Agentic data pipelines
ETL, enrichment, and classification crews
For: Data teams running batch or streaming enrichment through agents
Data pipelines push large volumes of records through agents, serializing each one and hitting the database concurrently. Throughput per dollar is the metric that matters.
The problem
- →Per-record JSON serialization dominates at scale
- →Concurrent workers contend for database connections
- →Memory lookups for dedup and enrichment slow down over a run
How Fast-CrewAI helps
serde serialization
34.5× faster encoding of every record passing between stages.
Pooled DB connections
r2d2 pooling lifts concurrent throughput across parallel workers.
FTS5 memory search
11.2× faster dedup and enrichment lookups on large stores.
Best for: Batch and streaming crews where per-record overhead multiplies across volume.
Ready to make CrewAI faster?
Talk to the team that wrote the acceleration layer. We take on performance audits, full system builds, and retained engineering.