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Probe
AI-friendly semantic code search engine for large codebases. Combines ripgrep speed with tree-sitter AST parsing. Powers AI coding assistants with precise, context-aware code…
Install
$
npx -y @probelabs/probe search "authentication AND login" ./srcClaude Code
claude mcp add probe -- npx -y @probelabs/probe search "authentication AND login" ./srcCursor / Claude Desktop (mcp.json)
{
"mcpServers": {
"probe": {
"command": "npx",
"args": [
"-y",
"@probelabs/probe",
"search",
"\"authentication",
"AND",
"login\"",
"./src"
]
}
}
}Install command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Probe
We read code 10x more than we write it. Probe is a code and markdown context engine, with a built-in agent, made to work on enterprise-scale codebases.
Today's AI coding tools use a caveman approach: grep some files, read random lines, hope for the best. It works on toy projects. It falls apart on real codebases.
Probe is a context engine built for reading and reasoning. It treats your code as code—not text. AST parsing understands structure. Semantic search finds what matters. You get complete, meaningful context in a single call.
The Probe Agent is purpose-built for code understanding. It knows how to wield the Probe engine expertly—searching, extracting, and reasoning across your entire codebase. Perfect for spec-driven development, code reviews, onboarding, and any task where understanding comes before writing.
One Probe call captures what takes other tools 10+ agentic loops—deeper, cleaner, and far less noise.
Table of Contents
- Why Probe?
- Quick Start
- Features
- Usage Modes
- LLM Script
- Installation
- Supported Languages
- Documentation
- Environment Variables
- Contributing
- License
Why Probe?
Most code search tools fall into two camps: text-based (grep, ripgrep) or embedding-based (vector search requiring indexing and an embedding model). Probe takes a third path: AST-aware structural search with zero setup.
| grep/ripgrep | Embedding tools (grepai, Octocode) | Probe | |
|---|---|---|---|
| Setup time | None | Minutes (indexing + embedding service) | None |
| Code understanding | Text only | Text chunks (can split mid-function) | AST-aware (returns complete functions/classes) |
| Search method | Regex | Vector similarity | Elasticsearch-style boolean queries + BM25 |
| Result quality | Line fragments | ~512-char chunks | Complete semantic code blocks |
| Ranking | None (line order) | Cosine similarity | BM25/TF-IDF/Hybrid with SIMD acceleration |
| External dependencies | None | Embedding API (Ollama/OpenAI) | None |
| Token awareness | No | Partial | Yes (--max-tokens, session dedup) |
| Works offline | Yes | Only with local model | Always |
| AI agent integration | None | MCP server | Full agent loop + MCP + Vercel AI SDK |
The key insight: AI agents don't need embedding search
Embedding-based tools solve vocabulary mismatch -- finding "authentication" when the code says verify_credentials. But when an AI agent is the consumer, the LLM already handles this:
User: "find the authentication logic"
-> LLM generates: probe search "verify_credentials OR authenticate OR login OR auth_handler"
-> Probe returns complete AST blocks in milliseconds
The LLM translates intent into precise boolean queries. Probe gives it a powerful query language (AND, OR, +required, -excluded, "exact phrases", ext:rs, lang:python) purpose-built for this. Combined with session dedup, the agent can run 3-4 rapid searches and cover more ground than a single embedding query -- faster, deterministic, and with zero setup cost.
Quick Start
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