TypedMemory – long-term memory and reflection for AI agents
TypedMemory is a system designed to provide AI agents with long-term, context-aware memory that evolves over time. It enables agents to remember information, recall relevant context, and reflect on contradictions or changes in decisions. By maintaining an auditable, non-destructive history of memory changes, it helps prevent hallucinations and improves agent reliability.
- ▪TypedMemory detects contradictions in AI agent memories instead of silently overwriting them.
- ▪It tracks memory changes with audit trails, allowing users to review decision history via the 'typedmem history' command.
- ▪The system supports goal resolution, memory deduplication, and summarization while preserving original data links.
- ▪Memory evolution includes policies for handling conflicts, reinforcing facts from multiple sources, and managing preference drift.
- ▪Agents using TypedMemory can maintain persistent, structured knowledge across sessions with improved debugging capabilities.
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TypedMemory Long-term memory and reflection for AI agents. Persistent, evolving, context-aware — improves agent behavior over time. 📦 PyPI · 📚 Docs · 🏷️ Releases · 📝 Changelog TL;DR TypedMemory gives AI agents long-term memory. remember new information recall relevant context reflect and improve over time The problem AI agents start believing their own hallucinations. They: contradict themselves silently — the last write wins, the conflict disappears overwrite past decisions with no audit trail — you can't debug what you can't see never resolve goals — yesterday's "I'll do X" looks identical to today's "I did X" TypedMemory makes that visible.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.