Agentic RAG Services | Custom RAG Agents & LangGraph Experts
Agentic RAG built for your production stack.
We specialise in designing and delivering agentic RAG systems—multi-step RAG agents that plan, retrieve, reason, call tools, and remember context to provide your users with accurate answers at speed.
Why Move Beyond Vanilla RAG?
- Chained Agent Calls: Complex questions require dynamic reasoning, web searches, and multiple rag agent calls. Vanilla RAG systems are often insufficient for complex queries, as they don’t handle dynamic interactions well.
- Large Corpora Handling: When dealing with massive datasets, agentic RAG efficiently routes queries to the right slice of data, bypassing single-query limits typical of vanilla RAG.
- Live APIs & CRM Integration: Rag agents can trigger tools like APIs, calendars, or CRMs, providing live, real-time interactions and structured outputs.
Bottom line: Vanilla RAG is limited to "retrieve & stitch." Agentic RAG moves beyond that, delivering actions and outcomes based on real-time context.
What We Deliver
Capability | Real‑world Impact |
|---|---|
Agent Orchestration | LangGraph graphs—planner, retriever, tool‑caller, answer composer—async, restart‑safe. |
Hybrid Retrieval | Dense + sparse search, rerankers, KB routing over millions of docs. |
Tool Use | web‑scrape, calendar, CRM—wired via function‑calling rag agents. |
Session & Chat History | Redis short‑term + persistent store; thread‑aware prompts. |
Long‑Term Memory | Mem0 or Zep backed by Qdrant / pgvector. |
Observability | LangSmith traces, OpenTelemetry spans, Prometheus metrics, alert hooks. |
QA Harness | Automated evals for factuality, completeness, regression diffing. |
Agentic RAG with LangChain & LangGraph
Watch our in‑depth tutorial on building production‑ready RAG agent build with LangChain & LangGraph embedded below. We show exactly "what is agentic rag" and walk through building your first router‑retriever‑tool pipeline.
Loading video...
All hops logged to LangSmith; nightly QA jobs catch regressions before users do.
Tech Stack at a Glance
Layer | Default | Options |
|---|---|---|
LLM | GPT‑4.1 / Claude 4 | Gemini 2.5, Llama 4, custom fine‑tunes |
Embeddings | OpenAI | BGE‑Large, Instructor, domain‑tuned |
Vector Store | Qdrant | Qdrant, Pinecone, Weaviate, pgvector |
Memory DB | Mem0 graph | Zep, custom Postgres tables |
Frameworks | LangGraph, LangChain | Autogen, CrewAI |
Runtime | FastAPI (async) | gRPC, WebSocket streaming |
Agentic RAG Service: Key Features & Advantages
The Agentic RAG service offers a range of advanced features to significantly enhance business operations. Here's a breakdown of what the service provides:
1. Custom Node Configuration
Adjust the settings at each node for optimal performance. You can tweak the LLM model, prompts, and other parameters to balance accuracy, cost, and latency.
2. Customizable Knowledge Base
You can use our knowledge base service or integrate your knowledge base seamlessly into the service. Our engineers ensure a smooth transition and smooth operation with chunk-enriched data stored in a vector database.
3. Custom Action and Integration Service
- Custom Actions: Leverage external APIs and integrate them directly into your agents' workflows, enhancing interactivity with systems like CRMs and order platforms.
- Integration Service: Secure, credential-based connections to managed services (e.g., Freshdesk, Odoo) through dedicated MCP (Model Context Protocol) servers, ensuring a secure and robust connection.
4. Base Service with Expansion Capabilities
Our base service is robust but flexible enough for expansion, allowing you to add custom features as your needs evolve.
5. Deployment Options
- Flexible Deployment: You can use our service or choose to deploy the service within your own infrastructure or database, granting you complete control over your data.
- Optimised Deployment: We offer optimised solutions, eliminating the need for you to build everything from scratch. As we have already based the service ready, the process will be quick for you; instead of months, it will be weeks.
6. Separate UI Option
If you're looking to build your own front-end, we offer a backend-only option that allows you to utilise the powerful features while you maintain control over your user interface design.
7. Real-Time Streaming & Regenerate Chat
Get near-instant responses for better user experience, minimising delays and improving interaction speed. Users can quickly regenerate the chat for a fresh response, eliminating the need to start over.
8. Follow-Up Questions & Source Display
The service allows follow-up questions based on prior queries, ensuring the conversation flows naturally. Metadata and retrieved chunks show the sources of responses, adding transparency to the process.
9. Multiple Agent Management with Session History
Manage several agents with unique roles and tasks, ensuring streamlined operations. Maintain separate conversation histories for each agent, providing easy access to past interactions for seamless task management.
FAQ
Agents vs RAG
Agents vs RAG is an evolution, not a debate. While standard RAG systems simply retrieve documents and feed them to an LLM, Agentic RAG builds on this by adding layers of planning, tool-calling, and memory management. This boosts accuracy and enables actions beyond simple retrieval.
Proof of Expertise
- Video Demo: “Agentic RAG LangChain Tutorial” – Embedded above.
Ready to Build?
We specialise in designing, deploying, and maintaining production-grade Agentic RAG systems. Contact us at contact@futuresmart.ai to discuss your AI needs.