> For the complete documentation index, see [llms.txt](https://hawkfi.gitbook.io/whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://hawkfi.gitbook.io/whitepaper/hawkfi-or-agent-terminal.md). # HawkFi | Agent Terminal {% hint style="info" %} Learn to deploy Market Making agents here: [hawkfi.gitbook.io/whitepaper/tutorials/market-making-agents](https://hawkfi.gitbook.io/whitepaper/tutorials/market-making-agents) {% endhint %} HawkFi is an Agent Terminal. Deploy 24/7 agents optimized for any asset and market regime. Outperform HODL by Market Making (MM) or Liquidity Providing (LP). ## The HawkFi Thesis
Every retail investor is moving toward their own quant agent: AI-native, data-driven, always-on, and built for fully or semi-automated execution. HawkFi is building the terminal where those agents are created, tested, deployed, and improved. HawkFi started on Solana spot markets with agentic liquidity provision because crypto is the fastest form factor for tokenized markets. From there, the same agent stack expands into permissionless market making, systematic spot trading, perpetuals, and eventually the most traded assets globally: crypto, commodities, public equities, and private markets as they become tokenized. The goal is a factory of specialized agents that can run market-specific models at machine speed, around the clock, across asset classes and regimes. Each agent is a step toward generalized agent intelligence for trading any asset, in any regime, anywhere. {% hint style="info" %} HawkFi backtests, models, and agents help users compare strategies against HODL and passive benchmarks. They do not guarantee future returns or remove market risk. {% endhint %} ## What can you deploy? | Agent path | Use it when you want to | What HawkFi runs | | ----------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | | [Market Making Agents](/whitepaper/hawkfi-agents/market-making-agents-mma.md) | Run permissionless MM by placing and refreshing buy and sell quotes around price. | Dynamic Limit Order mechanics, execution models, inventory controls, and quote management. | | [Screener Agents](/whitepaper/hawkfi-agents/screener-agents-osprey-v1.md) | Screen markets 24/7 and route qualified LP entries into a model. | Osprey screening, HawkFi Models, and LP position automations. | | [Liquidity Providing](/whitepaper/hawkfi-laboratory/liquidity-providing.md) | Research or deploy DLMM LP strategies that compete against passive LP and HODL. | Models, automations, and Laboratory backtests against passive LP and HODL benchmarks. | ## How HawkFi grows HawkFi's first production edge is agentic liquidity on Solana: LP automation, market making, and benchmark-driven model selection on tokenized spot markets. The next layer is a broader agent platform for retail investors who want systematic execution without building quant infrastructure themselves. Users choose an asset, market regime, and strategy path. HawkFi supplies the models, simulation tools, agent workflows, and execution surfaces. | Layer | What it means | | ------------------------------ | --------------------------------------------------------------------------------------------------------- | | Agent Terminal | The user surface for testing, selecting, deploying, and monitoring agents. | | Specialized agents | Market-making, screening, LP, and future systematic trading agents tuned for specific assets and regimes. | | Models and simulations | Historical testing, benchmark comparison, and configuration research before live deployment. | | Generalized agent intelligence | The long-term direction: agents that can adapt across assets, venues, and market regimes. | ## Documentation Hierarchy HawkFi documentation is organized from terminal overview to execution: | Layer | Purpose | What lives there | | ------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | | [HawkFi overview](https://hawkfi.gitbook.io/whitepaper) | Explains HawkFi as an Agent Terminal for MM and LP workflows. | Core product identity, agent paths, and where to start. | | [HawkFi Models](/whitepaper/hawkfi-or-agent-terminal/hawkfi-models.md) | Introduces LP models and helps users choose where to start. | Strategy picker, market scenarios, model overview, and LP model table of contents. | | [HawkFi Agents](/whitepaper/hawkfi-agents.md) | Explains 24/7 agents for market making and screened LP entries. | Agent taxonomy, Market-making Agent, Screener Agent, setup flows, and advanced agent controls. | | [HawkFi Laboratory](/whitepaper/hawkfi-laboratory.md) | Lets users test MM and LP configurations on historical pool conditions. | Market Making Mode, Liquidity Mode, metrics, backtests, and benchmark comparisons. | | [Build Your Own Strategy](/whitepaper/hawkfi-or-agent-terminal/hawkfi-models/build-your-own-models.md) | Explains the LP automation building blocks. | Auto-compound, Auto-accumulate, Auto-rebalance, Reshape Liquidity, Take Profit, and Stop Loss. | ## Start Here [HawkFi Agents](/whitepaper/hawkfi-agents.md)\ Start here if you want to deploy 24/7 agents for market making or screened LP entries. [HawkFi Laboratory](/whitepaper/hawkfi-laboratory.md)\ Use Laboratory to backtest Market Making Agent configurations, LP models, and custom setups before deploying real capital. [HawkFi Models](/whitepaper/hawkfi-or-agent-terminal/hawkfi-models.md)\ Use Models when you want a starting point for Liquidity Providing strategies across different market environments. [Build Your Own Strategy](/whitepaper/hawkfi-or-agent-terminal/hawkfi-models/build-your-own-models.md)\ Use Build Your Own Strategy when you want to combine HawkFi automations manually instead of starting from a model.