
Filesift
io.github.roshunsunderPublished by GitHub account roshunsunderv1.0.1Updated Oct 24, 2026
Local file search for AI agents — semantic + keyword indexing over your codebase.
Overview
Lets a coding assistant index a local codebase and search it by natural-language intent using hybrid keyword and semantic search.
- What you can do
- FileSift indexes a local project directory and exposes four MCP tools over STDIO: filesift_search for natural-language queries, filesift_find_related for files linked by imports or semantic similarity, filesift_index to build an index, and filesift_status to check indexing progress. Search combines BM25 keyword matching with FAISS vector search via Reciprocal Rank Fusion. A background daemon keeps indexes in memory so repeat searches are fast, and indexes are stored in a .filesift directory inside each indexed project.
- When to use it
- Useful when an assistant needs to locate relevant code by what it does rather than by exact strings, for example finding authentication middleware or retry logic. It suits coding agents that would otherwise grep and read whole files, saving context and tokens. Best for local projects the agent already works in.
- Requirements
- Runs locally as a Python package installed with pip (filesift), providing the filesift-mcp command over STDIO. No accounts, API keys, or environment variables are declared. It needs local disk access to the project being indexed and enough resources to run an embedding model and daemon.
Installation
In SourceWeft
- Open Filesift in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
FileSift
A local, open-source utility that helps AI coding agents intelligently search and understand codebases.
FileSift lets your AI coding agent search across a codebase based on what code does, rather than what it looks like. Instead of sifting through entire files after a grep, your agent can jump straight to the most relevant code using natural language queries like "authentication middleware" or "database connection pooling". Everything runs locally on your machine — your code never leaves your environment.
Key benefits:
- Smarter search — hybrid keyword + semantic search finds code by intent, not just string matching
- Less context wasted — agents get pointed to the right files immediately, saving token budget on exploration
Installation
Usage
There are three ways to use FileSift, depending on your workflow:
1. CLI
The most straightforward approach. Good for testing queries, managing indexes, and configuring settings.
2. MCP Server
Installing FileSift also provides a filesift-mcp command — a lightweight MCP server that exposes indexing and search as tools over STDIO. This works with most popular coding agents including Claude Code, Cursor, Copilot, and more.
Add it to your agent's MCP configuration:
The MCP server exposes four tools:
filesift_search— search an indexed codebase by natural language queryfilesift_find_related— find files related to a given file via imports and semantic similarityfilesift_index— index a directory to enable searchingfilesift_status— check indexing status of a directory
3. Skills
FileSift ships with a search-codebase skill that can be installed directly into your coding agent's skill directory. This lets the agent interact with the FileSift CLI through bash, without requiring MCP support.
Supported agents: claude, codex, cursor, copilot, gemini, roo, windsurf.
How It Works
FileSift uses a daemonized embedding model to keep searches fast. At its core, it generates embeddings from code descriptions and performs searches against small vector stores called indexes.
-
Indexing —
filesift indexfirst builds a fast keyword/structural index (completes in seconds), then triggers background semantic indexing that generates embeddings for each file. -
Daemon — A background daemon loads indexes into memory and automatically shuts down after a configurable period of inactivity. After the first cold-start search, subsequent searches are near-instant.
-
Search — Queries are matched using both keyword (BM25) and semantic (FAISS) search, then combined via Reciprocal Rank Fusion for the best of both approaches.
Indexes are stored in a .filesift directory within each indexed project.
Configuration
FileSift uses a TOML configuration file, manageable via the CLI:
Configuration sections: search, indexing, daemon, models, paths.
Contributing
Contributions are welcome! To get started:
- Fork the repository
- Create a feature branch (
git checkout -b feature/my-feature) - Commit your changes and open a pull request
License
Apache 2.0 — see LICENSE for details.
Source: README.md at commit 7a14f26
Tools
0Version history
1- v1.0.1LatestSep 16, 2026
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