What This Repo Claims
Orchestrate teams of AI agents that collaborate on complex tasks through role-based delegation. No cloud dependency required — works with local LLMs via ollama.
50.8k stars. Current version 1.14.4.
The core promise: pip install crewai → crewai create crew → define agents and tasks → run a crew where agents hand off work to each other and produce a final output.
What I Tested
Environment:
- macOS, Apple Silicon
- repoverifiertest isolated user — no prior crewAI install
- Python 3.13
- ollama/llama3.2 — no API key used at any point
pip install crewai
Works. Two dependency conflicts on install — not fatal but documented:
- litellm 1.83.14 requires openai==2.24.0, but installed openai 2.36.0
- streamlit 1.38.0 requires rich<14, but installed rich 14.3.4
Test 2: CLI availability
crewai --version
zsh: command not found: crewai
CLI installs to /Users/repoverifiertest/Library/Python/3.13/bin/ which is not in PATH by default. Fix:
export PATH="/Users/repoverifiertest/Library/Python/3.13/bin:$PATH"
crewai --version
# crewai, version 1.14.4
Not documented in the README. First-time users will hit this.
Test 3: Scaffold a crew
crewai create crew test_crew
Clean interactive setup. Prompts for LLM provider — selected ollama. Prompts for model — selected ollama/llama3.1. All files created correctly:
- test_crew/src/test_crew/crew.py
- test_crew/src/test_crew/main.py
- test_crew/src/test_crew/config/agents.yaml
- test_crew/src/test_crew/config/tasks.yaml
- test_crew/.env
crewai run
# ERROR: Model llama3.1 not found
The scaffold defaults to ollama/llama3.1 but the locally available model was llama3.2. The setup wizard offers model options from a list without checking what's actually installed in ollama. Fix:
sed -i '' 's/ollama\/llama3.1/ollama\/llama3.2/g' .env
Straightforward fix once you know what's installed. But a first-time user with no prior ollama experience would be stuck.
Test 5: Two-agent crew execution
crewai run
Crew ran successfully end to end:
- Agent 1 (
AI LLMs Senior Data Researcher) received the research task and produced output - Agent 2 (
AI LLMs Reporting Analyst) received Agent 1's output as context and expanded it into a structured report report.mdcreated in the project root with the final output
Findings
Finding 1: Core multi-agent orchestration works
Two agents ran sequentially, context passed correctly between them, final output written to file. The framework does what it claims.
Finding 2: CLI not in PATH after install
crewai command not found until PATH is manually updated. One-line fix but not documented. Affects all macOS users installing without a venv.
Finding 3: Default scaffold model doesn't match local ollama
crewai create crew defaults to ollama/llama3.1 without checking what's installed. If you don't have llama3.1 pulled, the first run fails. Fix is a one-line .env edit but adds friction for new users.
Finding 4: Dependency conflicts on install
Two version conflicts with openai and rich. Not fatal but signals that crewAI is opinionated about its dependency tree. Use an isolated venv.
Finding 5: No API key required
Entire test ran on local ollama with llama3.2. Zero external API calls. The local-first claim holds completely.
What I Did Not Test
- Hierarchical crew process (manager agent delegating to sub-agents)
- CrewAI Flows (event-driven workflows)
- Tool use (web search, file I/O)
- Multi-crew pipelines
- Memory and knowledge features
Verdict: Solid
crewAI delivers on its core claim. Install it, point it at a local ollama model, define two agents and two tasks, and they will collaborate and produce output — with no API key and no cloud dependency. The framework handles agent orchestration, context passing, and output generation correctly out of the box.
Two setup friction points worth knowing: the CLI isn't in PATH after install, and the default scaffold model may not match what you have in ollama. Both are one-line fixes. Neither is a framework failure.
Included in Solution #4: Multi-Agent Orchestration Stack.