BrowsingCode
AI coding assistants need relevant codebase context, but they don't know where to look. BrowsingCode automatically indexes repositories and retrieves the exact functions, classes, and documentation your LLM needs.
Ask questions in natural language, get structured code context back
Explore how retrieval-augmented generation chains are constructed, from document loaders to vector store integration.
Try thisWalk through the pipeline from index creation to query engine instantiation and response synthesis.
Try thisUnderstand the logging API, metric storage backends, and how runs are organized within experiments.
Try thisDiscover how agents bind tools to LLMs, parse structured output, and execute multi-step tool calls.
Try thisBrowsingCode uses a three-stage pipeline to transform raw codebases into searchable context. The offline indexing runs weekly, while retrieval and augmentation happen in real-time per query.
- Scan repository
- Parse AST trees
- Extract code headers
- Embed metadata tags
- Enrich query
- Search code objects
- Rerank retrieved objects
- Append source url's
- Append relevant code headers
- Generate answer
Try the context retriever below. Enter queries to gather relevant files, and get grounded answers. The interface displays token counts and context boundaries in real-time.
Watch this demonstration to see BrowsingCode in action. Learn how to gather context, manage token budgets, and integrate with your coding workflow.
Learn to build precise context snapshots for your agent. See how BrowsingCode gathers, scores, and packages relevant code files within your token budget.
Questions about BrowsingCode? Want to discuss integration or contribute to the project?