Microsoft AutoGen

v0.4

Microsoft Research

Agentmulti-agentmicrosoftopen-source
85
Strong
About This Agent

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.

Last Evaluated: November 9, 2025
Official Website

Trust Vector Analysis

Dimension Breakdown

🚀Performance & Reliability
+
task completion accuracy

Based on research benchmarks and model performance

Evidence
AutoGen Research Paper — Demonstrated high accuracy on complex multi-agent tasks
highVerified: 2025-11-09
tool use reliability

Tool integration testing

Evidence
AutoGen Code Execution — Robust code execution with Docker sandboxing support
highVerified: 2025-11-09
multi step planning

Complex task testing

Evidence
Conversational Patterns — Multiple conversation patterns for complex task decomposition
highVerified: 2025-11-09
memory persistence

Memory system evaluation

Evidence
Agent Context — Conversation history maintained within sessions
mediumVerified: 2025-11-09
error recovery

Error handling testing

Evidence
Human-in-the-Loop — Strong human-in-the-loop capabilities for error correction
highVerified: 2025-11-09
conversation quality

Conversation quality assessment

Evidence
AutoGen Paper — Conversational framework produces coherent multi-turn interactions
highVerified: 2025-11-09
🛡️Security
+
tool sandboxing

Security architecture review

Evidence
Docker Code Executor — Docker-based sandboxing for code execution
highVerified: 2025-11-09
access control

Access control assessment

Evidence
Agent Configuration — Agent-level access control via configuration
mediumVerified: 2025-11-09
prompt injection defense

Injection attack testing

Evidence
System Messages — System message separation provides some protection
mediumVerified: 2025-11-09
data isolation

Data architecture review

Evidence
Agent Conversations — Separate conversation contexts for different agent groups
highVerified: 2025-11-09
open source transparency

Source code review

Evidence
AutoGen GitHub — Apache 2.0 license, 30k+ stars, backed by Microsoft Research
highVerified: 2025-11-09
🔒Privacy & Compliance
+
data retention

Privacy architecture review

Evidence
Self-Hosted Architecture — Full control over data retention in self-hosted deployments
highVerified: 2025-11-09
gdpr compliance

Compliance capabilities assessment

Evidence
Microsoft Open Source — GDPR compliance achievable with proper deployment
mediumVerified: 2025-11-09
third party data sharing

Data flow analysis

Evidence
Model Integration — Data sent to configured LLM provider (OpenAI, Azure, etc.)
mediumVerified: 2025-11-09
local deployment option

Deployment options assessment

Evidence
Local Model Support — Supports local LLMs via Ollama, LM Studio, and vLLM
highVerified: 2025-11-09
👁️Trust & Transparency
+
documentation quality

Documentation completeness review

Evidence
AutoGen Documentation — Excellent documentation with tutorials, examples, and research papers
highVerified: 2025-11-09
execution traceability

Logging capabilities assessment

Evidence
Logging Features — Built-in logging with conversation history tracking
highVerified: 2025-11-09
decision explainability

Explainability features assessment

Evidence
Conversation Logs — Full conversation history provides context for decisions
mediumVerified: 2025-11-09
open source code

Open source assessment

Evidence
GitHub Repository — Apache 2.0, 30k+ stars, Microsoft Research backing
highVerified: 2025-11-09
research foundation

Academic backing assessment

Evidence
Academic Publications — Strong research foundation with published papers
highVerified: 2025-11-09
⚙️Operational Excellence
+
ease of integration

Integration complexity assessment

Evidence
AutoGen Quickstart — Clear quickstart but requires understanding of agent concepts
highVerified: 2025-11-09
scalability

Scalability testing

Evidence
Agent Orchestration — Designed for scalable multi-agent systems
highVerified: 2025-11-09
cost predictability

Pricing model analysis

Evidence
Open Source Framework — Free framework, costs limited to LLM API usage
highVerified: 2025-11-09
monitoring capabilities

Monitoring features assessment

Evidence
Observability — Good logging support, integrates with external monitoring
highVerified: 2025-11-09
community support

Community activity analysis

Evidence
GitHub Community — Very active community with Microsoft backing
highVerified: 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

Multi-agent conversations excellent for complex support scenarios

code generation

Outstanding with code execution, testing, and review agents

research assistant

Multi-agent research teams work well for comprehensive analysis

data analysis

Code execution capabilities excellent for data analysis

content creation

Good for collaborative content creation workflows

education

Human-in-the-loop features ideal for interactive tutoring

healthcare

Requires healthcare-specific security and compliance setup

financial analysis

Self-hosted with good security, suitable with proper configuration

legal compliance

Multi-agent analysis from different legal perspectives

creative writing

Agent debates and discussions excellent for creative ideation