Golf vs Lune: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Golf and Lune — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Golf
golf.dev
Production-ready MCP server framework and firewall that protects MCP providers from prompt injections and PII leaks.
Key features
- MCP Firewall: Network and prompt-level protection that detects and blocks prompt-injection attempts and prevents PII leakage from agent conversations, reducing data-exposure risk for users.
- Python-Based Server Framework: Define tools, prompts, and resources as conventional Python files; Golf auto-discovers, parses, and compiles these components into a runnable MCP server to minimize boilerplate.
- Built-in Auth & Access Control: Integrated authentication and authorization primitives to manage user and agent permissions for secure production deployments.
- Observability & Telemetry: Runtime telemetry, logs, and metrics collection plus anonymous CLI usage telemetry to monitor MCP health, performance, and usage patterns for debugging and optimization.
- Debugger & Runtime Tools: Developer-facing debugger and runtime facilities to run, inspect, and iterate on MCP behavior and tool integrations during development and testing.
- Testing Framework (golf-testing): CLI tooling to test MCPs for performance, security, and compliance, enabling validation before production rollout.
- Production Readiness: Features targeted at enterprise deployments such as scalable runtime components, telemetry hooks, and security-first defaults to run real-world MCPs powering AI agents.
- MCP firewall layer to detect and block prompt injection attempts
- PII leak detection and protection for user data
- Production-ready MCP server framework implemented in Python
- Built-in authentication and authorization components
- Observability and telemetry integration for monitoring MCPs
- Runtime tooling and debugger for developing and troubleshooting MCPs
- Companion testing CLI/framework (golf-testing) for performance, security and compliance
Best for
- Building production MCP servers that power multi-component AI agents with defined tools, prompts, and resource bindings authored in Python.
- Protecting hosted MCP endpoints from prompt-injection attacks and preventing accidental leaks of PII or sensitive responses to users.
- Running pre-deployment security, performance, and compliance tests using the golf-testing framework to validate MCPs at scale.
- Integrating observability and telemetry into agent infrastructure to trace incidents, monitor usage, and optimize runtime performance.
- Rapid prototyping and iteration of agent capabilities via the file-based component model and local debugger/runtime before production deployment.
- Managing authentication and access control for enterprise MCP deployments to enforce permissioned use of tools and data by agents.
- Protect enterprise MCP deployments from prompt-injection attacks and accidental PII exposure
- Build and run production MCP servers that power AI agents with integrated Auth, Telemetry and Debugger
- Run automated security, performance and compliance tests against MCP implementations using the golf-testing tool
- Add observability and telemetry to MCP runtimes to monitor usage and troubleshoot agent behavior
Lune
Retrograde Labs
MCP server that grounds AI agents in full-text papers from top-tier computer science conferences plus research best practices.
Key features
- Full-text Semantic Search: Search plain-language queries across papers from top-tier conferences all at once.
- Full-text Reading: Agents read whole papers via get_paper_fulltext rather than abstracts alone.
- Citation Tracing: See what a paper builds on and what later work builds on it.
- Research Best Practices: Guidance on novelty bars, baselines, ablations and review patterns surfaced when relevant.
- Claim Checking: Verify that cited claims match the source papers.
- Curated Venues: Only A/A* and CCF A conference papers, filtering out workshop and abstract-only noise.
- Works with Many Clients: Install in Claude, ChatGPT, Cursor, Claude Code and dozens of other AI clients.
Best for
- Literature Review: Find foundational papers and research gaps in a new area.
- Novelty Checks: Test whether an idea is already covered by prior work.
- Experiment Design: Get baseline and ablation expectations reviewers will look for.
- Related-work Writing: Draft positioning paragraphs with citations verified against sources.
- Peer Review Prep: Check a draft against common review patterns.
- Student Onboarding: Learn the key papers of an unfamiliar subfield.