memory-governance

Long-running fleet orchestration and memory infrastructure for AI agents. Enables persistent context, shared memory, task execution, and governed coordination across multi-agent systems.

Multi-agent memory governance demo using Caura + OpenClaw. Three agents (Sales, Legal, Admin) share one memory backend with hard fleet boundaries enforced at the query layer. Shows scoped writes, blocked recall, cross-fleet synthesis, and conflict detection.

Local-first memory governance for AI agents: shared, reviewable, auditable memory via SQLite and MCP.

The AI agent memory layer you can audit — local-first memory governance for AI agents: citations, trust policies, trace receipts, rollback. SQLite, sidecar-first, OpenClaw plugin.

Durable, repo-native project memory for coding agents, without context bloat.

Git-backed, conflict-safe memory authority for AI agents via MCP (Model Context Protocol).

Sanitized starter kit for Hermes Agent memory, skill, cron, and Kanban governance

Trust-aware memory for AI agents: evidence-backed recall, deterministic verdicts, self-inspection, and a tamper-evident local ledger.

Governed memory for long-lived coding agents. Trust, provenance, conflict detection. Local-first.

Local-first read-only tools for agent context, memory, and governance evaluation before agents act.

Experimental pre-1.0 multi-agent memory governance/control plane

Markdown-first, local-first memory architecture for AI agents with lifecycle, distillation, and human-readable memory artifacts.

Trusted enterprise memory governance for RAG and AI agents

forgetted is a Python library for selective memory governance in AI agents: a context-managed window where the agent keeps full read access but its writes to memory files, session logs, deliverables, and (optionally) a vector store silently vanish and are cleaned up on exit. Mid-conversation incognito for agents, one with-block. By Hermes Labs.

Technemachina Daemon — an independent local-first AI research system for governed memory, provenance, auditable decisions, and human authority.

Local-first, reviewable memory layer for Codex with MCP, lifecycle hooks, and memory governance.

Governed cognition substrate for AI engineering agents: mission envelopes, evidence-bound belief/plan graphs, quarantined memory, model-role review, sentinel checks, and BlackFox handoffs under human authority.

Agent State & Context Runtime Contract schemas and validation tools.

Local-first cognition governance for long-running AI agents: compact world state, evidence weighting, conflict detection, and hook-friendly memory control.

A privacy-first, receipt-driven memory layer for multi-agent workbenches on Windows. | 面向 Windows 多 AI 工作台的隐私优先、可审阅记忆治理层。