Agent Framework
CrewAI
Multiple AI agents working as a team
CrewAI lets you define a team of AI agents — each with a role, goal, and tool access — that collaborate to complete complex tasks. One agent researches, another writes, a third reviews. Complex workflows that would be a single long prompt elsewhere become structured, reliable processes.
Book a consultation to set up CrewAI →What it is
AI as a team, not a single assistant
A single AI model trying to do everything produces mediocre results. CrewAI breaks complex tasks into roles: a Researcher with web access, a Writer focused on style, a Reviewer enforcing standards. Each agent is focused on its job and passes work to the next.
The result is more reliable than a single prompt for multi-step workflows. Each agent's output becomes context for the next. Roles enforce quality constraints that a single model conversation doesn't.
We configure your crew for your workflow — the agents, their roles, their tools, and the task sequence. You run the crew; it executes the full pipeline.
How it works
Define once, run repeatedly
A Crew is defined as a Python configuration: agents with roles and goals, tasks with descriptions and expected outputs, a process flow (sequential or hierarchical). Once defined, you trigger the crew with an input, and it runs to completion.
All agents share the same local Ollama instance. Each agent is a separate model call with its own role context. The orchestrator manages task delegation and output passing between agents. Everything runs locally on your Mac.
Who it's for
Anyone with repeatable, multi-step workflows
- ✓Content teams running research → write → review pipelines
- ✓Businesses with repeatable multi-step analysis workflows
- ✓Researchers automating literature review + synthesis
- ✓Developers building AI-powered pipelines that need reliability
- ✓Anyone whose complex AI tasks benefit from structured role separation
Unique advantage
Complex workflows that would be a single exhausting prompt elsewhere become structured, repeatable processes. Define the crew once; run it for every client, every report, every cycle. Quality comes from structure, not from hoping the model stays on track.
Full stack
What gets installed
| Layer | Component | Purpose |
|---|---|---|
| AI Engine | Ollama (MLX backend) | Runs models on Apple Silicon |
| Agent Framework | CrewAI | Multi-agent orchestration with role-based collaboration |
| Chat UI | Open WebUI | Browser chat, always available |
| Networking | Tailscale | Secure access from anywhere |
| Runtime | Python environment | Crew definitions and tool integrations |
| Security | Hardened config | Loopback binding, egress logging |
Security
Local models, local execution
All agents run against your local Ollama instance — no API calls to cloud AI services. Tool use (web search, file access) is configured with defined scope. All data processed by the crew stays on your Mac unless you explicitly configure an external integration.
Recommended models
Models that pair well
Reliable instruction following — agents stay on task and execute roles consistently
Complex multi-agent reasoning — strong at planning and delegation across agents
Planning and reasoning — excellent for the orchestrator role in complex workflows
Ready to set up CrewAI?
Book a consultation. We'll design your crew for your specific workflow and configure it on your Mac.
Book a consultation to set up CrewAI →