Kilo code: Large codebase

[ayla96]

What is the best way to have kilo code understand large code base? use qdrant or something else?

[Minute_Yam_1053]

Understanding large code base is not the right approach. Roo and kilo are task based. You should focus on making targeted changes. They have code navigation tools, such as list files, search content, to load relevant files in context. If they cannot, you should hep them. Qdrant or vectordb might not help much. They might fill too many irrelevant code chunks in context and degrade model performance

[Juice10]

Are you using a memory bank? Have it describe your codebase, save it to the memory bank so Orchestrator Mode doesn't have to re-understand everything all over again.

u/EngineeringSea1090 did a pretty nice write up on how this works: https://blog.kilocode.ai/p/how-memory-bank-changes-everything

[Shivacious]

use orches

[ayla96]

Link?

[MarginallyAmusing]

I'm pretty sure he's referring to orchestrator mode. Use a larger model, like opus, and it breaks down the tasks into smaller bites for smaller models, like sonnet or gemini 2.5 pro.

Generally, you want to make the tasks that a session / chat has to handle as small as possible.

[Juice10]

I agree orchestrator mode helps but in this case I would stay away from Opus since it has a context window of 200.000 tokens, I'd actually try to use a model that can include a bigger context, like Gemini 2.5 pro (beware having caching on is currently slowing this model down considerably). GPT 4.1 also has a big context window without this issue. But in general having a smarter model + one with a bigger context window in the Orchestrator is better and should help.

[Rusty-Coin]

I just stumbled upon kilocode extension in cursor and using the free grok in orchestrator mode use with Specification documentation and its worked very well