Problem: AI coding performance dips when context windows exceed 50% Solution: Combine Cline's context window awareness with the `new_task` tool + .clinerules to create a workflow that autonomously hands off tasks before hitting limits, ensuring persistent memory. A guide: 🧵

Large context windows aren't a silver bullet. Models can still struggle or "forget" past ~50% usage, degrading performance. Plus, manually re-explaining project context each time you restart is a major workflow killer.

The first piece is awareness: Cline is aware of its own context window usage (visible in `environment_details`). It knows how much "memory" is being used relative to the model's limit (e.g., 105k/200k tokens = 53%).

The second piece is the `new_task` tool. This allows Cline to cleanly end the current session and immediately start a fresh one, crucially preloading it with specific context you define (summaries, next steps, file states, etc.).

The magic happens when you combine these in `.clinerules`. You define the trigger (e.g., "if context 50%, propose handoff") and exactly what context Cline should package using `new_task`. This creates an automated, proactive context management workflow.

The outcome? Cline intelligently manages its own context before performance degrades. No more manual resets or tedious re-explaining. For complex, multi-session tasks, it feels like working with an agent that has persistent memory.

Ready to build workflows that beat context limits? Learn how to implement this with `.clinerules` and the `new_task` tool in our docs:

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this is very cool. To solve this same issue, I created time travel tool to allow agent to partially clear the conversation and summarize it. I think it allow a more flexible way to manage context and make continuation more seamless.

Just curious: how are you measuring context windows? Simply token count input from the user?

cline just keeps consistently delivering. no wonder it's my go-to.