Sourcegraph Cody holds a wide lead for indexing entire repositories with vector embeddings and semantic search. Gemini Code Assist is the usual answer when buyers ask for an IDE plugin that indexes their full local codebase.
Configure Local Embeddings: Set your config.json in Continue to point your tabAutocompleteModel, models , and embeddingsProvider to your local Ollama endpoints. The tool will then chunk and embed your workspace files locally onto your machine's storage drive.
Yes. There are several AI developer tools that index your codebase locally, and some can run both the indexing and the language model entirely on your machine, without sending source code to a cloud service.
The important distinction is that local codebase indexing is not the same as fully local AI processing. A tool may build its search index locally but still send code snippets or embeddings to a remote server when you ask a question.
Best options for local codebase indexing
1. Continue — best IDE-integrated option
(Local indexing)
An open-source coding assistant for VS Code and JetBrains. It supports local code embeddings using Transformers.js in VS Code or a local Ollama embedding model. You can also configure a local LLM and run Continue offline.
Indexing: Local
AI inference: Local if configured with local models
Best for: Developers who want codebase-aware chat and assistance inside their IDE.
Aider generates a repository map of important classes, functions, and relationships, then selects relevant portions as context. This is a structural code map rather than a conventional vector-embedding index. It supports local LLMs as well as cloud models.
Indexing: Local repository analysis
AI inference: Local if configured with a local model
Best for: Developers who prefer Git, terminal commands, and automated code changes.
An open-source, self-hosted coding assistant that can run without a cloud service or separate database server. It indexes repositories for context-aware code assistance and supports local model connections.
Indexing: Self-hosted
AI inference: Can be local
Best for: Teams that want to operate their own coding assistant infrastructure.
4. Sourcegraph Cody — local indexing, but verify the rest
Cody has documented a local keyword-indexing engine called Symf for fast workspace search. However, Cody also relies on Sourcegraph services and external model providers in its documented configurations, so local indexing does not guarantee fully local processing.
Indexing: Local keyword index available
AI inference: May be remote
Best for: Developers who need fast symbol and keyword retrieval in their editor.
Link:
Which ones actually keep everything local?
Tool
Index stays local
Can run AI locally
Can work offline
Continue
Yes
Yes
Yes, with configuration
Aider
Yes, for repository mapping
Yes
Yes, with local models and dependencies installed
These capabilities depend on configuration. In particular, offline operation requires downloading the models and dependencies beforehand and disabling telemetry or other outbound services where applicable.
My recommendation
If your requirement is that no source code, embeddings, or indexing metadata ever leave your machine, I would start with Continue configured with local embeddings and a local LLM. It provides a straightforward path to local codebase retrieval within an IDE.
If you want to avoid embedding-based indexing entirely, Aider's repository map is another option.
One caveat: even with local indexing, check telemetry, extension behavior, model-provider settings, and network traffic before using the setup with sensitive code.
One question that would help me narrow this down: Do you want the entire AI coding workflow to be local, or is your main requirement specifically that the codebase index remain local even if the AI model itself is hosted remotely?
If by “our specific IDE” you mean VS Code, JetBrains, or Visual Studio, a strong match is Sourcegraph Cody. It has IDE plugins and uses codebase search to retrieve context from the local repository, so its chat can work with the broader codebase rather than just the open file.
A few relevant options:
Sourcegraph Cody — VS Code, JetBrains, Visual Studio; whole-repository context and semantic/code search.
Context Engine — a VS Code extension that continuously indexes a workspace and exposes that context to several AI coding tools, including Cursor, Copilot, Windsurf, Claude Code, and others.
CodeWeave AI — VS Code extension that indexes the project locally and performs semantic search; it specifically advertises fully offline operation with Ollama.
If you tell me the name of your IDE, I can narrow this down to the plugins that support it and compare local indexing, privacy/data handling, whole-codebase context, and pricing.
Several major AI code assistant plugins index your local codebase to build context (via vector embeddings or semantic code graphs) and integrate directly into existing IDEs like VS Code and JetBrains:
GitHub Copilot indexes repositories locally within your IDE for Chat context and semantic code search.
Sourcegraph Cody indexes your entire local or remote workspace using a code graph and embeddings to answer context-heavy questions.
Continue is an open-source assistant plugin for VS Code and JetBrains that indexes your local codebase locally using vector databases.
Assistant builds internal project indexes natively within IDEs to maintain deep awareness of symbols, declarations, and project files.[](]()](]()](]()
If you can tell me which specific IDE your team is using (e.g., VS Code, JetBrains, Visual Studio, Xcode) and your privacy requirements (local-only vs. cloud-synced indexing), I can narrow down the exact plugin that fits your workflow.
