LangChain_
>> In 2026 LangChain is the high-level assembly layer, not a throwaway prototype toolkit. We use create_agent and the ecosystem for standard tool loops, then drop to LangGraph when branching and durable state demand explicit graphs.
Calls/month
Production AI volume across live systems
PII detection
Compliance-ready detection accuracy in production AI
Projects
Shipped since 2012 with senior engineers only
Direct answer
Standard agent and RAG loops ship fastest when create_agent and middleware cover the job. We assemble LangChain on the LangGraph runtime, then harden with evals, tenancy, and an escape hatch to explicit graphs.
Trusted by leaders
>> System map
Adjacent AI layers we ship with this expertise. Technology-first, not vendor theater.
>> When this system fits
Honest fit gates. We will tell you when another approach is better.
Strong fit
- Standard tool-calling agents with middleware for PII, summarization, or approvals
- RAG and document workflows that need ecosystem connectors without custom orchestration graphs
- Teams standardizing on LangChain 1.x with LangGraph runtime underneath
Weak fit
- Complex multi-agent graphs that need StateGraph from day one without a high-level loop
- Greenfield work with no eval plan or ownership of prompts/tools
Stack: LangChain · create_agent · Python · TypeScript · RAG · Tools
What we deliver
_> Capabilities on this stack
create_agent delivery
Production agent loops with middleware for approvals, redaction, retries, and summarization.
RAG assembly
Retrieval chains and document workflows with citeable outputs and evaluation hooks.
Integration discipline
Typed tool contracts, tenancy-aware retrieval, and failure modes that ops can debug.
Escape hatch to LangGraph
Refactor to explicit graphs without a full rewrite when control requirements grow.
LangChain engagement model
Loop vs graph decision
Confirm whether create_agent covers the workflow or StateGraph is required immediately.
Ship with evals
Thin agent or RAG path with offline evals and tracing from the first merge.
Harden middleware
Add PII controls, approvals, cost caps, and persistence before scale.
Related projects
_> See how we've applied our expertise
>>Related guides
_> Cite-worthy depth behind this stack
Frameworks and scorecards buyers and answer engines can quote. Each guide links back to delivery proof.
LangChain vs LangGraph for Production Agents
2026 decision guide: LangChain create_agent on the LangGraph runtime vs explicit StateGraph for durable branching and HITL.
RAG Quality Guide: Evaluation That Holds Up in Production
A practical guide to RAG evaluation: offline test sets, retrieval and answer metrics, regression gates, and RAG monitoring to reduce hallucinations.
How to Choose a Production AI and RAG Partner
Hire a production AI partner when RAG or agents must survive security review and real traffic. Score vendors on shipped systems, compliance, senior delivery, and evaluation harnesses you keep.
Agent Eval, Cost, and CI Gates
Build offline eval suites, online scorers, cost budgets, and merge-blocking CI gates for production agents.
FAQ
Yes. They are complementary. create_agent runs on the LangGraph runtime. Start high-level; drop to graphs when you need explicit control.
We ship LangChain/LangGraph in Python and TypeScript depending on your product stack and hiring plan.
>> Where this goes next
Adjacent expertise and the engagement models we deliver it through.