Microsoft AutoGen
Microsoft Research
85·Strong
Overall Trust Score
Multi-agent conversation framework enabling next-gen LLM applications with conversable agents that can operate in various modes combining LLMs, human inputs, and tools. Supports complex workflows through agent conversations.
multi-agent
microsoft
open-source
Trust Vector
Performance & Reliability
86
task completion accuracy87
task completion accuracy
87
Methodology
Based on research benchmarks and model performance
Evidence
Confidence: highLast verified: 2025-11-09
tool use reliability90
tool use reliability
90
Methodology
Tool integration testing
Evidence
Confidence: highLast verified: 2025-11-09
multi step planning88
multi step planning
88
Methodology
Complex task testing
Evidence
Conversational Patterns
Multiple conversation patterns for complex task decomposition
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
memory persistence82
memory persistence
82
Methodology
Memory system evaluation
Evidence
Confidence: mediumLast verified: 2025-11-09
error recovery85
error recovery
85
Methodology
Error handling testing
Evidence
Confidence: highLast verified: 2025-11-09
conversation quality89
conversation quality
89
Methodology
Conversation quality assessment
Evidence
Confidence: highLast verified: 2025-11-09
Security
83
tool sandboxing90
tool sandboxing
90
Methodology
Security architecture review
Evidence
Confidence: highLast verified: 2025-11-09
access control78
access control
78
Methodology
Access control assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
prompt injection defense82
prompt injection defense
82
Methodology
Injection attack testing
Evidence
Confidence: mediumLast verified: 2025-11-09
data isolation85
data isolation
85
Methodology
Data architecture review
Evidence
Confidence: highLast verified: 2025-11-09
open source transparency93
open source transparency
93
Methodology
Source code review
Evidence
Confidence: highLast verified: 2025-11-09
Privacy & Compliance
84
data retention87
data retention
87
Methodology
Privacy architecture review
Evidence
Confidence: highLast verified: 2025-11-09
gdpr compliance83
gdpr compliance
83
Methodology
Compliance capabilities assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
third party data sharing78
third party data sharing
78
Methodology
Data flow analysis
Evidence
Confidence: mediumLast verified: 2025-11-09
local deployment option92
local deployment option
92
Methodology
Deployment options assessment
Evidence
Confidence: highLast verified: 2025-11-09
Trust & Transparency
88
documentation quality92
documentation quality
92
Methodology
Documentation completeness review
Evidence
AutoGen Documentation
Excellent documentation with tutorials, examples, and research papers
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
execution traceability85
execution traceability
85
Methodology
Logging capabilities assessment
Evidence
Confidence: highLast verified: 2025-11-09
decision explainability83
decision explainability
83
Methodology
Explainability features assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
open source code93
open source code
93
Methodology
Open source assessment
Evidence
Confidence: highLast verified: 2025-11-09
research foundation95
research foundation
95
Methodology
Academic backing assessment
Evidence
Confidence: highLast verified: 2025-11-09
Operational Excellence
85
ease of integration83
ease of integration
83
Methodology
Integration complexity assessment
Evidence
Confidence: highLast verified: 2025-11-09
scalability87
scalability
87
Methodology
Scalability testing
Evidence
Confidence: highLast verified: 2025-11-09
cost predictability89
cost predictability
89
Methodology
Pricing model analysis
Evidence
Confidence: highLast verified: 2025-11-09
monitoring capabilities82
monitoring capabilities
82
Methodology
Monitoring features assessment
Evidence
Confidence: highLast verified: 2025-11-09
community support88
community support
88
Methodology
Community activity analysis
Evidence
Confidence: highLast verified: 2025-11-09
✨ Strengths
- •Strong research foundation from Microsoft Research
- •Excellent code execution with Docker sandboxing
- •Flexible multi-agent conversation patterns
- •Outstanding documentation and examples
- •Powerful human-in-the-loop capabilities
- •Large active community with Microsoft backing
⚠️ Limitations
- •Can be complex to orchestrate many agents effectively
- •Conversation costs can accumulate quickly with many agents
- •Requires careful prompt engineering for agent roles
- •Limited built-in persistence for long-running workflows
- •Some learning curve for advanced features
- •Performance depends heavily on LLM quality
📊 Metadata
license: Apache 2.0
supported models:
0: OpenAI
1: Azure OpenAI
2: Anthropic
3: Local LLMs
4: Any OpenAI-compatible API
programming languages:
0: Python
deployment type: Self-hosted
tool support:
0: Code execution
1: Function calling
2: Custom tools
github stars: 50400+
first release: 2023
pricing: Free (Apache 2.0) - Costs only from LLM API usage
python requirement: Python 3.10+
contributors: 559+
transition notice: Microsoft Agent Framework is the recommended path forward; AutoGen receives maintenance and critical patches only
Use Case Ratings
customer support
88
Multi-agent conversations excellent for complex support scenarios
code generation
94
Outstanding with code execution, testing, and review agents
research assistant
89
Multi-agent research teams work well for comprehensive analysis
data analysis
91
Code execution capabilities excellent for data analysis
content creation
85
Good for collaborative content creation workflows
education
87
Human-in-the-loop features ideal for interactive tutoring
healthcare
79
Requires healthcare-specific security and compliance setup
financial analysis
82
Self-hosted with good security, suitable with proper configuration
legal compliance
84
Multi-agent analysis from different legal perspectives
creative writing
90
Agent debates and discussions excellent for creative ideation