Multi-agent scripts vs an agent operating system
CrewAI orchestrates role-based agent crews in Python. AGNT gives multi-agent work a full runtime: visual orchestration, memory, a tool vault, receipts, and a marketplace of reusable parts.
Where they differ
Python library vs local OS
CrewAI lives inside your codebase; AGNT is the environment your agents live in.
Roles vs jobs
CrewAI defines agent personas in code; AGNT assigns agents real jobs with tools, gates and verification.
Reuse
Crew definitions stay in your repo; AGNT agents, workflows and skills install from a marketplace.
Verification
CrewAI leaves auditing to you; AGNT produces receipts for every operation.
Which should you pick?
Choose CrewAI if
You are prototyping multi-agent patterns in Python and want a lightweight library.
Choose AGNT if
You want multi-agent operations with memory, tooling and audit built in — running on your own machine.
More comparisons: the full grid · vs Zapier · vs n8n
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How they compare, property by property
| Property | AGNT | CrewAI |
|---|---|---|
| What it is | A runtime you install and run | A Python framework |
| Time to a working agent | Minutes | Hours to days |
| Runs without writing code | Yes | No |
| Multi-agent orchestration | Sub-workflows and agent nodes | The core design target |
| Role and delegation patterns | Explicit, in the workflow | First-class abstraction |
| Visual canvas | Yes | No |
| Triggers and scheduling included | Yes | No — you supply the runner |
| Credential management included | Yes — a local vault | No |
| Integrations out of the box | 120+ native, plus MCP | Tool wrappers you write or import |
| Approval gates before an action | Built in, per step | You build them |
| Audit trail of what ran | A receipt per run | Whatever you log |
| Choose the model per step | Yes, in the UI | Yes, in code |
| Local model support | First-class | Supported |
| Runs on your own machine | Yes — a desktop app | Yes — wherever you run Python |
| Ceiling on customisation | High | Unlimited |
| You maintain the plumbing | No | Yes |
| Team needs an engineer | No | Yes |
| Deploys as a product feature | Not the design target | Yes |
| Vendor lock-in | None | None — it is your code |
| Best fit | Automating your own recurring work | Research-style multi-agent problems |
Across 20 properties: AGNT leads on 10, CrewAI leads on 4, and 6 are a genuine draw. Purpose-built for multi-agent role-play patterns, and excellent when the problem genuinely needs several specialised agents arguing toward an answer.
What it costs at three real usage levels
| Monthly volume | AGNT | CrewAI |
|---|---|---|
| A few hundred runs a month | $0 — Community Core | Usually inside a free or starter tier |
| Around 10,000 runs a month | $0 — Community Core | Typically $50–$300/mo depending on step count |
| 100,000+ runs a month | $0 — Community Core, plus your own model costs | Typically $500–$1,600/mo, plus AI usage |
AGNT runs on hardware you already own, so the marginal cost of a run is electricity plus whatever the model call costs you directly. Hosted tools meter the run itself. The comparison figures are list prices at the time of writing and are the part of this page most likely to age — check them before quoting them.