CrewAI made multi-agent setups feel approachable. You write a crew, a few agents, and the tasks that bind them. The structure is good but it stays trapped inside Python and the wiring still costs lines. Digitorn keeps the multi-agent shape and turns it into nested YAML.
CrewAI works best for one-off scripts. The minute you need scheduled runs, webhooks, real credentials, or a UI, you write the surrounding plumbing yourself. Digitorn gives you the same coordinator-and-specialists model with channel providers, the Hub registry, a credential vault, and a streaming UI already wired in.
Every CrewAI primitive maps to a Digitorn equivalent. Where the mapping is not 1-to-1, the notes call out what changed.
Real apps in both stacks. The Digitorn version is what you would commit to a repo, no scaffolding hidden offscreen.
1from crewai import Agent, Task, Crew, Process2from langchain_anthropic import ChatAnthropic34llm = ChatAnthropic(model="claude-haiku-4-5", api_key=API_KEY)56researcher = Agent(7 role="Researcher", goal="Find facts about the topic",8 backstory="...", llm=llm, tools=[search_tool],9)10writer = Agent(11 role="Writer", goal="Compose a clear summary",12 backstory="...", llm=llm,13)1415task1 = Task(description="Research {topic}", agent=researcher)16task2 = Task(description="Write summary based on research", agent=writer)1718crew = Crew(agents=[researcher, writer], tasks=[task1, task2],19 process=Process.sequential)2021crew.kickoff(inputs={"topic": "agentic frameworks"})1schema_version: 223app:4 app_id: research-crew5 name: "Research crew"6 version: "1.0.0"78modules:9 web: {}10 agent_spawn: {}1112runtime:13 mode: one_shot14 entry_agent: lead1516agents:17 - id: lead18 role: coordinator19 modules: [{agent_spawn: [agent]}]20 brain: { provider: anthropic, model: claude-sonnet-5, credential: anthropic_main }21 system_prompt: |22 You coordinate research. Call agent(agent="researcher", task="...") first,23 then call agent(agent="writer", task="...") with the findings.2425 - id: researcher26 role: specialist27 modules: [{web: [search, fetch]}]28 brain: { provider: anthropic, model: claude-haiku-4-5, credential: anthropic_main }29 system_prompt: "Find sources and return facts with citations."3031 - id: writer32 role: specialist33 brain: { provider: anthropic, model: claude-haiku-4-5, credential: anthropic_main }34 system_prompt: "Compose a clear summary from the research output."The crew, agent, and task triple collapses into a list of agents. The lead's prompt explains the dispatch order, no Process enum needed. Switching to parallel research is a prompt change, not a config flag.
Subtle differences that look the same on paper and break on first run. Read these before you start porting.
A Digitorn specialist is just an agent block the coordinator can spawn via agent_spawn. There is no separate Crew object to instantiate.
Where CrewAI uses Process.sequential or Process.hierarchical, you describe the dispatch logic in the coordinator's system prompt. The runtime does not enforce a process, the prompt does.
agent(agent, task) is deliberately minimal - no built-in wait-for-many or cancel modes to reach for. Design the coordinator's prompt around that.
Open Studio and tell the builder assistant what your CrewAI agent does. It rebuilds it for you, wires up the tools and triggers, and you refine it live on the canvas. It's vibe coding for agents, no rewrite by hand.
Open StudioEngineering notes from the Digitorn team. No marketing, no launch announcements, no "10 prompts that will change your life". Just the things we write that we'd want to read.