The Only Coding Agent You’ll Ever Need

I’ve been using LLM-backed coding agents in my professional work for nearly two years. They are my primary collaborators, helping me navigate eight-hour workdays and complex goals. Like many, my journey started with Cursor before I migrated to CLI-based tools like Claude Code and various wrappers for Gemini and OpenAI.

Two weeks ago, I dug into the source code of OpenClaw to understand its architecture and dependencies. That’s when I discovered the Pi Coding Agent by Mario Zechner.

I installed it, ran one prompt, and was immediately convinced: I’m never going back to vendor-specific CLI agents.

Why I’m sticking with Pi:

• Efficiency: My token limits last 10x longer for the same volume of work.
• Precision: The output quality is significantly higher, with far less LLM confusion or "weird" architectural decisions.
• Instruction Following: The model sticks accurately to my system prompts and constraints.
• Flexibility: I can switch between model providers seamlessly, even mid-session.
• Branching: It supports session branching, allowing me to form a "tree" of conversations to explore different solutions.
• Autonomy: It keeps working until the LLM determines the task is complete—"YOLO mode" by default.

What makes other CLIs worse?

My theory is simple: System Context Bloat.

By default, tools like Claude Code inject a massive amount of hidden text before your prompt even begins. This "bloat" burns through thousands of tokens before you’ve typed a single word. The creators of these tools make dozens of architectural decisions for you—whether you want them or not—which often leads to the LLM becoming "distracted" by its own internal instructions.

Another factor is Tool Over-engineering. These agents go far beyond create_file, edit_file, read_file and calling bash. Each of these tools carries its own heavy set of instructions (system prompts), further cluttering the context window and diluting the LLM's focus on your actual code.

The Result

By using a leaner agent like Pi, I’ve stripped away the "middleman" between my intent and the model's execution.

The cherry on top: Models have finally become a commodity to me. I’m currently cycling through Anthropic, z.ai, and Moonshot AI (Kimi) within the same session. I can swap the "brain" of the agent mid-stream, and the new model picks up the context seamlessly where the last one left off. I’m getting significantly more relevant output for a fraction of the cost.

If you want to hear more about my experience with "vibe-coding" or assisted development in production environments, drop me a message!