AI Agent Framework Benchmarks
Objective performance data — not marketing. Based on real production metrics, GitHub activity, and community-reported benchmarks.
Data sources: GitHub repository metrics, npm/PyPI download trends, community surveys, documented production case studies. Last updated: March 2026.
Master Framework Comparison
Five frameworks across eight performance and developer-experience metrics. Color coding reflects relative standing: green is best, yellow is acceptable, red requires consideration.
| Framework | Cold Start | Avg Latency / step | Cost / 1k tokens | GitHub Stars | npm Downloads / mo | Multi-Agent | Observability | Learning Curve |
|---|---|---|---|---|---|---|---|---|
| LangChain | 1.2s | 340ms | $0.0018 | 92k | 4.2M | ✓ LangGraph | LangSmith★★★★★ | Steep |
| CrewAI | 0.8s | 280ms | $0.0021 | 24k | 890k | ✓ Native | Limited★★★★★ | Moderate |
| AutoGen | 1.5s | 410ms | $0.0019 | 38k | 620k | ✓ Native | Custom needed★★★★★ | Steep |
| n8n | 0.3s | 120ms | $0.0008 | 48k | 310k | Partial | Built-in★★★★★ | Easy |
| LangGraph | 1.4s | 360ms | $0.0019 | 9.8k | 1.1M | ✓ Advanced | LangSmith★★★★★ | Very Steep |
Head-to-Head Comparisons
Detailed breakdowns of the most common framework decision points. Each comparison draws on the same production dataset.
LangChain's ecosystem breadth and 92k GitHub stars give it a decisive edge for production deployments requiring broad tool integrations. CrewAI wins on simplicity and lower cold-start latency for teams that need role-based multi-agent workflows fast.
See Full Benchmark →LangChain leads on download volume (4.2M/mo) and observability via LangSmith, making it more operationally mature. AutoGen's conversational multi-agent model is unmatched for research-oriented workflows but demands more custom infrastructure.
See Full Benchmark →CrewAI's 280ms average step latency is 32% faster than AutoGen and its role-based crew abstractions dramatically reduce boilerplate. AutoGen edges ahead for highly dynamic agent-to-agent conversation patterns in research environments.
See Full Benchmark →n8n dominates on cost efficiency ($0.0008/1k tokens) and raw step latency (120ms), making it the clear winner for automation-heavy workflows with human-readable visual graphs. LangChain wins wherever fine-grained LLM control and Python ecosystem depth matter.
See Full Benchmark →Methodology
How we collect, validate, and report benchmark data. Transparency is non-negotiable.
GitHub API
Star counts, commit frequency, open vs closed issue ratios, and contributor velocity are pulled from the GitHub public API and refreshed monthly. Forked and mirror repositories are excluded.
npm / PyPI Download Stats
Monthly download figures are sourced directly from npm's public download API and PyPI's BigQuery dataset. Figures represent package downloads, not unique installations.
Community Surveys
Discord and Reddit community surveys across r/MachineLearning, r/LangChain, and framework-specific Discord servers. Sample sizes range from 200 to 1,400 respondents per cycle.
Production Case Studies
Publicly documented production deployments, blog posts, and conference talks from engineering teams. Latency and cost figures are derived from cited production environments, not synthetic benchmarks.
Editorial independence: We do not accept payment to influence benchmark results. Framework vendors cannot purchase improved placement, scores, or comparisons. All data is sourced from public repositories and community reports.
Framework Quick Facts
Key facts for each framework as of March 2026.
- License
- MIT
- Language
- Python / TypeScript
- Created by
- LangChain, Inc.
- Primary use
- General-purpose LLM application orchestration and RAG pipelines
- License
- MIT
- Language
- Python
- Created by
- João Moura / CrewAI, Inc.
- Primary use
- Role-based multi-agent crews for autonomous task completion
- License
- MIT
- Language
- Python
- Created by
- Microsoft Research
- Primary use
- Conversational multi-agent systems for research and code generation
- License
- Sustainable Use License / Apache 2.0
- Language
- TypeScript
- Created by
- n8n GmbH
- Primary use
- Visual workflow automation with AI nodes and 400+ integrations
- License
- MIT
- Language
- Python / TypeScript
- Created by
- LangChain, Inc.
- Primary use
- Stateful, cyclical agent graphs with human-in-the-loop support