llm-tools

The missing standard library for Agentic Workflows. Native Go. Single Binary. 100x Faster than Python.

⚡ Why this exists

LLM Agents need to be fast. Waiting 400ms for a Python script or 100ms for Node.js to spin up just to read a file kills the flow of an autonomous loop.

llm-tools is a suite of high-performance, statically compiled tools designed to be the "hands" of your AI agent. It includes a native MCP Server for instant integration with Claude Desktop and Gemini.

The "Rewrite it in Go" Effect

I benchmarked this against equivalent Python and Node.js implementations on a real-world codebase. The difference is massive.

vs Python (llm-support)

Benchmarks run on M4 Pro 64gb macOS Darwin (arm64), 2025-12-26.

vs Node.js (llm-filesystem)

I ported the popular fast-filesystem-mcp from TypeScript to Go to create llm-filesystem.

Benchmarks run on M4 Pro 64gb macOS Darwin (arm64), 2025-12-31.

🚫 Zero Dependency Hell

Deploying agent tools in Python or Node is painful. You have to manage virtual environments, node_modules, pip install dependencies, and worry about version conflicts. llm-tools is a single static binary. It works instantly on any machine—no setup required.

🤖 Standardized LLM Orchestration

llm-tools isn't just for reading files; it's a reliability layer for your agent's cognitive functions.

Two ways to call LLMs:

prompt - Universal CLI Adapter

Wraps external LLM CLIs (gemini, claude, ollama, openai, octo) with retries, caching, and validation:

llm-support prompt \
  --prompt "Analyze this error log" \
  --llm gemini \
  --retries 3 \
  --cache \
  --min-length 50

complete - Direct API Access

Calls OpenAI-compatible APIs directly without external binaries:

# Uses OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL
llm-support complete \
  --prompt "Explain this code" \
  --system "You are a senior engineer" \
  --temperature 0.3

⚡ Advanced Workflows

Parallel Batch Processing (foreach)

Run prompts across thousands of files in parallel without writing a loop script. Perfect for migrations, code reviews, or documentation generation.

# Review all Go files in parallel (4 concurrent workers)
llm-support foreach \
  --glob "src/**/*.go" \
  --template templates/code-review.md \
  --llm claude \
  --parallel 4 \
  --output-dir ./reviews

🚀 Quick Start

Pre-built Binaries

Recommended: Download the latest binary for your OS. No dependencies required.

Installation (Go)

go install github.com/samestrin/llm-tools/cmd/llm-support@latest
go install github.com/samestrin/llm-tools/cmd/llm-filesystem@latest
go install github.com/samestrin/llm-tools/cmd/llm-semantic@latest

💡 Common Recipes

See what's possible with a single line of code:

# Find all TODOs and FIXMEs (Fast grep)
llm-support grep "TODO|FIXME" . -i -n

# Show project structure (3 levels deep)
llm-support tree --path . --depth 3

# Search for multiple definitions in parallel (Token optimized)
llm-support multigrep --path src/ --keywords "handleSubmit,validateForm" -d

# Extract data from JSON without jq
llm-support json query response.json ".users[0]"

# Calculate values safely
llm-support math "round(42/100 * 75, 2)"

# Generate config from template
llm-support template config.tpl --var domain=example.com --var port=8080

# Hash all Go files (Integrity check)
llm-support hash internal/**/*.go -a sha256

# Count completed tasks in a sprint plan
llm-support count --mode checkboxes --path sprint/plan.md -r

# Detect project stack
llm-support detect --path .

# Extract only relevant context (AI-filtered) - works with files, dirs, and URLs
llm-support extract-relevant --path docs/ --context "Authentication Config"
llm-support extract-relevant --path https://docs.example.com --context "API keys"

# Fetch a URL as Markdown, with proxy + FlareSolverr fallback (JS via --via flaresolverr)
llm-support fetch https://example.com --format markdown

# Extract and rank links from any webpage (heuristic scoring)
llm-support extract-links --url https://example.com/docs --json

# Extract links with LLM-based relevance scoring
llm-support extract-links --url https://example.com/docs --context "authentication" --json

# Summarize directory content for context window (Token optimized)
llm-support summarize-dir src/ --format outline --max-tokens 2000

# Batch process files with a template (LLM-driven)
llm-support foreach --files "*.ts" --template refactor.md --parallel 4

📚 Documentation

Detailed references for all 40+ commands:

• llm-support Commands - File ops, search, analysis, and data processing.
• llm-filesystem Commands - High-performance filesystem operations (drop-in replacement for fast-filesystem-mcp).
• llm-semantic Commands - Semantic code search with local embeddings.
• MCP Setup Guide - Integration with Claude Desktop & Gemini.
• Quick Reference - Cheat sheet.

🧠 How It Works

The Loop:

1. Agent receives a task.
2. llm-support provides fast codebase context (files, structure, search results).
3. llm-semantic memory recalls past decisions ("Use Jest, not Mocha") to prevent regression.
4. Agent generates code with full context.

sequenceDiagram
    participant U as 👤 User
    participant A as 🤖 Agent
    participant S as ⚡ Support
    participant M as 🧠 Memory
    participant C as 📂 Codebase

    U->>A: /execute-sprint
    
    rect rgb(30, 30, 30)
        note right of A: Fast Context
        A->>S: multiexists, count, report
        S-->>A: ✓ Context Loaded (22ms)
    end
    
    rect rgb(50, 20, 20)
        note right of A: Long-Term Memory
        A->>M: memory search
        M-->>A: ⚠ RECALL: "Use Jest"
    end

    A->>C: TDD Implementation (using Jest)

License

MIT License