Comparing TinyTroupes with similar approaches

LLM-based agents and multiagent systems are becoming popular ways of structuring applications. Understandably, current multiagent programming libraries and frameworks focus on problem-solving (e.g., programming) and assistive AI (e.g., chatbots or copilots). TinyTroupe, however, is a multiagent simulation library that focuses on simulation and experimentation, to support imagination enhancement and business insights. This brings unique benefits and requirements, which do not come naturally to existing problem-solving/assistive approaches. Below we compare (rather roughly) TinyTroupe with other LLM-based multiagent tools along some important dimensions:

Aspect	TinyTroupe	Autogen	Crew AI
Programmatic	✔️	✔️	✔️
Agent persona	✔️ Highly detailed	✔️ Short system message	✔️ Short background story
Composable cognitive capabilities	✔️ mental faculties via TinyMentalFaculty	❌	❌
Fine-grained cognitive control	✔️ TinyAgent methods like .think(), .internalize_goal(), etc.	❌	❌
Agent population sampling	✔️ TinyPersonFactory	❌	❌
Agent validation	✔️ TinyPersonValidator	❌	❌
Environments	✔️ TinyWorld	✔️ Teams	✔️ Crews
Message or interaction protocols	❌	✔️	✔️ Processes
Explicit tasks	❌	✔️	❌
Agent persistence	✔️ JSON specifications	❌	✔️ JSON specifications; pickle
Simulated tools	✔️ e.g., TinyWordProcessor	❌	❌
Real-world tools	❌ (not yet)	✔️	✔️
Code execution	❌	✔️	✔️
Simulated time	✔️ (e.g., granularity of minutes, hours, days, etc.)	❌	❌
Simulation steering	✔️ story-telling via TinyStory	❌	❌
Human input	✔️	✔️	✔️
Grounding data support	✔️	✔️	✔️
Structured information extraction	✔️ ResultsExtractor and ResultsReducer	❌	❌
Synthtetic data exports (e.g., Word files)	✔️ ArtifactExporter	❌	✔️ Pydantic models
Content enrichment	✔️ TinyEnricher	❌	❌
Simulation-optimized caching	✔️	❌	❌

As the field matures and the boundaries between simulation and problem-solving/assistive AI blur, it is possible -- maybe probable -- that these two overall stances will converge over time (e.g., via common higher-level abstractions). We hope to both learn from others and contribute original elements to this convergence.

Note: The above is a rough and incomplete comparison. It might also contain inaccuracies due to both our own ignorance and the fast-paced evolution of the area. We'll update, complete and correct it if needed over time.