Choosing Between LangChain, LlamaIndex, or No-Framework for LLM Apps

Decide the right approach for your LLM application—optimize performance, cost, and maintainability with a clear plan and checklist. Start building confidently today.

Picking the right developer path—LangChain, LlamaIndex, or no-framework—shapes delivery speed, scalability, and cost. This guide helps technical leads and engineers match tool strengths to project goals and avoid common traps.

• Quick recommendation for featured-snippet consumption.
• Side-by-side strengths and ideal scenarios for each option.
• Concrete integration and deployment checklist to move from decision to implementation.

Quick answer (one-paragraph)

For rapid prototyping, complex multi-step chains, and standardized connectors choose LangChain; for document-centric retrieval, metadata-driven indexing, and search-first apps choose LlamaIndex; choose no-framework when you need minimal dependencies, tight performance control, or bespoke model orchestration—each option trades developer velocity for customization and long-term maintainability.

Assess use cases and success criteria

Start by documenting the app’s primary user flows, data shape, latency tolerances, throughput expectations, and cost targets. Ask whether the product is retrieval-first (search, QA, knowledge base), generation-first (creative or template-driven output), or orchestration-heavy (multi-model pipelines, tool use).

• Data types: short prompts vs. long documents, structured data, streaming inputs.
• Latency: sub-200ms interactive vs. batch or background jobs.
• Scale: single-user prototype, few-power users, or millions of requests.
• Success metrics: accuracy, relevance, cost per request, deployment time.

Choose LangChain: strengths and ideal scenarios

LangChain excels at orchestrating multi-step LLM workflows and integrating tools (retrievers, agents, external APIs). Use it when flows require chains, memory, or agentic decision-making.

• Strengths:
  • Rich abstractions for chains, agents, memory, and tools.
  • Large ecosystem of connectors (vector stores, APIs, prompts).
  • Good for multi-model orchestration and stateful interactions.
• Ideal scenarios:
  • Conversational assistants with memory and tool calls.
  • Complex pipelines combining retrieval, LLM calls, and external APIs.
  • Prototypes that need quick assembly of components and community patterns.

Choose LlamaIndex: strengths and ideal scenarios

LlamaIndex focuses on indexing and retrieval over large, heterogeneous document collections. It shines where precise retrieval, metadata queries, and index design drive quality.

• Strengths:
  • Flexible index types (vector, tree, keyword + metadata).
  • Built-in tooling for document ingestion, chunking, and schema-aware retrieval.
  • Good integration with vector stores and evaluation tooling for relevance tuning.
• Ideal scenarios:
  • Knowledge bases, enterprise search, and document question-answering.
  • Use cases where retrieval quality and index design are primary levers.
  • Apps that require fine-grained control of metadata and provenance.

Choose No‑Framework: strengths and ideal scenarios

No-framework (hand-rolled orchestration) suits teams that need minimal runtime overhead, strict performance guarantees, or want to avoid dependency churn. It grants maximum control over request flow and cost optimization.

• Strengths:
  • Lean runtime, fewer external dependencies, and predictable behavior.
  • Full control over batching, caching, model selection, and retry logic.
  • Smaller attack surface and simpler upgrade matrix.
• Ideal scenarios:
  • High-performance, low-latency production systems where every ms and cent matters.
  • Tight GDPR/compliance contexts that restrict third-party libraries.
  • Long-term systems where internal expertise supports maintaining custom code.

Compare trade-offs: performance, cost, and maintainability

Weigh three axes: latency & throughput, per-request cost, and long-term maintenance. The right choice depends on where your project places priority.

• Performance:
  • No-framework often yields the lowest latency when implemented carefully (custom batching, async IO).
  • LangChain and LlamaIndex add abstraction layers that can increase latency but speed development.
• Cost:
  • Frameworks can increase compute cost via extra processing; but they reduce dev time, which has a cost benefit.
  • Custom implementations can optimize token use and caching to minimize API spend.
• Maintainability:
  • LangChain provides community patterns and updates; that helps teams keep pace but can introduce breaking changes.
  • LlamaIndex centralizes index-related complexity, simplifying long-term tuning of retrieval quality.
  • No-framework requires in-house documentation and tests to avoid knowledge rot.

Plan integration, orchestration, and deployment

Design the runtime architecture before implementation: components, interfaces, data contracts, and scaling points.

• Integration: define clear adapter interfaces for models, vector stores, databases, and external APIs.
• Orchestration: choose sync vs async, queueing (e.g., Kafka, Redis), and retry/circuit-breaker strategies.
• Deployment: containerize services, use autoscaling groups for stateless components, and plan stateful index hosting separately.
• Monitoring: track latency, error rates, tokens per request, and cost per user action.

Common pitfalls and how to avoid them

• Choosing based on familiarity rather than fit — map requirements to tool strengths first.
• Underestimating index design — run small relevance tests and iterate on chunking and metadata.
• Ignoring token and network costs — implement request-level caching and prompt trimming.
• Skipping observability — add metrics and logging before production traffic arrives.
• Excessive coupling to a framework — design thin adapter layers so you can swap implementations.
• Poor versioning and dependency control — pin framework versions and test upgrades in staging.

Implementation checklist

• Define success metrics: latency, accuracy, cost per request.
• Choose primary flow: retrieval-first, generation-first, or orchestration-heavy.
• Select tech path: LangChain / LlamaIndex / No-framework based on mapping above.
• Design adapters for models, stores, and external APIs (document interfaces).
• Implement caching, batching, and retry logic; add rate limiting where needed.
• Create tests for relevance, prompt regressions, and performance baselines.
• Instrument metrics (latency, tokens, costs) and set alerts.
• Prepare rollback and upgrade strategies for framework or model changes.

FAQ

Q: Can I combine LangChain and LlamaIndex?

A: Yes — LangChain for orchestration/agents and LlamaIndex for document indexing is a common hybrid.

Q: When should I avoid frameworks entirely?

A: Avoid them if you require the lowest possible latency, strict compliance restrictions, or have the engineering capacity to maintain custom code.

Q: How do I evaluate retrieval quality quickly?

A: Create a small labeled dataset, run A/B tests across index types and chunking strategies, and measure precision@k and user satisfaction.

Q: What are core observability metrics for LLM apps?

A: Request latency, LLM token usage, error rates, cache hit ratio, and cost per successful user interaction.

Q: How do I mitigate vendor lock-in with frameworks?

A: Implement thin adapter layers and keep critical logic (prompt templates, index schemas) in versioned, framework-agnostic formats.