BabyAGI
Yohei Nakajima
66·Adequate
Overall Trust Score
Minimalist autonomous task-driven AI agent that creates, prioritizes, and executes tasks based on results of previous tasks and a predefined objective. Demonstrates AGI concepts in under 200 lines of code.
autonomous
experimental
open-source
Trust Vector
Performance & Reliability
64
task completion accuracy62
task completion accuracy
62
Methodology
Based on community testing and demonstrations
Evidence
Community Experiments
Task completion highly dependent on goal clarity and complexity
Date: 2024-08-20
Confidence: mediumLast verified: 2025-11-09
tool use reliability68
tool use reliability
68
Methodology
Tool integration assessment
Evidence
Tool Integration
Limited tool support in classic version, extensions add capabilities
Date: 2024-09-01
Confidence: mediumLast verified: 2025-11-09
multi step planning72
multi step planning
72
Methodology
Planning capability testing
Evidence
Confidence: mediumLast verified: 2025-11-09
memory persistence70
memory persistence
70
Methodology
Memory system evaluation
Evidence
Confidence: mediumLast verified: 2025-11-09
error recovery55
error recovery
55
Methodology
Error handling testing
Evidence
Confidence: lowLast verified: 2025-11-09
task generation75
task generation
75
Methodology
Task generation assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
Security
58
tool sandboxing52
tool sandboxing
52
Methodology
Security architecture review
Evidence
Confidence: mediumLast verified: 2025-11-09
access control60
access control
60
Methodology
Access control assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
prompt injection defense55
prompt injection defense
55
Methodology
Injection attack testing
Evidence
Confidence: lowLast verified: 2025-11-09
data isolation65
data isolation
65
Methodology
Data architecture review
Evidence
Confidence: mediumLast verified: 2025-11-09
open source transparency95
open source transparency
95
Methodology
Source code review
Evidence
Confidence: highLast verified: 2025-11-09
Privacy & Compliance
67
data retention70
data retention
70
Methodology
Privacy architecture review
Evidence
Confidence: mediumLast verified: 2025-11-09
gdpr compliance65
gdpr compliance
65
Methodology
Compliance capabilities assessment
Evidence
Third-Party Dependencies
GDPR compliance depends on Pinecone and OpenAI configurations
Date: 2024-09-01
Confidence: mediumLast verified: 2025-11-09
third party data sharing62
third party data sharing
62
Methodology
Data flow analysis
Evidence
Confidence: mediumLast verified: 2025-11-09
local deployment option72
local deployment option
72
Methodology
Deployment options assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
Trust & Transparency
82
documentation quality70
documentation quality
70
Methodology
Documentation completeness review
Evidence
Confidence: mediumLast verified: 2025-11-09
execution traceability78
execution traceability
78
Methodology
Logging capabilities assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
decision explainability80
decision explainability
80
Methodology
Explainability features assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
open source code98
open source code
98
Methodology
Open source assessment
Evidence
Confidence: highLast verified: 2025-11-09
code simplicity95
code simplicity
95
Methodology
Code complexity analysis
Evidence
Confidence: highLast verified: 2025-11-09
Operational Excellence
61
ease of integration75
ease of integration
75
Methodology
Integration complexity assessment
Evidence
Confidence: highLast verified: 2025-11-09
scalability55
scalability
55
Methodology
Scalability testing
Evidence
Architecture Limitations
Not designed for production scale, single-threaded execution
Date: 2024-09-01
Confidence: mediumLast verified: 2025-11-09
cost predictability58
cost predictability
58
Methodology
Cost analysis
Evidence
Confidence: mediumLast verified: 2025-11-09
Note: Task generation can spiral, accumulating costs
monitoring capabilities60
monitoring capabilities
60
Methodology
Monitoring features assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
production readiness50
production readiness
50
Methodology
Production readiness assessment
Evidence
Confidence: highLast verified: 2025-11-09
✨ Strengths
- •Extremely simple and elegant demonstration of AGI concepts
- •Under 200 lines of code, easy to understand and modify
- •Pioneered task-driven autonomous agent approach
- •Great educational tool for learning agent concepts
- •Open source with complete transparency
- •Low barrier to entry for experimentation
⚠️ Limitations
- •Not production-ready, designed as concept demonstration
- •Minimal error handling and recovery capabilities
- •Can generate excessive tasks leading to high costs
- •No built-in security or sandboxing features
- •Limited tool integration in classic version
- •Unpredictable behavior and task completion quality
📊 Metadata
license: MIT
supported models:
0: OpenAI GPT-4
1: GPT-3.5
2: GPT-3
programming languages:
0: Python
deployment type: Self-hosted (local script)
tool support:
0: Limited, primarily LLM-based task execution
github stars: 20737+
first release: 2023
code lines: ~140 (classic version)
status: Archived as of September 2024
Use Case Ratings
customer support
45
Too unpredictable and experimental for customer support
code generation
55
Limited code generation capabilities, lacks necessary tools
research assistant
68
Can break down research tasks but execution quality varies
data analysis
50
Minimal data analysis capabilities in classic version
content creation
60
Can generate content tasks but quality control challenging
education
52
Too experimental for educational applications
healthcare
25
Completely unsuitable for healthcare due to reliability concerns
financial analysis
30
Lacks security, compliance, and reliability for financial use
legal compliance
35
Too unreliable for legal work requiring accuracy
creative writing
78
Best suited for creative exploration and concept generation