Mastering Long-Running AI Coding Tasks with GLM 5.2 and Claude Code

In AI-assisted software development, running large-scale projects over extended periods has long been a challenge. With models like Zhipu AI's GLM 5.2 and tools like Claude Code, it's now feasible to automate complex tasks for hours — even with massive codebases. This article dives into a practical case study: porting the 400,000-line TypeScript project OpenClaw to Python, showcasing the power of long-context AI models and structured task management.

1. Why GLM 5.2 + Claude Code Matters

GLM 5.2, released by Zhipu AI, stands out with its million-token context window and optimized attention mechanisms, making it capable of handling large-scale, long-duration tasks. Paired with Claude Code's goal command for looped execution, you get a robust setup for end-to-end AI-driven development.

Most AI models struggle with "lost in the middle" issues when context windows overflow. GLM 5.2's architectural improvements minimize this, ensuring it stays on track even after 13+ hours of continuous operation — critical for projects like OpenClaw with 10+ core modules and 400,000+ lines of code.

2. Step 1: Project Analysis and Task Breakdown

2.1 Reading and Analyzing the Codebase

First, clone the repository and use Claude Code's agent capabilities to map the architecture:

git clone --depth 1 <OpenClaw-repo-url>
cd python-openclaw

Then trigger multi-agent analysis via Claude Code:

Analyze all .ts files in the 'ts-reference/' directory.
1. Map import dependencies and draw a call graph.
2. Identify core modules (utils, agents, plugins, etc.).
3. Document the dependency hierarchy from bottom to top.
4. List each module's responsibilities and file count.

This generates a structured report outlining the project's architecture:

• Layer 1 (Utils): utils/shared-types (214 files) — pure utility functions.
• Layer 2 (Infrastructure): logging, infra, terminal (273 files) — logging, environment management, terminal rendering.
• Layer 3 (Agent Core): agents, auto-reply, context-engine (529 files) — core AI agent logic.

2.2 Breaking Down Tasks

With the architecture mapped, split the project into granular tasks:

For each module in docs/specs/, break down into function-level tasks.
- Each task must include:
  - A clear requirement (e.g., "Implement compaction for message summarization").
  - At least 3 test cases (e.g., test_empty_messages_returns_empty_string).
  - A validation method (e.g., "Run pytest and ensure 100% coverage").
- Generate a progress.json file to track task status.

This yields 443 tasks, each with precise acceptance criteria. Example task:

## Task 154: Compaction for Message Summarization
- Requirement: Implement summarize_messages(messages, model)
- Test Cases:
  1. test_summarize_single_message_returns_text
  2. test_empty_messages_returns_empty_string
  3. test_fallback_truncation_when_model_fails
- Validation: Run pytest --cov=agents, ensure coverage >= 90%

3. Step 2: Long-Running Execution with goal Command

Claude Code's goal command enables looped execution — the model fetches tasks from progress.json, executes them, and updates status iteratively.

/goal Iterate over all tasks in docs/plans until python check_progress.py returns 0.
- Read progress.json to get the first pending task.
- Execute all test cases defined in the task (at least 3).
- If tests fail (RED), stop and mark as failed.
- If tests pass (GREEN), run ruff check and ruff format --check; fix any issues.
- Commit changes with git commit --task=N.
- Update progress.json (done, completed+1, rounds+1).
- Stop if check_progress.py returns 0; else, continue.

Execute it in Claude Code:

/bypass permissions on
/goal <prompt-text>

During the 13+ hour run, the model handles code generation, automated testing with pytest, linting with ruff, git commits per task, and progress tracking via progress.json.

4. Step 3: Validation and Iteration

Post-execution, validate the output:

• Web Chat: Functional chat interface at http://localhost:8080.
• Skill Execution: Run pre-installed skills like image generation.
• Tool Usage: Fetch web content and save to local files.

If issues arise (e.g., repetitive task execution), simply prompt:

The agent is repeating historical tasks. Fix the loop to avoid duplication.

GLM 5.2's improved instruction-following ensures most fixes land in one shot.

FAQ

Can I use this approach with models other than GLM 5.2?

Yes, any model with a large enough context window can work. The key requirement is maintaining coherence over long sessions without "losing the thread." Claude's own Opus and Sonnet models also handle long-context tasks well, though they may have different pricing structures. The million-token window of GLM 5.2 simply makes it particularly cost-effective for marathon coding sessions. You can also experiment with Gemini 2.5 Pro or GPT-5, both of which offer large context windows.

What happens if the model gets stuck in an infinite loop?

This is a real risk with looped execution. To mitigate it: (1) set a maximum round limit in your goal prompt, (2) use the progress.json file to detect when the same task is attempted multiple times without completion, and (3) periodically check in on long-running sessions. The check_progress.py script mentioned above should include a guard clause that exits after N consecutive failures on the same task.

Is this approach suitable for production code, or just experiments?

It depends on the risk level. For low-risk modules like utility functions and data formatting, AI-generated code with automated testing is production-ready. For security-critical code (authentication, payment processing), combine AI generation with human code review. The 443-task OpenClaw porting project demonstrates that AI can handle the bulk of mechanical translation work, freeing developers to focus on architecture decisions and security audits. Treat it like you'd treat any junior developer's output — test thoroughly, review critical paths, and iterate.