Build agent workflows, RAG pipelines, and LLM orchestration with LangChain.
Fremen Consulting builds LangChain applications — agent workflows with tool use, RAG retrieval chains, multi-step reasoning, and LLM orchestration connecting OpenAI, Anthropic, and open-source models to your data and APIs.
Problems we solve for businesses like yours
Hand-rolled LLM orchestration code becomes spaghetti as tool calls, memory, and retry logic accumulate — every new feature breaks existing flows.
Naive chunk-and-embed approaches retrieve irrelevant context, causing LLM answers that sound confident but cite wrong information.
When LangChain agents fail silently or loop infinitely, teams cannot trace which step failed or why — debugging is guesswork.
Solutions tailored to your industry and growth goals
LangChain agents with custom tools, structured output parsing, human-in-the-loop checkpoints, and guardrails for multi-step autonomous tasks.
Hybrid search, reranking, contextual compression, and metadata filtering for high-precision retrieval before LLM generation.
LangSmith tracing for chain debugging, dataset evaluation, prompt hub management, and production monitoring dashboards.
Measurable outcomes from projects in this space
LangChain agent with tool use automated document extraction and classification, processing ten times more documents than the previous manual workflow.
Clear answers to common questions in this industry
LangChain is a framework for building LLM applications with chains, agents, and tool integration. Use it when you need multi-step reasoning, RAG pipelines, or agents that call external APIs — rather than simple single-prompt completions.
Yes. For complex agent workflows with cycles, state management, and human-in-the-loop, we use LangGraph — LangChain's graph-based orchestration framework for production agent systems.
Yes. LangChain supports OpenRouter as a unified gateway, plus OpenAI GPT 5.5, Anthropic Opus 4.8 and Sonnet, Mistral Large 3, xAI Grok 4.3, DeepSeek, Gemini, and open-source models — enabling model flexibility and automatic fallback strategies.
We implement hybrid search combining vector and keyword retrieval, cross-encoder reranking, contextual chunk compression, and metadata filtering to improve precision before the LLM generates an answer.
A RAG pipeline takes six to ten weeks. Complex agent systems with tool integration and evaluation typically take ten to sixteen weeks.
Tell us about your business and goals. We will recommend the right approach for your industry, timeline, and budget.