How to search across multiple git repositories with Claude

Why the usual way fails

Most modern AI tools and IDE extensions are designed to index a single workspace or a specific folder at a time. If your organization utilizes a microservices architecture or maintains separate repositories for documentation, infrastructure, and application code, the AI remains blind to the broader architectural picture. Standard command-line tools like grep or ripgrep can find specific strings across multiple folders, but they lack the semantic understanding required to identify symbol definitions or complex relationships that span repository boundaries. Manually selecting and feeding files into a chat interface is not only slow but also prone to human error, as you might miss a critical dependency that you didn't even know existed. Furthermore, many cloud-based AI agents operate in sandboxed environments, meaning they cannot access your local file system or private internal tools like Confluence and Jira where the actual business requirements and technical specifications are stored. This lack of visibility leads to hallucinations where the agent guesses how external components work instead of referencing the actual source code.

How Semlith helps

Semlith addresses these challenges by creating a single, unified index that encompasses all your repositories, documents, and project tickets. It functions as a central source of truth that any AI agent can query through a standardized Model Context Protocol (MCP) URL, regardless of where the agent is running. Instead of forcing you to re-index each repository for every new session or tool, Semlith indexes your entire workspace once and keeps that information fresh as you make changes. The underlying engine maps the intricate relationships between code symbols across all these different sources, which allows an AI agent to see exactly which functions in one repository call the code you are currently modifying in another. This provides the agent with the necessary reach to provide accurate, context-aware answers without relying on guesses about missing information. Because it uses a graph-based approach rather than just a generative model for retrieval, the answers it provides to the agent are based on hard facts found within your files.

Step by step

1. Install the Semlith binary on your local development machine using the provided one-line installer or the cargo package manager.
2. Execute the setup command to automatically register the Semlith server with your preferred AI clients, such as Claude Code, Cursor, or the ChatGPT desktop application.
3. Add your various git repositories, Confluence spaces, and Jira projects to the local indexer to begin the data ingestion process.
4. Allow the system a few moments to complete the initial indexing and build the cross-repository graph that maps symbol relationships.
5. Connect your Claude instance to the local or remote MCP URL generated by the Semlith portal to enable seamless communication.
6. Start asking Claude complex questions that span your entire toolset, such as identifying every location where a specific API endpoint is consumed across the organization.

Tips

• Utilize the automated impact check feature to visualize how a proposed code change in a pull request affects dependent callers in other repositories before you merge.
• Enhance the quality of AI responses by indexing your Confluence documentation and Jira tickets alongside your source code to provide full project context.
• Run the free version of the software locally to ensure that all your sensitive data remains on your own machine and is accessible even without an internet connection.
• Monitor the retrieval ledger in the portal to see exactly which code chunks and document sections the AI agent referenced to generate its specific answer.

Questions