Agentkeeper solved the Goldfish Memory problem in AI Agents.v1.1 out now
AgentKeeper has introduced a new version of its cognitive continuity infrastructure for AI agents. This update allows agents to maintain their identity, memory, and priorities across various model switches and process restarts. The system addresses cognitive continuity issues rather than just memory retention, ensuring that agents can operate seamlessly regardless of changes in their environment.
- ▪AgentKeeper enables AI agents to survive model switches and restarts while retaining their identity and memory.
- ▪The system includes features like memory expiration for GDPR compliance and structured relations alongside prose memory.
- ▪Users can easily integrate AgentKeeper with various AI models without extensive coding.
Opening excerpt (first ~120 words) tap to expand
AgentKeeper Cognitive continuity infrastructure for long-lived AI agents. Your agent survives model switches, crashes, context-window limits, and restarts — with the same identity, memory, and priorities it had before. Why this exists Agents don't fail because they forget facts. They fail because they lose cognitive continuity — their state, priorities, and identity drift the moment the model changes, the context window fills, or the process restarts. AgentKeeper treats this as a systems problem, not a memory problem. Install pip install agentkeeper-ai Zero required dependencies. No external infrastructure. Storage defaults to local SQLite.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.