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.
2 outlets in our directory ran this story, first to last over 6 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ Agent Memory Is a Cache Coherence Problem — r/programming
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,433 of its stories.
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/Thinklanceai/agentkeeper |
| Publication time | Fri, 29 May 2026 09:06:28 +0000 |
| Retrieval time | 2026-05-29T09:09:59.774Z |
| Last seen | 2026-05-29T09:09:59.774Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | _YPZ3JAu777D · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.