From source (requires CMake for llama.cpp)

cargo install --git https://github.com/devwhodevs/engraph

**Index your vault:**

Prerequisites

• engraph installed and indexed (engraph index ~/your-vault)
• A tunnel tool: Cloudflare Tunnel (recommended) or ngrok

Interactive setup — enables HTTP, creates API key, sets CORS

engraph configure --setup-chatgpt

Or configure manually in `~/.engraph/config.toml`:

[[http.api_keys]] key = "eg_your_key_here" # generate with: engraph configure --add-api-key --key-name chatgpt --key-permissions write name = "chatgpt" permissions = "write" # "read" for search-only, "write" to also create/edit notes

[http.plugin] name = "My Vault" description = "Search and manage my Obsidian vault" public_url = "https://your-tunnel-url.trycloudflare.com" # set after starting tunnel ```

Quick start

Install:

```bash

Example usage

4-lane search with intent classification:

engraph search "how does authentication work" --explain

 1. [97%] 01-Projects/API-Design.md > # API Design  #e3e350
    All endpoints require Bearer token authentication...

Intent: Conceptual

--- Explain ---
01-Projects/API-Design.md
  RRF: 0.0387
    semantic: rank #2, raw 0.38, +0.0194
    rerank: rank #2, raw 0.01, +0.0194
02-Areas/Development/Auth-Architecture.md
  RRF: 0.0384
    semantic: rank #1, raw 0.51, +0.0197
    rerank: rank #4, raw 0.00, +0.0187

The orchestrator classified the query as Conceptual (boosting semantic lane weight). The reranker scored each result for relevance as the 4th RRF lane.

Rich context for AI agents:

engraph context topic "authentication" --budget 8000

Returns a token-budgeted context bundle: relevant notes, connected people, related projects — ready to paste into a prompt or serve via MCP.

Person context:

engraph context who "Sarah Chen"

Returns Sarah's note, all mentions across the vault, connected notes via wikilinks, and recent activity.

Vault structure overview:

engraph context vault-map

Returns folder counts, top tags, recent files — gives an AI agent orientation before it starts searching.

Create a note via the write pipeline:

engraph write create --content "# Meeting Notes\n\nDiscussed auth timeline with Sarah." --tags meeting,auth

engraph resolves tags against the registry (fuzzy matching), discovers potential wikilinks ([[Sarah Chen]]), suggests the best folder based on semantic similarity to existing notes, and writes atomically.

Edit a specific section:

engraph write edit --file "Meeting Notes" --heading "Action Items" --mode append --content "- [ ] Follow up with Sarah"

Targets the "Action Items" section by heading, appends content without touching the rest of the note.

Rewrite a note (preserves frontmatter):

engraph write rewrite --file "Meeting Notes" --content "# Meeting Notes\n\nRevised content here."

Replaces the entire body while keeping existing frontmatter (tags, dates, metadata) intact.

Edit frontmatter:

engraph write edit-frontmatter --file "Meeting Notes" --op add_tag --value "actionable"

Granular frontmatter mutations: set, remove, add_tag, remove_tag, add_alias, remove_alias.

Delete a note:

engraph write delete --file "Old Draft" --mode soft   # moves to archive
engraph write delete --file "Old Draft" --mode hard   # permanent removal

Check vault health:

engraph context health

Returns orphan notes (no links in or out), broken wikilinks, stale notes, and tag hygiene issues.

Use cases

AI-assisted knowledge work — Give Claude or Cursor deep access to your personal knowledge base. Instead of copy-pasting context, the agent searches, reads, and cross-references your notes directly.

Developer second brain — Index architecture docs, decision records, meeting notes, and code snippets. Search by concept across all of them.

Research and writing — Find connections between notes that you didn't explicitly link. The graph lane surfaces related content through shared wikilinks and mentions.

Team knowledge graphs — Index a shared docs vault. AI agents can answer "who knows about X?" and "what decisions were made about Y?" by traversing the note graph.

Step 1: Configure engraph

```bash

Step 3: Update config with tunnel URL

Edit ~/.engraph/config.toml and set public_url to your tunnel URL:

[http.plugin]
public_url = "https://abc-xyz.trycloudflare.com"

Then restart engraph (Ctrl+C and re-run engraph serve --http). This ensures the OpenAPI spec points to the correct public URL.

Configuration

Optional config at ~/.engraph/config.toml:

```toml vault_path = "~/Documents/MyVault" top_n = 10 exclude = [".obsidian/", "node_modules/", ".git/"]

Local development without API keys (127.0.0.1 only)

engraph serve --http --no-auth

**API key management:**

Add a new API key (read or write permission)

engraph configure --add-api-key

HTTP REST API

engraph serve --http adds a full REST API alongside the MCP server, exposing the same capabilities over HTTP for web agents, scripts, and integrations.

26 endpoints:

Authentication:

All requests require an API key via the Authorization header:

curl -H "Authorization: Bearer eg_abc123..." http://localhost:3000/api/vault-map

Keys have either read or write permission. Write keys can access all endpoints; read keys are restricted to read-only endpoints. Use --no-auth for local development without keys (127.0.0.1 only).

curl examples:

```bash

Step 4: Verify endpoints

```bash

Obsidian CLI integration (auto-detected during init)

[obsidian]

Integration tests (downloads GGUF model)

cargo test --test integration -- --ignored ```

How it compares

engraph is not a replacement for Obsidian — it's the intelligence layer that sits between your vault and your AI tools.