Tech • IA • Crypto
Context Management in Claude Code
TL;DR
Efficient management of limited context windows is essential for maintaining performance and accuracy in AI-assisted coding workflows.
KEY POINTS
Finite context window as a core constraint
AI coding systems operate within a limited “context window,” which stores prompts, file reads, tool calls, and responses. Every interaction consumes space, making it a critical resource during development. As this window fills, performance can degrade unless actively managed.
Automatic and manual compaction
When the context approaches capacity, automatic compaction summarizes key information and removes less relevant data such as detailed tool outputs. Developers can also trigger this process manually using a /compact command to free space while retaining a condensed memory of prior work.
Risk of information loss during compaction
While compaction helps reclaim space, it can discard useful details from earlier interactions. This creates a trade-off between maintaining full conversational history and preserving capacity for continued work, especially in complex development tasks.
Clearing context for fresh workflows
A /clear command allows complete reset of the context window, removing all prior information. This is particularly useful when starting a new feature or project, preventing earlier work from biasing or interfering with new tasks.
Monitoring context usage
Developers can inspect context consumption through a /context command, which provides a breakdown of usage and highlights which elements consume the most space. This visibility supports better decision-making about when to compact or clear.
Strategic use of compaction vs. clearing
A practical guideline emerges: compact when continuing work on the same feature, and clear when transitioning to a new one. This ensures relevant information is preserved without carrying unnecessary context into unrelated tasks.
Persistent memory through external files
Important long-term information can be stored in dedicated files such as claw.md. This allows systems to retain key knowledge across sessions without repeatedly rediscovering it, reducing context usage over time.
Importance of precise prompting
Vague prompts increase context consumption because the system must explore more of the codebase and generate additional reasoning. Clear, specific instructions reduce unnecessary processing and help conserve context space.
Impact of tool and server loading
External integrations such as MCP servers can significantly inflate context usage by loading large sets of tools by default. Disabling unused servers or switching to lighter alternatives like skills can improve efficiency.
Use of sub-agents for isolated tasks
Sub-agents operate with separate context windows, allowing them to handle specific queries independently. They can return concise summaries to the main agent, minimizing the main context’s load while still providing needed results.
CONCLUSION
Managing context effectively—through compaction, clearing, precise prompting, and selective tool use—is critical to sustaining efficient and accurate AI-driven development workflows.
Full transcript
Context is Claw's working memory. Every file it reads, every command it runs, every message you send, it all takes up space in the context window. Think of the context window as the amount of space that Claude could hold in his memory. Whenever you enter a prompt, Claude reads a file, runs a tool call, gets a tool call result. This is added on to the context window. And since there's only a finite amount you can put in the context window, it becomes extremely important to optimize this as much as possible. Now, when you approach this limit, the context window is automatically compacted. Compaction will summarize important details and remove the unnecessary tool call results and free up a lot of space in your context window. Do note though that this could potentially lose details in your previous conversation. You can run the compaction manually as well with the /compact command. This will compact everything that you've done up to that point, which could be handy if you want to clear up context space, but also have a memory of what you previously worked on. If you want to completely start from scratch without memory of what was previously worked on, you can also run /cle and that will remove everything starting from scratch. To check the state of your context, run the /context command. Here you'll get a big picture of how large your context size is, the different categories that are taking up the most context, and a graphic showing you all of this. A general rule of thumb is when you're working on a specific feature and are going over the context window, but need to continue, then compact. Keeping the context relevant for this feature is important when continuing development. If you have finished the plan and want to start on a new feature, then clear. You don't want the previous conversation to present bias in anything new that you want to create. For things that you do want Claude to remember in other sessions, put it in the claw.md file. That way, it doesn't have to rediscover things from scratch all over again. Be specific. The irony behind writing a smaller prompt is that it in the long run, it will take up more context. Without being explicit, Claude is forced to look around your codebase more and do its own thinking, which takes up a lot more context. window space than if you were just a little bit more clear with a sentence or two. MCP servers load all of the tools available into context by default. So, if you have a lot of MCP servers for things that are unrelated to the project, it might be worth turning them off. You can also try out skills, which works similarly to MCP servers, but doesn't put the entire thing into context, saving you space. Sub agents run in parallel with your main agent but has a complete separate context window. So for tasks that require an answer without the journey like where is the authentication endpoints located, you can have the sub agent do the work and return just a summary to your main agent. Managing context within cloud code is crucial. Use slash compact to summarize long sessions and slashclear to start fresh. To use your context window effectively, be specific with what you want. Check what's using your current context window and use sub agents to delegate tasks you only need the answer for.