Knowledge
CrewAI, written clearly
Long-form guides on CrewAI performance, architecture, and migration — alongside commentary on long-standing upstream CrewAI issues. From the team that built Fast-CrewAI.
Choosing a multi-agent framework in 2026
An honest comparison of CrewAI, LangGraph, and AutoGen for teams that need to ship. Where each one shines, where each one hurts, and how Fast-CrewAI changes the calculus.
Memory search uses LIKE queries and gets quadratically slower
CrewAI's default RAG storage uses substring LIKE queries against SQLite, which are full table scans. As memory grows, every agent turn pays the full cost of the scan.
Production CrewAI architecture patterns
Architectural patterns for running CrewAI in production: memory layering, RAG pipelines, tool isolation, observability, and where Fast-CrewAI fits into each. Written for architects.
Fast-CrewAI vs CrewAI benchmarks, explained
Methodology, raw numbers, and honest caveats behind the 34.5× serialization, 17.3× tool execution, and 11.2× memory search claims. Everything you need to reproduce the results.
JSON serialization dominates agent message passing
Python's json module is the hidden tax on every agent-to-agent handoff. For memory-heavy crews, it can account for 20–40% of CPU time.
Long-term memory grows unbounded and nothing evicts it
CrewAI's long-term memory persists everything and has no default retention policy. Over weeks of production use, it grows without limit — degrading search quality and bloating SQLite files.
Observability is retrofittable but not first-class
CrewAI runs produce logs, but the structured spans you'd need for distributed tracing have to be added by hand. When things slow down in production, you're guessing.
Rust + Python interop with PyO3 for AI agents
A practical look at PyO3, serde, and Tokio in the context of AI agent frameworks. What Rust actually helps with, what it doesn't, and how Fast-CrewAI is structured under the hood.
Tools get executed repeatedly with identical arguments
LLMs call the same tool with the same arguments over and over. CrewAI has no default caching layer, so every invocation pays the full cost — including the ones that are wasteful.
Migrating to Fast-CrewAI: the zero-code-changes playbook
What the one-line import actually does, how to verify it's active, how to toggle individual components, and how to roll back if something breaks. The honest migration guide.
Independent tasks run sequentially when they could run in parallel
CrewAI's default task scheduler runs tasks in order, even when their dependencies don't require it. Teams leave multi-minute parallelism wins on the table without realizing.
Tool argument validation is slower than the tools themselves
CrewAI validates tool arguments with Python's jsonschema on every call. For simple tools that return instantly, validation can be 70% of the call cost.
Why CrewAI multi-agent systems get slow in production
A technical walkthrough of the four places CrewAI spends CPU time in real workloads — serialization, memory search, tool execution, and task scheduling — and what you can do about each of them today.
Concurrent workers fight over a single SQLite connection
CrewAI's default memory backend uses a single SQLite connection per process. Under concurrent load, workers serialize through it and throughput collapses.
No entries in this category yet.
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.