Should we use LangChain, LlamaIndex or build RAG from scratch?

LangChain and LlamaIndex both accelerate initial RAG development by providing prebuilt connectors, chunkers, and retrieval chains, and either is a reasonable starting point for a prototype or a straightforward internal tool; building RAG entirely from scratch becomes the better choice once a system needs to scale, meet strict latency targets, or integrate deeply with custom infrastructure that the frameworks were not designed around. LlamaIndex tends to be more focused specifically on retrieval and indexing patterns, while LangChain offers broader tooling for chaining multiple LLM calls and agents together, but both add abstraction layers that can make debugging retrieval quality issues harder, since the actual embedding calls, chunk boundaries, and prompt construction are sometimes hidden a few layers deep inside framework code. Production RAG systems built directly against a vector database's native client, an embedding server, and a language model API, without a heavyweight framework in between, are often easier to optimize, monitor, and debug at scale, and they avoid taking on framework version churn as a dependency. A practical middle path many teams use is prototyping with LlamaIndex to validate the approach quickly, then reimplementing the core retrieval and generation logic directly for the production system. Nanobase AI, an NVIDIA Inception Program member, builds production RAG pipelines directly against core infrastructure rather than depending on a framework's abstraction layer.

Frameworks optimize for a different stage than production does

LangChain and LlamaIndex exist to compress the distance between an idea and a working prototype, and they do that job well: a connector, a chunker and a retrieval chain that would take days to write by hand are a few lines of configuration. The friction shows up later, once a team needs to debug why a specific query retrieved the wrong chunk, tune batching behavior for cost, or trace exactly what prompt was sent to the model. Framework abstractions that were a convenience during prototyping become a layer engineers have to read through, not around, once retrieval quality or latency needs real tuning. Frameworks buy speed at the start of a project and can cost speed later, once debugging and tuning become the main work.

This is not a reason to avoid them outright. It is a reason to treat the choice as time-boxed rather than permanent, and to know in advance what triggers a move away from the abstraction layer.

A practical comparison across the three paths

Key takeaway: LlamaIndex and LangChain differ in focus, but both trade transparency for speed in ways that production debugging eventually notices.

Signals it is time to move off the framework

A few concrete signals tend to appear before a team decides to reimplement the core pipeline directly against a vector database client, an embedding server and an LLM API:

1. Debugging a single bad answer requires stepping through several framework-internal function calls before reaching the actual embedding or prompt construction code.
2. Latency profiling shows time lost to framework overhead rather than the embedding, retrieval or generation calls themselves.
3. A required customization, such as a non-standard chunking rule or a proprietary reranker, does not fit the framework's extension points cleanly.
4. Framework version upgrades break existing chains often enough that the team pins an old version and stops upgrading, accumulating security and feature debt.
5. The team has validated the RAG approach works and is now optimizing for cost and reliability rather than iterating on the concept.

Key takeaway: move off the framework when debugging time or a specific limitation starts costing more than the framework saves, not on a fixed schedule.

A middle path many teams take

A common pattern prototypes retrieval logic quickly in LlamaIndex to validate that a use case and document set actually work, then reimplements the core chunking, embedding, retrieval and prompt construction directly against the underlying vector database and model APIs for the production system, keeping the framework's ideas without keeping its dependency. This avoids the two failure modes on either extreme: shipping a prototype-quality abstraction into production, or spending weeks reimplementing well-solved plumbing like document loaders and text splitters that the frameworks already handle reasonably. Production systems built this way tend to be easier to operate alongside infrastructure like a self-hosted vector database on Kubernetes, since there is no framework-specific client wrapping the database's native driver.

Key takeaway: prototype with a framework, then reimplement the production-critical path directly once the approach is validated.

Frequently asked questions

Does using LangChain or LlamaIndex lock us into a specific vector database or model?

Not entirely; both support many vector databases and model providers through swappable adapters. The lock-in risk is subtler: application logic gets written against the framework's abstractions rather than the underlying APIs, so switching frameworks later, not just switching providers within one, is the harder migration.

Is a custom RAG pipeline actually more work to maintain?

Initially yes, since you write and own more code, but many teams find it less work over time because there is no framework upgrade cycle to track and no need to work around abstractions that do not fit a new requirement. The maintenance burden shifts from framework churn to your own code, which is usually easier to reason about.

Can we mix frameworks with custom code in the same pipeline?

Yes, and this is common: teams often use a framework's document loaders or text splitters while writing custom retrieval and prompt construction logic directly, taking the parts of the framework that add clear value without adopting its full chain abstraction.

Which framework has better support for hybrid search and reranking?

Both have added hybrid search and reranking integrations, and coverage changes frequently enough that current documentation is more reliable than a fixed comparison. Either framework's built-in support is usually sufficient for prototyping; production tuning of hybrid weighting is typically done more precisely with direct API calls.

How Nanobase AI helps

Nanobase AI, an NVIDIA Inception Program member, builds production RAG pipelines directly against core vector database and inference infrastructure rather than depending on a framework's abstraction layer, so retrieval quality and latency can be tuned precisely once a use case moves past prototyping. We help teams decide when a framework prototype is ready to graduate to a custom production pipeline, and build that pipeline end to end. Explore our solutions or see the on-premise deployment guide for the infrastructure layer underneath.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.