[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-f056d2fbc3259de2-optimize-live-agents-gepa-prompts-managed-vars-summary":3,"summaries-facets-categories":326,"summary-related-f056d2fbc3259de2-optimize-live-agents-gepa-prompts-managed-vars-summary":3912},{"id":4,"title":5,"ai":6,"body":13,"categories":291,"created_at":292,"date_modified":292,"description":146,"extension":293,"faq":292,"featured":294,"kicker_label":292,"meta":295,"navigation":161,"path":308,"published_at":309,"question":292,"scraped_at":310,"seo":311,"sitemap":312,"source_id":313,"source_name":314,"source_type":315,"source_url":316,"stem":317,"tags":318,"thumbnail_url":292,"tldr":322,"tweet":323,"unknown_tags":324,"__hash__":325},"summaries\u002Fsummaries\u002Ff056d2fbc3259de2-optimize-live-agents-gepa-prompts-managed-vars-summary.md","Optimize Live Agents: GEPA Prompts + Managed Vars",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",8380,2516,37110,0.0029115,{"type":14,"value":15,"toc":284},"minimark",[16,21,25,33,47,55,70,77,88,92,95,98,117,120,123,126,133,137,140,192,199,206,209,220,223,226,230,233,244,247,251,280],[17,18,20],"h2",{"id":19},"build-golden-datasets-and-custom-evals-for-reliable-agent-testing","Build Golden Datasets and Custom Evals for Reliable Agent Testing",[22,23,24],"p",{},"Samuel Colvin demonstrates optimizing agents post-deployment by first establishing a baseline with structured evaluations against a \"golden dataset\"—manually verified ground truth data. For the case study, he scrapes Wikipedia pages for UK MPs, extracts text via BeautifulSoup, and defines Pydantic schemas for MP details and political relations (focusing on ancestors like parents\u002Fgrandparents, excluding spouses\u002Fchildren).",[22,26,27,28,32],{},"The golden dataset (",[29,30,31],"code",{},"golden_relations.json",") contains exact relations for ~650 MPs, created by running a high-end model like Opus once and manual checks. Custom evaluators compare agent outputs to this truth:",[34,35,36,44],"ul",{},[37,38,39,43],"li",{},[40,41,42],"strong",{},"Accuracy",": Exact match on relations list (1.0 if perfect, partial scores like 0.9 for minor name\u002Fdescription diffs).",[37,45,46],{},"Assertions for relation types, roles, and ancestor filtering.",[22,48,49,50,54],{},"Key principle: Prefer deterministic, rule-based evals over \"LLM-as-judge\" to avoid bias. \"Defining your own ",[51,52,53],"span",{},"evaluators"," is far better than LLM as a judge because the LLM as a judge is effectively the kind of lunatics running the asylum.\"",[22,56,57,58,61,62,65,66,69],{},"To run: Load dataset with ",[29,59,60],{},"load_dataset()",", register evaluators, then ",[29,63,64],{},"dataset.evaluate(agent_func, name=\"eval-name\")"," using Pydantic AI's ",[29,67,68],{},"override"," for prompts\u002Fmodels. Concurrency limits (e.g., max=5) prevent rate limits. Results appear in Logfire UI: spans show inputs\u002Foutputs\u002Fcosts, evals tab aggregates metrics (e.g., 85% accuracy for simple prompt).",[22,71,72,73,76],{},"Common mistake: Over-relying on console logs—disable terminal output (",[29,74,75],{},"LOGFIRE_NO_CONSOLE=true",") for clean traces. Before: Simple one-liner prompt gets 85% accuracy, confuses non-ancestors\u002Fpolitical vs. public figures. After better prompt: Improves to ~90%+ by explicitly discounting same-gen relations.",[22,78,79,80,83,84,87],{},"Setup prerequisites: ",[29,81,82],{},"uv sync",", Logfire project (",[29,85,86],{},"logfire project use demo","), API keys (Pydantic Gateway for multi-model access or direct OpenAI\u002FAnthropic). Quality criteria: High accuracy on ancestors, low false positives on spouses\u002Fkids.",[17,89,91],{"id":90},"evolve-prompts-genetically-with-gepa-on-production-traces","Evolve Prompts Genetically with GEPA on Production Traces",[22,93,94],{},"GEPA (Genetic Evolutionary Prompt Algorithm, via \"Jepper\" library) optimizes prompts as strings or JSON by breeding top performers. It evaluates candidates on a dataset, selects Pareto frontier (best trade-offs), mutates\u002Fcrosses them (e.g., mix phrases from high-scorers), and iterates.",[22,96,97],{},"Process:",[99,100,101,104,107,114],"ol",{},[37,102,103],{},"Define initial prompts (simple vs. advanced) and models as Pydantic models.",[37,105,106],{},"Run evals on split dataset (e.g., 65 test cases for speed).",[37,108,109,110,113],{},"Launch GEPA: ",[29,111,112],{},"gepa.optimize(evaluate_fn, initial_candidates, generations=10, population_size=20)",". It parallelizes evals, instruments via Logfire for traces.",[37,115,116],{},"Output: Ranked prompts by composite score (accuracy + cost\u002Fefficiency).",[22,118,119],{},"In demo: Simple prompt → 85% acc; advanced (ancestor rules) → better; GEPA evolves hybrids exceeding both (e.g., 92%+ acc). Handles systemic errors like over-including spouses by evolving phrasing: \"Only ancestors (parents, grandparents)—exclude spouses, children, siblings.\"",[22,121,122],{},"Trade-offs: Compute-heavy (hundreds of evals\u002Fgeneration); start small dataset. Mistake: Random mutation—GEPA biases toward elites like horse breeding. \"It takes the best racehorses and breeds them... you take all of the best resources and breed them.\"",[22,124,125],{},"Extend to production: Use real traces\u002Ffeedback as eval inputs. Future: Autonomous optimization from Logfire.",[22,127,128,129,132],{},"Quote: \"GEPA is ultimately an optimization library ",[51,130,131],{},"that"," optimizes a string... it can be a simple text prompt or some JSON data.\"",[17,134,136],{"id":135},"enable-zero-downtime-tuning-with-managed-variables-in-production","Enable Zero-Downtime Tuning with Managed Variables in Production",[22,138,139],{},"Logfire's managed variables let you update any Pydantic-serializable object (prompts, models, params) live without restarts. Define as Pydantic model:",[141,142,147],"pre",{"className":143,"code":144,"language":145,"meta":146,"style":146},"language-python shiki shiki-themes github-light github-dark","from logfire.managed import managed_variable\n\nclass AgentConfig(BaseModel):\n    model: str = \"gateway:gpt-4o-mini\"\n    instructions: str = \"...\"\n\nconfig = managed_variable(AgentConfig)\n","python","",[29,148,149,156,163,169,175,181,186],{"__ignoreMap":146},[51,150,153],{"class":151,"line":152},"line",1,[51,154,155],{},"from logfire.managed import managed_variable\n",[51,157,159],{"class":151,"line":158},2,[51,160,162],{"emptyLinePlaceholder":161},true,"\n",[51,164,166],{"class":151,"line":165},3,[51,167,168],{},"class AgentConfig(BaseModel):\n",[51,170,172],{"class":151,"line":171},4,[51,173,174],{},"    model: str = \"gateway:gpt-4o-mini\"\n",[51,176,178],{"class":151,"line":177},5,[51,179,180],{},"    instructions: str = \"...\"\n",[51,182,184],{"class":151,"line":183},6,[51,185,162],{"emptyLinePlaceholder":161},[51,187,189],{"class":151,"line":188},7,[51,190,191],{},"config = managed_variable(AgentConfig)\n",[22,193,194,195,198],{},"In agent: ",[29,196,197],{},"agent = Agent(..., instructions=config.instructions, model=config.model)",". Changes in Logfire UI propagate instantly (poll every 30s).",[22,200,201,202,205],{},"Production demo: FastAPI server with ",[29,203,204],{},"\u002Fanalyze"," endpoint runs agent on live Wikipedia HTML. Update prompt\u002Fmodel via Logfire—tune for better ancestor detection without deploy.",[22,207,208],{},"Implicit feedback: Log user thumbs-up\u002Fdown, aggregate into evals. Q&A insights:",[34,210,211,214,217],{},[37,212,213],{},"Prompt bloat: GEPA prunes inefficient phrasing.",[37,215,216],{},"Context engineering: Chain-of-thought in prompts.",[37,218,219],{},"Internal use: Pydantic team tunes agents on traces.",[22,221,222],{},"Trade-offs: Polling overhead (low); free tier generous. Mistake: Mutable globals—managed vars are safe, versioned.",[22,224,225],{},"Quote: \"Managed variables... don't have to be just text they can be effectively any object that you can define with a Pydantic model.\"",[17,227,229],{"id":228},"from-manual-to-continuous-optimization-workflow","From Manual to Continuous Optimization Workflow",[22,231,232],{},"Full loop: Golden evals → GEPA on traces → Managed vars deploy → Feedback evals. Fits mid-workshop: Assumes Python\u002FPydantic familiarity, agent-building basics. Broader: Any structured output task (invoices, addresses) benefits.",[22,234,235,236,239,240,243],{},"Exercise: Fork repo (",[29,237,238],{},"github.com\u002Fpydantic\u002Ftalks\u002F2024-ai-engineer","), run ",[29,241,242],{},"uv run main.py eval --split test --prompt initial",", compare prompts, GEPA optimize, deploy to FastAPI.",[22,245,246],{},"Quote: \"Deploying an agent is only the start... change prompts, models... without redeploying.\"",[17,248,250],{"id":249},"key-takeaways","Key Takeaways",[34,252,253,256,259,262,265,268,271,274,277],{},[37,254,255],{},"Create golden datasets from high-model runs + manual verification for deterministic evals—beats LLM judges.",[37,257,258],{},"Use GEPA to breed prompts: Start with 2-5 candidates, 10 generations on 65-case split for quick wins.",[37,260,261],{},"Define managed variables as Pydantic models for instant prod updates—no restarts needed.",[37,263,264],{},"Instrument everything with Logfire: Traces reveal confusions (e.g., spouses as ancestors).",[37,266,267],{},"Prioritize ancestor filtering in political\u002Frelation extraction: Evolve phrasing like \"exclude same-gen or descendants.\"",[37,269,270],{},"Run evals in parallel (max_concurrency=5) to optimize costs during optimization.",[37,272,273],{},"For FastAPI agents: Override configs live, log implicit feedback for GEPA inputs.",[37,275,276],{},"Avoid hype: \"I don't really believe in AI observability I think it's a feature not a category.\"",[37,278,279],{},"Scale: Free Logfire tier handles workshops; Gateway simplifies multi-model testing.",[281,282,283],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":146,"searchDepth":158,"depth":158,"links":285},[286,287,288,289,290],{"id":19,"depth":158,"text":20},{"id":90,"depth":158,"text":91},{"id":135,"depth":158,"text":136},{"id":228,"depth":158,"text":229},{"id":249,"depth":158,"text":250},[],null,"md",false,{"content_references":296,"triage":305},[297,301],{"type":298,"title":299,"context":300},"podcast","The Rest is Politics","mentioned",{"type":302,"title":303,"context":304},"other","Jepper (GEPA)","recommended",{"relevance":177,"novelty":171,"quality":171,"actionability":177,"composite":306,"reasoning":307},4.55,"Category: AI & LLMs. The article provides a detailed approach to optimizing AI agents using specific techniques like golden datasets and custom evaluations, addressing a key pain point for developers looking to improve production AI features. It includes actionable steps and code snippets that developers can implement directly.","\u002Fsummaries\u002Ff056d2fbc3259de2-optimize-live-agents-gepa-prompts-managed-vars-summary","2026-05-07 17:00:06","2026-05-08 11:03:29",{"title":5,"description":146},{"loc":308},"263bbb77349e4ef1","AI Engineer","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=A48uhxfxbsM","summaries\u002Ff056d2fbc3259de2-optimize-live-agents-gepa-prompts-managed-vars-summary",[319,320,145,321],"agents","prompt-engineering","ai-tools","Tune production agents without redeploys using Logfire's managed variables for prompts\u002Fmodels and GEPA's genetic algorithm to evolve better prompts from evals on golden datasets.","Hands-on workshop by Pydantic's Samuel Colvin: codes along optimizing an agent for extracting political relations from Wikipedia pages using Logfire evals, GEPA prompt evolution on a golden dataset, and managed variables for live prompt\u002Fmodel tweaks in a FastAPI app—no redeploys 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This shifts effort to refinement over rote creation.",[17,3938,3940],{"id":3939},"manual-vs-ai-generated-concrete-fastapi-example","Manual vs AI-Generated: Concrete FastAPI Example",[22,3942,3943],{},[40,3944,3945],{},"Manual (error-prone start):",[141,3947,3949],{"className":143,"code":3948,"language":145,"meta":146,"style":146},"@app.post(\"\u002Fusers\")\ndef create_user(user: UserCreate):\n    if not user.email:\n        raise ValueError(\"Email required\")\n    db_user = …\n",[29,3950,3951,3956,3961,3966,3971],{"__ignoreMap":146},[51,3952,3953],{"class":151,"line":152},[51,3954,3955],{},"@app.post(\"\u002Fusers\")\n",[51,3957,3958],{"class":151,"line":158},[51,3959,3960],{},"def create_user(user: UserCreate):\n",[51,3962,3963],{"class":151,"line":165},[51,3964,3965],{},"    if not user.email:\n",[51,3967,3968],{"class":151,"line":171},[51,3969,3970],{},"        raise ValueError(\"Email required\")\n",[51,3972,3973],{"class":151,"line":177},[51,3974,3975],{},"    db_user = …\n",[22,3977,3978],{},"AI output starts complete with validation, errors, and service integration, eliminating copy-paste bugs and accelerating iteration.",[281,3980,283],{},{"title":146,"searchDepth":158,"depth":158,"links":3982},[3983,3984,3985],{"id":3925,"depth":158,"text":3926},{"id":3932,"depth":158,"text":3933},{"id":3939,"depth":158,"text":3940},[329],{},"\u002Fsummaries\u002Fprompt-ai-to-end-boilerplate-drudgery-summary","2026-04-08 21:21:18",{"title":3915,"description":146},{"loc":3988},"aa74cd8bd7ebfa34","Python in Plain English","https:\u002F\u002Funknown","summaries\u002Fprompt-ai-to-end-boilerplate-drudgery-summary",[145,320,321],"Manual boilerplate is bug-prone transcription that wastes focus—prompt AI like 'Create a FastAPI endpoint with validation, error handling, and service layer' for complete drafts in seconds.",[],"7-niqiCUTVz34nsU6kuL4KZNLDUHZ2muTI7rj2XoX7Y",{"id":4001,"title":4002,"ai":4003,"body":4008,"categories":4102,"created_at":292,"date_modified":292,"description":146,"extension":293,"faq":292,"featured":294,"kicker_label":292,"meta":4103,"navigation":161,"path":4122,"published_at":292,"question":292,"scraped_at":4123,"seo":4124,"sitemap":4125,"source_id":4126,"source_name":4127,"source_type":315,"source_url":4128,"stem":4129,"tags":4130,"thumbnail_url":292,"tldr":4131,"tweet":292,"unknown_tags":4132,"__hash__":4133},"summaries\u002Fsummaries\u002Fb6c275efa5018657-google-s-adk-code-first-python-ai-agent-toolkit-summary.md","Google's ADK: Code-First Python AI Agent Toolkit",{"provider":7,"model":8,"input_tokens":4004,"output_tokens":4005,"processing_time_ms":4006,"cost_usd":4007},9732,1740,12684,0.00230955,{"type":14,"value":4009,"toc":4097},[4010,4014,4017,4074,4077,4081,4084,4088,4095],[17,4011,4013],{"id":4012},"define-agents-and-tools-directly-in-code","Define Agents and Tools Directly in Code",[22,4015,4016],{},"ADK uses a code-first approach to create testable, versionable agents. Start with a single agent by specifying name, model (e.g., gemini-2.5-flash), instructions, description, and tools like google_search:",[141,4018,4020],{"className":143,"code":4019,"language":145,"meta":146,"style":146},"from google.adk.agents import Agent\nfrom google.adk.tools import google_search\n\nroot_agent = Agent(\n    name=\"search_assistant\",\n    model=\"gemini-2.5-flash\",\n    instruction=\"You are a helpful assistant. Answer user questions using Google Search when needed.\",\n    description=\"An assistant that can search the web.\",\n    tools=[google_search]\n)\n",[29,4021,4022,4027,4032,4036,4041,4046,4051,4056,4062,4068],{"__ignoreMap":146},[51,4023,4024],{"class":151,"line":152},[51,4025,4026],{},"from google.adk.agents import Agent\n",[51,4028,4029],{"class":151,"line":158},[51,4030,4031],{},"from google.adk.tools import google_search\n",[51,4033,4034],{"class":151,"line":165},[51,4035,162],{"emptyLinePlaceholder":161},[51,4037,4038],{"class":151,"line":171},[51,4039,4040],{},"root_agent = Agent(\n",[51,4042,4043],{"class":151,"line":177},[51,4044,4045],{},"    name=\"search_assistant\",\n",[51,4047,4048],{"class":151,"line":183},[51,4049,4050],{},"    model=\"gemini-2.5-flash\",\n",[51,4052,4053],{"class":151,"line":188},[51,4054,4055],{},"    instruction=\"You are a helpful assistant. Answer user questions using Google Search when needed.\",\n",[51,4057,4059],{"class":151,"line":4058},8,[51,4060,4061],{},"    description=\"An assistant that can search the web.\",\n",[51,4063,4065],{"class":151,"line":4064},9,[51,4066,4067],{},"    tools=[google_search]\n",[51,4069,4071],{"class":151,"line":4070},10,[51,4072,4073],{},")\n",[22,4075,4076],{},"This integrates pre-built tools, custom functions, OpenAPI specs, or MCP tools, optimized for Google ecosystem but model-agnostic. Add tool confirmation (HITL) to require explicit user approval before execution, preventing unintended actions.",[17,4078,4080],{"id":4079},"build-scalable-multi-agent-hierarchies","Build Scalable Multi-Agent Hierarchies",[22,4082,4083],{},"Compose specialized agents into hierarchies for complex workflows. Define root and sub-agents with shared or unique tools, enabling orchestration where agents delegate tasks. Supports Agent Config for no-code agent building alongside code definitions. Recent updates include rewind to replay sessions pre-invocation, custom service registration for FastAPI servers, and AgentEngineSandboxCodeExecutor for safe code execution via Vertex AI sandbox.",[17,4085,4087],{"id":4086},"install-evaluate-and-deploy-seamlessly","Install, Evaluate, and Deploy Seamlessly",[22,4089,4090,4091,4094],{},"Install stable via ",[29,4092,4093],{},"pip install google-adk"," (bi-weekly releases) or dev version from git main for latest fixes. Evaluate agents with built-in metrics; deploy containerized to Cloud Run or scale on Vertex AI Agent Engine. Integrates A2A protocol for remote agent communication. Use 18.9k-starred repo's samples for patterns like skill activation via environment tools or BigQuery integration (now stable). Trade-off: Dev version risks bugs but accesses unshipped features like Parameter Manager for secret handling.",[281,4096,283],{},{"title":146,"searchDepth":158,"depth":158,"links":4098},[4099,4100,4101],{"id":4012,"depth":158,"text":4013},{"id":4079,"depth":158,"text":4080},{"id":4086,"depth":158,"text":4087},[],{"content_references":4104,"triage":4120},[4105,4108,4111,4114,4117],{"type":302,"title":4106,"url":4107,"context":304},"ADK Documentation","https:\u002F\u002Fgoogle.github.io\u002Fadk-docs\u002F",{"type":302,"title":4109,"url":4110,"context":304},"ADK Samples","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-samples",{"type":302,"title":4112,"url":4113,"context":300},"Java ADK","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-java",{"type":302,"title":4115,"url":4116,"context":300},"Go ADK","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-go",{"type":302,"title":4118,"url":4119,"context":300},"A2A Protocol","https:\u002F\u002Fgithub.com\u002Fgoogle-a2a\u002FA2A\u002F",{"relevance":177,"novelty":171,"quality":171,"actionability":177,"composite":306,"reasoning":4121},"Category: AI & LLMs. This article provides a detailed overview of Google's ADK, a toolkit for building AI agents, which directly addresses the needs of developers looking to integrate AI into their products. The code examples and deployment instructions offer practical, actionable steps for the audience.","\u002Fsummaries\u002Fb6c275efa5018657-google-s-adk-code-first-python-ai-agent-toolkit-summary","2026-04-15 15:35:01",{"title":4002,"description":146},{"loc":4122},"b6c275efa5018657","__oneoff__","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-python","summaries\u002Fb6c275efa5018657-google-s-adk-code-first-python-ai-agent-toolkit-summary",[319,145,321],"Build, evaluate, and deploy modular AI agents in Python using Google's ADK—pip install google-adk for code-first logic, rich tools, multi-agent hierarchies, and deployment to Cloud Run or Vertex AI.",[],"tfGNqAYRA0hvN_aLK0bF0iyMsIaLQJVjLxwYUgfHF-Y",{"id":4135,"title":4136,"ai":4137,"body":4142,"categories":4269,"created_at":292,"date_modified":292,"description":146,"extension":293,"faq":292,"featured":294,"kicker_label":292,"meta":4270,"navigation":161,"path":4282,"published_at":4283,"question":292,"scraped_at":4284,"seo":4285,"sitemap":4286,"source_id":4287,"source_name":4288,"source_type":315,"source_url":4289,"stem":4290,"tags":4291,"thumbnail_url":292,"tldr":4293,"tweet":292,"unknown_tags":4294,"__hash__":4295},"summaries\u002Fsummaries\u002Fa690c3914c9d11ae-memori-persistent-memory-for-multi-user-llm-agents-summary.md","Memori: Persistent Memory for Multi-User LLM Agents",{"provider":7,"model":8,"input_tokens":4138,"output_tokens":4139,"processing_time_ms":4140,"cost_usd":4141},8671,1734,17086,0.0025787,{"type":14,"value":4143,"toc":4264},[4144,4148,4187,4191,4218,4222],[17,4145,4147],{"id":4146},"seamless-client-integration-for-automatic-memory","Seamless Client Integration for Automatic Memory",[22,4149,4150,4151,4154,4155,4158,4159,4162,4163,4166,4167,4170,4171,4174,4175,4178,4179,4182,4183,4186],{},"Register synchronous and asynchronous OpenAI clients with Memori using ",[29,4152,4153],{},"mem.llm.register(client)"," and ",[29,4156,4157],{},"mem.llm.register(async_client)",". This intercepts all ",[29,4160,4161],{},"chat.completions.create"," calls to inject relevant memories as context, eliminating manual retrieval logic. Setup in Colab: ",[29,4164,4165],{},"pip install memori>=3.3.0 openai>=1.40.0 nest_asyncio",", set ",[29,4168,4169],{},"OPENAI_API_KEY"," (required) and optional ",[29,4172,4173],{},"MEMORI_API_KEY"," for non-rate-limited access. Use ",[29,4176,4177],{},"gpt-4o-mini"," as the model and a 6-second ",[29,4180,4181],{},"WRITE_DELAY"," after each call to ensure memory persistence. Result: Every LLM interaction becomes stateful, recalling facts like \"Alice loves hiking, Italian food, allergic to peanuts\" across turns via simple ",[29,4184,4185],{},"mem.attribution(entity_id=\"[email protected]\", process_id=\"personal-assistant\")"," before prompting.",[17,4188,4190],{"id":4189},"multi-tenant-isolation-via-entity-process-and-session-scoping","Multi-Tenant Isolation via Entity, Process, and Session Scoping",[22,4192,4193,4194,4197,4198,4201,4202,4205,4206,4209,4210,4213,4214,4217],{},"Scope memories hierarchically: ",[29,4195,4196],{},"entity_id"," (e.g., user email) isolates users—Bob's \"vegetarian, Rust developer, Berlin\" doesn't leak to Alice. ",[29,4199,4200],{},"process_id"," separates agent roles for one user: Alice's \"sub-25-minute 5K goal\" stays with ",[29,4203,4204],{},"fitness-coach",", while \"low-carb dinners\" is siloed to ",[29,4207,4208],{},"meal-planner",". Sessions group turns: ",[29,4211,4212],{},"mem.set_session(\"project-fastapi-abc123\")"," captures \"FastAPI app 'Lighthouse', Python 3.12, Fly.io, SQLAlchemy + Alembic\", excluding unrelated \"puppy named Mochi\" from ",[29,4215,4216],{},"mem.new_session()",". Recall by re-attributing and setting session: Agent summarizes project decisions accurately. Trade-off: Rate-limited tier suffices for demos; paid key needed for production scale.",[17,4219,4221],{"id":4220},"production-ready-features-streaming-async-and-workflows","Production-Ready Features: Streaming, Async, and Workflows",[22,4223,4224,4225,4228,4229,4232,4233,4236,4237,4240,4241,4244,4245,4247,4248,4251,4252,4258,4259,4263],{},"Streaming works out-of-box: ",[29,4226,4227],{},"stream=True"," on ",[29,4230,4231],{},"client.chat.completions.create"," pulls Alice's facts incrementally without breaking memory flow. Async calls via ",[29,4234,4235],{},"AsyncOpenAI"," recall restrictions like peanut allergy seamlessly: ",[29,4238,4239],{},"await async_client.chat.completions.create(...)",". Build multi-session agents like support bots: ",[29,4242,4243],{},"mem.attribution(entity_id=\"[email protected]\", process_id=\"support-bot\")","; new ",[29,4246,4216],{}," per turn still remembers \"Pro plan, ",[51,4249,4250],{},"email protected","\" across interactions. System prompt: \"You are a calm, helpful customer support agent. Use what you remember about the user.\" Inspect memories at ",[4253,4254,4255],"a",{"href":4255,"rel":4256},"https:\u002F\u002Fapp.memorilabs.ai",[4257],"nofollow"," or use BYODB for Postgres. Full notebook: ",[4253,4260,4261],{"href":4261,"rel":4262},"https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FAgentic%20AI%20Memory\u002Fmemori_agent_native_memory_infrastructure_tutorial_Marktechpost.ipynb",[4257],". This scales to personalized assistants, multi-agent systems, and customer support retaining context long-term.",{"title":146,"searchDepth":158,"depth":158,"links":4265},[4266,4267,4268],{"id":4146,"depth":158,"text":4147},{"id":4189,"depth":158,"text":4190},{"id":4220,"depth":158,"text":4221},[],{"content_references":4271,"triage":4280},[4272,4276,4278],{"type":4273,"title":4274,"url":4275,"context":304},"tool","Memori","https:\u002F\u002Fgithub.com\u002FMemoriLabs\u002FMemori",{"type":4273,"title":4277,"url":4255,"context":300},"Memori Dashboard",{"type":302,"title":4279,"url":4261,"context":300},"Full Codes with Notebook",{"relevance":177,"novelty":171,"quality":171,"actionability":177,"composite":306,"reasoning":4281},"Category: AI & LLMs. The article provides a detailed implementation guide for integrating persistent memory in multi-user LLM applications, addressing a specific pain point of managing context across interactions. It includes actionable code snippets and setup instructions that developers can directly apply to their projects.","\u002Fsummaries\u002Fa690c3914c9d11ae-memori-persistent-memory-for-multi-user-llm-agents-summary","2026-05-11 07:34:38","2026-05-11 15:04:13",{"title":4136,"description":146},{"loc":4282},"a690c3914c9d11ae","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F11\u002Fa-coding-implementation-to-build-agent-native-memory-infrastructure-with-memori-for-persistent-multi-user-and-multi-session-llm-applications\u002F","summaries\u002Fa690c3914c9d11ae-memori-persistent-memory-for-multi-user-llm-agents-summary",[4292,319,321,145],"llm","Register OpenAI clients with Memori to automatically store\u002Fretrieve scoped memories by user entity, agent process, and session, enabling context-aware agents across turns, users, and interactions without manual prompt management.",[],"IOxDEqszxB6CWccOlNI-E5tVk_XdhVTT78_E4jNp8d8",{"id":4297,"title":4298,"ai":4299,"body":4304,"categories":4422,"created_at":292,"date_modified":292,"description":146,"extension":293,"faq":292,"featured":294,"kicker_label":292,"meta":4423,"navigation":161,"path":4437,"published_at":4438,"question":292,"scraped_at":4439,"seo":4440,"sitemap":4441,"source_id":4442,"source_name":4443,"source_type":4444,"source_url":4445,"stem":4446,"tags":4447,"thumbnail_url":4449,"tldr":4450,"tweet":4451,"unknown_tags":4452,"__hash__":4453},"summaries\u002Fsummaries\u002F1efced8af0da0fa8-ai-agents-need-scaffolding-prompts-to-plugins-guid-summary.md","AI Agents Need Scaffolding: Prompts to Plugins Guide",{"provider":7,"model":8,"input_tokens":4300,"output_tokens":4301,"processing_time_ms":4302,"cost_usd":4303},8571,2639,26226,0.00301105,{"type":14,"value":4305,"toc":4414},[4306,4310,4313,4316,4319,4323,4326,4329,4332,4335,4338,4342,4345,4348,4351,4354,4357,4361,4364,4367,4370,4373,4377,4380,4383,4386,4388],[17,4307,4309],{"id":4308},"agents-arent-just-llmsthey-need-a-mech-suit-scaffold","Agents Aren't Just LLMs—They Need a 'Mech Suit' Scaffold",[22,4311,4312],{},"LLMs alone handle simple tasks but fail on messy, multi-part workflows like planning, tool use, or ambiguity navigation—core strengths of models like GPT-5.5 or Claude. The fix isn't smarter models; it's scaffolding: prompts, skills, plugins, MCPs, hooks, and scripts that wrap the LLM like Darth Vader's suit or a Transformer's armor. This lets non-engineers customize agents for real work, saving hours wasted on repetitive prompting.",[22,4314,4315],{},"Nate Jones argues people get stuck because they overload prompts, missing how to layer these components. Without clear boundaries, agents produce generic output; with scaffolding, they apply your standards, pull live data, and run deterministic checks. Tradeoff: Prompts are instant but non-reusable; full scaffolds require upfront setup but yield 10x leverage for teams.",[22,4317,4318],{},"\"It's like Darth Vader has a mech suit right and that's how Darth Vader works or Transformers have these huge metal suits and that's how they get the job done—this is how LLMs work they have these suits around them that help them get work done.\" (Nate Jones uses this metaphor to explain why raw LLMs need external structure for production workflows, shifting focus from model intelligence to customizable harnesses.)",[17,4320,4322],{"id":4321},"prompts-for-one-offs-skills-for-reusable-house-style","Prompts for One-Offs, Skills for Reusable House Style",[22,4324,4325],{},"Prompts suit temporary, specific tasks: a unique client note with custom backstory. They're plain text, carrying no permissions, tools, or team-sharing ease—fine for one-offs but wasteful for repeats, burning hours on copy-paste tweaks.",[22,4327,4328],{},"Skills upgrade reusability: Markdown docs encoding your \"house style\" for processes like PR reviews, marketing docs, or cold emails. Write once (AI can help), invoke across LLMs like Codex or Claude. Example: A skill for outbound emails specifies structure—paragraphs, data pulls from docs, strong closes—consistent for batches without per-use prompting.",[22,4330,4331],{},"Decision chain: If one-off → prompt. Repeatable\u002Fhigh-sensitivity → skill. Skills follow power law: 20% deliver 80% value; prioritize frequent, error-prone tasks (e.g., customer service, engineering reviews). Track them to avoid sprawl—divide\u002Fconquer by workflow chunks.",[22,4333,4334],{},"Tradeoffs: Skills are universal\u002Fportable but text-only—no live integrations. Over-prompting mimics skills poorly, lacking enforcement.",[22,4336,4337],{},"\"A prompt is what you do when you want to do something once... a prompt is not a great home for anything that you repeat constantly or consistently.\" (Jones contrasts to push viewers from prompt dependency, estimating massive time waste from non-packaged repeats.)",[17,4339,4341],{"id":4340},"plugins-package-full-workflows-for-team-reuse","Plugins Package Full Workflows for Team Reuse",[22,4343,4344],{},"Plugins scale beyond skills: Bundles including skills, MCPs, hooks, scripts, assets, commands, and metadata. Install once, run entire workflows like Salesforce-pulled outbound emails or architecture reviews. Not just app add-ons—workflow wrappers making humans obsolete as the \"human plugin\" (copy-paste across apps).",[22,4346,4347],{},"Why plugins now? Agents handle rich work; tools like Codex simplify non-coder builds. Example: Editorial plugin for first-pass reviews—reads text multiple ways, flags rough spots\u002Fincoherence\u002Ffacts—faster than humans for routine, saving time without full automation.",[22,4349,4350],{},"Build logic: Identify repeatable structures with clear boundaries (e.g., refunds vs. upgrades in customer success—not one mega-plugin). Plugins aren't app-store shopping; they're active packaging: \"What part of my work has enough repeatable structure that the agent should be able to inherit it?\"",[22,4352,4353],{},"Tradeoffs: High setup (but easier in 2026—no engineering needed); golden skill to bound workflows right, avoiding bloated\u002Fover-wide packages prone to failure.",[22,4355,4356],{},"\"You are literally the human plugin cuz you copy from one app you paste into the chat... if you don't want to be the human plugin consider making an actual plugin.\" (Jones demystifies plugins, showing serious AI users already do this manually—formalize for leverage.)",[17,4358,4360],{"id":4359},"mcpsconnectors-for-live-data-hooksscripts-for-determinism","MCPs\u002FConnectors for Live Data, Hooks\u002FScripts for Determinism",[22,4362,4363],{},"MCPs (Managed Compute Providers?) and app connectors plug agents into live systems: Slack, Figma, GitHub, Salesforce. Like old-school internet plugs—fetch real data, not imagined. Plugins often contain MCPs but add workflow layers (e.g., data → process → output).",[22,4365,4366],{},"Hooks\u002Fscripts handle non-LLM-trustable parts: Format code (run formatter), validate schemas\u002FJSON, execute tests, enforce pre-stop reviews. Deterministic, not model's \"best judgment.\" Embed in plugins for full loops.",[22,4368,4369],{},"Confusion fix: MCPs ≠ plugins (data access vs. full package); hooks\u002Fscripts ≠ MCPs (internal checks vs. external plugs). All Lego bricks; plugins pre-assemble into structures.",[22,4371,4372],{},"\"Some things ought to be deterministic by which I mean some things should not be left to the model... if the tests need to pass run the test don't ask the model to imagine running the test.\" (Emphasizes reliability—agents shine on ambiguity, falter on facts; scripts ensure quality.)",[17,4374,4376],{"id":4375},"marketplaces-evolve-beyond-app-stores","Marketplaces Evolve Beyond App Stores",[22,4378,4379],{},"Plugins distribute via marketplaces, but undersold as \"extensions.\" Think workflow sharing: Team installs PR review plugin—your standards, tools, checks—once. Enables semantic workflow design: Right unit? (E.g., separate refund\u002Fupgrade plugins over monolithic customer success.)",[22,4381,4382],{},"Result: Scaffolded agents 10x generic prompting. Same LLM, better output via human-provided structure. Non-engineers build now; 2025 impossible, 2026 routine.",[22,4384,4385],{},"\"If you think of plugins as workflow packaging you're going to ask a much sharper question... 'What part of my work has enough repeatable structure?'\" (Challenges passive consumption, urging proactive bounding for team-scale reuse.)",[17,4387,250],{"id":249},[34,4389,4390,4393,4396,4399,4402,4405,4408,4411],{},[37,4391,4392],{},"Audit workflows: One-off → prompt; repeatable style → skill (Markdown); full package → plugin.",[37,4394,4395],{},"Prioritize 20% high-value skills\u002Fplugins (frequent, sensitive tasks like PRs\u002Fmarketing).",[37,4397,4398],{},"Bound plugins tightly: One job per (e.g., refunds ≠ full customer success) to avoid failure.",[37,4400,4401],{},"Embed determinism: Hooks\u002Fscripts for formatting\u002Ftests\u002Fvalidation inside plugins.",[37,4403,4404],{},"Use MCPs\u002Fconnectors for live data (Salesforce\u002FGitHub); plugins wrap them with process.",[37,4406,4407],{},"Build now: Non-coders can; formalize your \"human plugin\" habits for 10x team leverage.",[37,4409,4410],{},"Test trust: Checklist before install—does it match standards? Pull right data?",[37,4412,4413],{},"Distribute via marketplaces: Sharable team assets, not solo hacks.",{"title":146,"searchDepth":158,"depth":158,"links":4415},[4416,4417,4418,4419,4420,4421],{"id":4308,"depth":158,"text":4309},{"id":4321,"depth":158,"text":4322},{"id":4340,"depth":158,"text":4341},{"id":4359,"depth":158,"text":4360},{"id":4375,"depth":158,"text":4376},{"id":249,"depth":158,"text":250},[],{"content_references":4424,"triage":4434},[4425,4428,4431],{"type":302,"title":4426,"url":4427,"context":300},"Ultimate Codex Plugin Guide","https:\u002F\u002Fnatesnewsletter.substack.com\u002Fp\u002Fcodex-plugins-bottleneck-moved?r=1z4sm5&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true",{"type":298,"title":4429,"url":4430,"context":300},"AI News & Strategy Daily","https:\u002F\u002Fopen.spotify.com\u002Fshow\u002F0gkFdjd1wptEKJKLu9LbZ4",{"type":298,"title":4432,"url":4433,"context":300},"AI News & Strategy Daily with Nate B Jones","https:\u002F\u002Fpodcasts.apple.com\u002Fus\u002Fpodcast\u002Fai-news-strategy-daily-with-nate-b-jones\u002Fid1877109372",{"relevance":177,"novelty":171,"quality":171,"actionability":171,"composite":4435,"reasoning":4436},4.35,"Category: AI & LLMs. The article provides a deep exploration of how to effectively scaffold AI agents for complex workflows, addressing a key pain point for builders who struggle with prompt overload. It offers actionable insights on creating reusable skills and plugins, which can directly enhance productivity in AI-powered product development.","\u002Fsummaries\u002F1efced8af0da0fa8-ai-agents-need-scaffolding-prompts-to-plugins-guid-summary","2026-05-09 15:00:09","2026-05-10 15:02:24",{"title":4298,"description":146},{"loc":4437},"2557f3671dbddfb1","AI News & Strategy Daily | Nate B Jones","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=647pSnX5H_Y","summaries\u002F1efced8af0da0fa8-ai-agents-need-scaffolding-prompts-to-plugins-guid-summary",[319,320,321,4448],"ai-automation","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002F647pSnX5H_Y\u002Fhqdefault.jpg","Most waste 40% of AI time on prompts for repeatable tasks. Build agent 'mech suits' with skills for house style, plugins for full workflows, MCPs for data access, and hooks\u002Fscripts for reliability—reusable across teams and LLMs.","Breakdown of \"agentic scaffolding\" around LLMs in Codex: prompts for one-offs, skills for reusable \"house styles,\" plugins for installable workflows, plus MCPs\u002Fconnectors and hooks\u002Fscripts. Links to a [Substack guide](https:\u002F\u002Fnatesnewsletter.substack.com\u002Fp\u002Fcodex-plugins-bottleneck-moved?r=1z4sm5&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true) with decision trees and examples.",[4448],"gP1VRYquj61rbJeB3ga584672Yfi069-FO1bPSgFFjk"]