SYSTEM ACTIVE
Home›Agents›OpenAI Swarm

OpenAI Swarm

OpenAI

74·Adequate

Overall Trust Score

Experimental educational framework from OpenAI for building multi-agent systems with lightweight orchestration. Demonstrates ergonomic patterns for agent coordination and handoffs using simple Python primitives.

openai
assistants
experimental
open-source
Version: Experimental (Deprecated)
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

73
agent orchestration
78
Methodology
Orchestration testing
Evidence
Swarm Documentation
Lightweight multi-agent orchestration patterns
Date: 2024-10-20
Confidence: mediumLast verified: 2025-11-09
agent handoffs
80
Methodology
Handoff testing
Evidence
Handoff Pattern
Ergonomic agent-to-agent handoff mechanisms
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09
simplicity
85
Methodology
Code complexity assessment
Evidence
Design Philosophy
Minimalist design with simple Python primitives
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
context management
72
Methodology
Context handling testing
Evidence
Context Variables
Basic context management for agent coordination
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
experimental status
60
Methodology
Status assessment
Evidence
README Warning
Explicitly marked as experimental, not for production
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
latency
Value: Variable (OpenAI API dependent)
Methodology
Performance monitoring
Evidence
Performance
Performance depends on OpenAI API and agent complexity
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Security

70
minimal dependencies
85
Methodology
Dependency analysis
Evidence
Dependencies
Minimal dependencies reduce attack surface
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
openai trust
88
Methodology
Source trust assessment
Evidence
OpenAI Repository
Official OpenAI project with trusted maintainers
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
experimental risks
55
Methodology
Security maturity assessment
Evidence
Experimental Status
Experimental status means security not production-hardened
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
open source
90
Methodology
Open source assessment
Evidence
GitHub
MIT license, 13k+ stars, transparent code
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
api security
68
Methodology
API security review
Evidence
OpenAI Integration
Security depends on OpenAI API key handling
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Privacy & Compliance

75
local execution
82
Methodology
Privacy architecture review
Evidence
Framework Architecture
Python library runs locally, orchestration in your environment
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
openai data sharing
70
Methodology
Data flow analysis
Evidence
OpenAI API
All agent interactions sent to OpenAI API
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
no telemetry
85
Methodology
Telemetry assessment
Evidence
Code Review
No telemetry in framework code
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
gdpr considerations
72
Methodology
Compliance assessment
Evidence
OpenAI Privacy
Privacy depends on OpenAI's data policies
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
data control
68
Methodology
Data control assessment
Evidence
Framework Design
Limited data control, OpenAI API required
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Trust & Transparency

82
documentation quality
80
Methodology
Documentation completeness review
Evidence
README and Examples
Good README with examples, limited detailed docs
Date: 2024-10-20
Confidence: mediumLast verified: 2025-11-09
code clarity
90
Methodology
Code quality assessment
Evidence
Source Code
Clean, readable code demonstrating patterns
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
openai backing
88
Methodology
Source trust assessment
Evidence
Official OpenAI
Official OpenAI project, trusted source
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
open source
90
Methodology
Open source assessment
Evidence
GitHub
MIT license, 13k+ stars, open development
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
educational purpose
75
Methodology
Purpose assessment
Evidence
Purpose Statement
Explicitly designed for educational and experimental use
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

Operational Excellence

68
ease of use
85
Methodology
Usability assessment
Evidence
Simplicity
Very simple API, easy to understand patterns
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
production readiness
45
Methodology
Production readiness assessment
Evidence
README Warning
Explicitly not recommended for production use
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
scalability
65
Methodology
Scalability assessment
Evidence
Design
Designed for experimentation, not large-scale deployment
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
cost predictability
88
Methodology
Pricing model analysis
Evidence
Pricing
Free MIT library, costs only for OpenAI API usage
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
monitoring
58
Methodology
Monitoring features assessment
Evidence
Features
Minimal monitoring, experimental framework
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
learning value
92
Methodology
Educational value assessment
Evidence
Educational Design
Excellent for learning multi-agent patterns
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

✨ Strengths

  • •Official OpenAI educational framework for multi-agent patterns
  • •Extremely simple and ergonomic API design
  • •Clean code that demonstrates best practices
  • •MIT licensed with minimal dependencies
  • •Excellent for learning agent orchestration concepts
  • •13k+ GitHub stars showing community interest

⚠️ Limitations

  • •Explicitly experimental, not for production use
  • •OpenAI API only, no support for other LLM providers
  • •Limited features compared to production frameworks
  • •No built-in monitoring, error handling, or scaling features
  • •Minimal documentation beyond examples
  • •Not actively maintained for production use cases

📊 Metadata

license: MIT
supported models:
0: OpenAI GPT-4
1: GPT-3.5
programming languages:
0: Python
deployment type: Self-hosted Python library
tool support:
0: Function calling
1: Agent handoffs
pricing model: Free open source (OpenAI API costs apply)
github stars: 13000+
first release: 2024
status: Experimental / Educational - Replaced by OpenAI Agents SDK
deprecation notice: OpenAI recommends migrating to the Agents SDK for production use. Swarm was experimental only and is not officially supported.
github repo: https://github.com/openai/swarm
successor: OpenAI Agents SDK (production-ready evolution of Swarm)

Use Case Ratings

customer support

70

Good for prototyping agent handoff patterns

code generation

68

Can demonstrate multi-agent code workflows

research assistant

72

Good for experimenting with research agent patterns

data analysis

71

Can prototype multi-agent data workflows

content creation

74

Good for prototyping content agent collaboration

education

90

Excellent for learning about multi-agent systems

healthcare

50

Experimental status not suitable for healthcare

financial analysis

48

Not suitable for production financial systems

legal compliance

65

Can prototype multi-agent legal workflows

creative writing

76

Good for experimenting with creative agent collaboration