llm-tools

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 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 my original Python implementation on a real-world codebase (21k files). The difference was massive.

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

🚫 No Python Venv Hell

Deploying Python-based agent tools is painful. You have to manage virtual environments, 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.

The prompt command acts as a Universal Adapter for almost any LLM CLI (gemini, claude, ollama, openai, octo). It wraps them with:

• Retries & Backoff: Automatically retries failed API calls.
• Caching: Caches expensive results to disk (--cache-ttl 3600).
• Validation: Ensures output meets criteria (--min-length, --must-contain) or fails fast.

# Reliable, cached, validated prompt execution
llm-support prompt \
  --prompt "Analyze this error log" \
  --llm gemini \
  --retries 3 \
  --cache \
  --min-length 50

⚡ 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-clarification@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)
llm-support extract-relevant --path docs/ --context "Authentication Config"

# 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-clarification Commands - Long-term memory and decision tracking system.
• 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-clarification 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

    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: match-clarification
        M-->>A: ⚠ RECALL: "Use Jest"
    end

License

MIT License