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AutoGen

Agents
85

Framework enabling multi-agent conversations for complex task solving

Microsoft-backed multi-agent conversation framework

Metrics

licenseMIT
providerMicrosoft
agent typemulti_agent
github stars35,000
base model requirementsgpt-4, claude-3, llama-3
tool integrations count25

Score Breakdown

adoption86
tool use84
memory context85
self reflection82
planning reasoning88

Compatibility

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Scoring Methodology

planning_reasoning25% weight

Task decomposition and strategic execution capabilities

Source: AgentBench, WebArena, SWE-bench Verified

tool_use25% weight

Intent recognition, function selection, and execution accuracy

Source: ToolBench, API-Bank, Berkeley Function Calling Leaderboard

memory_context20% weight

Long-context handling and information retention

Source: LongBench, RULER, InfiniteBench

self_reflection15% weight

Error recognition and behavior adjustment

Source: ReAct benchmark, Reflexion evaluation

adoption15% weight

GitHub stars, community usage, ecosystem maturity

Source: GitHub API, PyPI downloads, Stack Overflow activity

Related Signals

Multi-Agent Frameworks See Enterprise Adoption

Agents1d ago

Enterprise teams are increasingly adopting multi-agent frameworks like CrewAI and AutoGen for complex workflows, moving beyond single-agent implementations to orchestrated agent teams.

87

Data Sources

github.commicrosoft.github.io
Last updated: December 24, 2025