Neureus vs LangChain
LangChain is powerful for teams that want to build AI orchestration from the ground up. Neureus is for teams that want to skip the orchestration code entirely — RAG, agents, workflows, and routing already implemented, accessible from one REST endpoint.
A Python/TypeScript library that runs in your environment. You write orchestration code — chains, retrieval strategies, prompt templates, agent logic. You manage the vector DB, the hosting, the observability stack, and package version compatibility.
Good for: ML teams building custom AI frameworks. Direct Python ecosystem integration.
A managed REST API running on 300+ global edge locations. RAG, agents, workflows, multi-model routing, and auth are already implemented. You call endpoints — no orchestration code, no infra to manage.
Good for: Product teams shipping AI features. Any language. Zero ops overhead.
LangChain is free. The infrastructure around it isn't.
langchain.RAG → POST /rag/ingest + POST /rag/query Auto-chunks, embeds, stores, retrieves langchain.ReActAgent → POST /agents / POST /agents/:id/execute Persistent state, audit log, tool use LLMChain with multiple models → POST /ai/chat with model param 10 providers, switch by changing model ID LangChain Hub templates → POST /templates/:id/use 20 production templates across 5 verticals Custom embedding pipeline → POST /rag/ingest Handles chunking + Workers AI embedding automatically LangSmith observability → Built-in — Analytics Engine + /monitoring/* Per-request metrics, health, alerts from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.chains import RetrievalQA
from langchain.llms import ChatOpenAI
# Load and split document
loader = WebBaseLoader("https://your-docs.com/guide")
docs = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
chunks = splitter.split_documents(docs)
# Embed and store (Pinecone $70+/mo)
embeddings = OpenAIEmbeddings()
vectorstore = Pinecone.from_documents(
chunks, embeddings, index_name="my-index"
)
# Query
qa = RetrievalQA.from_chain_type(
llm=ChatOpenAI(), retriever=vectorstore.as_retriever()
)
result = qa.run("What is the return policy?")# Ingest (auto-chunks, embeds, stores — no config)
curl -X POST https://app.neureus.ai/rag/ingest \
-H "Authorization: Bearer nr_key" \
-d '{"url": "https://your-docs.com/guide"}'
# Query (retrieves + LLM answer + sources)
curl -X POST https://app.neureus.ai/rag/query \
-H "Authorization: Bearer nr_key" \
-d '{
"query": "What is the return policy?",
"model": "gpt-4o-mini"
}'
# Response: { "answer": "...", "sources": [...] }| Dimension | LangChain | Neureus |
|---|---|---|
| Deployment | Self-hosted (your infrastructure) | Managed API (300+ edge locations) |
| Language | Python / TypeScript library | Any language via REST + TypeScript SDK |
| RAG pipeline | Manual: embed + store + retrieve code | POST /rag/ingest + POST /rag/query |
| Vector DB | Self-managed (Pinecone, Chroma, etc.) | Included (Cloudflare Vectorize) |
| Embedding service | Configure separately | Included (Workers AI, automatic) |
| Agent execution | Write your own ReAct loop | POST /agents/:id/execute |
| Multi-model routing | Manual per-provider SDK calls | Built-in (10 providers, 35+ models) |
| Batch inference | N/A | 40% off via provider Batch APIs |
| HITL approvals | Build from scratch | Built-in workflow approval step |
| Auth + multi-tenancy | Build from scratch | WebAuthn, SSO, RBAC included |
| MCP server | N/A | Included (9 tools) |
| Monitoring + metrics | Build from scratch (LangSmith $39/seat) | Built-in (health, alerts, AE metrics) |
| Ops overhead | High — you maintain all infra | Zero — fully managed |
| Open source | ✓ (MIT) | Closed (API service) |
| Pricing | Free + your infra costs | From $0 (500 Neurons/mo) |
500 Neurons/month free. RAG, agents, and workflows from the first API call.