Working With LangChain In Your Stack

Most of the LangChain work we do is maintenance of something a previous developer started, and a fair amount of it ends with taking LangChain out of the hot path in favour of plain Python. We are comfortable either way. What we are not is a LangChain shop: we treat it as one library among several, and if it is the wrong tool for your case we will say so before you pay us to extend it.

LangChain tutorials make it look easy. Real LangChain deployments are not. The framework evolves rapidly, the abstractions leak, and the default patterns are great for prototypes and terrible for production. Most teams that start with LangChain end up forking it, rewriting chains as plain Python, or migrating to LlamaIndex/Haystack/AutoGen.

The right way to use LangChain is to treat it as a toolkit, not a framework. Use the components you genuinely benefit from (loaders, splitters, vector store wrappers, callback handlers) and write your own orchestration layer. Trying to use chains and agents as the orchestration substrate ships code that is hard to debug, expensive to run, and impossible to evaluate systematically.

Tracing matters more than the docs suggest. LangSmith integration is the right answer, but only if instrumented correctly. Without per-step traces, you cannot debug why a chain returned the wrong answer on input 4731. Without cost-per-trace, you cannot tell which chain is bankrupting you. Production LLM apps without tracing are flying blind.

Cost controls in LangChain are easy to miss. Each retry doubles cost. Each tool call adds tokens. Each retrieved chunk adds tokens. A chain that "works" can cost $0.30 per request — fine at 1000/day, ruinous at 1M/day. We design with per-step token budgets and route to cheaper models where appropriate.

AsyncForge has senior engineers shipping production LangChain apps. Submit chain implementations, agent designs, RAG pipelines, evals, or migrations away from LangChain. Light 4 days, Standard 48 hours, Pro 1 day.