LangChain vs Qdrant

LangChain

Qdrant

LangChain · 18 features

• Agents — Tool-using agents with reasoning loops.
• Chains (LCEL) — LangChain Expression Language — pipe primitives into chains.
• Checkpointers (LangGraph) — Persist agent state to SQL, Mongo, Redis, Postgres.
• Document Loaders — 150+ loaders for PDF, HTML, Notion, Google Drive, S3, GitHub, etc.
• Human-in-the-loop — Pause agent for approval, then resume.
• LangGraph — Stateful graph-based agent runtime. Durable, replayable, human-in-the-loop.
• LangGraph Platform — Managed hosting for LangGraph agents with state persistence.
• LangGraph Studio — Desktop IDE for debugging agent graphs.
• LangServe — Deploy chains as FastAPI endpoints.
• Memory — Buffer, summary, entity, vector memory stores.
• Output Parsers — Structured JSON, Pydantic schemas, function calling.
• Prompt Templates — Templating + partial filling + output parsers.
• RAG (Retrieval-Augmented Generation) — Standard patterns + 50+ retrievers.
• Streaming — First-class streaming at every layer.
• Subgraphs — Compose agent graphs hierarchically.
• Text Splitters — Recursive, token, semantic splitters for chunking.
• Tools — 400+ pre-built tools (web search, code, databases, APIs).
• Vector Store Integrations — 60+ vector DBs (Pinecone, Chroma, Weaviate, PGVector, Qdrant, Milvus).

Qdrant · 13 features

• BYOC (Hybrid Cloud) — Managed Qdrant in your cloud account.
• Cloud Inference — Hosted embedding models for free tokens.
• Cluster Monitoring — Prometheus metrics + health.
• Collections — Typed collections with named vectors + payload schema.
• Distributed — Horizontal sharding + Raft replication.
• Hybrid Search — Sparse + dense + keyword in one query.
• Multi-Vector — Multiple vectors per point (text + image, etc.).
• Open Source — Apache 2.0 licensed.
• Payload Filters — Rich filter DSL with indexed fields.
• Quantization — Scalar + product + binary for memory reduction.
• RBAC — API-key scopes + roles.
• Snapshots + Restore — Backup + DR primitives.
• Sparse Vectors — BM25 + SPLADE sparse embeddings natively.