Technology
LLMs & AgentsLangChain, LlamaIndex, Tools
The LLM stack we reach for, the trade-offs we name, and when we use something else — LangChain and LlamaIndex for the orchestration, the right model for the use case, the right tools for the agent, with the eval suite as the safety net.
What we use it for
LangChain and LlamaIndex for the orchestration — the model call, the prompt, the context, the retrieval, the tool call, the memory. The right model for the use case — OpenAI, Anthropic, Google or open source. The right tools for the agent — the API, the database, the file system.
We extend the stack with the eval suite the model needs — LangSmith, Helicone, custom evals. The architecture is model-agnostic, with the swap path written, and the eval is part of the deploy.
When we choose it over the alternative
LangChain is right when the orchestration is well-known, the team is comfortable with the framework, and the use case is a standard one. LlamaIndex is right when the retrieval is the focus, the data is documents, and the team needs a retrieval-first framework. A custom pipeline is right when the use case is novel and the framework does not fit.
The right answer depends on the use case, the data and the team. We will say so on the call, with a written rationale for the choice and the trade-offs named.
When we do not choose it
We do not choose a framework when the use case is a single model call and the framework is over-engineering. We do not choose a custom pipeline when the framework fits and the team is comfortable with it. We do not choose the default when the eval suite and the guardrails are not part of the plan.
Frequently asked
The questions the team asks
- LangChain or LlamaIndex?
- It depends on the use case. LangChain is the right default for most LLM apps, with the broadest ecosystem. LlamaIndex is the right answer when the retrieval is the focus, the data is documents, and the team needs a retrieval-first framework. We will say so on the call.
- How do you handle the agent?
- The planner, the specialist agents, the tool access, the memory, the budget, the audit log, the eval suite. The agent is part of the architecture from sprint one, with the guardrails and the eval as the safety net.
- How do you handle the model swap?
- A documented swap path, with the eval as the gate. The architecture is model-agnostic, so the next model is a configuration change, not a rewrite. The cost, the latency and the quality are part of the eval, and the result is in the deploy log.
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