Strong for orchestration, tool calling, and composable agent workflows. Keep interfaces explicit so failures can be traced quickly.

LlamaIndex excels in retrieval pipelines; DSPy excels in systematic prompt/program optimization. Measure impact with shared eval and observability metrics.

All three frameworks can solve overlapping problems, but forcing one tool to do everything often increases complexity without improving reliability. Introduce complexity only when a clear requirement justifies it.

Define chains or agents, connect tools, and instrument traces for debugging and iteration. Review framework boundaries regularly as the product evolves.

Guardrails and observability are essential as workflow complexity grows. Keep interfaces explicit so failures can be traced quickly.

Without explicit state and error handling, agent workflows become hard to debug and expensive to operate under real traffic variability. Measure impact with shared eval and observability metrics.

Build ingestion, indexing, and retrieval layers with configurable strategies and vector backends. Introduce complexity only when a clear requirement justifies it.

Evaluation of retrieval quality is critical before tuning generation behavior. Review framework boundaries regularly as the product evolves.

Weak chunking or indexing decisions can dominate downstream quality, even when model choice and prompting are strong. Keep interfaces explicit so failures can be traced quickly.

Define task modules, compile against examples, and optimize for measurable objective functions. Measure impact with shared eval and observability metrics.

You need representative training/eval examples to realize DSPy benefits. Introduce complexity only when a clear requirement justifies it.

Define measurable objectives up front, then optimize against stable datasets. Optimization without clear targets tends to overfit style rather than task quality.

Use LlamaIndex for retrieval, LangChain for orchestration, and DSPy for optimizing critical reasoning steps. Review framework boundaries regularly as the product evolves.

Keep interfaces explicit so framework boundaries remain maintainable. Keep interfaces explicit so failures can be traced quickly.

Assign clear ownership for retrieval, orchestration, and optimization layers to prevent unclear failure ownership in production incidents. Measure impact with shared eval and observability metrics.

Track retrieval precision, tool-call accuracy, response quality, and latency/cost metrics per route. Introduce complexity only when a clear requirement justifies it.

Use eval data to refine prompts, retrieval settings, and model routing rules continuously. Review framework boundaries regularly as the product evolves.

Capture traces and quality signals in a shared schema across frameworks so cross-layer debugging remains consistent and fast. Keep interfaces explicit so failures can be traced quickly.

Complex tool orchestration: start with LangChain. Retrieval-heavy knowledge apps: start with LlamaIndex. Programmatic optimization: bring in DSPy.

Add the other frameworks only when requirements clearly justify extra complexity. Measure impact with shared eval and observability metrics.

Start with one framework that solves the immediate bottleneck, stabilize evaluation, then compose additional frameworks incrementally. Introduce complexity only when a clear requirement justifies it.