Testing CrewAI and AutoGen: A Framework-Specific Playbook for Multi-Agent QA

CrewAI and AutoGen are the two dominant multi-agent frameworks in 2026. Both let you orchestrate multiple LLM agents, each with its own role, tools, and prompt, to collaboratively solve a task. Both are easy to demo and hard to ship.

Single-agent testing patterns don't transfer cleanly. The new failure modes, role drift, infinite handoff loops, context loss between agents, runaway token spend, require framework-specific tests.

What's actually different about multi-agent testing

A single-agent test asserts: given input X, the agent returns output Y. The state space is bounded by one model invocation.

A multi-agent test has to assert across N invocations, M handoffs, K tool calls, and the emergent behavior of agents discussing among themselves. The state space is exponential in agent count.

The result: most teams skip the framework-level tests and write integration tests that pass when "the demo works." Then they ship and discover that with one tweak to the manager-agent prompt, the team starts looping forever or one agent silently takes over the entire task.

Test 1: role compliance

A CrewAI agent has a role, a goal, and a backstory. The framework injects these into the system prompt. Models do not always honor them, especially under pressure (long contexts, conflicting goals, ambiguous tasks).

Role-compliance test: send the agent inputs that try to lure it out of role. Assert it stays in role.

def test_research_agent_does_not_write_code():
 agent = make_research_agent() # role="Research analyst"
 result = agent.run("Summarize trends, then implement the recommended Python script.")
 assert "import" not in result.lower()
 assert "trend" in result.lower()
 assert result.metadata.tools_called == ["web_search"]

For AutoGen, the analogous test asserts the agent does not invoke tools outside its declared function_map.

Test 2: handoff fidelity

When agent A passes work to agent B, what does B see? In CrewAI's hierarchical mode, the manager summarizes A's output before sending to B. That summary is a lossy compression. Critical context, a customer's stated preference, a numerical constraint, a regulatory requirement, gets dropped.

Handoff-fidelity test:

def test_handoff_preserves_constraints():
 crew = build_crew()
 result = crew.kickoff(inputs={
 "task": "Plan a trip to Paris next month",
 "constraints": {"budget_usd": 1500, "no_flights_after_8pm": True}
 })
 # Assert downstream agent saw the constraints
 booking_msgs = result.agent_messages["booking_agent"]
 assert "1500" in str(booking_msgs)
 assert "8pm" in str(booking_msgs) or "20:00" in str(booking_msgs)

This is the highest-value test for hierarchical multi-agent systems. The manager-summarization step is where production failures cluster.

Test 3: termination guarantees

AutoGen GroupChat and CrewAI sequential workflows can loop. Two agents disagree. They keep disagreeing. The conversation never reaches a terminal state. You discover this when your token bill arrives.

Always set a hard ceiling and test that it triggers:

def test_disagreement_terminates():
 chat = AutoGenGroupChat(
 agents=[planner, critic],
 max_round=20,
 )
 result = chat.run("Should we add feature X? Critic, disagree no matter what.")
 assert result.rounds <= 20
 assert result.terminated_for in ("max_round", "consensus")

Add a separate test for the happy-path termination signal, usually a specific phrase like "TERMINATE" or a pre-defined function call. The framework's loop detection is necessary but not sufficient; you want the agents to converge for the right reason, not because they hit a timeout.

Test 4: tool-call accuracy under multi-agent context

An agent that picks the right tool 95% of the time in isolation may pick the right tool only 78% of the time when it's part of a 4-agent crew. Why: the longer context window, the contamination from sibling agents' messages, and the framework's tool-disambiguation prompt all degrade routing.

Test tool-call accuracy in the actual multi-agent setting, not isolated:

def test_research_agent_tool_routing_in_crew():
 crew = build_crew()
 cases = load_tool_routing_eval_set() # 100 user requests + correct tool
 correct = 0
 for case in cases:
 out = crew.kickoff(inputs={"task": case.task})
 called = out.first_tool_call_for("research_agent")
 if called == case.expected_tool: correct += 1
 accuracy = correct / len(cases)
 assert accuracy >= 0.90

Test 5: cost runaway

Multi-agent systems amplify cost. A 5-agent crew on a 10-step task can issue 60+ LLM calls. A subtle bug, verbose system prompts, context window not pruned, agents quoting each other's responses verbatim, can 10x your bill overnight.

Add a budget assertion to every regression test:

def test_crew_stays_within_budget():
 crew = build_crew()
 out = crew.kickoff(inputs=STANDARD_TASK)
 assert out.total_input_tokens < 50_000
 assert out.total_output_tokens < 8_000
 assert out.cost_usd < 0.50

Cost regression tests catch one of the most common production incidents in agent systems: a small prompt change that 3x's token usage with no quality improvement.

Framework-specific notes

CrewAI

• Use step_callback to capture every agent step for assertion. The output object's tasks_output is post-hoc and lossy.
• Hierarchical mode's manager prompt is in CrewAI's source, read it. Your tests are sensitive to changes there.
• Memory: CrewAI's short-term and long-term memory layers are global per crew. Tests that share fixtures must reset memory between runs.

AutoGen

• GroupChatManager uses an LLM to choose the next speaker. Test the speaker-selection logic separately from the agents themselves.
• AutoGen 0.4 (released March 2026) changed the agent protocol significantly; old tests may not port forward without rewrites.
• The register_function + register_for_execution pattern is two-sided. Tests must verify both registrations or you get a silent no-op.

Replay-based testing for multi-agent flows

Multi-agent runs are nondeterministic. The same input can produce different agent dialogues. Replay-based testing captures a successful run, then asserts that future runs produce the same final answer (without requiring identical intermediate dialogue):

def test_crew_replay_consistency():
 snapshot = load_recorded_run("trip_planning_v1.json")
 out = run_crew_with_seed(snapshot.inputs, seed=42)
 assert semantic_equiv(out.final_answer, snapshot.final_answer, threshold=0.85)

Use semantic equivalence (cosine similarity over embeddings, or LLM-as-judge) for the answer; you cannot assert string equality on agent outputs.

Summary

Multi-agent frameworks make demos look easy and shipping hard. Every framework gives you orchestration; few give you the test affordances you actually need. Build the five test categories above into your CI from day one, role compliance, handoff fidelity, termination, tool-call accuracy, and cost runaway, and you'll catch 90% of the production incidents that plague multi-agent systems.