CogniCore started from a simple frustration: AI agents kept repeating the same mistakes. Most frameworks focus on making models smarter, adding more agents, or building more complex workflows. While experimenting with autonomous agents, I found that the bigger problem was often memory. Agents would fail, forget why they failed, and repeat the exact same strategy again. That led to a different idea: what if memory lived in the runtime instead of the agent? I started building CogniCore as an open-source runtime with persistent memory, replay, reflection, and failure-aware retrieval. Along the way, some of the results surprised me. In our benchmarks, memory improved performance significantly, while adding reviewer agents often increased token usage and reduced solve rates. The project has grown through community feedback, open-source contributions, and thousands of downloads. Most recently, we've added MCP support so tools like Claude Desktop and Cursor can access memory and replay capabilities directly. We're still early, but the goal remains the same: help AI agents learn from experience and stop making the same mistake twice. I'd love feedback from builders working on agents, memory systems, and AI infrastructure.

Cognicore

The memory layer that makes Claude never forget you

The memory layer that makes Claude never forget you