Agentic RAG Services | Custom RAG Agents & LangGraph Experts
Agentic RAG built for your production stack.
We design and ship agentic RAG systems—multi‑step rag agents that plan, retrieve, reason, call tools, and remember context so your users get accurate answers fast.
Why Move Beyond Vanilla RAG?
- Complex questions often need chained rag agent calls, web search, and dynamic reasoning.
- Large corpora break single‑query limits; agentic RAG routes to the right slice of data.
- Live APIs, calendars, or CRMs require rag agents that can trigger tools and return structured output.
Bottom line: vanilla RAG stops at "retrieve & stitch." Agentic RAG delivers answers that act.
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.
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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 |
FAQ
Agents vs RAG
Standard RAG retrieves documents and feeds them to an LLM. Agents vs RAG is not a debate; it’s an evolution. Agentic RAG keeps retrieval but layers planners, tool callers, and memory on top, boosting accuracy and enabling actions.
Proof of Expertise
- Video Demo: “Agentic RAG LangChain Tutorial” – embedded above.
Ready to Build?
We design, deploy, and hand off production‑grade agentic RAG systems. Email us at contact@futuresmart.ai to discuss your ai requirements.