Exercise 1: Same Agent, Two Frameworks (LangGraph + CrewAI)

Pairs with Stage 4 — Agent Frameworks Exercise 1.

🎓 How to use this: starter.py is the complete solution, not a TODO skeleton. The active approach works better — mv starter.py starter_reference.py, read the signatures but not the bodies, write your own starter.py from scratch, then run python test.py to check it; if you are stuck for 20 minutes, go back and compare against the reference. Full methodology in docs/HOW_TO_USE.md.

📚 Want the chapter-length version? The starter in this folder is an illustrative build focused on the core pattern plus two SDK paths — it is not in-depth teaching material. Recommended for depth:

• datawhalechina/hello-agents ⭐ the most complete Chinese-language course out there — chapter by chapter, plus 16 production capabilities. This exercise maps to hello-agents' framework comparison / orchestration chapter
• LangGraph Quickstart + CrewAI official docs
• Full references in Stage 4 Curated Projects

#Task

A minimal search + summarize agent:

• Given a query (e.g. "summarize Taipei")
• Agent uses a search tool to hit a knowledge base
• LLM summarizes the result in 1-2 sentences

Built once in LangGraph and once in CrewAI — compare styles.

#How to run — two paths + two frameworks

#Path A (default, free, local)

pip install -r requirements.txt
ollama pull qwen2.5:3b
ollama serve

python starter.py         # LangGraph + Ollama
python starter_crewai.py  # CrewAI + Ollama (comparison)

Budget: $0.

#Path B (Anthropic, cloud-quality)

pip install -r requirements.txt
export ANTHROPIC_API_KEY=sk-ant-...
python starter_anthropic.py   # LangGraph + Claude

Budget: ~$0.001 per run (claude-haiku-4-5).

#Validate the logic (mock-based)

python test.py             # LangGraph + mock LLM
python test_anthropic.py   # starter_anthropic loads + ChatAnthropic constructs
python test_crewai.py      # CrewAI tool + module loads

#Side-by-side framework comparison

#LangGraph style (condensed)

g = StateGraph(State)
g.add_node("agent", agent_node)
g.add_node("tools", tool_node)
g.add_conditional_edges("agent", should_continue, {"tools": "tools", END: END})
g.add_edge("tools", "agent")

"I tell the system explicitly: state shape, nodes, edges, branching via should_continue."

#CrewAI style (condensed)

researcher = Agent(role="Researcher", goal="...", tools=[search], llm=MODEL)
task = Task(description=query, expected_output="...", agent=researcher)
crew = Crew(agents=[researcher], tasks=[task])
crew.kickoff()

"I describe: who plays this role, what task, what tools. Framework decides how to run."

#What to observe

1. Abstraction cost: CrewAI hides more, writes less code; but stack depth grows when debugging
2. Small-model friendliness: LangGraph is more stable with qwen2.5:3b; CrewAI's denser prompts can confuse small models
3. Controllability: LangGraph exposes state transitions; CrewAI is "result-oriented"
4. When to pick: production / audit → LangGraph. Multi-agent prototypes / role-based → CrewAI

#Common pitfalls

• LangGraph bind_tools: must llm.bind_tools([search]) to expose tool schema. Without it the model doesn't know the tool exists
• CrewAI model spec: needs LiteLLM format ("ollama/qwen2.5:3b", not "qwen2.5:3b"). Misspell and framework silently falls back to OpenAI default
• CrewAI return type: crew.kickoff() returns a CrewOutput object; str(result) to get text. Bare print(result) may show repr

#Want smarter answers?

MODEL=claude-sonnet-5 python starter_anthropic.py    # more stable
MODEL=qwen2.5:7b python starter.py                      # larger local model

#Extensions

• Streaming: LangGraph graph.stream(...), CrewAI crew.kickoff(stream=True)
• Checkpointing: LangGraph + MemorySaver for time-travel debug
• Human-in-the-loop: see Exercise 3