Why AI Coding Agents Feel Like a Black Box
AI coding agents have fundamentally changed the way developers write, review, and debug code. Tools like Claude Code and OpenAI Codex can generate entire functions, refactor complex logic, and even navigate multi-file projects with impressive accuracy. But if you've spent any real time working with them, you've likely hit a wall of uncertainty: what is the agent actually doing behind the scenes?
What's in that system prompt the agent is using? How much context is being passed to the model on every single turn? Are you burning through tokens unnecessarily? When the agent gives a strange or unexpected response, what information did it actually have when it made that decision? These are not trivial questions. Token costs add up quickly, and debugging a poorly behaved agent is nearly impossible when you can't see what it's working with.
Until recently, there was no easy way to answer any of this without diving deep into API logs, setting up complex interceptors, or installing certificates just to inspect HTTPS traffic. That friction meant most developers simply accepted the black box and moved on. A tool called ccglass changes that completely.
What Is ccglass?
ccglass is a lightweight, locally run tool that acts as a reverse proxy between your AI coding agent and the model's API. In plain terms, it sits in the middle of the conversation between your agent and the model, quietly capturing every request and response that passes through. It then logs all of that data and presents it inside a clean, easy-to-read web dashboard that runs locally in your browser.
This means you get full visibility into the raw payloads being sent and received — including the system prompt, the complete message history, token counts, and more — without touching your network configuration, installing CA certificates, or modifying any source code. It works transparently alongside your existing workflow and supports multiple popular agents including Claude Code, OpenAI Codex, and DeepSeek-TUI.
For developers who care about understanding their tools, optimizing costs, or simply satisfying their curiosity about how these agents frame their instructions, ccglass is genuinely exciting.
Getting Started with ccglass
One of the most appealing things about ccglass is how easy the setup process is. The only prerequisite is Node.js version 18 or higher, which most active developers already have installed. From there, a single command gets you up and running.
To install ccglass globally, open your terminal and run the following:
npm install -g ccglass
Once installed, you simply run ccglass and select your agent interactively, or pass it directly as an argument:
ccglass claude— launches the proxy and dashboard configured for Claude Codeccglass codex— sets up everything for OpenAI Codexccglass deepseek— configures the tool for DeepSeek-TUI
When you run the command, ccglass takes care of everything automatically. It starts a local proxy server, sets the appropriate environment variables so your agent routes traffic through the proxy, launches the agent itself, and opens the web dashboard in your browser. There are no certificates to install, no complicated network routing to configure, and no manual environment setup. The entire process takes under a minute, which is a remarkable achievement for a tool this capable.
What You Can Actually See Inside the Dashboard
The ccglass web dashboard is where the real value lives. Once your agent is running and you start issuing prompts, the dashboard populates in real time with structured, readable data that would otherwise be completely hidden from you. Here's a breakdown of what you get access to.
The Full System Prompt
This is arguably the most fascinating part of the entire experience. Every AI coding agent ships with a system prompt — a set of instructions that tells the model how to behave, what its role is, what constraints it operates under, and how to handle specific situations. These system prompts are typically proprietary and not published anywhere. With ccglass, you can read them in full.
Claude Code's system prompt, in particular, is a detailed and thoughtfully written document. Reading it gives you real insight into how Anthropic has engineered Claude's coding persona, what priorities it has been given, and how it is instructed to reason about your requests. For anyone interested in prompt engineering or AI agent design, this alone is worth the installation.
Complete Message History Per Turn
Beyond the system prompt, ccglass shows you the complete message array being sent to the model on every turn. This is crucial for understanding how context accumulates over a session. You can see exactly which prior messages are retained in the context window, which ones have been dropped, and how the conversation history evolves as you keep working. This visibility is invaluable for diagnosing situations where the agent seems to have "forgotten" something you told it earlier, or where it appears to be working from stale information.
Token Usage and Cost Awareness
Token consumption is one of the biggest hidden costs of working with AI coding agents at scale. ccglass surfaces token counts for each request and response, giving you a clear picture of where your budget is going. If you're running an agent in a large codebase and notice your costs creeping up, the dashboard helps you identify which interactions are the heaviest consumers — whether that's long file reads, verbose tool outputs, or accumulating context that could be pruned.
Why Transparency in AI Tools Matters
There's a broader principle at work here that goes beyond developer convenience. As AI agents become more capable and more deeply integrated into professional workflows, the ability to inspect and understand their behavior becomes a matter of trust and accountability. A developer who can see what their agent is doing is a developer who can catch errors, optimize performance, and make informed decisions about when to trust the output.
Tools like ccglass represent a healthy trend toward making AI systems more auditable at the individual developer level. You don't have to accept the black box. You can look inside, understand the mechanics, and work more intelligently as a result.
Who Should Use ccglass?
ccglass is useful for a wide range of people working with AI coding tools. If you are a developer trying to debug unexpected behavior from your agent, it gives you the raw data you need to diagnose the problem. If you are a team lead trying to manage API costs across multiple engineers, it gives you the token-level visibility to make smarter decisions. If you are a prompt engineer or AI researcher curious about how commercial agents structure their instructions, the system prompt access alone is a goldmine of practical insight.
Even if you're simply someone who likes to understand the tools they rely on, there is real satisfaction in finally being able to see what's happening beneath the surface of your AI assistant.
Final Thoughts
ccglass solves a real problem in a clean, elegant way. It requires almost no setup, supports the most popular AI coding agents, and delivers genuinely useful visibility into the inner workings of your AI-powered development workflow. If you use Claude Code, Codex, or DeepSeek-TUI regularly, installing ccglass takes less than two minutes and will immediately change how you understand and interact with these tools. Transparency is a feature, and ccglass delivers it without compromise.