Ship reliable LLM products with LangChain
Oodles builds production-ready LLM systems using LangChain and LangGraph. We design orchestration graphs, retrieval pipelines, tool integrations, and safety layers that keep LangChain-based applications grounded, observable, and scalable under real-world traffic.
Orchestrate tools, data, and guardrails
Our LangChain developers implement end-to-end workflows using LangChain, LangGraph, and LangServe to connect models, tools, retrieval layers, and safety policies. Every flow is instrumented with tracing, evaluations,and alerts to support fast debugging and reliable production releases.
What we implement
- • LangChain orchestration with tools, agents, routers, and memory
- • RAG pipelines using chunking, embeddings, vector search, and re-ranking
- • LangGraph for stateful, controllable, multi-step workflows
- • LangServe deployments with authentication, rate limits, and tracing
- • Evaluation, guardrails, and observability with traces, metrics, and logs
Why teams choose us
- • Architecture-first LangChain design for model choice and latency targets
- • Grounded outputs through retrieval tuning, deduplication, and citations
- • Safety in-loop with jailbreak testing, PII masking, and abuse filters
- • Cost efficiency using caching, batching, and token budgeting
- • Reliability ensured by evals, regression tests, and SLOs before launch
Where LangChain fits
Targeted support for product, data, and platform teams building LLM experiences.
RAG assistants
LangChain-powered retrieval-augmented assistants with citations, fallback prompts, and hallucination controls.
Tool-using agents
LangChain agents with tool calling, API orchestration, and policy-aware execution flows.
Document pipelines
Document ingestion, chunking, embeddings, summarization, and re-ranking built with LangChain components.
Ops & analytics
Tracing, metrics, evaluation dashboards, and cost controls for LangChain applications in production.
Need LangChain experts fast?
Oodles provides experienced LangChain engineers to embed with your team or deliver a managed pod with weekly demos, shipped code, and production-ready workflows.
How we build with LangChain
A structured LangChain delivery process used by Oodles to design, test, and deploy reliable LLM workflows with guardrails at every step.
Blueprint
Select LLMs, tools, context limits, latency budgets, and safety requirements.
Data & retrieval
Configure chunking, embeddings, vector databases, and retrieval strategies.
Flows & tools
Build LangChain and LangGraph flows with tool use, routing, and tracing.
Evals & safety
Run evaluations, regression tests, PII checks, and jailbreak resistance tests.
Deploy & observe
Deploy via LangServe or APIs with dashboards, alerts, and cost monitoring.
FAQs (Frequently Asked Questions)
LangChain is a framework for chaining LLMs with tools, data, and memory. Use it for RAG, agents, chatbots, and multi-step workflows. Best when you need composable, production-ready orchestration.
Built-in support for Pinecone, Weaviate, Chroma, pgvector, and others. Use vector stores as retrievers in RAG chains. We configure and tune for your data and latency needs.
Agents let the LLM choose which tools to call (search, calculator, APIs). Tools are functions the model can invoke. Enables dynamic, multi-step workflows like research assistants.
LangServe exposes chains as REST APIs. Deploy on Kubernetes, serverless, or managed platforms. We add observability, rate limiting, and error handling for production readiness.
LangChain is broader: chains, agents, tools. LlamaIndex focuses on data ingestion and RAG. Use LangChain for full pipelines; LlamaIndex when RAG and indexing are the core need.
LangSmith for traces, debugging, and eval. Integrate with LangFuse, OpenTelemetry, or custom logging. Track latency, token usage, and chain steps for production monitoring.
Simple RAG or chatbot: 2–4 weeks. Full agent with tools: 4–8 weeks. Enterprise pipeline with observability: 2–3 months. Depends on data, integrations, and scale.
Ready to build with LangChain? Let's talk
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