agentica

Max Spero @max_spero_ · 15m Interesting harness. Seems like the key improvement here is letting the LLM burn more test-time-compute without overfilling context with bad attempts [Embedded card, "Agentica and ARC-AGI":] Agentica is a framework for building agents with a persistent Python REPL. It provides a stateful workspace where agents can run code, keep objects in memory, and call user-defined Python objects and tools via code. This persistent REPL allows agents to: • Use tools in a more complex manner than schema-based tool calls allow • Keep and mutate objects in memory • Interleave reasoning and execution in one persistent workspace In practice, this improves performance on long-context tasks. As is standard, types are enforced in the REPL, avoiding compounding errors throughout prolonged reasoning. User-defined objects are virtualized in the agent's REPL and, from the agent's perspective, they are native Python objects that it has access to. This also allows agents to return objects to the user or pass objects into the REPL of another agent. Consequently, the framework supports building both code mode agents and RLMs [2].