OpenAI Swarm

vExperimental (Deprecated)

OpenAI

Agentopenaiassistantsexperimentalopen-source
74
Adequate
About This Agent

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.

Last Evaluated: November 9, 2025
Official Website

Trust Vector Analysis

Dimension Breakdown

🚀Performance & Reliability
+
agent orchestration

Orchestration testing

Evidence
Swarm Documentation — Lightweight multi-agent orchestration patterns
mediumVerified: 2025-11-09
agent handoffs

Handoff testing

Evidence
Handoff Pattern — Ergonomic agent-to-agent handoff mechanisms
highVerified: 2025-11-09
simplicity

Code complexity assessment

Evidence
Design Philosophy — Minimalist design with simple Python primitives
highVerified: 2025-11-09
context management

Context handling testing

Evidence
Context Variables — Basic context management for agent coordination
mediumVerified: 2025-11-09
experimental status

Status assessment

Evidence
README Warning — Explicitly marked as experimental, not for production
highVerified: 2025-11-09
latency

Performance monitoring

Evidence
Performance — Performance depends on OpenAI API and agent complexity
mediumVerified: 2025-11-09
🛡️Security
+
minimal dependencies

Dependency analysis

Evidence
Dependencies — Minimal dependencies reduce attack surface
highVerified: 2025-11-09
openai trust

Source trust assessment

Evidence
OpenAI Repository — Official OpenAI project with trusted maintainers
highVerified: 2025-11-09
experimental risks

Security maturity assessment

Evidence
Experimental Status — Experimental status means security not production-hardened
highVerified: 2025-11-09
open source

Open source assessment

Evidence
GitHub — MIT license, 13k+ stars, transparent code
highVerified: 2025-11-09
api security

API security review

Evidence
OpenAI Integration — Security depends on OpenAI API key handling
mediumVerified: 2025-11-09
🔒Privacy & Compliance
+
local execution

Privacy architecture review

Evidence
Framework Architecture — Python library runs locally, orchestration in your environment
highVerified: 2025-11-09
openai data sharing

Data flow analysis

Evidence
OpenAI API — All agent interactions sent to OpenAI API
mediumVerified: 2025-11-09
no telemetry

Telemetry assessment

Evidence
Code Review — No telemetry in framework code
highVerified: 2025-11-09
gdpr considerations

Compliance assessment

Evidence
OpenAI Privacy — Privacy depends on OpenAI's data policies
mediumVerified: 2025-11-09
data control

Data control assessment

Evidence
Framework Design — Limited data control, OpenAI API required
mediumVerified: 2025-11-09
👁️Trust & Transparency
+
documentation quality

Documentation completeness review

Evidence
README and Examples — Good README with examples, limited detailed docs
mediumVerified: 2025-11-09
code clarity

Code quality assessment

Evidence
Source Code — Clean, readable code demonstrating patterns
highVerified: 2025-11-09
openai backing

Source trust assessment

Evidence
Official OpenAI — Official OpenAI project, trusted source
highVerified: 2025-11-09
open source

Open source assessment

Evidence
GitHub — MIT license, 13k+ stars, open development
highVerified: 2025-11-09
educational purpose

Purpose assessment

Evidence
Purpose Statement — Explicitly designed for educational and experimental use
highVerified: 2025-11-09
⚙️Operational Excellence
+
ease of use

Usability assessment

Evidence
Simplicity — Very simple API, easy to understand patterns
highVerified: 2025-11-09
production readiness

Production readiness assessment

Evidence
README Warning — Explicitly not recommended for production use
highVerified: 2025-11-09
scalability

Scalability assessment

Evidence
Design — Designed for experimentation, not large-scale deployment
mediumVerified: 2025-11-09
cost predictability

Pricing model analysis

Evidence
Pricing — Free MIT library, costs only for OpenAI API usage
highVerified: 2025-11-09
monitoring

Monitoring features assessment

Evidence
Features — Minimal monitoring, experimental framework
mediumVerified: 2025-11-09
learning value

Educational value assessment

Evidence
Educational Design — Excellent for learning multi-agent patterns
highVerified: 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

Good for prototyping agent handoff patterns

code generation

Can demonstrate multi-agent code workflows

research assistant

Good for experimenting with research agent patterns

data analysis

Can prototype multi-agent data workflows

content creation

Good for prototyping content agent collaboration

education

Excellent for learning about multi-agent systems

healthcare

Experimental status not suitable for healthcare

financial analysis

Not suitable for production financial systems

legal compliance

Can prototype multi-agent legal workflows

creative writing

Good for experimenting with creative agent collaboration