Re:Brokk: AI for Large Codebases
by lutzleonhardt on Thursday May 08, 2025 @04:04PM
(#43930721)
We implemented a multi-step process to find the required context:
1. Quick Context
Shows the most relevant files based on a pagerank algorithm (static analysis) and semantic embeddings (JLama inference engine). The input are the instructions and the AI workspace fragments (i.e. files).
2. Deep Scan
A richer LLM receives the summaries of the AI workspace files (+instructions) and returns a recommendation of files and tests. It also recommends the type of inclusion (editable, read-only, summary/skeleton).
3. Agentic Search
The AI has access to a set of tools for finding the required files. But the tools are not limited to grep/rg. Instead you can:
- find symbols (classes, methods, ...) in the project
- ask for summaries/skeletons of files
- provide class or method implementations
- find usages of symbols (where is x used?)
- call sites (in/out)
...
1. Quick Context Shows the most relevant files based on a pagerank algorithm (static analysis) and semantic embeddings (JLama inference engine). The input are the instructions and the AI workspace fragments (i.e. files).
2. Deep Scan A richer LLM receives the summaries of the AI workspace files (+instructions) and returns a recommendation of files and tests. It also recommends the type of inclusion (editable, read-only, summary/skeleton).
3. Agentic Search The AI has access to a set of tools for finding the required files. But the tools are not limited to grep/rg. Instead you can: - find symbols (classes, methods, ...) in the project - ask for summaries/skeletons of files - provide class or method implementations - find usages of symbols (where is x used?) - call sites (in/out) ...
You can read more about this in the Brokk.ai blog: https://brokk.ai/blog/brokk-under-the-hood