LangChain development for language-model applications and agents
LangChain is an open-source framework, available for Python and JavaScript, for building applications on top of language models. It provides common building blocks for prompts, retrieval, tool calling and multi-step agents, and works with most model providers.
LangChain speeds up development by offering integrations with many models, vector stores and data sources, and its companion tools help orchestrate agents and trace what they do. That makes swapping providers or adding tools easier. The abstraction layer can also add complexity and make debugging harder than a direct API call. For a simple, single-step feature we often call the model API directly; for retrieval-heavy work we may prefer LlamaIndex.
What we build with LangChain
- Agents that call internal tools and APIs
- Retrieval-augmented question answering
- Multi-step document workflows
- Provider-neutral model integrations
- Chatbots with memory and guardrails
LangChain questions
Do we need LangChain to build an AI feature?
Not always. Simple features can call the model API directly. LangChain earns its place when you need retrieval, tools, multiple steps or the flexibility to switch providers.
Can LangChain work with self-hosted models?
Yes. It integrates with open models served on your own infrastructure as well as hosted APIs.
What is LangGraph and when is it used?
LangGraph is a companion library for building agents as explicit graphs of steps with state. We use it when a workflow needs loops, branching, retries or human approval.
How do you debug and monitor LangChain applications?
We trace each run so every prompt, tool call and response can be inspected, using LangSmith or an open-source tracing tool, and add alerts on errors and latency.
How do you evaluate a LangChain agent?
We build test cases covering typical and difficult requests and check both the final answer and the steps taken, rerunning them whenever prompts, tools or models change.
Does LangChain make it easy to switch model provider?
It offers a common interface across providers, so switching needs few code changes. Prompts and behaviour still need retesting because models respond differently.
How do you stop an agent taking the wrong action?
We limit which tools it can use, validate inputs to each tool, cap the number of steps and require human approval for anything consequential.
Can LangChain keep costs under control?
We track token usage for every step in traces, cache repeated calls, keep context short and route simple steps to cheaper models.
Is LangChain suitable for production systems?
Yes, with discipline. We pin versions, keep the chain logic small and well tested, and avoid unnecessary abstraction so the code stays maintainable.
Can LangChain use our internal APIs as tools?
Yes. We wrap your APIs as tools with clear descriptions and input schemas, and apply the same authentication and permission checks as any other client.
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Tell us what you are building. We reply within one business day with next steps — and we are happy to sign an NDA first.