Millions spend hours with AI characters. But characters forget, creators don't earn, and the best experiences sit behind paywalls. We raised $1.5M to build the character economy with @lattice_fund, @cbventures' Base Ecosystem Fund, @JME_Ventures; bootstrapped by @worldcoinfnd.

Announcing the Honcho CLI 🎉 Manage peers, sessions, and memory for your agents straight from the terminal 👇 Install with: uv tool install honcho-cli

On memory harnesses for agents: 1. I remain unconvinced that the best “single-agent” memory harnesses will be external to the foundation model providers. Genuinely believe that both claude and codex will iterate heavily on this. Memory gives personality and is therefore too strategically important to out-source. The memory systems that survive will have to offer more than a single-agent solution. 2. As it stands today - the best memory harnesses are the ones with the best models doing inference. Virtually all “good” memory harnesses are doing a combination of retrieval + inference on retrieved facts to answer queries. A lot of the performance in the benchmarking of “good” memory systems is a direct result of the inference. Obviously the prompts and workflow matters too; but it is undoubtedly the case that much of the "problems" of semantic reasoning is being solved by the inference of a smart model. This paradigm is essentially “memory as an agent” and derives performance and resolves contradictions from the underlying intelligence of the model doing the inference. Suffice to say, you want the smartest model doing this. 3. Virtually all memory systems have settled on “consolidation” or “dreaming” as a core mechanism. It’s basically a process to correct contradictions in your facts and consolidate memory into hierarchical layers. -- If you're looking for a memory harness to implement and don't want to wait around for codex/claude to "solve memory", I've really come to like what Honcho is doing and you should check them out. They have an open-sourced repository that lets you plug and play any model for inference. It should go far enough for 99% of use-cases, but if you really want a hands-off solution... Their managed solution comes with a specialized, fine-tuned model. I think it gives them a huge edge because - specialized models beat generalized models at specific tasks and inference on facts to answer queries accurately and to be helpful is such an example of a specialized task.