'memory management' 검색 결과

Maintain the harness memory files (USER.md, MEMORY.md, AGENTS.md) so that long-term knowledge persists across conversations.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Expert guide for long-term episodic memory integration (Mem0 v2, Letta/MemGPT, Zep v2), memory tier architecture, pgvector HNSW storage, and unified context management for autonomous AI agents / Panduan ahli untuk integrasi memori episodik jangka panjang (Mem0 v2, Letta/MemGPT, Zep v2), arsitektur tier memori, penyimpanan pgvector HNSW, dan manajemen konteks terpadu untuk agen AI otonom.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Reflective Memory. Use when working with memory, reflection, context management, or intent tracking. Provides the reflective memory practice — how and when to use keep tools for capturing insights, tracking commitments, and maintaining awareness.

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Design AI memory systems with context management. TRIGGERS - Use when user needs help with ai-memory-system related tasks.

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Complete zero-dependency memory system for AI agents — file-based architecture, daily notes, long-term curation, context management, heartbeat integration, and memory hygiene. No APIs, no databases, no external tools. Works with any agent framework.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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F Agent Memory: A Git-Native Persistent Memory Solution for AI Coding Agents and Project Knowledge Management OKF Agent Memory introduces a standardiz

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Use when the user asks to "remember project context", review saved findings, initialize runtime memory, archive stale work, reconcile notes, or erase a subject; manages authorized HOT/WARM/COLD working memory across all disciplines while preserving registry event ownership and privacy controls. Not for changing canonical registry facts - route those through the owning registry. 项目记忆/跨会话

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Memory management and FreeRTOS context for the Megahub ESP32 project. Use when dealing with heap allocation, stack size, task creation, inter-task communication, semaphores, queues, or any ESP32 memory constraints.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

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Design AI memory systems with context management. TRIGGERS - Use when user needs help with ai-memory-system related tasks.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Automate checks, code formatting, agent coordination, and session records around Claude Code operations. Uses hooks, actions triggered before or after work, to preserve context and connect development tasks with shared memory.

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• Automatically format and validate edited code
• Share progress across multiple agents
• Save and restore development sessions

Automate checks, code formatting, agent coordination, and session records around Claude Code operations. Uses hooks, actions triggered before or after work, to preserve context and connect development tasks with shared memory.

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• Automatically format and validate edited code
• Share progress across multiple agents
• Save and restore development sessions

Analyze how ransomware encrypts files and manages keys to assess whether decryption is feasible. Examine implementation weaknesses, check existing decryptors, and document recovery options with technical reasons for the assessment.

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• Assessing whether ransomware-encrypted files can be decrypted
• Examining encryption routines and key management
• Investigating key recovery from memory dumps

Record founder-approved decisions with chosen options, success measures, stop conditions, and dissent. Keep approved decisions separate from discussion transcripts so future advisory sessions use confirmed choices, with a review scheduled after 90 days.

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• Recording an approved management decision
• Preserving dissent and stop conditions
• Scheduling a decision review after 90 days

Review Doris code changes using project-specific checklists for data correctness, concurrency, memory safety, compatibility, performance, and tests. Trace related code paths and explain findings with concrete failure scenarios, using query plan trees when reviewing optimizer behavior.

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• Review newly completed Doris code changes
• Check concurrency and memory management in related code paths
• Assess compatibility and test coverage before merging

Help QM organization admins inspect settings, stored memory, conversations, files, and operational records across the organization. It also supports confirmed configuration changes and external user management, while keeping admin grant changes in the dashboard.

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• Check whether the requester is an organization admin
• Inspect or update organization and channel settings
• Investigate agent behavior using transcripts and captured prompts

Plan and configure command and control (C2) systems for red team exercises. Covers major frameworks, communication methods, relay servers, certificate management, and the network or endpoint signals defenders use to detect these systems.

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• Compare C2 frameworks for a red team exercise
• Plan communication methods and relay infrastructure
• Review C2 detection signals from a defender's perspective

Compare batching, request scheduling, and memory management for a single model serving instance. Assess concurrent request capacity, response times, generation speed, and GPU time per result that meets the required quality and deadline.

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• Comparing batching and request scheduling options
• Checking memory capacity for concurrent requests
• Assessing cache reuse and offloading tradeoffs