Laplace (ラプラス Rapurasu) is the Wizard partner of Solo in Mega Man Star Force 3. According to the game Laplace is the last EM Being from Le Mu, awakened after the continent Mu fell in to the ocean. He is unregistered under Project TC, while Solo is registered as No. 002.
Because Solo can already EM Wave Change by himself, he doesn't perform Wave Change with Laplace; instead, Laplace transforms into his sword, which he can use in both human and Wave Changed forms. This replaces his sword from the previous version, which he pulled from his left arm.
Very little is revealed about Laplace, other than his origins as the last EM Being from Mu. When Geo Stelar and his friends first see Solo in the third game, Laplace is already his partner. This lasts throughout the game, where Laplace is inseparable from Solo/Rogue, and where he is always pulled out at the beginning of a battle.
Just before Mega Man enters the Black Hole Server, he is challenged by Rogue to a fight to decide who will battle Sirius. It is revealed then, by Mega Man, that while Solo rejects all bonds with humans, he feels a deep connection to his own culture and Laplace along with it. This is why Solo has formed such a close partnership despite his normal attitude towards companions, and proves that he sees Laplace as much more than just a tool and that Laplace shares Solo's pain.
- In Mega Man Star Force 3, if the player equips Humor Word, they will learn that he enjoys deleting Solo's save files from the game Burger Quest as a prank.
- Laplace never speaks once in the game. However, Solo seems to able to understand the static-like sounds Laplace makes, and judging by Solo's responses, it seems Laplace is something of a smart aleck towards his master and at one point even appeared to be teasing him. Solo's acceptance of this behavior from Laplace, but not from others, would seem to further prove his bonds to his ancestors from Mu, but not to people of the modern world.
- Laplace has four Battle Cards in the game: SpinSword 1, 2, 3, and X, randomly obtained through noise data.