Multi-Agent Framework Performance Benchmark: Speed, Cost & Quality (2026)

Picking a multi-agent AI framework is a high-stakes decision. You are committing to an orchestration layer that touches every workflow your team builds. The wrong choice means slow iterations, bloated costs, or unreliable outputs.

We ran 30 standardized tasks through 5 frameworks -- Ivern, CrewAI, AutoGen, LangGraph, and n8n -- and measured execution speed, cost per task, output quality, and reliability. Every test used the same underlying model (Claude 3.5 Sonnet) to isolate framework-level differences from model performance.

This benchmark is the most direct multi-agent framework comparison we have published. For broader context, see our AI agent cost benchmark report and our guide to choosing an AI agent platform.

TL;DR: Benchmark Results Summary

Ivern finished first overall, driven by the fastest execution times, lowest cost per task, and highest reliability. LangGraph led on output quality for complex code review tasks. CrewAI and AutoGen offered strong customization but required significantly more setup time.

Overall scores are normalized on a 0-100 scale across all four metrics. Full methodology and raw numbers follow.

Benchmark Methodology

Framework Versions

All frameworks were tested on their latest stable releases as of April 2026:

Model Configuration

To ensure a fair comparison, every framework used Claude 3.5 Sonnet as the sole LLM provider. This eliminates model-level variance and isolates how each framework handles agent orchestration, prompt routing, and token management.

For Ivern, which supports cross-provider workflows natively, we configured a single-provider setup to match the other frameworks. See our BYOK AI platform comparison for benchmarks that leverage Ivern's multi-model capabilities.

Task Execution Protocol

• Each of the 30 tasks was run 5 times per framework (150 runs per framework, 750 total)
• Runs were executed between April 14-21, 2026, during US business hours (9 AM - 5 PM ET)
• Failed runs were retried once; if the retry failed, the task was marked as a reliability failure
• Quality was evaluated by three independent reviewers using a standardized rubric

Environment

• Python frameworks: Python 3.11, 8-core CPU, 16 GB RAM
• n8n: Docker container, same hardware
• Ivern: Cloud platform (no local compute)
• Network latency was measured and subtracted from timing data

Test Categories and Task Descriptions

We designed 30 tasks across three categories, each representing a common enterprise use case for multi-agent systems. For more examples of real-world agent workflows, see our AI agent workflow examples.

Category 1: Content Creation (10 tasks)

Each task required a minimum of two agents: one for research and one for writing. Some tasks added a third agent for editing or SEO optimization.

Category 2: Code Review (10 tasks)

Each task required agents to analyze code, identify issues, and produce actionable review feedback.

Category 3: Research Reports (10 tasks)

Each task required agents to gather information, synthesize findings, and produce a structured report. For a deeper dive into research automation, see our guide to automating research with AI agents.

Speed Benchmark Results

Execution speed measures wall-clock time from task submission to final output delivery, with network latency subtracted. Faster frameworks complete multi-agent coordination with less overhead.

Average Execution Time by Framework

Key Speed Observations

Ivern was 38-57% faster than the next closest framework across all three categories. The speed advantage comes from three factors:

1. Pre-optimized agent routing. Ivern's squad architecture assigns tasks to specialized agents without the round-trip negotiation overhead that plagues conversation-based frameworks like AutoGen.
2. Streaming-first architecture. Output delivery begins as soon as the first agent completes its work, rather than waiting for the full pipeline to finish.
3. Managed infrastructure. No cold starts, no container spin-up time. The other four frameworks all incurred 2-8 seconds of initialization overhead per run.

LangGraph placed second due to its efficient graph traversal. Since LangGraph defines explicit edges between agents, it avoids the back-and-forth message passing that slowed AutoGen and CrewAI.

n8n was the slowest because its AI nodes add abstraction layers on top of the underlying model calls. Each workflow step involves JSON parsing, node transitions, and webhook callbacks that add measurable latency.

For teams that need fast iteration cycles, speed directly maps to developer productivity. Our AI coding tools benchmark found similar patterns: frameworks with lower per-task latency enable 2-3x more iterations per day.

Cost Benchmark Results

Cost per task measures total LLM API spend, including input tokens, output tokens, and any repeated calls from retry logic. Lower is better.

Average Cost Per Task by Framework

Key Cost Observations

Ivern's BYOK model means you pay only for the underlying API tokens with zero platform markup. The cost advantage comes from efficient prompt management: Ivern's pre-configured agent roles use shorter system prompts and avoid redundant context injection.

CrewAI and AutoGen both inject substantial context into each agent conversation (role definitions, task descriptions, delegation instructions). This adds 800-1,500 input tokens per agent per turn. Over a multi-agent pipeline with 3-4 turns, that compounds quickly.

LangGraph sits in the middle because its explicit state management lets developers control exactly what context gets passed between nodes. Well-optimized LangGraph workflows can approach Ivern's efficiency, but the default configuration is more token-heavy.

n8n's AI nodes include template injection and output parsing that adds roughly 400-600 tokens of overhead per step. For multi-step workflows, this overhead is multiplicative.

For teams running hundreds or thousands of tasks per month, these per-task differences add up. Our AI agent cost calculator shows that at 500 tasks/month, the difference between Ivern ($20.50) and n8n ($36.00) is $186/month or $2,232/year.

Quality Benchmark Results

Three independent evaluators scored each output on a 1-5 scale across four dimensions: accuracy, completeness, coherence, and actionability. The final quality score is the average across all evaluators and dimensions.

Average Quality Score by Framework (1-5 scale)

Key Quality Observations

LangGraph led on code review quality (4.7/5), a meaningful result. LangGraph's graph-based architecture lets developers define precise review checklists as separate nodes, producing more thorough and structured code analysis. For teams where code review quality is the top priority, LangGraph deserves serious consideration.

Ivern led on content creation quality (4.4/5). The pre-configured agent squads with specialized roles (Researcher, Writer, Editor, SEO Specialist) produce well-structured content with strong factual grounding. The built-in review step catches common issues before output delivery.

n8n scored lowest across all categories. Its generic AI nodes lack the specialized prompting that purpose-built agent frameworks provide. Outputs were functional but often lacked depth and nuance.

The quality gap between frameworks narrowed compared to our previous benchmark round. As underlying models improve, framework-level quality differences matter less for simple tasks and more for complex, multi-step workflows.

Reliability Benchmark Results

Reliability measures the percentage of tasks that completed successfully without errors, timeouts, or malformed output. A task was marked as failed if it produced an error, timed out (120-second limit), or returned output that did not meet the minimum structural requirements (e.g., a blog post with fewer than 500 words).

Task Completion Rate by Framework

Key Reliability Observations

Ivern's 97% reliability rate reflects its managed infrastructure and pre-tested agent configurations. When an agent encounters an issue, Ivern's orchestration layer handles retries and fallbacks transparently.

LangGraph's reliability dipped on research reports (87%) because long-running graph executions occasionally hit state serialization issues. These are known bugs in the LangGraph runtime that are being addressed.

AutoGen's conversation-based architecture is inherently less reliable. Agents occasionally enter infinite conversation loops, exceed token limits, or produce malformed JSON when parsing each other's responses. This is a trade-off of AutoGen's flexible multi-agent dialogue model.

n8n's lower reliability stems from workflow node failures. When an AI node produces unexpected output, downstream nodes receive malformed input and fail. n8n's error handling has improved but still lags behind purpose-built agent frameworks.

For production deployments, reliability is often more important than speed or cost. A framework that fails 18% of the time (n8n) requires substantially more monitoring and manual intervention than one that fails 3% of the time (Ivern).

Overall Scores and Rankings

Each metric was normalized to a 0-100 scale (higher is better for all metrics, with cost inverted so lower cost = higher score). The overall score is an unweighted average of all four normalized metrics.

Framework-by-Framework Summary

Ivern (91.4) -- Best for teams that want the fastest results at the lowest cost with minimal setup. The no-code interface and pre-built agent squads make it the most accessible option. Its weakness is customization: you cannot define custom graph topologies or write custom agent logic. For most business use cases, the trade-off is worth it. See our Ivern vs CrewAI detailed comparison and Ivern vs AutoGen comparison for deeper dives.

LangGraph (76.3) -- Best for engineering teams that need fine-grained control over agent workflows. The graph-based architecture produces the highest quality output for structured tasks like code review. The trade-off is a steep learning curve and more verbose code. See our LangGraph vs CrewAI comparison for a head-to-head breakdown.

CrewAI (68.0) -- A solid middle ground for Python developers. The role-based agent model is intuitive, and the framework is well-documented. It lacks LangGraph's control precision and Ivern's speed, but it ships real work reliably. Setup takes 30-60 minutes.

AutoGen (63.0) -- Best for research and experimentation. The conversation-based multi-agent model is the most flexible, but also the most prone to reliability issues. AutoGen excels when you need agents to brainstorm or debate; it struggles with structured, repeatable production workflows. See our multi-agent AI orchestration guide for more on when AutoGen fits.

n8n (56.0) -- Best for teams already using n8n for workflow automation who want to add AI capabilities incrementally. It is not a purpose-built agent framework, and the benchmark reflects that. For multi-agent orchestration specifically, the other four options are stronger choices.

What These Results Mean for Your Team

Choose Ivern if:

• You want to go from zero to a working multi-agent workflow in under 5 minutes
• Your team includes non-technical stakeholders who need to create and manage agent workflows
• Cost efficiency and speed are priorities
• You want a managed platform with a free tier and BYOK pricing
• You need reliable, repeatable outputs for production workloads

Start with Ivern's free tier at ivern.ai/signup -- 15 tasks, no credit card required.

Choose LangGraph if:

• You have a strong Python engineering team
• You need custom graph topologies with conditional branching and cycles
• Code review and structured analysis are your primary use cases
• You are willing to invest 1-2 weeks in framework learning

Choose CrewAI if:

• You want a Python-native framework with a gentler learning curve than LangGraph
• Role-based agent teams match your mental model for task delegation
• You need moderate customization without the complexity of graph-based state management

Choose AutoGen if:

• You are conducting research or experimentation with multi-agent systems
• Conversation-based agent interaction is a feature, not a bug
• You need agents that can negotiate, debate, or collaboratively solve open-ended problems

Choose n8n if:

• You already use n8n for workflow automation
• AI is one component of a larger automation pipeline
• You do not need advanced multi-agent orchestration

For a deeper look at how these frameworks compare on setup time, see our Ivern vs AutoGen vs CrewAI comparison.

FAQ

How were the quality scores calculated?

Three independent evaluators scored each output on four dimensions (accuracy, completeness, coherence, actionability) using a 1-5 rubric. The final quality score is the mean of all evaluator-dimension combinations. Evaluators did not know which framework produced each output.

Why did you use Claude 3.5 Sonnet for all frameworks?

Using a single model eliminates model-level variance. If one framework used GPT-4o and another used Gemini, the results would conflate framework performance with model capability. Claude 3.5 Sonnet is widely available across all five frameworks and represents a strong mid-tier model.

Can Ivern's results be replicated with other models?

Yes. Ivern supports cross-provider workflows, meaning you can use Claude, GPT-4o, Gemini, or any combination. Our BYOK guide explains how to configure multi-model squads. The speed advantages in this benchmark come from framework-level orchestration, not model selection.

Why is n8n included in a multi-agent framework benchmark?

n8n has added dedicated AI agent nodes and is marketed as an AI workflow platform. Many teams consider it alongside purpose-built agent frameworks. Including it provides an honest comparison: n8n is a capable automation tool, but it is not optimized for multi-agent orchestration.

How does Ivern's free tier affect the cost comparison?

Ivern's free tier includes 15 tasks at no cost. For the benchmark, we calculated cost using BYOK pricing (API token costs only) to ensure an apples-to-apples comparison. The free tier makes Ivern even more cost-effective for teams that stay within its limits.

What about LangGraph Cloud pricing?

LangGraph Cloud charges $0.03 per step, which adds up quickly for multi-step agent workflows. Our benchmark used self-hosted LangGraph with direct API calls to match the cost model of the other frameworks. Production deployments using LangGraph Cloud would see higher per-task costs than our benchmark numbers.

Were the Python frameworks optimized before testing?

Yes. We followed each framework's official best practices documentation and configured optimal agent counts, temperature settings, and output parsers. We did not apply custom optimizations beyond what the documentation recommends. This ensures the benchmark reflects the experience a typical user would have.

Should I benchmark these frameworks on my own workloads?

Absolutely. This benchmark covers 30 general-purpose tasks across three categories. Your specific workloads may produce different results. Most of these frameworks are free to try. Ivern offers a free tier, and the Python frameworks are open source. Run your own tasks and measure what matters to your team.

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