LangGraph vs Qdrant for AI agents: Which Should You Use?

LangGraph and Qdrant solve different problems, and treating them as substitutes is how teams waste weeks. LangGraph is an orchestration framework for agent state, control flow, tool execution, and multi-step reasoning; Qdrant is a vector database for retrieval, similarity search, and filtering over embeddings.

For AI agents: use LangGraph to run the agent, and Qdrant to give it memory and retrieval. If you must pick one first, pick LangGraph for agent logic; add Qdrant when your agent needs real knowledge access.

Quick Comparison

When LangGraph Wins

• •

  You need deterministic control over agent behavior.
  If your agent has branches like “if confidence < threshold, ask a human” or “if tool A fails twice, switch to tool B,” LangGraph is the right layer. StateGraph gives you explicit nodes and edges instead of hoping the model behaves.

• •

  You need durable state across turns or long-running workflows.
  LangGraph’s checkpointing pattern is built for agents that pause, resume, and recover. That matters in banking or insurance flows where a conversation can span minutes or days.

• •

  You want multi-agent coordination without spaghetti code.
  When one node handles extraction, another handles policy checks, and another handles customer communication, LangGraph keeps the control plane readable. The graph structure beats hand-rolled async chains every time.

• •

  You need human-in-the-loop approval steps.
  LangGraph fits review gates cleanly: generate draft → pause → approve/reject → continue. That pattern is common in regulated workflows like claims handling or account changes.

A typical setup looks like this:

from langgraph.graph import StateGraph

builder = StateGraph(dict)

def classify(state):
    return {"route": "search"}

def search(state):
    return {"answer": "..."}

builder.add_node("classify", classify)
builder.add_node("search", search)
builder.set_entry_point("classify")
builder.add_edge("classify", "search")

graph = builder.compile()

That is the point: explicit execution flow. You know what runs next.

When Qdrant Wins

• •

  You need semantic retrieval at scale.
  Qdrant is built for fast nearest-neighbor search over embeddings. If your agent needs to pull relevant policies, tickets, contracts, or call notes from a large corpus, Qdrant is the right backend.

• •

  You need metadata filtering with vector search.
  Qdrant’s payload filtering is the difference between “find similar documents” and “find similar documents for this customer in this region from this date range.” For enterprise agents, that filter layer matters more than raw cosine similarity.

• •

  You want hybrid retrieval patterns.
  Qdrant supports combining dense vectors with structured payloads so your agent can retrieve by meaning and constraints. That’s how you keep responses grounded in the right slice of data.

• •

  You are building persistent memory outside the model context window.
  If your agent should remember past interactions across sessions without stuffing everything into prompts, Qdrant gives you a real memory store. Use it as retrieval-backed memory instead of fake “memory” in prompt text.

Example query pattern:

from qdrant_client import QdrantClient
from qdrant_client.models import Filter, FieldCondition, MatchValue

client = QdrantClient(url="http://localhost:6333")

results = client.search(
    collection_name="customer_docs",
    query_vector=[0.12] * 1536,
    query_filter=Filter(
        must=[
            FieldCondition(key="customer_id", match=MatchValue(value="C123")),
            FieldCondition(key="doc_type", match=MatchValue(value="policy"))
        ]
    ),
    limit=5
)

That’s production-relevant retrieval: scoped results with actual business constraints.

For AI agents Specifically

My recommendation is simple: build the agent workflow in LangGraph and back its knowledge/memory layer with Qdrant. LangGraph controls what the agent does next; Qdrant controls what the agent knows when it decides.

If you force one tool to do both jobs, you get either brittle orchestration or weak retrieval. The clean architecture is graph for execution, vector DB for context.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.