By
Andrea Candela
January 12, 2026
4 minutes
In the fast-moving world of Solana DeFi, innovation often outpaces documentation. Not long ago, we published a deep dive into Making Sense of Prop AMMs on Solana, where we explored the unique architecture of proprietary automated market makers. Unlike traditional open-source AMMs, these "Prop AMMs" often operate with opaque logic, making it difficult for developers and researchers to truly understand their efficiency, slippage profiles, and execution quality.
Today, LimeChain is taking the next step in bringing transparency to this ecosystem. We are excited to announce our latest R&D project: pmm-sim — a comprehensive simulation and benchmark environment designed specifically for Solana’s most popular proprietary AMMs.
The Solana ecosystem has seen a surge in Proprietary AMMs. These protocols often offer tighter spreads or unique liquidity management strategies, but for an aggregator or a researcher, they can be a "black box." Without a standardized way to measure performance, it’s nearly impossible to determine which AMM provides the best quote at any given time or how their internal logic reacts to market volatility.
Our goal with pmm-sim is to provide the machinery required to connect to, execute against, and analyze these protocols in a controlled, reproducible environment.
The pmm-sim repository is a specialized toolkit that allows anyone to run simulations and benchmarks against a suite of implemented proprietary AMMs. It provides the infrastructure to measure and plot the differences between quotes at specific points in time, offering a data-driven look into the "inner workings" of these protocols.
At launch, the environment supports benchmarking for:
(Note: The environment is built to be modular; we intend to add more protocols as they emerge in the ecosystem.)
Measure exactly how different Prop AMMs respond to the same trade size at the same block height. By standardizing the environment, we eliminate the noise of network latency and focus purely on the protocol's pricing algorithm.
The tool includes capabilities to visualize performance delta. Seeing a side-by-side comparison of slippage curves or price impact across different AMMs provides immediate insights that raw logs cannot.
Beyond just quotes, the repository showcases the specific "machinery" needed to interact with these protocols. This is invaluable for developers who need to understand the connection and execution patterns required for Prop AMM integration.
We see this tool as a foundational layer for several key players in the Solana space:
pmm-sim provides clear and reproducible steps that'd allow anyone to inspect and tap into the deep liquidity provided by the Prop AMMs, completely bypassing potential obfuscation techniques and benefitting from the uniformity of the used methods.
Quantitative researchers can use this environment as a "lab" to deconstruct Prop AMM behavior. By harvesting high-fidelity data on how these markets move, analysts can produce research papers and market reports that were previously impossible due to the closed nature of these protocols.
If you are building a new AMM, you need to know how you stack up against the competition. pmm-sim provides a neutral ground to benchmark your protocol's performance against the market leaders, helping you iterate faster and optimize your pricing curves.
At LimeChain, we believe that open R&D is the tide that lifts all boats. By providing a standardized way to analyze these markets, we are providing the community with the means to conduct deeper academic and financial research.
The data derived from pmm-sim can serve as the primary source for papers regarding Solana’s market efficiency, liquidity fragmentation, and the evolution of PMMs (Private Market Makers) on-chain.
The repository is now public and ready for exploration. Whether you are a researcher looking for your next paper topic or a developer building the next great Solana aggregator, we invite you to dive in.
Unlike traditional AMMs (like Uniswap or Orca) where the math is entirely on-chain and transparent, Proprietary AMMs often use off-chain pricing engines or "black-box" smart contracts. This allows them to offer sophisticated pricing but makes it harder for the community to verify their efficiency without specialized tools like pmm-sm.
The simulation environment is designed to minimize external noise. By querying multiple AMMs for the same trade size at the same block height, we isolate the protocol's pricing logic from Solana's network fluctuations, providing a pure look at the AMM's performance.
Absolutely. The repository is designed to be modular. We encourage developers to add adapters for new protocols or suggest new metrics for benchmarking. You can find the contribution guidelines in our GitHub repository.
While pmm-sim provides the logic to connect and interact with these AMMs, its primary purpose is simulation and analysis. However, developers can certainly leverage the connection logic as a foundation for building more robust execution engines or aggregators.
