The 4 Stacks of LLM Apps & Agents

Building useful LLM apps and agents comes down to assembling four distinct “stacks” in the right places: the model itself, the data/search/memory layer, the reasoning-and-action layer, and the personalization layer. The practical value of this framework is that it turns vague “LLM app” ideas into an architecture you can plan—what needs to be trained or served, what information must be retrieved and injected, how decisions and tool use should work, and how the system should speak and behave.

At the foundation sits the LLM stack: everything tied to the language model’s capabilities and deployment. That includes how the model was created—pre-training, fine-tuning, and whether RLHF was used—and whether additional fine-tuning is worth doing for a specific domain, use case, or writing style. Deployment choices matter too. For open-source models, teams must decide between cloud serving (paying per token or compute) and local hosting, potentially using quantized “4-bit” style models. The tradeoff is not just cost; quantization can reduce performance on tasks beyond simple chat, especially where logic or mathematics is involved.

Above that is the search, memory, and data stack, which focuses on getting the right information into prompts. This layer typically uses semantic search and vector stores, with decisions such as whether to use Faiss or hosted options like ChromaDB or Pinecone. It also covers how data is sourced—whether from conventional databases, knowledge graphs, or the live web via tools like Google or Duck Duck Go—and how it’s extracted and injected into the model. Context window size changes what’s feasible: a 16K context window allows more retrieved content to be included. For agents, persistent memory is crucial: saving and retrieving information for in-context learning so the system can condition responses on prior facts.

The third layer—reasoning and actions—handles decision-making and tool use. Instead of leaving every choice to the language model alone, this stack often incorporates structured approaches like ReAct-style tool calling and external solvers. Mathematical or logic solvers can run heuristics over inputs and return answers that the LLM then uses to take the next step. This layer is also where API calls and “go fetch information” behaviors belong. The expectation is that this stack will grow quickly as systems adopt reasoning methods beyond pure text generation.

At the top is the personalization stack, which shapes how the system sounds and relates to users. It includes prompt engineering for personality and brand, plus customization of output style and conversational behavior. Sometimes it involves tracking user-specific details; often it’s about controlling tone, format, and interaction patterns through prompts and related manipulations. The framework helps teams allocate effort: role-playing and stylistic fidelity demand more personalization work, while knowledge-heavy applications demand more investment in the data/search/memory layer.

Finally, architecture choices determine where each stack lives. Mobile apps may push much of the heavy lifting to the cloud while keeping some personalization on-device. Agent design requires deciding which tools the agent can use, what reasoning mechanisms it relies on, and whether heuristic systems should assist. Tools like LLaMA index are positioned as strong interfaces for moving data from databases and vector stores into prompt-ready formats for in-context learning. The overall message: treat LLM apps as systems of components, not monolithic prompts, and build each stack deliberately.