Mini Agent Emerges as Powerful Open‑Source Alternative to Claude, Code and Manus
Mini Agent Emerges as Powerful Open‑Source Alternative to Claude, Code and Manus
Introduction
The AI agent landscape has been dominated for months by proprietary solutions such as Anthropic’s Claude, OpenAI’s Code interpreter, and the research‑focused Manus platform. MiniAgent, an open‑source project from the Miniax team, now offers a compelling, low‑cost alternative that leverages the new Miniax M2 model. Built on an Anthropic‑compatible API, MiniAgent delivers a full agent execution loop, persistent memory, and a suite of professional skills—all while remaining affordable and highly extensible.
Overview of MiniAgent
MiniAgent is positioned as a minimal yet professional demo project that showcases best practices for building autonomous agents with the Miniax M2 model. It aims to provide developers with a reliable foundation for creating agents capable of long‑running, complex tasks such as deep research, document generation, and code development.
Key Features
- Anthropic‑compatible API – Works with Miniax M2, Sonnet, or any compatible endpoint.
- Interleaved Thinking – Unlocks powerful reasoning for extended tasks.
- Full Execution Loop – Handles tool calls, file‑system interactions, and shell commands out‑of‑the‑box.
- Persistent Memory – An active session‑note tool retains critical information across interactions.
- Automatic Summarization – Keeps context within a configurable token limit, enabling virtually infinite task lengths.
- 15 Built‑in Professional Skills – Cover document creation, design, testing, and development (e.g., PDF/DOCX generation, web page rendering).
- Extensible Toolset – Supports additional MCP (Multi‑Channel Proxy) servers for web browsing or external APIs.
- Robust Logging & Simple Design – Facilitates debugging and customization.
Installation & Configuration
- Install the package – Run the provided installation command (e.g.,
pip install mini-agent). - Generate configuration files – Execute the setup script that creates a default
config.yaml. - Edit the configuration – Populate the following fields:
base_url– Your Anthropic‑compatible endpoint.model_name– Typicallyminiax-m2.max_steps– Default is 100; increase to 1,000+ for longer sessions.workspace_dir– Directory for temporary files and outputs.
- Launch the agent – Run
mini-agentfrom the terminal. The interface appears within seconds.
Using MiniAgent: A Practical Demo
After starting the CLI, a handful of slash commands are available:
/help– Lists shortcuts./clear– Clears session history while preserving the system prompt./history– Shows message count./stats– Displays session statistics./exit– Terminates the program.
Keyboard shortcuts include Ctrl+U to clear the input line, J to insert a new line, and Tab for command autocomplete.
Example: Building a Simple Minesweeper Game
The user requests a “simple Minesweeper game displayed in the browser.” MiniAgent proceeds as follows:
- Task Decomposition – Breaks the request into subtasks (HTML layout, CSS styling, JavaScript logic).
- Tool Invocation – Calls the file system tool to create project files, then uses a shell tool to launch a local server.
- Iterative Refinement – Adjusts code based on intermediate outputs, all within a handful of seconds.
- Result – The game opens automatically in the default browser.
The entire process demonstrates MiniAgent’s snappy tool‑calling loop and its ability to handle multi‑step workflows without manual intervention.
Comparison with Existing Solutions
| Feature | MiniAgent | Claude (Anthropic) | Manus | OpenAI Code Interpreter |
|---|---|---|---|---|
| Open‑Source | ✅ | ❌ | ❌ | ❌ |
| Pricing | $10‑$50/month plans, cheap per‑token rates | Higher tier pricing | Enterprise pricing | Pay‑as‑you‑go |
| Persistent Memory | ✅ (session notes) | Limited | ✅ | ❌ |
| Tool Set | File system, shell, custom MCP | Limited | Extensive (web, DB) | Limited to Python |
| Maximum Steps | Adjustable up to 1,000+ | Fixed | Configurable | Fixed |
| Extensibility | Easy to add new skills via system prompt | Proprietary | Plugin ecosystem | Limited |
MiniAgent’s open‑source nature and flexible configuration make it especially attractive for developers who need long‑running autonomous agents without the cost or vendor lock‑in of commercial platforms.
Potential Use Cases
- Deep Research Agents – Combine multiple MCP servers for web crawling, citation gathering, and summarization.
- Automated Documentation – Generate PDFs, DOCX files, or markdown reports from raw data.
- Rapid Prototyping – Spin up front‑end prototypes (e.g., landing pages, simple games) with a single prompt.
- Testing Frameworks – Use built‑in testing skills to create unit tests for code snippets.
- Custom CLI Personas – Deploy multiple MiniAgent instances each tuned to a specific domain (e.g., finance, legal, DevOps).
Conclusion
MiniAgent represents a significant step forward for developers seeking a capable, cost‑effective, and extensible autonomous agent framework. By harnessing the Miniax M2 model and an Anthropic‑compatible API, it delivers persistent memory, robust tool integration, and a rich skill set—all within an open‑source package that can be tailored to virtually any workflow. Whether you are building deep‑research assistants, automating documentation, or simply need a reliable CLI tool for rapid prototyping, MiniAgent offers a compelling alternative to the dominant proprietary agents on the market.