nixagent 1.23

A sophisticated AI agent toolkit supporting multiple AI providers with tool calling capabilities, enterprise state management, HITL, Graph routing, LangChain support, and structured outputs.

nixagent

A generic, multipurpose nixagent library in Python. This framework is completely agnostic to specific use cases and architectures, serving as a robust foundation for building autonomous, collaborative AI agents that can manage their own context, interface with each other, and securely use external tools.

🚀 Quick Start

Installation

pip install -r requirements.txt

Command Line Usage

First, set up your environment configuration by copying .env.example to .env and adding your API keys.

# Ask a question directly
python app.py "What files are in the current directory?"

# Interactive mode
python app.py

# With custom settings
python app.py "Analyze the code structure" --no-save

Python Library Usage

from nixagent import Agent

# Initialize the core agent
agent = Agent(
    name="MainAgent",
    system_prompt="You are a highly capable AI assistant that uses available tools to accomplish goals."
)

result = agent.run(user_prompt="List all Python files in the project")
print(result)

✨ Features

• 🌐 Standardized API Interface: Uses pure requests following the OpenAI native JSON structure. Compatible with OpenAI, Vertex, Local LLMs (via Ollama/vLLM), Groq, and more.
• 🤖 Autonomous Agents: Agents maintain independent conversation histories and automatically delegate sub-tasks when needed.
• 🔌 Model Context Protocol (MCP): Dynamic tool extension via MCP Servers via .mcp.json.
• 🛠️ Rich Built-In Tools: Deep system-level tools covering regex-based file searching, exact content mapping, disk manipulation, and secure subprocess execution.
• 🗣️ Inter-Agent Collaboration: Support for multiple sub-agents operating concurrently under the same framework via .register_collaborator(agent).

📦 Project Structure

framework/
├── app.py                # Main CLI application
├── nixagent/             # Core Framework Mechanics
│   ├── __init__.py       # Library exports
│   ├── agent.py          # Core contextual autonomous Agent
│   ├── llm.py            # Central HTTP-based LLM orchestration
│   ├── logger.py         # Central system execution logger
│   ├── mcp.py            # Model Context Protocol definition and bindings
│   ├── providers/        # LLM Vendor specific HTTP adapters
│   │   ├── openai.py
│   │   ├── anthropic.py
│   │   ├── gemini.py
│   │   ├── vertex.py
│   └── tools/            # Default Native Tools
│       ├── __init__.py   # Tool bindings & descriptions
│       ├── cmd.py        # Subprocess shell extensions
│       └── fs.py         # File system native operations
├── mcp.json              # Model Context Protocol Server mapping
├── docs/                 # Additional Documentation
├── requirements.txt      # Python dependencies
├── .env                  # Operational mapping variables
└── README.md             # This file

⚙️ Configuration

Create a .env file in your project root:

# LLM Provider (openai, anthropic, gemini, or vertex)
PROVIDER=openai

# OpenAI Configuration
OPENAI_API_KEY=your_api_key_here
OPENAI_BASE_URL=https://api.openai.com/v1
OPENAI_MODEL=gpt-4o

# Anthropic Configuration
ANTHROPIC_API_KEY=your_anthropic_api_key_here
ANTHROPIC_BASE_URL=https://api.anthropic.com/v1
ANTHROPIC_MODEL=claude-3-opus-20240229

# Gemini Configuration
GEMINI_API_KEY=your_gemini_api_key_here
GEMINI_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai
GEMINI_MODEL=gemini-2.5-flash

# Vertex AI Configuration
VERTEX_API_KEY=your_vertex_api_key_here
VERTEX_BASE_URL=https://aiplatform.googleapis.com/v1
VERTEX_MODEL=gemini-2.5-flash-lite

# Tool and Processing Configuration
MAX_ITERATIONS=25

# Logging Configuration
LOG_LEVEL=INFO
LOG_FILE=agent.log  # (Optional) Route all agent tool execution traces to this file instead of stdout

🔌 Using MCP Servers

Add server definitions to your mcp.json file in the root directory:

{
  "mcpServers": {
    "sqlite": {
      "command": "uvx",
      "args": ["mcp-server-sqlite", "--db-path", "./database.db"],
      "active": true
    }
  }
}

The framework's MCPManager automatically bootstraps all active MCP servers, parses their schemas, and loads their tools natively alongside standard tools upon Agent initialization.

🤝 Collaborative Agents

Agents can securely establish communication networks.

from nixagent import Agent

research_agent = Agent("Researcher", "You perform file system research.")
writer_agent = Agent("Writer", "You answer questions accurately.")

writer_agent.register_collaborator(research_agent)

writer_agent.run("Ask the Researcher to find all text files and read them to me.")

🤖 AI Skill

nixagent is available as an installable AI coding agent skill — giving your AI assistant full knowledge of the framework, its API, and usage patterns.

Install the Skill

npx skills add technicalheist/nixagent

This installs the skill into your project's agent directories (Cursor, Copilot, Cline, Antigravity, and more) so your AI assistant can immediately understand and use nixagent without extra explanation.

What's Included

The skill (skills/nixagent/SKILL.md) contains:

• Full installation & environment setup guide
• Documentation for all 7 core features
• Provider-specific usage (OpenAI, Anthropic, Gemini, Vertex)
• Code reference examples in skills/nixagent/examples/

Find the Skill

GitHub	technicalheist/nixagent › skills/nixagent
Skill Registry	skills.sh

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.