**: Advanced tool use and function calling.

- **Groq**: High-performance inference for faster responses.

- **Deep infra**: more options for open-source models.

- **Gemini (Google)**: Multimodal processing and advanced content generation.



## Additional Capabilities



- **Web Content Processing**: Fetch and process web content as markdown.

- **Web Search**: Search the internet for current information.

- **Web Content Extraction**: Extract and analyze content from specific URLs.

- **Model Context Protocol (MCP)**: Connect to MCP-compatible servers to extend

  AI capabilities.

- **Memory System**: Store, retrieve, and manage conversation history.

- **Code Structure Analysis**: Generate structural maps of source code.



## Chat Features



- **File Integration**: Include files in your conversation.

- **Web Integration**: Ask the AI to scrape URLs or search the web directly.

- **Copy Responses**: Easily copy AI responses to your clipboard with Alt+C.

- **Multiline Input**: Write complex messages with proper formatting.

- **Code Analysis**: Analyze and understand the structure of code repositories.



## Extended Thinking Mode



The system supports extended thinking capability (currently only with Claude),

which allows the AI to:



- Work through complex problems step-by-step.

- Show its reasoning process before providing a final answer.

- Allocate a specific token budget for the thinking process.



Enable thinking mode during chat with `/think ` command, where budget is

the token allocation (minimum 1024 tokens). Support reasoning effort of openai,

`budget` can be `low`, `medium` or `high` when using openai reasoning model



## Chat Commands



During an interactive chat session, you can use these commands:



- `/file ` - Include a file in your message.

- `/clear` - Clear the conversation history.

- `/think ` - Enable extended thinking mode with specified token budget

  (min 1024). for openai reasoning model, `` can be `low`, `medium`,

  `high`

- `/think 0` - Disable thinking mode.

- `/model ` - Switch to a different AI model during your chat session.

- `/models` - List all available models you can switch to.

- `/agent ` - Switch to a different specialized agent.

- `/agents` - List all available specialized agents.

- `exit` or `quit` - End the chat session.

- Press `Enter` for a new line, `Ctrl/Cmd+Enter` to submit your message.



## Memory System



The tool includes a persistent memory system that:



- Automatically stores all conversations.

- Allows the AI to retrieve relevant past conversations using the

  `retrieve_memory` tool.

- Automatically cleans up conversations older than one month.

- Intelligently chunks and stores conversations for efficient retrieval.



## Universal MCP Server Support



The system supports connecting to any MCP-compatible server using the following

configuration format in your `mcp_servers.json` file:



```json

{

    "server_id": {

        "name": "string",

        "command": "string",

        "args": ["string"],

        "env": {

            "key": "value"

        },

        "enabledForAgents": ["string"]

}

```



- `server_id`: Unique identifier for the server.

- `name`: Human-readable name for the server.

- `command`: The command to execute to start the server.

- `args`: List of arguments to pass to the command.

- `env`: Environment variables to set for the server process.

- `enabledForAgents`: List of agent names that are allowed to use tools from

  this server.



## Roadmap



The following features are planned for future releases:



### Enhanced Assistant Capabilities



- [x] **Web Search Integration**: Allow AI to search the web for real-time

      information.

- [x] **Conversation Memory**: Store and retrieve past conversations.

- [x] **Code Analysis**: Analyze code structure in git repositories.

- [x] **Model Context Protocol (MCP) Support**: Implement MCP for improved

      context management.

- [x] **Command-line Thinking Mode Options**: Enable and configure thinking mode

      with the `/think` command.

- [x] **Agents through config**: Dynamic agents configurations through config

      file

- [ ] **Google A2A Compatible**: Support Google A2A protocol for both client and

      server



### Advanced Personalization



- [ ] **Enhanced Memory Management**: Configure memory retention periods and

      retrieval preferences.

- [ ] **Local Model Support**: Add ONNX runtime integration.

- [ ] **Advanced Memory Indexing**: Improve memory retrieval accuracy and

      performance.

- ~~[x] **Adaptive User Context**: Agent can build up a context and knowledge

  base when interact with user and use it to tailor the response~~ => due to

  messing around with current request, will comeback on this later

- [x] **Multi-Agent Collaboration**: Enable multiple agents to work together on

      complex tasks.



### User Experience



- [x] **Session Management**: Save, resume, and manage multiple conversation

      threads.

- [x] **UI Improvements**: Enhanced terminal interface with better visualization

      options.

- [ ] **Configuration Profiles**: Create and switch between different tool

      configurations.

- [ ] **Memory Visualization**: Browse and manage stored conversations.

- [ ] **Agent Performance Metrics**: Track and analyze agent effectiveness.



## License



[MIT License](LICENSE)