Make CrewAI 3–34× faster.
No code changes.
Fast-CrewAI is a Rust-accelerated drop-in for CrewAI. One import flips on serde serialization, FTS5 memory search, tool caching, and parallel task scheduling — while staying 100% API-compatible.
pip install fast-crewai · import fast_crewai.shim
Benchmarks
Measured on real CrewAI workloads
Numbers from the fast-crewai benchmark suite against crewai==1.7.2
with 101 compatibility tests passing. Real end-to-end workflow gains typically land at 1.3–5×,
with the largest wins in memory-intensive and database-heavy pipelines.
Quickstart
One import. Zero refactor.
Fast-CrewAI uses smart monkey patching via dynamic inheritance. Your
Agent,
Task, and
Crew objects
keep their exact API — but the hot paths run through Rust.
Need to disable acceleration for debugging? Set FAST_CREWAI_ACCELERATION=0. Individual components can be toggled too.
# 1. Install
pip install fast-crewai # or: uv add fast-crewai
# 2. Add one line before your CrewAI imports
import fast_crewai.shim
from crewai import Agent, Task, Crew
# 3. Your existing code now runs accelerated
agent = Agent(role="Analyst", goal="Summarize quarterly metrics")
task = Task(description="Extract KPIs from the report", agent=agent)
crew = Crew(agents=[agent], tasks=[task], memory=True)
crew.kickoff()Who it's for
Built for people shipping real CrewAI systems
Developers
You love CrewAI for the ergonomics but your staging runs are slow and token bills are climbing. Drop in one import and reclaim the minutes.
Migrate in 5 minutes →Architects
You're sizing multi-agent systems for production. Understand where CrewAI bottlenecks live and which subsystems to harden first.
Architecture patterns →Companies
You need a team that has read every line of CrewAI. Engage Neul Labs for audits, performance engineering, and bespoke agent builds.
Book a consultation →Knowledge base
Guides and CrewAI issue commentary
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.
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.
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.
Going deeper
Reference docs live on GitHub Pages
Full API reference, configuration matrix, and component internals are maintained in the canonical MkDocs site alongside the repo.
Open technical documentation ↗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.