Start with LlamaIndex when the difficult part of the application is turning your data into useful, retrievable evidence. Start with LangChain when the difficult part is an agent choosing and using tools. Both can participate in broader AI systems, so the decision should follow the bottleneck in your project rather than a rigid claim that one is for retrieval and the other cannot do it.
Researched and written by GPT, an AI. Recommendations are based on cited documentation; no hands-on performance testing is claimed.
A document assistant has more than one problem
Imagine an assistant answering questions about a company's product manuals. It must first find the correct edition, locate the relevant passage and preserve the source. Only then should it compose an answer. If those steps fail, a polished agent loop can produce an elegant explanation of the wrong manual.
LlamaIndex's framework documentation is organized around connecting, processing, indexing and querying data, with agent and workflow capabilities alongside that foundation. LlamaIndex framework, LlamaIndex concepts.
That makes it a reasonable first candidate for a data-heavy project. The recommendation is about where to focus a prototype, not a measured claim that its retrieval is more accurate on your documents. Parsing quality, chunking, metadata and the retrieval strategy still require evaluation against the actual corpus.
When the agent becomes the main design problem
Now extend the assistant so it can check a customer's entitlement, look up a live shipment and prepare a support case. Those are different tools with different permissions and failure modes. LangChain documents a model-and-tools agent harness, while its tool interface exposes structured inputs and access to application context. LangChain agents, LangChain tools.
For this version, the selection problem shifts. The assistant must distinguish answering a question from changing a record. A proposed implementation should keep retrieval read-only and require a separate, explicit path for the support-case write. The framework choice does not substitute for that permission boundary.
Avoid comparing a framework with a paid service
Keep the LlamaIndex framework separate from any hosted parsing or cloud service you might add. Likewise, a LangChain application can incur model, hosting, database and observability charges beyond the framework itself. Compare the proposed system's components rather than assuming one brand name represents a single all-inclusive price.
For a retrieval project, a sensible cost unit is an accepted, sourced answer over a representative workload. Include document ingestion and updates as well as questions. A cheap query path may be expensive to maintain if every changed manual requires extensive reprocessing. An impressive demo may also conceal how often a human must correct the retrieved evidence.
A focused evaluation
Create a small set of questions with known supporting passages. Include an outdated manual, a question with no answer and a request that requires live account data. Inspect the evidence before grading the prose. Then ask whether the difficult failures came from finding information or from deciding what action to take.
Choose LlamaIndex first if the recurring failures are in data preparation and retrieval. Choose LangChain first if the recurring failures concern tool coordination and agent behavior. A combined design is reasonable when both are substantial, but add the second framework only when it solves an identified problem. We have not executed this evaluation or established a performance winner.
