[{"data":1,"prerenderedAt":434},["ShallowReactive",2],{"blog-tag-hu-context-engineering":3},[4,83,132,214,314,382],{"slug":5,"published":6,"minutes":7,"category":8,"tags":9,"tagPages":15,"keywords":17,"about":29,"sources":39,"cover":73,"og":74,"expertise":75,"locales":76,"lang":79,"title":80,"description":81,"coverAlt":82},"context-rot-compaction-long-running-agents","2026-10-16",10,"agents",[10,11,12,13,14],"Context rot","Compaction","Context engineering","Long-running agents","Agent memory",[16],"context-engineering",[18,19,20,21,22,23,24,25,26,27,28],"kontextusromlás","llm kontextusromlás","tömörítés ai ügynökök","kontexttervezés","hosszan futó ügynökök kontextablak","claude compaction api","kontextus szerkesztés eszközeredmények törlése","openai responses compaction","lost in the middle llm","ügynök kontextuskezelés","alügynökök kontextus izolálása",[30,33,36],{"name":31,"url":32},"Context window","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLarge_language_model#Context_window",{"name":34,"url":35},"Attention (machine learning)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAttention_(machine_learning)",{"name":37,"url":38},"Intelligent agent","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FIntelligent_agent",[40,43,46,49,52,55,58,61,64,67,70],{"title":41,"url":42},"Chroma: Context Rot: How Increasing Input Tokens Impacts LLM Performance (Jul 2025)","https:\u002F\u002Fresearch.trychroma.com\u002Fcontext-rot",{"title":44,"url":45},"Liu et al.: Lost in the Middle: How Language Models Use Long Contexts (2023)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.03172",{"title":47,"url":48},"Modarressi et al.: NoLiMa: Long-Context Evaluation Beyond Literal Matching (2025)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.05167",{"title":50,"url":51},"Anthropic: Effective context engineering for AI agents (Sep 2025)","https:\u002F\u002Fwww.anthropic.com\u002Fengineering\u002Feffective-context-engineering-for-ai-agents",{"title":53,"url":54},"Anthropic: Effective harnesses for long-running agents (Nov 2025)","https:\u002F\u002Fwww.anthropic.com\u002Fengineering\u002Feffective-harnesses-for-long-running-agents",{"title":56,"url":57},"Claude API docs: Compaction at a token threshold","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fbuild-with-claude\u002Fcompaction-threshold",{"title":59,"url":60},"Claude API docs: Compaction on demand","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fbuild-with-claude\u002Fcompaction-on-demand",{"title":62,"url":63},"Claude API docs: Context editing","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fbuild-with-claude\u002Fcontext-editing",{"title":65,"url":66},"Claude API docs: Memory tool","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fagents-and-tools\u002Ftool-use\u002Fmemory-tool",{"title":68,"url":69},"OpenAI API docs: Compaction","https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Fcompaction",{"title":71,"url":72},"Cognition: Don't Build Multi-Agents","https:\u002F\u002Fcognition.ai\u002Fblog\u002Fdont-build-multi-agents","\u002Fimages\u002Fblog\u002Fcontext-rot-compaction-long-running-agents\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fcontext-rot-compaction-long-running-agents\u002Fog.jpg","ai-engineer",[77,78,79],"en","de","hu","Kontextusromlás hosszan futó ügynököknél: törlés, tömörítés, memória és al-ügynökök","Miért romlik az LLM pontossága a kontextus növekedésével, és hogyan tartja pályán a hosszú ügynökfutást a törlés, a tömörítés, a jegyzet és az al-ügynök.","Kontextusromlás hosszan futó ügynököknél: papírkupac alatt ül egy robot, a prés egyetlen lappá préseli.",{"slug":84,"published":85,"minutes":86,"category":87,"tags":88,"tagPages":92,"keywords":93,"about":103,"sources":107,"cover":126,"og":127,"expertise":75,"locales":128,"lang":79,"title":129,"description":130,"coverAlt":131},"zep","2026-10-06",11,"rag",[14,89,90,12,91],"Knowledge graph","Temporal graph","RAG",[16,87],[94,95,96,97,98,99,100,101,102],"zep ai","zep teszt","zep ügynök memória","graphiti tudásgráf","zep árak","zep vagy mem0","ügynök memória hosszú távon","időbeli tudásgráf","zep cloud",[104],{"name":105,"url":106},"Zep","https:\u002F\u002Fwww.getzep.com\u002F",[108,111,114,117,120,123],{"title":109,"url":110},"Zep pricing: plans, credits and limits","https:\u002F\u002Fwww.getzep.com\u002Fpricing",{"title":112,"url":113},"Zep documentation","https:\u002F\u002Fhelp.getzep.com\u002F",{"title":115,"url":116},"Graphiti on GitHub","https:\u002F\u002Fgithub.com\u002Fgetzep\u002Fgraphiti",{"title":118,"url":119},"Graphiti product page","https:\u002F\u002Fwww.getzep.com\u002Fplatform\u002Fgraphiti\u002F",{"title":121,"url":122},"Announcing a new direction for Zep's open-source strategy","https:\u002F\u002Fwww.getzep.com\u002Fblog\u002Fannouncing-a-new-direction-for-zeps-open-source-strategy\u002F",{"title":124,"url":125},"Graphiti: temporal knowledge graphs for AI agents (arXiv)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2501.13956","\u002Fimages\u002Fblog\u002Fzep\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fzep\u002Fog.jpg",[77,78,79],"Zep ismertető: ügynök-memory temporális gráfon","A Zep hostolt memória-API temporális tudásgráffal: kreditek íráskor, lekérdezés ingyen, Flex 125 dollártól havonta, a Graphiti pedig önhosztolható.","Zep ismertető: robot egy falon lévő, összekötött kártyákból álló ábrát néz, mindegyiken kis óra.",{"slug":133,"published":134,"updated":135,"minutes":136,"category":8,"tags":137,"tagPages":141,"keywords":143,"about":154,"sources":162,"cover":208,"og":209,"expertise":75,"locales":210,"lang":79,"title":211,"description":212,"coverAlt":213},"ai-agent-memory-design","2026-09-10","2026-10-02",13,[138,12,139,140],"AI agent memory","Memory poisoning","GDPR",[16,142],"gdpr",[144,145,146,147,148,149,150,151,152,153],"ai ügynök memória tervezés","hosszú távú memória ai ügynökökhöz","epizodikus szemantikus procedurális memória llm","ügynök memória architektúra","chatgpt memória vs claude memória","claude memory tool","ai memória mérgezés","llm kontextus tömörítés","ai ügynök memória gdpr","rövid távú vs hosszú távú memória ügynökök",[155,156,159],{"name":37,"url":38},{"name":157,"url":158},"General Data Protection Regulation","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGeneral_Data_Protection_Regulation",{"name":160,"url":161},"Prompt injection","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FPrompt_injection",[163,165,167,169,172,175,178,181,184,187,190,193,196,199,202,205],{"title":164,"url":66},"Anthropic docs: Memory tool",{"title":166,"url":63},"Anthropic docs: Context editing",{"title":168,"url":51},"Anthropic Engineering: Effective context engineering for AI agents",{"title":170,"url":171},"Sumers et al.: Cognitive Architectures for Language Agents (CoALA)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.02427",{"title":173,"url":174},"Packer et al.: MemGPT, Towards LLMs as Operating Systems","https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.08560",{"title":176,"url":177},"Park et al.: Generative Agents, Interactive Simulacra of Human Behavior","https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.03442",{"title":179,"url":180},"Unit 42: When AI Remembers Too Much, persistent behaviors in agents memory","https:\u002F\u002Funit42.paloaltonetworks.com\u002Findirect-prompt-injection-poisons-ai-longterm-memory\u002F",{"title":182,"url":183},"From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents (preprint)","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2606.04329v1",{"title":185,"url":186},"The Hacker News: ChatGPT macOS flaw could have enabled long-term spyware via memory function","https:\u002F\u002Fthehackernews.com\u002F2024\u002F09\u002Fchatgpt-macos-flaw-couldve-enabled-long.html",{"title":188,"url":189},"Vectorize: OWASP ASI06, Memory and Context Poisoning explained","https:\u002F\u002Fvectorize.io\u002Farticles\u002Fowasp-asi06",{"title":191,"url":192},"Claude Help Center: Use chat search and memory to build on previous context","https:\u002F\u002Fsupport.claude.com\u002Fen\u002Farticles\u002F11817273-use-claude-s-chat-search-and-memory-to-build-on-previous-context",{"title":194,"url":195},"OpenAI Help Center: Memory in ChatGPT","https:\u002F\u002Fhelp.openai.com\u002Fen\u002Farticles\u002F8590148-memory-faq",{"title":197,"url":198},"OpenAI Help Center: Dots privacy, security, and safety FAQs","https:\u002F\u002Fhelp.openai.com\u002Fen\u002Farticles\u002F20001529-dots-privacy-security-and-safety-faqs",{"title":200,"url":201},"Flavio Copes: A deep dive into OpenAI dots (quotes the dots documentation on memory)","https:\u002F\u002Fflaviocopes.com\u002Fopenai-dots\u002F",{"title":203,"url":204},"GDPR Article 5: Principles relating to processing of personal data","https:\u002F\u002Fgdpr-info.eu\u002Fart-5-gdpr\u002F",{"title":206,"url":207},"GDPR Article 17: Right to erasure","https:\u002F\u002Fgdpr-info.eu\u002Fart-17-gdpr\u002F","\u002Fimages\u002Fblog\u002Fai-agent-memory-design\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fai-agent-memory-design\u002Fog.jpg",[77,78,79],"AI-ügynökök memóriájának tervezése: szintek, írási szabályok, poisoning és GDPR","Hogyan tervezz memóriát AI-ügynököknek: kontextus, session és hosszú távú szint, mit tárolj és mit soha, retrieval, compaction, poisoning, GDPR-törlés.","MI-ügynökök memóriatervezése: egy robot kartotékkártyát tesz egy háromfiókos szekrény nyitott fiókjába.",{"slug":215,"published":216,"updated":217,"minutes":218,"category":8,"tags":219,"tagPages":223,"keywords":225,"about":237,"sources":247,"cover":308,"og":309,"expertise":75,"locales":310,"lang":79,"title":311,"description":312,"coverAlt":313},"token-saving-tools-coding-agents-top-20","2026-08-26","2026-09-23",17,[220,221,12,222],"Claude Code","token usage","developer tools",[224,16],"claude-code",[226,227,228,229,230,231,232,233,234,235,236,21],"claude code tokenhasználat csökkentése","tokenspóroló eszközök kódoló ügynökökhöz","rtk token killer","lean-ctx","context-mode mcp","serena mcp","repomix compress","mcp tool search","prompt caching","ccusage","kódoló ügynök költség",[238,241,244],{"name":239,"url":240},"Large language model","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLarge_language_model",{"name":242,"url":243},"Claude (language model)","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FClaude_(language_model)",{"name":245,"url":246},"Prompt engineering","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FPrompt_engineering",[248,251,254,257,260,263,266,269,272,275,278,281,284,287,290,293,296,299,302,305],{"title":249,"url":250},"rtk: a CLI proxy that filters shell output for coding agents (GitHub)","https:\u002F\u002Fgithub.com\u002Frtk-ai\u002Frtk",{"title":252,"url":253},"lean-ctx: context read modes and shell compression for coding agents (GitHub)","https:\u002F\u002Fgithub.com\u002Fyvgude\u002Flean-ctx",{"title":255,"url":256},"context-mode: sandboxed tool output with SQLite FTS5 search (GitHub)","https:\u002F\u002Fgithub.com\u002Fmksglu\u002Fcontext-mode",{"title":258,"url":259},"Serena: semantic code retrieval and editing over MCP (GitHub)","https:\u002F\u002Fgithub.com\u002Foraios\u002Fserena",{"title":261,"url":262},"token-savior: symbol index, memory and bash compaction over MCP (GitHub)","https:\u002F\u002Fgithub.com\u002FMibayy\u002Ftoken-savior",{"title":264,"url":265},"code-review-graph: a code graph for blast-radius reviews (GitHub)","https:\u002F\u002Fgithub.com\u002Ftirth8205\u002Fcode-review-graph",{"title":267,"url":268},"Aider documentation: repository map","https:\u002F\u002Faider.chat\u002Fdocs\u002Frepomap.html",{"title":270,"url":271},"Repomix: pack a repository into one AI-friendly file (GitHub)","https:\u002F\u002Fgithub.com\u002Fyamadashy\u002Frepomix",{"title":273,"url":274},"Context7: up-to-date library documentation for LLMs (GitHub)","https:\u002F\u002Fgithub.com\u002Fupstash\u002Fcontext7",{"title":276,"url":277},"claude-context: hybrid code search MCP (GitHub)","https:\u002F\u002Fgithub.com\u002Fzilliztech\u002Fclaude-context",{"title":279,"url":280},"caveman: terse output modes for coding agents (GitHub)","https:\u002F\u002Fgithub.com\u002FJuliusBrussee\u002Fcaveman",{"title":282,"url":283},"claude-token-efficient: an eight-rule CLAUDE.md (GitHub)","https:\u002F\u002Fgithub.com\u002Fdrona23\u002Fclaude-token-efficient",{"title":285,"url":286},"claude-code-router: a local model gateway for coding agents (GitHub)","https:\u002F\u002Fgithub.com\u002Fmusistudio\u002Fclaude-code-router",{"title":288,"url":289},"ccusage: token and cost reports from local agent logs (GitHub)","https:\u002F\u002Fgithub.com\u002Fryoppippi\u002Fccusage",{"title":291,"url":292},"LLMLingua: prompt compression (Microsoft, GitHub)","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLLMLingua",{"title":294,"url":295},"ComputingForGeeks: tools that reduce Claude Code token usage, tested (April 2026)","https:\u002F\u002Fcomputingforgeeks.com\u002Freduce-claude-code-token-usage-tools\u002F",{"title":297,"url":298},"Claude Code docs: manage costs effectively","https:\u002F\u002Fcode.claude.com\u002Fdocs\u002Fen\u002Fcosts",{"title":300,"url":301},"Claude Code docs: MCP, output limits and tool search","https:\u002F\u002Fcode.claude.com\u002Fdocs\u002Fen\u002Fmcp",{"title":303,"url":304},"Claude API docs: tool search tool","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fagents-and-tools\u002Ftool-use\u002Ftool-search-tool",{"title":306,"url":307},"Claude API docs: prompt caching","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fbuild-with-claude\u002Fprompt-caching","\u002Fimages\u002Fblog\u002Ftoken-saving-tools-coding-agents-top-20\u002Fcover.webp","\u002Fimages\u002Fblog\u002Ftoken-saving-tools-coding-agents-top-20\u002Fog.jpg",[77,78,79],"20 módszer, hogy kevesebb tokent égessenek a kódoló ügynökök: rtk, lean-ctx, Serena és társaik, bizonyítékok szerint rangsorolva","Az rtk, a lean-ctx, a context-mode, a Serena és 16 további tokenspóroló eszköz kódoló ügynökökhöz, bizonyítékok és saját Nuxt-mérések alapján rangsorolva.","Tokenspórolás kódoló ügynököknél: prés magas érmekupacot alacsonnyá nyom, mellette kupás robot.",{"slug":315,"published":316,"updated":317,"minutes":318,"category":8,"tags":319,"tagPages":323,"keywords":325,"about":336,"sources":342,"cover":376,"og":377,"expertise":75,"locales":378,"lang":79,"title":379,"description":380,"coverAlt":381},"multi-agent-systems-when-worth-it","2026-08-07","2026-09-04",12,[320,321,322,12],"Multi-agent systems","AI agents","Orchestrator-worker",[324,16],"ai-agents",[326,327,328,329,330,331,332,333,334,335],"multi-agent rendszerek megéri","multi-agent vs egy ügynök","orchestrator-worker minta","multi-agent llm token költség","miért buknak el a multi-agent rendszerek","ne építsünk multi-agent rendszert","subagentek kontextus izolálása","multi-agent vita megéri","ügynök átadás vs alügynökök","mikor használjunk több ai ügynököt",[337,340,341],{"name":338,"url":339},"Multi-agent system","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMulti-agent_system",{"name":37,"url":38},{"name":239,"url":240},[343,346,349,352,355,358,361,364,367,370,373],{"title":344,"url":345},"Anthropic Engineering: How we built our multi-agent research system","https:\u002F\u002Fwww.anthropic.com\u002Fengineering\u002Fmulti-agent-research-system",{"title":347,"url":348},"Anthropic: Building effective agents","https:\u002F\u002Fwww.anthropic.com\u002Fengineering\u002Fbuilding-effective-agents",{"title":350,"url":351},"Cognition: Don't Build Multi-Agents (12 June 2025)","https:\u002F\u002Fcognition.com\u002Fblog\u002Fdont-build-multi-agents",{"title":353,"url":354},"Cognition: Multi-Agents: What's Actually Working (22 April 2026)","https:\u002F\u002Fcognition.com\u002Fblog\u002Fmulti-agents-working",{"title":356,"url":357},"Google Research: Towards a science of scaling agent systems","https:\u002F\u002Fresearch.google\u002Fblog\u002Ftowards-a-science-of-scaling-agent-systems-when-and-why-agent-systems-work\u002F",{"title":359,"url":360},"arXiv 2512.08296: Towards a Science of Scaling Agent Systems","https:\u002F\u002Farxiv.org\u002Fabs\u002F2512.08296",{"title":362,"url":363},"arXiv 2503.13657: Why Do Multi-Agent LLM Systems Fail? (MAST)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2503.13657",{"title":365,"url":366},"arXiv 2305.14325: Improving Factuality and Reasoning in Language Models through Multiagent Debate","https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.14325",{"title":368,"url":369},"arXiv 2502.08788: Stop Overvaluing Multi-Agent Debate","https:\u002F\u002Farxiv.org\u002Fabs\u002F2502.08788",{"title":371,"url":372},"OpenAI Agents SDK: Handoffs","https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002Fhandoffs\u002F",{"title":374,"url":375},"Claude Code documentation: Subagents","https:\u002F\u002Fcode.claude.com\u002Fdocs\u002Fen\u002Fsub-agents","\u002Fimages\u002Fblog\u002Fmulti-agent-systems-when-worth-it\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fmulti-agent-systems-when-worth-it\u002Fog.jpg",[77,78,79],"Multi-agent rendszerek: mikor verik az egyetlen ügynököt, és mikor nem","Orchestrator-worker, fan-out, critic, handoff: mit ad valójában a multi-agent rendszer, mennyi tokenbe kerül, hogyan hibázik, és egy döntési táblázat.","Több ügynökös rendszerek: egy robot kis dobozt visz, négy robot egy hosszú gerendát, köztük mérleg.",{"slug":383,"published":384,"updated":385,"minutes":386,"category":8,"tags":387,"tagPages":391,"keywords":393,"about":402,"sources":408,"cover":428,"og":429,"expertise":75,"locales":430,"lang":79,"title":431,"description":432,"coverAlt":433},"agents-md-skills-mcp-cli-decision-matrix","2026-06-19","2026-07-16",8,[12,388,389,390],"AGENTS.md","Agent Skills","MCP",[16,392],"mcp",[21,394,395,396,397,398,399,400,401],"agents.md","ügynök skillek","mcp vagy cli","context rot","ügynök kontextablak","token keret ai ügynökökhöz","mikor használjunk mcp-t cli helyett","alügynökök ai ügynökökhöz",[403,404,407],{"name":239,"url":240},{"name":405,"url":406},"Model Context Protocol","https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FModel_Context_Protocol",{"name":245,"url":246},[409,410,413,416,419,422,425],{"title":50,"url":51},{"title":411,"url":412},"Chroma: Context Rot (Jul 2025)","https:\u002F\u002Fwww.trychroma.com\u002Fresearch\u002Fcontext-rot",{"title":414,"url":415},"AGENTS.md: the open format for agent instructions","https:\u002F\u002Fagents.md",{"title":417,"url":418},"Agent Skills specification","https:\u002F\u002Fagentskills.io\u002Fspecification",{"title":420,"url":421},"Anthropic: Advanced tool use, tool search and programmatic tool calling (Nov 2025)","https:\u002F\u002Fwww.anthropic.com\u002Fengineering\u002Fadvanced-tool-use",{"title":423,"url":424},"Blocks.ai: MCP vs CLI, the context window cost","https:\u002F\u002Fblocks.ai\u002Fblog\u002Fmcp-vs-cli-context-window-cost",{"title":426,"url":427},"Thoughtworks Technology Radar: Context engineering (Adopt, Apr 2026)","https:\u002F\u002Fwww.thoughtworks.com\u002Fradar\u002Ftechniques\u002Fcontext-engineering","\u002Fimages\u002Fblog\u002Fagents-md-skills-mcp-cli-decision-matrix\u002Fcover.webp","\u002Fimages\u002Fblog\u002Fagents-md-skills-mcp-cli-decision-matrix\u002Fog.jpg",[77,78,79],"Kontexttervezés kódoló ügynökökhöz: AGENTS.md, skillek, MCP vagy CLI?","Mi van egy kódoló ügynök kontextusában: szabályok az AGENTS.md-ben, eljárások a skillekben, és mikor ver egy MCP eszköz egy shell parancsot.","AGENTS.md, skillek, MCP, CLI: egy robot négyirányú útjelző táblát néz, rajta lap, szerszámláda, csatlakozó és terminál.",1793451238355]