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.

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Table of Contents

• Why Probe?
• Quick Start
• Features
• Usage Modes
  • Probe Agent (MCP)
  • Raw MCP Tools
  • CLI Agent
  • Direct CLI Commands
  • Node.js SDK
• LLM Script
• Installation
• Supported Languages
• Documentation
• Environment Variables
• Contributing
• License

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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.

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.

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Quick Start

Option 1: Probe Agent via MCP (Recommended)

Our built-in agent natively integrates with Claude Code, using its authentication—no extra API keys needed.

Add to ~/.claude/claude_desktop_config.json:

{
  "mcpServers": {
    "probe": {
      "command": "npx",
      "args": ["-y", "@probelabs/probe@latest", "agent", "--mcp"]
    }
  }
}

The Probe Agent is purpose-built to read and reason about code. It piggybacks on Claude Code's auth (or Codex auth), or works with any model via your own API key (e.g., GOOGLE_API_KEY).

Option 2: Raw Probe Tools via MCP

If you prefer direct access to search/query/extract tools without the agent layer:

{
  "mcpServers": {
    "probe": {
      "command": "npx",
      "args": ["-y", "@probelabs/probe@latest", "mcp"]
    }
  }
}

Option 3: Direct CLI (No MCP)

Use Probe directly from your terminal—no AI editor required:

# Semantic search with Elasticsearch syntax
npx -y @probelabs/probe search "authentication AND login" ./src

# Extract code block at line 42
npx -y @probelabs/probe extract src/main.rs:42

# AST pattern matching
npx -y @probelabs/probe query "fn $NAME($$$) -> Result<$RET>" --language rust

Option 4: CLI Agent

Ask questions about any codebase directly from your terminal:

# One-shot question (works with any LLM provider)
npx -y @probelabs/probe@latest agent "How is authentication implemented?"

# With code editing capabilities
npx -y @probelabs/probe@latest agent "Refactor the login function" --allow-edit

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Features

• Code-Aware: Tree-sitter AST parsing understands your code's actual structure
• Semantic Search: Elasticsearch-style queries (AND, OR, NOT, phrases, filters)
• Complete Context: Returns entire functions, classes, or structs -- not text chunks that break mid-function
• Zero Indexing: Instant results on any codebase. No embedding models, no vector databases, no setup
• Deterministic: Same query always returns the same results. No model variance, no stale indexes
• Fully Local: Your code never leaves your machine. No API calls for search
• Blazing Fast: SIMD-accelerated pattern matching + ripgrep scanning + rayon parallelism
• Smart Ranking: BM25, TF-IDF, and hybrid algorithms with optional BERT reranking
• Token-Aware: --max-tokens budget, session-based dedup to avoid repeating context
• Built-in Agent: Multi-provider (Anthropic, OpenAI, Google, Bedrock) with retry, fallback, and context compaction
• Multi-Language: Rust, Python, JavaScript, TypeScript, Go, C/C++, Java, Ruby, PHP, Swift, Solidity, Crystal, C#, and more

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Usage Modes

Probe Agent (MCP)

The recommended way to use Probe with AI editors. The Probe Agent is a specialized coding assistant that reasons about your code—not just pattern matches.

{
  "mcpServers": {
    "probe": {
      "command": "npx",
      "args": ["-y", "@probelabs/probe@latest", "agent", "--mcp"]
    }
  }
}

Why use the agent?

• Purpose-built to understand and reason about code
• Piggybacks on Claude Code / Codex authentication (or use your own API key)
• Smarter multi-step reasoning for complex questions
• Built-in code editing, task delegation, and more

Agent options:

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Raw MCP Tools

Direct access to Probe's search, query, and extract tools—without the agent layer. Use this when you want your AI editor to call Probe tools directly.

{
  "mcpServers": {
    "probe": {
      "command": "npx",
      "args": ["-y", "@probelabs/probe@latest", "mcp"]
    }
  }
}

Available tools:

• search - Semantic code search with Elasticsearch-style queries
• query - AST-based structural pattern matching
• extract - Extract code blocks by line number or symbol name
• symbols - List all symbols in a file (functions, classes, constants) with line numbers

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CLI Agent

Run the Probe Agent directly from your terminal:

# One-shot question
npx -y @probelabs/probe@latest agent "How does the ranking algorithm work?"

# Specify search path
npx -y @probelabs/probe@latest agent "Find API endpoints" --path ./src

# Enable code editing
npx -y @probelabs/probe@latest agent "Add error handling to login()" --allow-edit

# Use custom persona
npx -y @probelabs/probe@latest agent "Review this code" --prompt code-review

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Direct CLI Commands

For scripting and direct code analysis.

Search Command

probe search <PATTERN> [PATH] [OPTIONS]

Examples:

# Basic search
probe search "authentication" ./src

# Boolean operators (Elasticsearch syntax)
probe search "error AND handling" ./
probe search "login OR auth" ./src
probe search "database NOT sqlite" ./

# Search hints (file filters)
probe search "function AND ext:rs" ./           # Only .rs files
probe search "class AND file:src/**/*.py" ./    # Python files in src/
probe search "error AND dir:tests" ./           # Files in tests/

# Limit results for AI context windows
probe search "API" ./ --max-tokens 10000

Key options:

Extract Command

probe extract <FILES> [OPTIONS]

Examples:

# Extract function at line 42
probe extract src/main.rs:42

# Extract by symbol name
probe extract src/main.rs#authenticate

# Extract line range
probe extract src/main.rs:10-50

# From compiler output
go test | probe extract

Symbols Command

probe symbols <FILES> [OPTIONS]

Examples:

# List symbols in a file
probe symbols src/main.rs

# JSON output for programmatic use
probe symbols src/main.rs --format json

# Multiple files
probe symbols src/main.rs src/lib.rs

Query Command (AST Patterns)

probe query <PATTERN> [PATH] [OPTIONS]

Examples:

# Find all async functions in Rust
probe query "async fn $NAME($$$)" --language rust

# Find React components
probe query "function $NAME($$$) { return <$$$> }" --language javascript

# Find Python classes with specific method
probe query "class $CLASS: def __init__($$$)" --language python

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Node.js SDK

Use Probe programmatically in your applications.

import { ProbeAgent } from '@probelabs/probe/agent';

// Create agent
const agent = new ProbeAgent({
  path: './src',
  provider: 'anthropic'
});

await agent.initialize();

// Ask questions
const response = await agent.answer('How does authentication work?');
console.log(response);

// Get token usage
console.log(agent.getTokenUsage());

Direct functions:

import { search, extract, query, symbols } from '@probelabs/probe';

// Semantic search
const results = await search({
  query: 'authentication',
  path: './src',
  maxTokens: 10000
});

// Extract code
const code = await extract({
  files: ['src/auth.ts:42'],
  format: 'markdown'
});

// List symbols in a file
const fileSymbols = await symbols({
  files: ['src/auth.ts']
});

// AST pattern query
const matches = await query({
  pattern: 'async function $NAME($$$)',
  path: './src',
  language: 'typescript'
});

Vercel AI SDK integration:

import { tools } from '@probelabs/probe';

const { searchTool, queryTool, extractTool } = tools;

// Use with Vercel AI SDK
const result = await generateText({
  model: anthropic('claude-sonnet-4-6'),
  tools: {
    search: searchTool({ defaultPath: './src' }),
    query: queryTool({ defaultPath: './src' }),
    extract: extractTool({ defaultPath: './src' })
  },
  prompt: 'Find authentication code'
});

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LLM Script

Probe Agent can use the execute_plan tool to run deterministic, multi-step code analysis tasks. LLM Script is a sandboxed JavaScript DSL where the AI generates executable plans combining search, extraction, and LLM reasoning in a single pipeline.

// AI-generated LLM Script example (await is auto-injected, don't write it)
const files = search("authentication login")
const chunks = chunk(files)
const analysis = map(chunks, c => LLM("Summarize auth patterns", c))
return analysis.join("\n")

Key features:

• Agent integration - Probe Agent calls execute_plan tool to run scripts
• Auto-await - Async calls are automatically awaited (don't write await)
• All tools available - search(), query(), extract(), LLM(), map(), chunk(), plus any MCP tools
• Sandboxed execution - Safe, isolated JavaScript environment with timeout protection

See the full LLM Script Documentation for syntax and examples.

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Installation

NPM (Recommended)

npm install -g @probelabs/probe

curl (macOS/Linux)

curl -fsSL https://raw.githubusercontent.com/probelabs/probe/main/install.sh | bash

PowerShell (Windows)

iwr -useb https://raw.githubusercontent.com/probelabs/probe/main/install.ps1 | iex

From Source

git clone https://github.com/probelabs/probe.git
cd probe
cargo build --release
cargo install --path .

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Supported Languages

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Documentation

Full documentation available at probelabs.com/probe or browse locally in docs/.

Getting Started

• Quick Start - Get up and running in 5 minutes
• Installation - NPM, curl, Docker, and building from source
• Features Overview - Core capabilities

Probe CLI

• Search Command - Elasticsearch-style semantic search
• Extract Command - Extract code blocks with full AST context
• Symbols Command - List all symbols in files with line numbers
• Query Command - AST-based structural pattern matching
• CLI Reference - Complete command-line reference

LSP & Indexing

• LSP Features - What --lsp adds for semantic code intelligence
• LSP Quick Reference - Day-to-day LSP command cheatsheet
• Indexing Overview - Project indexing concepts and workflow
• Indexing CLI Reference - probe lsp index* command reference

LSP capabilities include call hierarchy enrichment (extract --lsp), direct symbol operations (probe lsp call definition|references|hover|...), daemon diagnostics (probe lsp logs --analyze), and workspace indexing (probe lsp index, probe lsp index-status).

Probe Agent

• Agent Overview - What is Probe Agent and when to use it
• API Reference - ProbeAgent class documentation
• Node.js SDK - Full Node.js SDK reference
• MCP Integration - Editor integration guide
• LLM Script - Programmable orchestration DSL

Guides & Reference

• Query Patterns - Effective search strategies
• How Probe Compares - vs embedding search, knowledge graphs, LSP tools
• Architecture - System design and internals
• Environment Variables - All configuration options
• FAQ - Frequently asked questions

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Environment Variables

# AI Provider Keys
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...
GOOGLE_API_KEY=...

# Provider Selection
FORCE_PROVIDER=anthropic
MODEL_NAME=claude-sonnet-4-6

# Custom Endpoints
ANTHROPIC_API_URL=https://your-proxy.com
OPENAI_API_URL=https://your-proxy.com

# Debug
DEBUG=1

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Contributing

We welcome contributions! See our Contributing Guide.

For questions or support:

• GitHub Issues
• Discord Community

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License

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For questions or contributions, please open an issue on GitHub or join our Discord community for discussions and support. Happy coding—and searching!