Additional Usage Pointers

Prompting Strategies

We found that it is often a good idea to spend some time conceptualizing and planning a task before actually implementing it, especially for non-trivial task. This helps both in achieving better results and in increasing the feeling of control and staying in the loop. You can make a detailed plan in one session, where Serena may read a lot of your code to build up the context, and then continue with the implementation in another (potentially after creating suitable memories).

Running Out of Context

For long and complicated tasks, or tasks where Serena has read a lot of content, you may come close to the limits of context tokens. In that case, it is often a good idea to continue in a new conversation. Serena has a dedicated tool to create a summary of the current state of the progress and all relevant info for continuing it. You can request to create this summary and write it to a memory. Then, in a new conversation, you can just ask Serena to read the memory and continue with the task. In our experience, this worked really well. On the up-side, since in a single session there is no summarization involved, Serena does not usually get lost (unlike some other agents that summarize under the hood), and it is also instructed to occasionally check whether it’s on the right track.

Serena instructs the LLM to be economical in general, so the problem of running out of context should not occur too often, unless the task is very large or complicated.

Serena and Git Worktrees

git-worktree can be an excellent way to parallelize your work. More on this in Anthropic: Run parallel Claude Code sessions with Git worktrees.

When it comes to serena AND git-worktree AND larger projects (that take longer to index), the recommended way is to COPY your $ORIG_PROJECT/.serena/cache to $GIT_WORKTREE/.serena/cache. Perform pre-indexing of your project to avoid having to re-index per each worktree you create.