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SuperAGI

Community

75·Strong

Overall Trust Score

Open-source autonomous AI agent framework for running and managing multiple AI agents concurrently. Features GUI-based management, tool marketplace, and agent templates for building production-ready autonomous agents.

autonomous
self-hosted
open-source
Version: 0.x
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

76
autonomous execution
80
Methodology
Autonomous task testing
Evidence
SuperAGI Documentation
Autonomous agent execution with goal-oriented planning
Date: 2024-10-15
Confidence: mediumLast verified: 2025-11-09
multi agent orchestration
78
Methodology
Multi-agent coordination testing
Evidence
Agent Management
Run multiple agents concurrently with resource management
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
tool integration
82
Methodology
Tool integration testing
Evidence
Tool Marketplace
Extensive tool marketplace with 40+ pre-built tools
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
planning capability
74
Methodology
Planning effectiveness testing
Evidence
Agent Planning
Multi-step planning with goal decomposition
Date: 2024-09-20
Confidence: mediumLast verified: 2025-11-09
memory management
72
Methodology
Memory system evaluation
Evidence
Vector Memory
Vector-based memory for agent context retention
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
latency
Value: 2-15s per iteration
Methodology
Performance monitoring
Evidence
Performance
Depends on LLM calls, tool usage, and planning complexity
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Security

70
tool sandboxing
65
Methodology
Security architecture review
Evidence
Tool Execution
Limited sandboxing, tools execute with agent permissions
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
self hosting
85
Methodology
Deployment security assessment
Evidence
Deployment
Full self-hosting with Docker Compose
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
authentication
72
Methodology
Authentication assessment
Evidence
Security Features
Basic authentication, requires additional hardening
Date: 2024-09-15
Confidence: mediumLast verified: 2025-11-09
open source
88
Methodology
Open source assessment
Evidence
GitHub
MIT license, 15k+ stars, open source community
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
api security
68
Methodology
API security review
Evidence
API Documentation
Basic API security, needs production hardening
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Privacy & Compliance

78
data retention
80
Methodology
Privacy architecture review
Evidence
Data Management
Local database storage with configurable retention
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
gdpr compliance
77
Methodology
Compliance capabilities assessment
Evidence
Self-Hosted
GDPR compliance possible with self-hosted deployment
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
local deployment
88
Methodology
Deployment options assessment
Evidence
Installation
Complete local deployment with Docker Compose
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
llm data sharing
72
Methodology
Data flow analysis
Evidence
LLM Integration
Agent data sent to configured LLM providers
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
agent logs
76
Methodology
Data storage assessment
Evidence
Logging
Agent execution logs stored locally
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Trust & Transparency

80
documentation quality
78
Methodology
Documentation completeness review
Evidence
SuperAGI Docs
Good documentation but still evolving
Date: 2024-10-15
Confidence: mediumLast verified: 2025-11-09
gui interface
85
Methodology
UI/UX assessment
Evidence
UI Features
Web-based GUI for agent management and monitoring
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
open source
90
Methodology
Open source assessment
Evidence
GitHub
MIT license, 15k+ stars, active community
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
agent traceability
76
Methodology
Traceability features assessment
Evidence
Agent Logs
Agent execution history and decision logs
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
community support
75
Methodology
Community engagement analysis
Evidence
Community
Growing community, Discord support
Date: 2024-10-15
Confidence: mediumLast verified: 2025-11-09

Operational Excellence

72
ease of setup
75
Methodology
Setup complexity assessment
Evidence
Installation Guide
Docker Compose setup, requires technical knowledge
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
scalability
68
Methodology
Scalability testing
Evidence
Architecture
Designed for small-medium scale, scaling requires work
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
cost predictability
90
Methodology
Pricing model analysis
Evidence
Open Source
Free MIT license, costs only for infrastructure and LLMs
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
monitoring
70
Methodology
Monitoring features assessment
Evidence
Monitoring Features
GUI-based monitoring, limited production metrics
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
tool marketplace
82
Methodology
Tool ecosystem assessment
Evidence
Tool Ecosystem
Growing marketplace with 40+ tools
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
production readiness
67
Methodology
Production readiness assessment
Evidence
Maturity
Early stage project, production use requires hardening
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

✨ Strengths

  • •GUI-based agent management for easy monitoring and control
  • •Open source (MIT) with growing community (15k+ stars)
  • •Tool marketplace with 40+ pre-built tools and extensibility
  • •Multi-agent orchestration for concurrent autonomous tasks
  • •Self-hosted deployment for data privacy and control
  • •Agent templates for quick start with common use cases

⚠️ Limitations

  • •Early stage project with limited production maturity
  • •Limited enterprise features (security, monitoring, RBAC)
  • •Can be expensive due to multiple LLM calls in autonomous loops
  • •Autonomous agent reliability can be unpredictable
  • •Requires technical knowledge for setup and customization
  • •Limited documentation compared to more mature frameworks

📊 Metadata

license: MIT
supported models:
0: OpenAI GPT-4
1: GPT-3.5
2: Claude
3: PaLM
programming languages:
0: Python
deployment type: Self-hosted (Docker Compose)
tool support:
0: 40+ marketplace tools
1: Custom tools
2: API integrations
pricing model: Free open source
github stars: 16600+
first release: 2023
database: PostgreSQL
agent features:
0: Goal-oriented
1: Multi-agent
2: Tool marketplace
3: Web GUI
pricing: Free (MIT license)

Use Case Ratings

customer support

75

Can build support agents but better suited for autonomous tasks

code generation

78

Good for autonomous coding agents with tool integration

research assistant

80

Suitable for autonomous research and web scraping tasks

data analysis

76

Can integrate analysis tools for autonomous data work

content creation

73

Autonomous content generation possible but limited

education

70

Better for autonomous learning tasks than tutoring

healthcare

65

Early stage, not suitable for clinical use

financial analysis

68

Limited enterprise features for compliance

legal compliance

74

Can build document analysis agents with tools

creative writing

77

Good for autonomous ideation and research tasks