Product
RAG Chatbot Systems
Production RAG chatbots with document ingestion, chunking, embedding, retrieval, and GPT-4 generation — grounded in your specific data.
LangChain Development — Production RAG Pipelines and AI Agent Systems
We build production LangChain applications — RAG chatbots, multi-step AI agents, document processing pipelines, and LLM orchestration systems that go from proof-of-concept to production.
15+ LangChain systems in production | LangChain, LangGraph, RAG, Agents, Tools
- Projects delivered
- 0+
- Clients worldwide
- 0+
- Client satisfaction
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Projects delivered
Clients worldwide
Client satisfaction
Avg first response
WHY CODEFLAMME
- Production-grade architecture from day one
- TypeScript, testing, and accessibility built in
- Dedicated engineers — no freelancers or hand-offs
- NDA protected | reply within 24 hours
RESPONSE TIME
< 24h
We reply to every inquiry within one business day with a structured plan.
What We Build
What We Build With LangChain Development
Production applications across product types — scoped to your users, stack, and growth stage.
6 product types — compare what we ship with LangChain in production.
- Platform
Multi-Step AI Agents
LangGraph-based agents that use tools, call APIs, query databases, and execute multi-step tasks autonomously with state management.
- SaaS
Document Processing Pipelines
LangChain document loaders, text splitters, and chains for automated extraction, classification, and summarisation of large document sets.
- Commerce
Conversational AI with Memory
Chat applications with conversation memory — window buffer, summary buffer, and vector store memory for contextually aware multi-turn conversations.
- Internal
LLM Evaluation Systems
RAGAS and custom evaluation pipelines for testing RAG accuracy, faithfulness, and context relevance in production.
- Design
LangServe API Deployment
Productionising LangChain chains as REST APIs using LangServe — standardised endpoints, streaming support, and LangSmith tracing.
WHY CODEFLAMME
Not Another Offshore Vendor
We know the hesitation. Here's exactly how we're different.
You Talk to the People Building It
No account managers relaying messages. You're in direct contact with the founder and senior engineers on your project — every sprint, every decision.
No Bench Rotation
The team that scopes your project is the team that ships it. We don't swap engineers mid-project to free them up for someone else.
NDA Before We Talk Details
Your idea and IP are protected from the first real conversation — not after contracts are signed.
Full IP, Zero Strings
Every line of code, every design file, transfers to you on delivery. No licensing, no retained rights, no surprises.
Our Capabilities
Our LangChain Development Services
Core delivery areas for LangChain — architecture, implementation, and production hardening.
- 01
RAG Architecture Design
Chunking strategy selection (recursive, semantic, parent-document), embedding model choice, retriever configuration, and context window management.
- 02
Vector Store Integration
Pinecone, Weaviate, pgvector, and Chroma integration — index design, metadata filtering, and hybrid search (dense + sparse) setup.
- 03
LangGraph for Agents
Stateful agent graphs with LangGraph — nodes, edges, conditional routing, parallel execution, and human-in-the-loop checkpoints.
- 04
Prompt Template Management
LangChain prompt templates, few-shot examples, output parsers, and chain composition for maintainable LLM pipelines.
- 05
LangSmith Observability
LangSmith tracing setup, run inspection, evaluation datasets, and production monitoring for LangChain-based applications.
- 06
Memory Systems
ConversationBufferWindowMemory, ConversationSummaryMemory, and VectorStoreRetrieverMemory for appropriate conversation history management.
When to Choose
When LangChain Development Is the Right Choice
Decision scenarios where LangChain is the strongest fit — and why it earns the recommendation.
SCENARIO 01
Primary use case
You are building a RAG application
LangChain provides the most mature and flexible RAG tooling available — document loaders, text splitters, retrievers, and chains for every component of a RAG pipeline.
02
Scenario
You need AI agents with tools
LangGraph's stateful agent framework is the current best practice for building reliable multi-step AI agents — better than React alone.
03
Scenario
You want to switch LLM providers
LangChain's LLM abstraction layer makes switching between OpenAI, Anthropic, Cohere, and local models a single-line change.
04
Scenario
You need production observability for LLM workflows
LangSmith provides the only purpose-built observability solution for LangChain applications — trace inspection, latency analysis, and evaluation.
In Practice
How We Use LangChain on Real Builds
RAG and agent systems we ship, when LangChain is warranted vs raw provider SDKs, and how Python backends host the stack.
LangChain engagements at CodeFlamme are mostly retrieval-augmented assistants over PDFs, tickets, or CMS content, plus tool-calling agents that hit internal APIs under human-readable policies. We use document loaders and splitters tuned per corpus, a vector store (often pgvector or Pinecone), and LangSmith traces so failures are inspectable. LangGraph appears when the workflow needs durable multi-step state rather than a single chain.
We reach for LangChain when orchestration, retrievers, and provider swaps matter. A thin OpenAI or Claude client is enough for one-shot prompts with no retrieval. Python FastAPI or Django hosts most pipelines; Redis queues handle long jobs. Embeddings and LLM choice stay swappable behind LangChain abstractions so product code does not hard-code a single vendor.
Projects often sit behind the same auth and tenancy as the product API — chat widgets in React or Next.js, admin reindex jobs, and evaluation suites that score answer faithfulness against golden sets. We version index schemas and chunking rules so re-ingests stay repeatable when the corpus changes. Tool policies list which internal endpoints an agent may call; anything outside that list stays unreachable from the chain.
- 01
OpenAI / Claude
LangChain sits above provider SDKs — GPT and Claude models are common defaults, selected per latency, context, and cost needs.
- 02
Python / Django
RAG services, agent runners, and evaluation jobs typically run in Python backends alongside the application's primary data store.
- 03
AI Development
LangChain work is delivered as part of our AI practice — architecture, eval harnesses, and production ops, not a prototype notebook.
Complementary Stack
The Stack We Use Alongside LangChain Development
The tools we pair with LangChain in production — organised by layer, not hype.
LAYERS
06
TOOLS
37
04
STACK_LAYER
Embeddings
05
STACK_LAYER
Document Loaders
06
STACK_LAYER
Backend
FAQ
Frequently Asked Questions
Can't find what you need? Talk directly with our team.
Book a Discovery CallLangChain for applications needing flexible chain composition, agent tooling, and multi-LLM support. LlamaIndex for RAG-focused applications where document indexing and retrieval is the primary concern.
Ready to build something powerful?
Tell us what you are building. We will respond within 24 hours with a clear, honest assessment — no pressure, no sales pitch.
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