“persistent memory”的搜索结果

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

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Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

中文简介正在准备中。当前显示原文说明。

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

中文简介正在准备中。当前显示原文说明。

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

中文简介正在准备中。当前显示原文说明。

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

中文简介正在准备中。当前显示原文说明。

Build, update, and query a persistent project knowledge graph from skills, memory, docs, and code structure — stdlib Python only, no external tools. Dual-mode: skill-library (agent-loom) or application (any consumer repo). Load when the user asks for a knowledge graph, project map, skill relationships, query the graph, update the graph, or trace how components connect. Auto-runs on memory-handoff and project-setup bootstrap. Also triggers on "build the graph", "what connects to X", "map this project".

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A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

中文简介正在准备中。当前显示原文说明。

Design bounded applications and workflows for an always-on AI agent with episodic memory. Covers product ideas, persistence-dependent workflows, and constructed examples that make delegation, interruption, evidence, and opt-out explicit. Activate on: "what can an always-on agent do", "persistent agent use cases", "agent applications", "proactive agent ideas", "what to build with episodic memory", "always-on agent product", "personal AI assistant ideas", "/always-on-agent-applications". NOT for: building the architecture (use always-on-agent-architecture), designing inputs (use always-on-agent-inputs), safety and privacy (use always-on-agent-safety).

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A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

中文简介正在准备中。当前显示原文说明。

Tracks complex, multi-session work using the Beads issue tracker and dependency graphs, and provides persistent memory that survives conversation compaction. Use when work spans multiple sessions, has complex dependencies, or needs persistent context across compaction cycles. Trigger with phrases like "create task for", "what's ready to work on", "show task", "track this work", "what's blocking", or "update status".

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Persistent AI companion with long-term memory, project context awareness, and multi-session continuity across any LLM.

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A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

中文简介正在准备中。当前显示原文说明。

按项目保存Claude Code中的工作进度、决策和后续步骤，在其他会话中读取摘要，接着上次中断的位置继续工作，也可在会话开始时自动加载。

• 结束会话前保存进度和后续步骤
• 接着上次会话继续工作
• 查看多个项目的状态

分析 Git 变更历史中的开发成果、工作时段、测试代码比例和频繁变更的文件，汇总个人与团队的改进方向，保存指标并比较不同时期的趋势。

• 汇总每周开发成果
• 回顾个人贡献与改进方向
• 查看测试比例和频繁变更的文件

Set up cognee and run a first Python example that stores information and retrieves it with a question. Covers virtual environments, API key configuration, optional packages, and basic troubleshooting.

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• Installing cognee in a new Python environment
• Trying a minimal example that stores and retrieves information
• Separating stored information by project

Build a workflow for importing large collections of audio, email, documents, or chats into knowledge pages. Test varied samples first, track progress in a persistent JSON file, and verify outputs before marking work complete.

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• Importing a large email export into knowledge pages
• Converting document collections into linked pages
• Resuming imports that span multiple sessions

Manage shared project tasks in Beads, from finding available work and claiming tasks to tracking dependencies, recording follow-up work, and closing completed items. Keep project status available across sessions and handoffs.

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• Find tasks that are ready to start
• Claim tasks and record completion
• Track blockers and dependencies

Diagnoses storage failures in GKE, Google's managed Kubernetes service, including stuck Pods, disk attachment errors, storage pressure, and missing volume expansion. It provides diagnostic commands and reviewable configuration patches for deployment through GitOps.

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• Diagnose Pods stuck in ContainerCreating
• Investigate slow disks and node storage pressure
• Find why expanded storage is missing inside a container

Guide an AI agent through ClawWork, an economic survival simulation where task rewards must cover API token costs. Defines a daily choice between work and learning, artifact submission, knowledge storage, and priorities based on the remaining balance.

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• Choose work or learning based on the remaining balance
• Create and submit task artifacts for evaluation
• Save research findings to persistent memory

Audit Claude, Codex, or Pi configuration, instruction conflicts, hooks, and MCP connections to external tools. It also checks safeguards for maintaining AI-assisted projects and returns prioritized findings with evidence and concrete corrective or diagnostic steps.

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• Investigate agent settings or instructions that seem ineffective
• Audit hooks, MCP connections, and permission boundaries
• Check verification gaps and stale project guidance

Keeps multi-step work organized with Markdown files for the plan, research notes, and final deliverable. Tracks completed phases, decisions, and errors so the agent can revisit goals and resume work with durable records.

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• Tracking progress through a multi-step task
• Keeping research findings and sources on disk
• Resuming work after a pause

Coordinate specialist agents in Claude Flow under a lead agent. Distribute development or research tasks, use voting to make decisions, preserve shared knowledge, and pause or resume work through managed sessions.

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• Splitting application development among specialist agents
• Comparing technologies through research and voting
• Coordinating code reviews across specialties

Use DeepAPI to search the web, collect website and social platform data, and produce sourced research. It also routes tasks to services for public website actions, email, image generation, and persistent agent memory.

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• Collecting information from websites and social platforms
• Researching a question with multiple cited sources
• Navigating and interacting with public websites

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

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