# How to Use the Hivemind Graph Module for Data Visualization > Visualize code relationships with the Hivemind Graph module. Explore virtual filesystem using shell commands for powerful data visualization. No disk storage needed. - Repository: [Activeloop/hivemind](https://github.com/activeloopai/hivemind) - Tags: how-to-guide - Published: 2026-06-11 --- **Hivemind exposes a virtual filesystem at `~/.deeplake/memory/graph/` that lets you visualize code relationships using standard shell commands like `cat` and `ls`, with no real files stored on disk.** The Hivemind graph module provides a **read-only virtual filesystem (VFS)** interface for exploring code-graph relationships in your repository. According to the activeloopai/hivemind source code, agents intercept shell commands targeting paths under `~/.deeplake/memory/graph/` and synthesize textual views of symbols, dependencies, and impact analysis on demand. ## Understanding the Graph Module Architecture When you use the graph module for data visualization, you interact with a sophisticated routing layer that translates standard file operations into graph queries. ### Command Parsing and Routing In [`src/graph/graph-command.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/graph-command.ts), the system parses rewritten shell commands (`cat`, `head`, `tail`, `ls`) to detect graph VFS targets. The `parseReadTargetPath` function extracts virtual paths, while `tryGraphRead` determines whether the command targets the graph subsystem. If the path matches, the system forwards the request to the VFS handler. ### Virtual Filesystem Handler The `handleGraphVfs` function in [`src/graph/vfs-handler.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/vfs-handler.ts) serves as the central router. It loads the latest local graph snapshot and dispatches to specific renderers based on the sub-path: - **index** → Overview statistics - **find/** → Symbol search - **show/** → Node details - **query/** → Search with expansion - **impact/** → Transitive dependents - **path//** → Shortest path analysis Each renderer in `src/graph/render/*.ts` produces markdown-formatted text that appears as file contents. ### Background Synchronization The [`src/hooks/graph-pull-worker.ts`](https://github.com/activeloopai/hivemind/blob/main/src/hooks/graph-pull-worker.ts) maintains snapshot freshness by pulling updates from remote sources, while [`src/hooks/graph-on-stop.ts`](https://github.com/activeloopai/hivemind/blob/main/src/hooks/graph-on-stop.ts) handles graceful shutdown of graph workers. ## Core Graph Endpoints for Visualization When you use the graph module for data visualization, you access specific endpoints by reading virtual paths. All endpoints are **best-effort**—errors return friendly "no-graph" messages rather than exceptions. - **index.md**: Returns commit ID, node/edge counts, top files, and node-/edge-kind statistics. Generated by `renderIndex` in [`vfs-handler.ts`](https://github.com/activeloopai/hivemind/blob/main/vfs-handler.ts). - **find/**: Performs case-insensitive substring search on node IDs and labels. Returns up to 50 matches and persists a handle table ([`.find-handles.json`](https://github.com/activeloopai/hivemind/blob/main/.find-handles.json)) for subsequent lookups. - **show/**: Resolves numeric keys via the saved handle table or treats the key as a pattern. Displays full node details and 1-hop neighborhoods grouped by edge type. - **query/**: Combines `find` and `show` operations—searches for the pattern and immediately expands each top match with its 1-hop neighbors. - **impact/**: Lists transitive dependents (blast radius) for impact analysis. - **neighborhood/**: Shows symbols defined in a specific file plus their cross-file neighbors. - **layers**: Groups symbols by path heuristics for architectural overview. - **tour**: Provides a deterministic dependency-ordered walkthrough of the entire graph. - **path//**: Computes and displays the shortest dependency path between two symbol patterns. ## Querying the Graph VFS To use the graph module for data visualization, interact with the virtual filesystem using standard shell commands. The following examples assume the default DeepLake memory root at `~/.deeplake/memory`. ### Display the Graph Overview View high-level statistics and navigation help: ```bash cat ~/.deeplake/memory/graph/index.md ``` This executes `renderIndex` in [`src/graph/vfs-handler.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/vfs-handler.ts), returning commit metadata, node/edge totals, and a query cheat-sheet. ### Search for Symbols Find symbols matching a keyword and generate reusable handles: ```bash cat ~/.deeplake/memory/graph/find/auth ``` This invokes the `renderFind` function, which searches node IDs and labels for "auth", displays up to 50 results, and caches handles in [`.find-handles.json`](https://github.com/activeloopai/hivemind/blob/main/.find-handles.json). ### Inspect Specific Nodes View detailed information using a handle from a previous search: ```bash cat ~/.deeplake/memory/graph/show/3 ``` The `renderShow` function resolves handle `3` from the saved table and displays the node's full details plus inbound/outbound edges grouped by type. ### Explore Dependencies and Impact Analyze symbol relationships and transitive dependencies: ```bash # Query pattern with immediate neighborhood expansion cat ~/.deeplake/memory/graph/query/Cache # View impact analysis for a symbol cat ~/.deeplake/memory/graph/impact/Database ``` The `query` endpoint combines `find` and `show` operations, while `impact` traverses the dependency graph to find all transitive dependents. ### Navigate Between Symbols Find the shortest path between two code elements: ```bash cat ~/.deeplake/memory/graph/path/Cache/Storage ``` This executes the shortest-path algorithm implemented in [`src/graph/render/path.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/render/path.ts), returning the dependency chain between symbols matching "Cache" and "Storage". ### List Available Graph Resources Discover endpoints using standard directory listing: ```bash ls ~/.deeplake/memory/graph ``` This triggers `tryGraphRead` to return the directory structure, including [`index.md`](https://github.com/activeloopai/hivemind/blob/main/index.md) and available subfolders. ## Summary - **Hivemind's graph module** exposes a virtual filesystem at `~/.deeplake/memory/graph/` for code visualization. - **Architecture**: [`src/graph/graph-command.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/graph-command.ts) parses commands, [`src/graph/vfs-handler.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/vfs-handler.ts) routes requests, and `src/graph/render/*.ts` generates markdown output. - **Key endpoints**: [`index.md`](https://github.com/activeloopai/hivemind/blob/main/index.md), `find/`, `show/`, `query/`, `impact/`, `path//`, and `layers`. - **Usage**: Standard shell commands (`cat`, `ls`) read virtual paths that return synthesized text representing graph relationships. - **Handles**: The `find` and `query` endpoints persist numeric handles in [`.find-handles.json`](https://github.com/activeloopai/hivemind/blob/main/.find-handles.json) for referencing results in subsequent `show` commands. ## Frequently Asked Questions ### How do I access the graph module if I have a custom DeepLake memory location? Replace `~/.deeplake/memory` with your configured memory root path. The graph VFS always resides under the `graph/` subdirectory of your DeepLake memory folder, regardless of the base location. ### Why does the graph module use a virtual filesystem instead of traditional visualization tools? The VFS approach allows AI agents to "read" code relationships using standard file operations. According to the activeloopai/hivemind source code, this design lets agents intercept `cat`, `ls`, and similar commands to synthesize textual views on demand, integrating naturally with existing agent workflows without requiring specialized visualization APIs. ### What happens if no graph snapshot exists for my repository? The system returns a friendly "no-graph" message rather than throwing an exception. As implemented in [`src/graph/vfs-handler.ts`](https://github.com/activeloopai/hivemind/blob/main/src/graph/vfs-handler.ts), the graph module is best-effort: if the snapshot is missing or malformed, handlers gracefully degrade to informative error text, keeping the agent responsive. ### Can I write data to the graph module or modify the visualization output? No. The graph VFS is **read-only**. You cannot create, delete, or modify files under `~/.deeplake/memory/graph/`. The background workers in [`src/hooks/graph-pull-worker.ts`](https://github.com/activeloopai/hivemind/blob/main/src/hooks/graph-pull-worker.ts) manage snapshot updates automatically based on remote graph builds.