MagiCrew
Give everyone their own AI workforce in one platform
735 followers
Give everyone their own AI workforce in one platform
735 followers
MagiCrew is an open-source AI Agent platform that gives everyone their own AI workforce. Instead of simply chatting with AI, deploy specialized digital workers that research, analyze, create reports, generate presentations, and complete real business tasks. With multi-agent collaboration, enterprise controls, and deliverable-ready outputs, MagiCrew helps teams turn AI from a tool they use into a workforce they can manage.
That alone could save so much time.
Can I observe the handoff process through MagiCrew or will it be more like a black box until I get the end product?
Interested in knowing about the context transfer process in MagiCrew. In case the research agent is completed and then taken up by the presentation agent, is there any stitching process involved here?
PaymentKit
Congratulations on your launch! The "Costco of AI" line stayed with me, curating rather than dropping everything possible on users makes sense.
FunBlocks MindMax
Congrats on this launch!
MagiCrew
@peng_wood 😊 thank you!
MagiCrew
Great question. The short answer: the user doesn't have to decide — the main agent does.
Here's how it works in practice:
Super Magic has a generalist main agent that receives your request first. It handles most tasks on its own — research, writing, data analysis, code, file operations — because switching agents has a cost (context, latency, coordination overhead). So the default bias is: do it yourself unless there's a good reason not to.
The main agent delegates when:
A task needs a specialized capability it doesn't have — e.g., slide design, canvas generation, or interacting with a specific platform API (Lark, DingTalk, etc.). These are handled by dedicated sub-agents or skills with domain-specific tools.
Independent subtasks can run in parallel — e.g., "research competitor A, B, and C" can fan out to multiple agents working simultaneously instead of sequentially, then merge results back.
The user explicitly asks for it — you can @ mention a specific agent to force-route a task.
The key design choice is that all agents share the same workspace filesystem. So when the main agent delegates to a sub-agent, it doesn't need to serialize the full context into a prompt — the sub-agent can just read the files. And when the sub-agent finishes, its output is already in the shared workspace for the next step. No "handoff summaries," no lost context.
Think of it less like "which tasks need collaboration" and more like one person deciding when to ask a specialist vs. doing it themselves — except the specialist already has access to everything on your desk.
Thanks for the kind words! 🙌