RAG over private corpora
Document ingestion, chunking, embedding and hybrid BM25 + vector retrieval over internal knowledge bases — with cross-encoder reranking, source attribution and RAGAS-measured quality.
LangGraph RAG EU AI Act GDPR
LangChain Development for Production RAG and AI Agents
LangChain and LangGraph are our tools of choice for production AI agents and RAG pipelines — not for proof-of-concepts. A senior-only engineering team (80+ engineers, in business since 2017) that instruments every chain with LangSmith, runs RAGAS evals in CI, and designs agents with explicit approval gates for actions that cannot be undone. This page is an honest evaluation of when LangChain is the right call — and when it isn't — plus how we build with it at scale.
LangChain is an open-source framework for composing LLM calls, tools, retrieval and memory into applications; LangGraph is its stateful graph runtime for agents that branch, loop and pause for human approval; LangSmith is the observability layer that traces every step. We use the three together to ship production RAG pipelines and multi-step AI agents — not proof-of-concepts. The rest of this page is a straight answer to two questions a CTO or architect actually asks: is LangChain the right tool for this workload, and is this team genuinely deep in it.
Evaluation
When to use LangChain — and when not
LangChain is not a plug-and-play library, and it is not the right default for every LLM feature. The honest decision comes down to how much orchestration state you actually have. Here is how we choose it on real projects.
| Reach for LangChain / LangGraph when… | Choose something lighter when… |
|---|---|
| You have a genuine agent — multi-step reasoning, tool use, branching and looping — that benefits from LangGraph's state machine. | It is a single prompt-and-response feature. A direct SDK call to the model is fewer moving parts and easier to reason about. |
| Agents must persist state and resume — pause mid-workflow, wait for a human, and continue hours later. LangGraph's built-in checkpointing is a real advantage here. | The workload is retrieval-heavy and the indexing pipeline is the hard part. LlamaIndex often reaches good retrieval with less code; we frequently pair it under LangGraph rather than replace it. |
| You want vendor-neutral orchestration across OpenAI, Anthropic and self-hosted models behind one interface, with fallback and per-tier routing. | Every millisecond of framework overhead counts on a hot path. A thin, hand-rolled orchestration layer avoids the abstraction tax. |
| You need first-class observability — LangSmith traces every LLM call, tool invocation and chain step out of the box, which shortens production debugging. | You cannot absorb version churn. LangChain's API moved significantly across 0.0.x→0.3.x and LangGraph still deprecates; if you will not pin and maintain, choose a slower-moving stack. |
Our default on larger systems is a hybrid: a purpose-built retrieval layer (often pgvector or Qdrant with LlamaIndex) feeding a LangGraph orchestration layer above it. That keeps retrieval quality high and orchestration explicit, and it is the pattern behind our RAG implementation and AI agent work.
Capabilities
What we build with LangChain
Multi-step AI agents
LangGraph agents that reason, call tools, branch on results and pause for human approval — with max-iteration caps and cycle detection so they never run away.
LLM orchestration layers
Vendor-neutral routing across OpenAI, Anthropic and self-hosted models — with fallback, cost tracking and latency SLAs per model tier.
Conversational assistants
Chat interfaces over product documentation, internal knowledge and customer data — with tiered context management and source-cited answers.
Document extraction pipelines
Structured extraction from PDFs, contracts and forms — schema-driven with Pydantic, RAGAS faithfulness scoring and human review queues for low-confidence output.
Multi-agent systems
Supervisor-and-specialist architectures where a routing agent delegates to domain-specific sub-agents via LangGraph subgraphs — for complex analytical and research tasks.
Stack
LangChain in our stack
LangChain rarely stands alone. On our projects it sits on a Python and FastAPI service, with OpenAI and Anthropic as interchangeable model providers behind vendor-neutral routing. Retrieval runs on pgvector or Qdrant, frequently with LlamaIndex owning the indexing pipeline. LangGraph handles agent state, LangSmith handles tracing, and RAGAS gates quality in CI. Pydantic schemas keep tool inputs and structured output typed end to end.
LangChain · LangGraph · LangSmith · LlamaIndex · OpenAI · Anthropic · pgvector · Qdrant · RAGAS · FastAPI · Python · Pydantic · Docker · Kubernetes.
Adoption & migration
Bringing LangChain into your codebase
Greenfield
We start with the eval set, not the prompt. A RAGAS harness built from real user queries goes in first, then LangSmith tracing, then the smallest LangGraph that solves the task. Approval gates for irreversible tool actions and minimal-privilege API keys are designed in from day one, not retrofitted.
Migration into an existing app
Into a live system we introduce LangChain behind a single service boundary — usually a FastAPI endpoint — so the rest of the codebase is untouched. We pin versions, track breaking changes across the 0.0.x→0.3.x history before upgrading, and migrate chain-by-chain rather than in one risky rewrite.
Compliance
Compliance for LLM applications
LLM systems carry real regulatory weight — data handling, automated decisions and model risk. We treat this as part of delivery, and offer dedicated EU AI Act compliance support where the risk tier demands it.
EU
- EU AI Act — risk classification, technical file, transparency obligations.
- GDPR Art. 22 — automated decision-making, DPIA, human oversight.
- DSA — transparency for recommender and content-moderation systems.
- GDPR — data residency, zero-data-retention endpoint configuration.
US
- NIST AI RMF — govern, map, measure, manage.
- CCPA/CPRA — automated decision opt-out.
- SR 11-7 — model risk management for regulated finance.
- HIPAA — minimum necessary, de-identification.
Shared: OWASP LLM Top 10, prompt-injection hardening, LangSmith tracing as the audit trail.
Why our team
Why our team is deep in LangChain
A senior-only engineering company in business since 2017 — 80+ engineers, 120+ projects delivered — with LLM engineering treated as core, not a bolt-on.
LangGraph in production
We have shipped LangGraph-orchestrated agents to production users — not just run demos. State machines, interrupt gates and cycle detection built from real agent-runaway incidents.
RAGAS eval from day one
Every RAG pipeline has a RAGAS eval harness before the first prompt goes live. Quality metrics gate every PR merge — no silent degradation.
Safety & compliance built in
Minimal-privilege tools, human-in-the-loop approval gates and EU AI Act risk classification are part of our standard AI engagement — not extras billed separately.
Ready to build rather than evaluate? Our productised delivery lives on the AI agent development, RAG implementation and generative AI integration service pages.
FAQ
LangChain technical FAQ
LangChain or LlamaIndex — which framework do you use?
LangChain for agentic workflows with tool use, multi-step reasoning and complex chain composition. LlamaIndex for RAG-heavy workloads where the indexing pipeline, retrieval strategies and structured output extraction are the primary concern. We often use both in the same project — LlamaIndex for the retrieval layer, LangChain for agent orchestration above it.
LangGraph or standard LangChain chains?
LangGraph for anything requiring branching, looping, parallel tool execution or human-in-the-loop interrupts. Standard chains for linear prompt pipelines where the complexity overhead of a state machine is not justified.
How do you evaluate RAG quality?
RAGAS metrics: faithfulness (answer grounded in retrieved context), answer relevance, context precision and recall. We build the eval set from real user queries before writing the first prompt, run it in CI on every change, and alert when any metric drops below threshold.
How do you design AI agents safely?
Tool schema design is the first safety layer — we define what each tool can and cannot do, use minimal-privilege API keys, and require explicit approval for irreversible actions (send email, modify database, call external API with side effects). LangGraph's interrupt mechanism is our standard for human-in-the-loop gates.
How do you keep agent context within token limits?
Conversation summarisation with a dedicated compression LLM call, selective memory via a retrieval-augmented history store, and tiered context: always-on system context, recent messages, and retrieved relevant history. We profile token usage per agent step and set budgets per turn.
Can you build multi-agent systems?
Yes. We design multi-agent architectures where a supervisor routes tasks to specialist agents — a document agent, a calculation agent, a search agent — and aggregates results. LangGraph's subgraph feature handles agent-to-agent communication cleanly.
How do you debug LangChain applications in production?
LangSmith is our default observability layer — every LLM call, tool invocation and chain step is traced with latency, token count and inputs/outputs. We set up LangSmith from day one, not as a retrofit, and use it to catch regressions before they reach users.
Not sure LangChain fits your workload? Get an architecture review
Talk to a LangChain engineer about whether it is the right call for your system — no pitch. Response within 1 business day, NDA on request. Ready to build? See AI agent development.
Get a proposal
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