LangChain Development Service
Senior engineers building LangChain agents, chains, and RAG pipelines with proper evals, cost controls, and tracing.
LangChain in Production Looks Nothing Like the Tutorials
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.
What You Get
Chains that survive code review
Chains structured as plain Python with LangChain components used selectively. Clear data flow, typed inputs and outputs, testable in isolation.
Agents with bounded loops
Tool-using agents with explicit max-steps, per-step token budgets, and fallback paths when tools fail. No runaway agents.
RAG with reranking
Document loading, chunking (with metadata), embedding, retrieval, reranking with Cohere/Voyage, generation with citations.
LangSmith tracing
Every chain instrumented with LangSmith. Per-step latency, cost, inputs, outputs visible. CI runs evals against tagged datasets.
Migration paths
When LangChain becomes the bottleneck, we migrate to plain Python + lightweight orchestration (e.g., guidance, instructor, or hand-rolled).
Cost-bounded execution
Per-request token caps, per-user daily quotas, model-tier routing (Haiku/Mini for easy, Opus/Sonnet for hard).
Technologies We Use
How It Works With AsyncForge
Subscribe
Plan picked.
Submit LLM work
Chains, agents, RAG, evals, migrations.
We deliver
Evaluated, traced, cost-bounded.
Iterate
Unlimited revisions.
Frequently Asked Questions
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