LangChain Development Services

We provide end-to-end LangChain development services including custom chain and agent architecture design, RAG pipeline development with LangChain and vector databases, LangGraph stateful agent and multi-agent system development, LangSmith observability and evaluation implementation, tool and API integration for LangChain agents, memory and persistence configuration, LLM integration across OpenAI, Anthropic, Google, and open-source models, production deployment, and ongoing optimisation. We build both standalone LangChain applications and integrate LangChain capabilities into existing products and enterprise systems.

LangChain is the leading open-source framework for building LLM-powered applications. It provides a composable architecture of chains, agents, tools, memory, and retrieval components that dramatically accelerate AI application development compared to building from scratch. LangChain abstracts LLM provider differences, standardises tool use and retrieval patterns, and provides built-in integrations with over 100 data sources, vector databases, and API services. For production applications, LangChain combined with LangGraph and LangSmith provides a complete development, orchestration, and observability stack for enterprise-grade AI systems.

LangGraph is LangChain's framework for building stateful, multi-actor AI applications as graph-based state machines. While standard LangChain chains work well for linear workflows, LangGraph is the right choice when you need persistent state across agent steps, conditional branching logic based on agent decisions, parallel execution of agent branches, human-in-the-loop approval checkpoints, or multi-agent architectures where specialised agents collaborate on complex tasks. We use LangGraph for any production agent system that requires robust state management, reliable recovery from errors, and complex decision logic beyond simple linear chain execution.

We build complete LangChain RAG pipelines covering document ingestion and parsing, text splitting with optimised chunking strategies, embedding generation using OpenAI, Cohere, or open-source models, vector database indexing (Pinecone, Weaviate, Chroma, pgvector), retrieval chain configuration with hybrid search and reranking, and LLM generation with citation-aware prompting. LangChain's built-in retrieval chain abstractions accelerate RAG development while our implementation goes beyond defaults to optimise chunking, retrieval quality, and generation accuracy for your specific content types.

LangChain provides standardised integrations with virtually every major LLM provider, and we leverage this to select and integrate the optimal model for each use case. We work with OpenAI (GPT-4o, GPT-4, GPT-3.5), Anthropic (Claude Opus, Sonnet, Haiku), Google (Gemini Pro, Gemini Flash), Meta (LLaMA 3), Mistral, Cohere, and open-source models hosted on Hugging Face or self-hosted infrastructure. LangChain's provider abstraction means we can swap or route between models without rewriting chain logic.

Yes. We design and build multi-agent architectures using LangGraph where orchestrator agents delegate tasks to specialised subagents—each with their own tools, memory, and LLM configurations. We implement agent communication patterns, shared state management, result aggregation, error recovery, and human-in-the-loop interruption points. Multi-agent systems built with LangGraph are particularly powerful for complex enterprise workflows that require parallel execution, specialised domain agents, and robust state persistence across long-running tasks.

LangSmith is Anthropic's observability and evaluation platform for LangChain applications. It provides full tracing of every chain and agent execution—capturing inputs, outputs, intermediate steps, token usage, latency, and errors across every component in your LangChain application. We implement LangSmith as standard on all production LangChain deployments, using it for debugging complex agent behaviour, evaluating RAG quality, A/B testing chain variants, monitoring production performance, and maintaining prompt version history.

LangChain provides multiple memory types for different use cases: ConversationBufferMemory for maintaining recent conversation context, ConversationSummaryMemory for compressing long histories, ConversationBufferWindowMemory for sliding window context, and VectorStoreRetrieverMemory for semantic memory retrieval from long interaction histories. We select and implement the appropriate memory configuration for your application's conversation pattern, token budget constraints, and the level of personalisation required across sessions.

Yes. LangChain's tool framework provides a standardised way to expose any API, database, or service as a callable tool for LangChain agents. We build custom tool wrappers for your internal APIs, CRM systems, databases, document repositories, and third-party services—enabling LangChain agents to retrieve live data and take real-world actions within your enterprise environment. We also use LangChain's built-in document loaders for direct integration with SharePoint, Confluence, Notion, Google Drive, SQL databases, and other enterprise data sources.

Winklix brings production-grade LangChain engineering expertise that goes beyond tutorials and demos. We design robust LangChain architectures with LangGraph stateful agents, optimised RAG pipelines, comprehensive LangSmith observability, cost-efficient LLM routing, and the error handling and retry logic that enterprise-grade AI applications require. Every engagement is focused on measurable outcomes—systems that perform reliably in production, not fragile prototypes that work in notebooks.