The cost estimates were rough illustrations for high-usage cloud setups (100 devs × 100 searches/day = ~3.65M queries/year):

Vector embeddings: ~$0.003/query (OpenAI embeddings + managed DB like Pinecone) → $10,950/yr Sourcegraph: Older Enterprise rate (~$91/user/mo) → $109k/yr Mantic: $0 (local, no APIs/DBs)

You're spot on—these are high-end, Weaviate (esp. self-hosted/compressed) can be way cheaper for moderate use like your ~60 users.

I leaned toward worst-case managed pricing to highlight the "no ongoing cost" upside.

Let me know how the trial feels!

I just shipped v1.0.13 (literally 5 minutes ago) that implements all three environment variables:

• MANTIC_IGNORE_PATTERNS - Custom glob patterns to exclude files • MANTIC_MAX_FILES - Limit number of files returned • MANTIC_TIMEOUT - Search timeout in milliseconds

Also fixed the regex bug that was breaking glob pattern matching.

Appreciate you pointing it out, having users actually test the features is way more valuable than my own QA.

Instead of converting queries to vectors, Mantic.sh uses structural inference, it ranks files based on path components, folder depth, naming patterns, and git metadata.

So "stripe webhook" matches /services/stripe/webhook.handler.ts highly because the path literally contains both terms in logical positions, not because their embeddings are close in vector space.

"Cognitive" just means it mirrors how developers already think about code organization, it encodes intent into paths, so searching paths directly often beats semantic similarity.

1. Use `git ls-files` instead of walking the filesystem (huge speed win - dropped Chromium 59GB scan from 6.6s to 0.46s) 2. Parse each file path into components (folders, filename, extension) 3. Score each file based on how query terms match path components, weighted by position and depth 4. Return top N matches sorted by score

The core insight: /services/stripe/webhook.handler.ts already encodes the semantic relationship between "stripe" and "webhook" through its structure. No need to read file contents or generate embeddings.

I should add an architecture doc to the repo, thanks for the nudge.

For Cursor:

Click the "Install in Cursor" badge at the top of the README, or Use this deep link: https://cursor.com/en-US/install-mcp?name=mantic&config=eyJ0...

For VS Code:

Click the "Install in VS Code" badge, or Use this deep link: https://vscode.dev/redirect/mcp/install?name=mantic&config=%...

Manual Installation: Add this to your MCP settings (e.g., ~/Library/Application Support/Claude/claude_desktop_config.json):

json { "mcpServers": { "mantic": { "type": "stdio", "command": "npx", "args": ["-y", "mantic.sh@latest", "server"] } } }

Once installed, Claude Desktop (or any MCP client) can call the search_codebase tool to find relevant files before making code changes.

The MCP server implementation is in src/mcp-server.ts

if you want to see the code.

A few debugging questions:

- What query did you run? (e.g., mantic "auth logic") - What's your project structure? (Is it a monorepo, or does it have a non-standard layout?) - Can you share the output of mantic "your query" --json?

If it's only returning package.json, it likely means:

- The query is too generic (e.g., mantic "project"), OR - The file scanner isn't finding your source files (possible .gitignore issue)

Tip: Try running git ls-files | wc -l in your project, if that returns 0 or a very small number, Mantic won't have files to search.

Happy to debug further if you can share more details!

Chromium timeout - Increased default to 30s (configurable via MANTIC_TIMEOUT). Now completes ~23s on 481k files.

Ranking (extract exports) - Added two-pass scoring, ultra-fast path/filename first, then lightweight regex extraction of function/class names from top 50 files.

Exact match: +200 boost Partial: +100 Result: dependency-graph.ts (with extractExports) now ranks #1.

Extras from your feedback:

Added Python (def), Rust (fn), Go (func) patterns Better camel/snake/keyword handling

New env var: MANTIC_FUNCTION_SCAN_LIMIT

Performance: +100-200ms overhead. Tested on Chromium (23s) and Cal.com (220ms).

Huge thanks—feedback like yours is gold!