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AI agent frameworks compared — 2026

Data-driven comparison of 11 major AI agent frameworks from the Nerq index of 5M+ assets. Published 2026-06-15.

11
frameworks compared
24,450
total agents using these
Anthropic
most adopted

comparison table

frameworkagentsavg trusttop agentlanguagenew (30d)
Anthropic 7,502 61.0 auth0/auth0-mcp-server (85.3) Python 0
OpenAI 6,330 60.8 arabold/docs-mcp-server (83.9) Python 0
LangChain 2,680 60.7 arabold/docs-mcp-server (83.9) Python 0
MCP 2,055 60.1 cyanheads/git-mcp-server (89.0) Python 0
Ollama 1,900 61.2 arabold/docs-mcp-server (83.9) Python 0
HuggingFace 1,238 61.0 cortex-net (72.9) Python 0
AutoGen 1,114 60.7 aas_customersupportbot_2026011 (74.2) Python 0
CrewAI 809 61.0 AI-Forensic-Investigator (72.7) Python 0
LlamaIndex 465 58.6 Bangladeshi_Chef (72.7) Python 0
A2A 191 59.9 specs (72.7) Python 0
Semantic Kernel 166 58.0 Josephrp/LablabAutogen (72.8) Python 0

adoption — agent count

Number of agents in the Nerq index using each framework

Anthropic
7,502
OpenAI
6,330
LangChain
2,680
MCP
2,055
Ollama
1,900
HuggingFace
1,238
AutoGen
1,114
CrewAI
809
LlamaIndex
465
A2A
191
Semantic Kernel
166

average trust score

Mean trust score of agents using each framework (0-100)

Ollama
61
Anthropic
61
HuggingFace
61
CrewAI
61
OpenAI
61
LangChain
61
AutoGen
61
MCP
60
A2A
60
LlamaIndex
59
Semantic Kernel
58

methodology

Framework association is determined from the frameworks array in the Nerq agent database. An agent is counted under a framework if that framework appears in its declared dependencies, metadata, or detected integrations. Trust scores are the Nerq Trust Score v2 — a composite of security practices, compliance, maintenance activity, community trust, and ecosystem compatibility. Data updates hourly from the live index.

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Data from the Nerq index. JSON: /v1/agent/stats · API docs · all reports

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