I've started building Lore because none of the existing "memory" solutions solved my real problem: my agent stopped every 5-10 minutes to "compact" and forget every important thing we covered before these compaction stages.
I hate repeating myself and I was a very strong AI-skeptic so I almost gave up until I came by Mastra's Observational Memory post. Soon after I saw Sanity's Nuum and I knew I had to try porting this to OpenCode.
Lore is the evolved version of this: it is harness-agnostic, works with OpenAI and Anthropic backends and I added Vertex and Bedrock support just recently (hopefully works? :D).
Would love to hear your thoughts about the memory craze of now and using solely MCP-based solutions while ignoring the context management in the active sessions and how we ended up accepting this primitive and terrible solution as our daily driver :D
I've started building Lore because none of the existing "memory" solutions solved my real problem: my agent stopped every 5-10 minutes to "compact" and forget every important thing we covered before these compaction stages.
I hate repeating myself and I was a very strong AI-skeptic so I almost gave up until I came by Mastra's Observational Memory post. Soon after I saw Sanity's Nuum and I knew I had to try porting this to OpenCode.
Lore is the evolved version of this: it is harness-agnostic, works with OpenAI and Anthropic backends and I added Vertex and Bedrock support just recently (hopefully works? :D).
Would love to hear your thoughts about the memory craze of now and using solely MCP-based solutions while ignoring the context management in the active sessions and how we ended up accepting this primitive and terrible solution as our daily driver :D
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