At Praxium Labs — Nepal's AI and automation consultancy — we see this pattern across most Nepali engagements. The LangChain ecosystem has split into LangChain (the high-level framework) and LangGraph (a graph-based execution engine for agents). For most production AI work in Nepal, the choice is between these two and "just write it in Python without a framework".
When LangChain is the right choice
- You are doing standard RAG and want the loaders / splitters / retrievers off the shelf
- You want vector-database abstraction (Pinecone, Qdrant, pgvector all behind the same interface)
- Your team prefers configuration-style code
- You are iterating fast and consistency matters more than performance
When LangGraph is the right choice
- You are building an agent with multiple tools and the control flow is non-trivial (branches, retries, loops, human-in-the-loop steps)
- You need durability — agents that can pause for hours then resume
- You want to checkpoint state for replay or debugging
- You need to coordinate multiple agents (multi-agent systems)
When you should use neither
- A single-call chatbot — just use the model SDK directly
- A small RAG pipeline (<200 LOC) — abstraction adds more reading than it saves writing
- When your team is more comfortable in their language's idioms than in a framework's DSL
Production trade-offs
LangChain pros: rapid prototyping, abundant examples, broad community. Cons: heavy abstractions that hide what the model is actually doing, frequent breaking-API changes, layered debugging. LangGraph pros: explicit graph means the flow is auditable, durability is real, debug tooling is better. Cons: steeper learning curve, fewer pre-built examples, opinionated.
What we use at Praxium Labs
For client RAG chatbots: usually plain Python + the model SDK + pgvector. For agentic workflows (research agents, document-processing pipelines, multi-step internal automations): LangGraph because the durability and state-checkpoint primitives are genuinely useful. LangChain we use rarely now — once the team is past the prototype phase, the framework usually adds more friction than it saves.
Alternatives to know
- LlamaIndex: RAG-focused, great for document workflows
- Haystack: mature, modular, popular in enterprise
- DSPy: Stanford-originated, focuses on prompt-optimisation; growing fast
- Pydantic AI: typed agent framework; cleaner DX than LangChain
When LangChain is enough
For most Nepali AI projects in 2026 — RAG over a knowledge base, simple agent that calls 2-3 tools, customer-support chatbot — LangChain remains the pragmatic choice. The ecosystem is broader (more retrievers, more LLM connectors), the documentation has more examples, and Nepali engineers find it easier to hire for. Save LangGraph for when you have a real reason: multi-step agent workflows with branching, stateful conversations beyond simple message history, parallel sub-agent execution, or explicit need to checkpoint and resume an in-progress agent run. See our deep-agent guide for when the orchestration complexity justifies LangGraph.
Total cost of ownership comparison
- LangChain build: 2-6 weeks for a typical Nepali RAG / chatbot use case; 1-2 engineers
- LangGraph build: 4-10 weeks for a comparable multi-step agent workflow; 1-2 senior engineers
- Maintenance: LangChain is more "boring" — fewer abstractions to debug. LangGraph requires deeper graph-thinking; debugging is harder
- Cost of running: LLM costs dominate either way; orchestration framework choice does not meaningfully change inference cost
- Migration: moving LangChain → LangGraph is incremental (LangChain runnables embed in LangGraph nodes); the reverse is a rewrite
Frequently asked questions
Is LangChain still relevant in 2026?
Yes, with caveats. It remains the default many tutorials assume. For real production, many teams have moved to lighter frameworks or no framework. Knowing it remains valuable; defaulting to it is no longer required.
Can I mix LangChain and LangGraph?
Yes — they are designed to interoperate. Many production systems use LangChain for retrieval + LangGraph for orchestration. The boundary is clean.
How does this compare to writing agents with the OpenAI / Anthropic SDK directly?
For simple agents (one tool, one or two turns), the SDKs are sufficient. For complex agents (many tools, branching, durable state) LangGraph saves substantial work. Below ~500 LOC of orchestration, raw SDK wins on simplicity; above it, LangGraph wins on maintainability.
What about Anthropic's Computer Use or OpenAI's Assistants API?
They are useful for specific use cases (browser automation, structured assistants) but lock you to one provider. For provider-portable agents, LangGraph + your own tool catalogue is more flexible.
For a Nepali team picking up agent work, where to start?
A two-week investment: read the LangGraph docs end-to-end, build one agent (e.g., a research assistant that reads three sources and summarises), deploy it. Skip the LangChain ecosystem at first; it will distract from the fundamentals.
Should I use LlamaIndex instead?
LlamaIndex is strong for retrieval-heavy applications (data ingestion, complex query engines). LangChain is broader. For pure RAG, LlamaIndex often produces cleaner code. For agents + RAG, LangChain wins.
What about no-code (n8n, Make, Zapier)?
No-code is faster to a working prototype but hits ceilings on complex agent logic and observability. For one-off automation, n8n is great (see our n8n guide). For a real production AI product, code with LangChain / LangGraph wins on flexibility.
Who can build this in Nepal?
Praxium Labs — Nepal's AI and automation consultancy, based in Lalitpur — designs and builds the systems described in this guide for Nepali businesses and for international teams hiring from Nepal. Start a project or see all services.