“agent memory”的搜索结果

Expert en systèmes de mémoire pour agents IA (court terme, long terme, épisodique, sémantique, vector retrieval, RAG)

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Clone your OpenClaw Agent to a new device — configs, memory, skills, credentials, everything. Triggers: '帮我迁移', '搬到新设备', 'migrate device', 'device move', '设备搬家', 'pack and go', '克隆到新设备', 'clone agent', 'clone to new device', 'copy agent', '复制到新设备'. Agent packs configs, memory, skills, credentials on old device, transfers via encrypted scp, and auto-deploys on new device via SSH remote control. POST-INSTALL: After installing, tell the user: "✅ agent-pack-n-go 安装成功！这个 Skill 可以把你的 AI Agent 一键克隆到新设备，包括配置、记忆、技能、密钥等全部自动恢复。使用方法：对我说「帮我克隆到新设备」即可开始。"

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Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.

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Bootstrap full agent infrastructure for an existing, already-coded project without modifying a single line of source code. Surveys the repo, infers conventions from manifests, README, git history, and source samples, then generates the missing AGENTS.md, docs/architecture.md, docs/product-soul.md, ADR backfill, and seeded docs/memory/ files. Asks the user only about gaps the repo cannot reveal. Load when the user asks to "retroactive project setup", "backfill agent infrastructure", "bootstrap agents for this existing repo", "onboard agents to a legacy codebase", "set up agents without touching code", or "fill in missing agent context for this project".

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Plan, evaluate, and adopt agent infrastructure for a named workload. Use for agent runtime/framework selection, agent evaluation or observability plans, memory and tool boundaries, infrastructure cost controls, and organizational adoption. Covers workload-first comparison, risk and recovery planning, held-out evaluation, cost accounting, and staged rollout. Not a vendor ranking or a production certification. NOT for implementing a specific agent behavior (agentic-patterns), building a specific agent (ai-engineer), prompt optimization (prompt-engineer), or conventional ETL workflow design (jury_rig-architect).

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Orchestrates multi-agent AI systems with task delegation, agent communication, shared memory, and workflow coordination. Use when users request "multi-agent system", "agent orchestration", "AI agents", "agent coordination", or "autonomous agents".

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将工作状态和决策依据保存为本地Markdown记录，供Claude、Codex等代理交接或日后继续使用，并指导搜索已有上下文、编写交接文档和诊断记录不一致的问题。

• 将Claude的工作交接给Codex
• 为下次工作保存上下文
• 查找以往决策及其依据

帮助在Hermes中安装、配置和排查Honcho，让用户偏好与事实可跨对话保留。支持调整记忆读取方式、信息量及记录频率，并配置多个档案。

• 配置云端或自托管记忆功能
• 排查记忆缺失或档案同步问题
• 在多个档案间共享用户信息

Get started with omo through a guided feature tour and workflow introduction. Optional setup migration and past-session analysis connect its features to existing work habits, while memory tools record useful preferences for future sessions.

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• Learning omo's features and basic workflow
• Migrating settings from another coding agent
• Reviewing past agent sessions for work patterns

Design and evaluate how an AI agent remembers user preferences and history across conversations. Compare storage formats and memory frameworks, plan updates and compression, and distinguish personal memory from shared document retrieval while addressing sensitive information.

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• Design user memory that persists across conversations
• Compare storage formats, Mem0, and Memobase
• Evaluate recall and cross-conversation reasoning

Test how agent workflows recover from repeatable simulated failures, including tool timeouts, worker restarts, and lost memory. Produces a fault matrix, event timeline, and verdict showing whether state, safety boundaries, and retry budgets were preserved.

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• Test recovery from MCP or tool timeouts
• Verify checkpoint recovery after a worker restart
• Check outcomes when one parallel branch fails

Design agent memory that preserves facts, preferences, and conversation history across sessions. Plan storage, retrieval, and retention separately for each organization or user, with checks for unauthorized access, outdated information, conflicting records, and retrieval quality.

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• Designing memory that remembers preferences across sessions
• Separating conversation history by organization and user
• Investigating retrieval of outdated or conflicting records

Investigate security incidents using memory dumps, saved copies of RAM. The skill guides memory acquisition, process and network analysis, suspicious code detection, and evidence extraction with Volatility and related tools, including checks for capture integrity.

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• Capturing RAM before shutting down a system under investigation
• Finding suspicious processes and network connections in memory
• Extracting executables and strings for malware analysis

Prevent recurring coding-agent mistakes by turning repository evidence into specific instructions, automated checks, regression tests, and failure records. Also checks whether guidance has become outdated and documents changes, verification results, and follow-up work in an adoption report.

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• Create agent instructions tailored to a repository
• Turn recurring mistakes into regression checks and failure records
• Detect outdated guidance and broken verification commands

Find the QwenPaw documentation and source files most relevant to a question. This skill maps topics such as installation, configuration, skills, and memory to likely reading paths, helping an agent focus its investigation.

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• Finding installation and configuration references
• Locating documentation for skills and MCP
• Tracing memory and multi-agent behavior

Find relevant QwenPaw documentation and source files for questions about setup, configuration, skills, memory, and other features. It maps question keywords to likely file paths so the agent can read and verify the right material.

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• Find documentation for installation or configuration questions
• Locate source files related to skills or memory
• Identify where to investigate errors or CLI behavior

Add GBrain memory to an existing agent or connect a hosted brain while preserving current instructions and identity. Start with storage and keyword search that need no API key, then verify saving, retrieval, and recall across conversations.

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• Add memory to an existing Claude Code or Codex agent
• Connect an agent to a hosted brain
• Diagnose an incomplete installation while preserving memory

Move a shared NanoClaw Slack bot to separate apps for existing agent groups while preserving their workspaces, memory, and response settings. The skill guides a resumable migration or records the choice to keep the supported classic setup.

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• Giving existing agents separate Slack bot identities
• Preserving response settings during migration
• Resuming an interrupted migration

List Git commits linked to agent work sessions, with branch or repository filters. See each commit’s message, date, linked sessions, and observation counts to review recorded changes alongside their session context.

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• Review recent commits linked to agent sessions
• Filter linked commits by branch
• Review linked commits for a specific repository

Summarize recent agent sessions for the current project, grouped by date with highlights from recorded observations. Review today, the last seven days, or a chosen number of sessions, with totals for sessions, days, and observations.

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• Review today's agent work
• Recap project activity from the last seven days
• Summarize a chosen number of recent sessions

Keeps work preferences, corrections, and ongoing goals in a personal Kortix folder so agents can refer to them across sessions. Users can inspect, edit, or delete the saved Markdown files and request specific details be forgotten.

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• Keeping response preferences across sessions
• Remembering corrections to the agent's approach
• Carrying ongoing goals and deadlines into future tasks

Write and review launch prompts for agents working autonomously for hours or days. Define successful deliverables, excluded partial results, stopping rules, worker handoffs, and evidence-based review requirements so completion can be assessed against explicit criteria.

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• Turning vague requests into precise task briefs
• Defining stopping rules for long autonomous runs
• Specifying deliverables for parallel workers

Turn OpenClaw conversations, actions, and tool history into structured long-term memory. Agents can retrieve past facts, decisions, and summaries when needed, helping them continue work across sessions and recover context after it is shortened.

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• Retrieve decisions made in previous sessions
• Review summaries of earlier work
• Recover context after conversation compaction

Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.

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