A multi-agent system can hand you the right answer and still be broken. The planner looped three times, two subagents researched the same thing, and the verifier signed off without checking. The output scores well. Next week, on a slightly different input, the same coordination bug produces a wrong answer nobody catches.
This guide compares six tools for multi-agent evaluation on what they actually score (the outcome, each agent, each handoff), how they test before launch, and what happens once a score says something went wrong.
Get the short answer
- Lyzr: evaluation built into a governed agent platform, with Agent Eval reasoning traces, Opencontroller audit trails and request-path policy enforcement.
- DeepEval: Apache 2.0, pytest-style framework that scores agent trajectories in CI.
- Arize Phoenix: self-hostable, source-available tracing with session-level evals.
- LangSmith: managed multi-turn evaluators that score whole agent conversations.
- Maxim AI: simulation-first testing for multi-turn agents, with human review.
- Galileo: nine agentic metrics, including Agent Flow, now part of Cisco’s Splunk.
What is multi-agent evaluation?
Multi-agent evaluation is scoring how a group of cooperating agents performs on your own tasks at three levels: the end-to-end outcome, each agent’s contribution, and each handoff between agents. Single-agent evaluation stops at the first level, which in a multi-agent system is where the least useful information sits.
The best evidence for why comes from UC Berkeley researchers who built MAST, the first taxonomy of multi-agent failures. They annotated more than 1,600 traces from seven popular frameworks and found 14 failure modes in three groups: system design issues, inter-agent misalignment and task verification. Annotators agreed at a kappa of 0.88, so these are repeatable patterns. Two of the three groups describe things an output-only score can’t see.
Cost is the other reason. Anthropic reported that agents use about 4 times more tokens than a chat, and its multi-agent research system about 15 times more, so an extra round of delegation is a line item even when the answer is fine. And the problem is growing: Gartner recorded a 1,445% jump in multi-agent system inquiries between Q1 2024 and Q2 2025.
See how we scored these multi-agent evaluation tools
We assessed each tool on the same seven dimensions, using public documentation as of September 2026. Lyzr publishes this guide and is listed first; we say where a leaner tool is the better call.
| Dimension | What we checked | Why it matters |
|---|---|---|
| Per-agent tracing | Separate spans and scores per agent | One blended score hides which agent failed |
| Multi-turn simulation | Synthetic users, personas, multi-turn runs | Multi-agent bugs show up on turn six, not turn one |
| Trajectory and handoff metrics | Plan adherence, tool selection, path quality | Most MAST failures happen between agents |
| CI/CD and benchmark runs | Test runners, datasets, version comparison | You need to prove version B coordinates better than A |
| Framework coverage | Agents built on different frameworks | Most multi-agent systems mix frameworks |
| Policy enforcement | Can a failing score stop the next call | A score without a consequence is a dashboard |
| Audit trail | Decisions tied to agent identity | Compliance asks who acted, not just what happened |
Multi-agent evaluation tools reviewed
1. Lyzr: evaluation inside a governed agent platform
The five tools after this one are built mainly to tell you a multi-agent run went wrong. Lyzr is built for the step after that. Its Simulation Engine, inside Agent Studio, runs full conversations from a scenario and a persona, then scores context retention, instruction adherence, reasoning across turns, response relevance and outcome completeness. Agent Eval adds truthfulness and groundedness checks on individual outputs.
Opencontroller is where the score gets teeth. It enforces policy in the request path of agent, model and tool calls, so when an agent fails a check it can be stopped, restricted or isolated, or have its next call refused. Every decision is traceable and every identity attributable, so in a chain of agents you can see which one acted, under which policy, and what stopped it.
| Capability | Evaluation tools provide this | Lyzr adds this |
|---|---|---|
| Per-agent scoring | Usually, through traces | Scores tied to agent identity |
| Audit trail | Rarely beyond logs | Every decision attributable and traceable |
| Enforcement | Rarely | Stop, restrict, isolate or refuse a call in the request path |
Pick it if you are
- Running agents that touch money, customer data or regulated workflows.
- Asked by compliance to show which agent did what, and under which policy.
Skip it if you are
- Only after trajectory tests in CI. DeepEval is free and faster to set up.
- Committed to an open-source-only stack.
Not sure which camp you’re in? The Agent Governance Maturity Assessment shows where your controls are thin.
2. DeepEval: agent tests that run like pytest
DeepEval, from Confident AI, brings pytest-style testing to agents. Plan Quality and Plan Adherence score reasoning, Tool Correctness and Argument Correctness score each action, and Task Completion and Step Efficiency score the whole trajectory. An @observe decorator marks spans as agents, tools or LLM calls, and metrics attach to whichever span you care about.
The catch: none of these metrics has a built-in idea of a handoff. You can score each agent as its own span, but handoff quality is something you’ll define yourself with a custom G-Eval rubric.
License: Apache 2.0; Confident AI is the optional hosted layer.
Best for: engineers who want a broken agent to fail the build.
Not ideal if: you need production monitoring.
3. Arize Phoenix: self-hosted traces with tool-level evals
Phoenix is built on OpenTelemetry and OpenInference, so traces from most agent frameworks land in one view, with each agent’s spans nested under the run. Its pre-built agent evaluators target the tool layer: Tool Selection, Tool Invocation and Tool Response Handling, which checks whether an agent used a tool’s result (or error) correctly.
That matters more than it sounds, because a subagent misreading a tool result is how bad data quietly passes up the chain. Ownership is shifting: Dynatrace announced on August 13, 2026 that it’s acquiring Arize for about $915 million.
License: Elastic License 2.0 (source-available); Arize AX is the managed product.
Best for: teams that need traces and evals on their own infrastructure.
Not ideal if: you want simulation or policy enforcement.
4. LangSmith: whole-conversation scoring for LangGraph teams
LangSmith’s multi-turn online evaluators score an entire thread, judging the user’s intent, whether the task succeeded and the trajectory of tool calls in between. A thread is scored once it has been idle for a set period (10 minutes by default).
For LangGraph users, the link between evaluation and the trace viewer is the selling point: a low score sits right next to the run that earned it. Know the limits, though. Each workspace caps out at 10 multi-turn online evaluators.
License: Commercial; self-hosting needs an Enterprise license.
Best for: teams already building on LangChain or LangGraph.
Not ideal if: your agents span several frameworks.
5. Maxim AI: simulation before you ship
Maxim’s strength is the pre-launch phase. It simulates multi-turn interactions across scenarios and personas, for text and voice agents, at thousands of test cases, and scores them with pre-built, programmatic or human evaluators.
Simulation is underrated for multi-agent work. The failures MAST calls “task derailment” and “premature termination” rarely show up on a clean first turn; they surface when a simulated user pushes back.
License: Commercial; SOC 2 Type 2 and ISO 27001, with in-VPC deployment.
Best for: teams that want to break their agents before customers do.
Not ideal if: you want an open-source tool.
6. Galileo: agentic metrics inside Splunk
Galileo ships nine agentic metrics, and Agent Flow is the one multi-agent teams should look at first. It validates a trajectory against tests you write in plain language (for example, “the billing agent must confirm the account before the refund agent acts”), the closest thing here to an out-of-the-box handoff check. Galileo has also open-sourced Agent Control, a guardrail framework, under Apache 2.0.
Cisco completed its acquisition of Galileo on May 22, 2026 and is folding it into Splunk Observability. If your ops teams already live in Splunk, that’s a reason to shortlist it. If not, ask about the roadmap.
License: Commercial; SaaS, VPC or on-premises.
Best for: enterprises already standardized on Splunk.
Not ideal if: you want vendor independence from a large observability suite.
Use public benchmarks as a sanity check
Benchmarks show how an architecture behaves on someone else’s tasks. Useful for picking a starting point, close to useless for approving a release.
MultiAgentBench (ACL 2025) scores task completion and collaboration quality with milestone-based KPIs, across star, chain, tree and graph coordination structures. τ-bench, from researchers at Sierra, introduced pass^k, which asks whether an agent succeeds on the same task across k repeated tries; GPT-4o-based agents solved under half the tasks, and scored under 25% on pass^8 in the retail domain. That idea is the one to steal. Borrow the method, not the dataset: build milestone checks and repeated trials around your own workflows.
Compare all multi-evaluation tools side by side
Key: • full support · ◐ partial support · ○ not the focus. Editorial assessment, September 2026, based on public documentation.
| Capability | Lyzr | DeepEval | Arize Phoenix | LangSmith | Maxim AI | Galileo |
|---|---|---|---|---|---|---|
| Per-agent tracing | • | ◐ | • | • | • | • |
| Multi-turn simulation | • | ◐ | ○ | ◐ | • | ◐ |
| Trajectory / handoff metrics | ◐ | • | ◐ | • | • | • |
| CI/CD and benchmark runs | ◐ | • | ◐ | • | • | ◐ |
| Works across agent frameworks | • | • | • | ◐ | • | • |
| Policy enforcement in the request path | • | ○ | ○ | ○ | ◐ | ◐ |
| Audit trail tied to agent identity | • | ○ | ○ | ○ | ○ | ◐ |
| License | Commercial | Apache 2.0 | Elastic License 2.0 | Commercial | Commercial | Commercial |
Lyzr leads on enforcement and audit; DeepEval, LangSmith and Maxim AI go deeper on trajectory metrics and CI.
Tooling isn’t the bottleneck it looks like. In LangChain’s State of Agent Engineering survey of 1,340 practitioners, 89% had some form of agent observability, but only 52.4% ran offline evals on test sets and 37.3% ran online evals on production traffic.
Match your scenario to a tool
| If your situation is… | Pick… | Why… |
|---|---|---|
| A failing score needs to stop the agent, on the record | Lyzr | Enforcement and audit trail in the request path |
| You want agent regressions to fail the build | DeepEval | Trajectory metrics in pytest |
| Traces can’t leave your infrastructure | Arize Phoenix | Self-hosted, OpenTelemetry-native |
| Your agents are LangGraph graphs | LangSmith | Thread-level evals beside the trace |
| You need to stress-test before launch | Maxim AI | Large-scale text and voice simulation |
| Your SOC already runs on Splunk | Galileo | Agent Flow inside Splunk Observability |
Run these three tests on any shortlist
- The blame test. Break one subagent on purpose. If the tool flags a bad answer but can’t name the agent or handoff, it’s evaluating a single agent wearing a costume.
- The repeat test. Run the same task ten times. A tool that only shows the average hides the flakiness pass^k exposes.
- The consequence test. Ask what happens after a failing score in production. If the answer is “someone gets a Slack alert,” decide whether that’s enough.
For what else must be in place before launch, see the playbook on how to take agents to production.
See Opencontroller on your own agents
If your agents only draft content, any tool above will serve you. Once they move money, change records or talk to customers, the score has to be able to stop something. Opencontroller does that, for agents built in Lyzr or on any other framework.
Book a demo of Opencontroller →
FAQ
Lyzr suits teams needing evaluation inside a governed platform with audit trails. DeepEval suits CI regression tests, Arize Phoenix self-hosted tracing, LangSmith multi-turn scoring, Maxim AI scenario simulation, and Galileo agentic metrics.
Single-agent evaluation stops at one agent’s output or trajectory. Multi-agent evaluation also scores handoffs, attributes each failure to a specific agent and checks passed context, which a final-answer score cannot isolate.
Track task completion, tool-call accuracy, handoff quality and token cost, per agent and end to end. Anthropic reports multi-agent systems use about 15 times more tokens than chats, so cost belongs on the dashboard.
Score each handoff as its own step: did the receiving agent get the context it needed, choose the right tool next, and stay on goal? Galileo’s Agent Flow and LangSmith’s trajectory evaluation test this, and Phoenix scores tool selection.
MultiAgentBench (Zhu et al.) measures task completion and milestone-based collaboration across star, chain, tree and graph structures. τ-bench (Yao et al.) simulates a user talking to a tool-equipped agent and adds a pass^k consistency metric.
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