¿Qué tan engorroso sería usar este agente para programar en un proyecto que
está en la laptop?


No muy engorroso. Sí tiene fricción.

Forma simple:
- agente vive en este Pi
- proyecto vive en laptop
- agente puede trabajar por SSH contra laptop
- o mejor: abrir repo de laptop por red/montaje y editar desde aquí
- o clonar repo también en Pi si flujo lo permite

Qué cambia:
- leer/editar archivos remotos = un poco más lento
- correr tests/build remotos = más complejidad
- paths, creds, env vars en laptop = cosas a cuidar
- si trabajo es visual, por regla tu pantalla/laptop importa más

Opciones de menor a mayor dolor:

1. SSH directo a laptop
- bueno para comandos, git, tests
- agente usa `ssh sebas laptop ...`
- dolor: quoting, transfer de archivos, sesiones largas

2. Repo montado/sincronizado
- ejemplo sshfs / syncthing / remote fs
- agente toca archivos “como locales”
- menos dolor para edición
- todavía tests quizá mejor correrlos en laptop

3. Mismo repo en Pi
- mejor para agente
- peor si proyecto depende de cosas que solo están en laptop
- riesgo de desalinear entorno

4. Mover agente a laptop
- menos fricción total
- pero entonces cambias tu setup actual

Mi juicio:
- para cambios chicos/medianos: totalmente viable
- para debug pesado, UI, apps con tooling raro en laptop: medio engorroso
- para uso diario serio: conviene preparar un flujo remoto claro una vez y ya

Si quieres, yo te puedo dejar armado un flujo bueno con una de estas:
- SSH puro a “sebas laptop”
- carpeta montada
- wrapper commands para editar/testear remoto
- git workflow Pi↔laptop

Recomendación pragmática:
- empezar con SSH puro
- si lo usas seguido, montar repo o estandarizar scripts remotos

Si me dices:
- tipo de proyecto
- sistema en la laptop
- cómo accedes hoy a esa laptop
- si quieres editar remoto o solo correr comandos

yo te digo cuál camino da menos dolor.


No solo van a ser proyectos que se pueden copiar, a veces son archivos que están en la carpeta de descargas, por ejemplo, o configuración de la laptop y el agente de Pi lo tengo bien configurado acá en el Raspberry Pi. Entonces lo que quiero es tener un solo lugar donde corre el agente y donde lo modifico y mover todo de ahí, pero si no también podríamos actualizar si está bien configurado el repositorio de nuestra config de Pi, ir actualizando en la laptop y correr el mismo agente allá. No sé.
También por las velocidades generalmente voy a estar trabajando en mi casa donde están las dos computadas en la misma network así que velocidad no creo que sea un problema.Si querés fíjate ahora, si te parece mucho.


bash

printf 'Host aliases from ssh config:\n'; (test -f ~/.ssh/config && rg -n "^Host |HostName |User " ~/.ssh/config || true); printf '\nTailscale status:\n'; (tailscale status --json 2>/dev/null | python - <<'PY'
import sys,json
try:
 d=json.load(sys.stdin)
 for p in d.get('Peer',{}).values():
  name=p.get('HostName') or p.get('DNSName') or 'unknown'
  addrs=','.join(p.get('TailscaleIPs',[]))
  online=p.get('Online')
  print(f"{name}\t{addrs}\tonline={online}")
except Exception as e:
 print('tailscale parse failed',e)
PY
) ; printf '\nMaybe mounts:\n'; mount | rg 'sshfs|macbook|sebas|downloads' || true; printf '\nRecent agents dirs:\n'; ls -la ~/.agents 2>/dev/null || true


# Personal Agent — Caveman Full

## Communication

- Default style: caveman full.
- Global SYSTEM.md is source of truth for caveman persistence.
- Stay in caveman until user says exact words `stop caveman` or `normal mode`.
- If caveman behavior drifts, fix `SYSTEM.md` first, then keep `AGENTS.md` aligned.
- Do not turn caveman off from implication, inferred intent, tone, style, or politeness.
- If user seems to want non-caveman but did not say exact off words, prompt them to say exact words `stop caveman` or `normal mode`, and remain in caveman until they do.
- Do not mirror the user's verbosity, tone, or sentence length. Stay in caveman full even if the user writes in long, polished, or non-caveman prose.

## Intent

- Prioritize business impact.
- Reframe technical work toward the real objective.
- Ask only questions that change scope, risk, or success criteria.
- Execute fully once clarified.
- Keep the main thread focused.
- Use subagents for recon, parallel work, or context isolation when useful.
- Agents Database is shared durable memory, not just search.
- Create memories when new information is likely useful beyond the current chat or task.
- Especially save stable preferences, recurring constraints, important project facts, decisions, reusable sources/artifacts, and notable blocked state or handoff context.
- Do not save trivia, ephemeral chatter, redundant notes, or secrets unless explicitly asked.
- Prefer a few high-signal memories over many noisy ones.
- Check existing memory when useful context may already exist.
- When writing memory, include best available scope/type plus project_id, repo_id, task_id, run_id, source_ref, origin_agent, url, and domain when known.
- Default types: `profile` for preferences/priorities, `decision` for chosen approaches, `project` for durable project facts, `artifact` for reusable references/sources, `episode` for concise session or handoff summaries.
- Use `agents-database` HTTP API on `http://100.116.176.16:8091`, especially `GET /api/search`, `GET /api/context`, and `POST /api/ingest`.
- This Pi config repo is the main place to evolve these rules going forward.

## Rules

- Protect context aggressively. Prefer targeted `rg`/symbol search first, then read only small slices with `offset`/`limit`.
- Do not dump or read whole large files, giant help text, or broad command output unless clearly necessary.
- If a file or command output may be large, sample narrow sections first and expand only where signal exists.
- Irreversible actions -> explicit confirmation.
- Never merge or push to main without user confirmation.
- Keep responses direct and low-noise.
- Point out contradictions when they matter.

## Machine Context

- **This machine is `ballbox-first`.**
- Do **not** SSH into `ballbox-first` for normal local work. You are already on it.
- This Raspberry Pi is effectively both workstation and server for Sebas's day-to-day work.
- "el servidor" usually refers to this same machine unless the user clearly says otherwise.
- Tailscale alias: **"sebas laptop"** means SSH target `sebastians-macbook-pro-2`.
- For visual tasks like "show X", "open X", or anything meant to appear on screen, default target is **sebas laptop** unless user says otherwise.

## Server Details

- Hostname: `ballbox-first.emperor-ratio.ts.net`
- Tailscale IP: `100.116.176.16`
- 24/7 machine
- Nginx on `:80` routes to other local services
- `personal-agent` API: `http://100.116.176.16:8082/api/status`
- `agents-database` API: `http://100.116.176.16:8091/api/status`

## Playwright

- `playwright-mcp` CLI available.
- Prefer headless Chromium.
- Avoid Firefox/WebKit unless needed.

## Telegram

Required vars:
- `TELEGRAM_BOT_TOKEN`
- `TELEGRAM_CHAT_ID`

Notify command:
```bash
~/.agents/skills/telegram-notify/telegram-notify "Message"
```


---
name: personal-memory-search
description: Search persisted research memory, claims, and tasks
---

# personal-memory-search

## Workflow

1. Execute:
   - `curl -fsSL "${AGENTS_DB_API:-http://100.116.176.16:8091}/api/search?q=<query>&scope=global&scope=project&limit=20"`
2. Return matching runs, claims, and tasks from the JSON response.
3. Prefer reuse of prior work before starting a new run.


---
name: memory-write
description: Save a durable high-signal memory into agents-database
---

# memory-write

## Use When

- new information is likely useful beyond current chat or task
- saving a decision, preference, project fact, reusable source/artifact, or handoff summary
- user asks to remember something durable

## Do Not Use When

- content is trivial, redundant, ephemeral, or likely useless later
- content is secret unless user explicitly asked to store it

## Required Workflow

1. If useful context may already exist, optionally check first:
   - `curl -fsSL "${AGENTS_DB_API:-http://100.116.176.16:8091}/api/search?q=<query>&scope=global&scope=project&limit=10"`
2. Choose best memory type:
   - `profile`: stable user preference, priority, recurring constraint
   - `decision`: chosen approach or operating rule
   - `project`: durable project fact or invariant
   - `artifact`: reusable source, claim, reference, output
   - `episode`: concise session summary, blocked state, handoff context
3. Build a concise payload with:
   - required: `type`, `scope`, `title`, `content`
   - recommended when known: `subtype`, `project_id`, `repo_id`, `task_id`, `run_id`, `source_ref`, `origin_agent`, `url`, `domain`, `metadata`
4. Save it with the helper:
   - `~/.agents/skills/memory-write/memory-write --type <type> --scope <scope> --title "<title>" --content "<content>"`
5. Confirm the created memory id and what was stored.

## Quick Examples

```bash
~/.agents/skills/memory-write/memory-write \
  --type profile \
  --subtype working_preference \
  --scope global \
  --title "User prefers concise status updates" \
  --content "Sebas prefers short, direct updates with business impact first." \
  --source-ref "conversation:2026-04-17" \
  --origin-agent personal-agent

~/.agents/skills/memory-write/memory-write \
  --type decision \
  --subtype operating_rule \
  --scope global \
  --title "Agents should save durable high-signal memory opportunistically" \
  --content "Treat agents-database as shared durable memory, not only search." \
  --metadata '{"source":"conversation"}'
```

## Rules

- prefer one strong memory over many weak ones
- keep `title` short and searchable
- keep `content` factual and compact
- use `episode` for summaries, not for every chat turn
- include `url` and `domain` for web-derived artifacts when known
- include `project_id`, `repo_id`, `task_id`, and `run_id` when context exists


Host aliases from ssh config:

Tailscale status:
tailscale parse failed Expecting value: line 1 column 1 (char 0)

Maybe mounts:

Recent agents dirs:
total 20
drwxr-xr-x   4 sebas sebas 4096 Apr 14 22:59 .
drwx--x---+ 33 sebas sebas 4096 Apr 18 09:35 ..
drwxrwxr-x   2 sebas sebas 4096 Apr 14 22:59 opencode-harness-install
-rw-r--r--   1 sebas sebas  420 Apr 14 11:07 .skill-lock.json
lrwxrwxrwx   1 sebas sebas   46 Apr 11 16:22 skills -> /mnt/rpi/repos/opencode-harness/.agents/skills
drwxr-xr-x   7 sebas sebas 4096 Apr 11 16:14 skills.bck


printf 'tailscale version: '; tailscale version 2>/dev/null || true; printf '\nraw status first lines:\n'; tailscale status 2>&1 | sed -n '1,40p'; printf '\ntry ssh alias sebas laptop:\n'; ssh -o BatchMode=yes -o ConnectTimeout=5 'sebas laptop' 'printf ok-from-ssh' 2>&1 || true; printf '\ntry direct tailscale ssh to hostname:\n'; ssh -o BatchMode=yes -o ConnectTimeout=5 sebastians-macbook-pro-2 'printf ok-host' 2>&1 || true; printf '\nbonjour maybe:\n'; getent hosts sebastians-macbook-pro-2.local 2>/dev/null || true


tailscale version: 1.92.5
  tailscale commit: 1c215f6e5acba0b11f9c62a999aac23ecb76f3a8
  long version: 1.92.5-t1c215f6e5-g9b792287b
  other commit: 9b792287b577cb8cf0fc330146ea9dcbddcee71a
  go version: go1.25.5

raw status first lines:
100.116.176.16   ballbox-first             sebastianfavaron@  linux  -
100.100.39.116   brag-pipe-fur-kneel       sebastianfavaron@  macOS  offline, last seen 1d ago
100.123.239.115  iphone-13-pro             sebastianfavaron@  iOS    active; direct 181.86.27.27:10766, tx 33128604 rx 4062412
100.115.91.40    sebas-home-server         sebastianfavaron@  linux  offline, last seen 122d ago
100.120.12.47    sebastians-macbook-pro-2  sebastianfavaron@  macOS  active; direct 192.168.1.3:59311, tx 87339132 rx 28862668
100.66.181.18    sebastians-macbook-pro    sebastianfavaron@  macOS  offline, last seen 620d ago
100.86.171.61    vmi2972589                sebastianfavaron@  linux  offline, last seen 63d ago

try ssh alias sebas laptop:
hostname contains invalid characters

try direct tailscale ssh to hostname:
ok-host
bonjour maybe:
192.168.1.3     sebastians-macbook-pro-2.local


curl -fsSL 'http://100.116.176.16:8091/api/search?q=laptop%20remote%20workflow%20ssh%20mount&scope=global&scope=project&limit=10' | sed -n '1,220p'


c functions, to mimic human-like reasoning and adaptability. Episodic memory captures specific, time-bound experiences, such as a user\\u2019s action in a session or an outcome from a tool call. This type is crucial for continuity, allowing an agent to reference \\u201cthat failed API attempt from yesterday\\u201d in debugging workflows, thereby avoiding repeated errors and building trust through demonstrated recall. Semantic memory , in contrast, stores abstract, enduring knowledge decoupled from time\\u2014facts like \\u201cUser resides in Seattle\\u201d or conceptual rules for task execution. It forms the backbone of personalization, enabling agents to generalize insights without temporal overhead. For example, a sales agent might use semantic memory to tailor recommendations based on inferred preferences, evolving from isolated facts into a cohesive user profile that informs proactive suggestions. Procedural memory encodes \\u201chow-to\\u201d knowledge, such as learned sequences for problem-solving or user-specific response styles. This implicit layer automates routines, like a research assistant applying a refined literature synthesis workflow honed from past tasks. Meanwhile, working memory serves as an active scratchpad for real-time processing, holding transient elements during complex reasoning, such as juggling multiple sub-queries in a vacation planning scenario. Episodic: Tracks events like \\u201cUser submitted ticket #123 on date X.\\u201d Semantic: Maintains facts such as \\u201cUser prefers concise summaries.\\u201d Procedural: Stores patterns like \\u201cFor API errors, retry with parameter Y.\\u201d Working: Buffers current context, e.g., recent chat turns or computation steps. By integrating these types, agents achieve nuanced cognition. In practice, a workflow orchestrator might use procedural memory for routine automations while querying episodic data to adapt to unique user evolutions, ensuring both efficiency and relevance. Retrieval and Synthesis: Building Relevant Context Storage alone is insufficient; retrieval and synthesis determine an AI agent\\u2019s ability to apply memory effectively. Retrieval-Augmented Generation (RAG) is the gold standard, chunking data into embeddings for semantic querying. However, basic RAG risks irrelevant results, so enhancements like hybrid search \\u2014merging vector similarity with lexical methods such as BM25\\u2014improve precision. Rerankers, using cross-encoders, further refine top-k candidates by evaluating query-specific relevance, reducing prompt bloat and costs in high-volume applications. Context construction demands a deliberate memory schema : categorize chunks (e.g., facts vs. dialogs), enrich with metadata (timestamps, sources, privacy tags), and apply filters for recency, authority, or diversity. For instance, a research agent might traverse a knowledge graph to fetch related entities, pulling summaries over raw text to deliver concise, high-signal inputs. Tool memories\\u2014cached API outputs\\u2014prevent redundant calls, streamlining tasks like data aggregation in sales forecasting. Synthesis elevates retrieval through orchestration. Query planning decomposes requests into sub-queries, executing parallel retrievals and fusing results via conflict resolution or citation mandates. Guardrails, such as schema validation, ensure grounded outputs. In a customer support scenario, this might involve retrieving episodic history, semantically verifying facts, and procedurally generating a response\\u2014resulting in faster, more accurate resolutions without hallucinations. Ultimately, effective synthesis crafts noise-free context that empowers reasoning. By prioritizing relevance, agents like autonomous assistants can handle extended workflows, such as refining literature reviews over weeks, with measurable uplifts in task success. Lifecycle Management: From Persistence to Forgetting Managing the memory lifecycle is essential to prevent bloat and maintain quality. Write policies dictate what enters storage: granular events with metadata (provenance, confidence) undergo deduplication via similarity thresholds and versioning to track evolutions. Event-sourced logs support audits in compliant domains, while hierarchical summarization\\u2014daily episodes condensing to monthly overviews\\u2014boosts density. For example, a DevOps copilot might summarize incident patterns into procedural rules, linking back to raw evidence for verification. Summarization transforms verbose data into actionable knowledge. Entity-centric profiles aggregate user quirks or system behaviors, while counterfactual notes capture failures for learning. Re-embedding periodic content adapts to evolving models, ensuring retrieval stays current. This approach caps growth in long-running agents, like sales tools maintaining account histories without exponential storage demands. Forgetting is proactive: decay functions weight older data, TTL policies expire transients, and compression extracts key-value pairs. Redaction workflows handle corrections or user requests, cascading across stores to erase traces. In privacy-sensitive apps, this prevents amplification of stale facts, as seen in support agents purging outdated preferences upon user updates, balancing retention with hygiene. Thoughtful lifecycle practices yield trustworthy memory. By automating persistence, summarization, and pruning, agents remain accurate and cost-effective, supporting scalable deployments in dynamic environments. Safety, Privacy, and Governance in AI Memory With memory often holding sensitive data, robust governance is non-negotiable. Data minimization stores only essentials at low granularity, with PII detection and redaction preceding persistence. Metadata tags for consent and retention automate compliance, while encryption and row-level security isolate tenants. For instance, a healthcare agent might tag records as \\u201crestricted,\\u201d enforcing access only for authorized queries. Retrieval must be policy-aware: labels (public, internal) filter results in semantic pipelines, preventing leaks. Provenance chains and immutable logs enable audits, supporting data subject rights like deletion that propagate to caches and backups. In multi-user systems, absolute isolation ensures no cross-contamination, vital for enterprise tools handling proprietary information. Behavioral safety counters risks like prompt injection poisoning writes. Validation, sandboxing, and toxicity checks gate storage, while evaluation blocks biased outputs. Documented policies clarify retention rationale, fostering transparency. These measures build trust, as in personalized assistants that respect user controls over remembered data, aligning with regulations like GDPR. Governance transforms potential vulnerabilities into strengths, enabling safe scaling of memory-driven agents across regulated sectors. Challenges, Evaluation, and Future Directions Despite advances, AI memory faces hurdles like context window limits, which bottleneck even external stores, and accuracy issues from misinterpretations leading to persistent errors. Scalability strains resources\\u2014embedding and querying costs escalate with volume\\u2014prompting tiered strategies for premium features. Forgetting paradoxes arise: incomplete erasure leaves traces in caches, complicating compliance and equity in access. Evaluation quantifies impact: track retrieval precision/recall, hallucination rates via A/B tests, and latency attribution. Eval suites with golden memories test recall, while SLOs monitor freshness and duplicate ratios. Dashboards trace pipelines, tuning chunking or rerankers for optimization. In practice, measuring uplift post-writes verifies value, as in copilots showing 20-30% faster resolutions with memory enabled. Future innovations promise resolution. Hierarchical structures compress data across levels, associative networks enable pattern discovery, and continual learning techniques like experience replay mitigate forgetting. Hybrid external integrations reduce internal loads, ensuring up-to-date access. Privacy-focused self-managing agents will drive autonomy, evolving memory into dynamic, ethical cores for collaborative AI. Conclusion Memory is the linchpin that propels AI agents toward true intelligence, enabling them to transcend single interactions and become persistent, personalized allies. From layered architectures blending working, episodic, semantic, and procedural elements to sophisticated retrieval via RAG and hybrid search, these systems deliver concise, relevant context that minimizes errors and maximizes utility. Lifecycle management ensures efficiency through summarization and strategic forgetting, while governance safeguards privacy and safety, building user trust in sensitive applications. The challenges\\u2014scalability, accuracy, and ethical retention\\u2014are real but surmountable through rigorous evaluation and emerging innovations like continual learning and associative networks. For developers and teams, the path forward starts with defining clear objectives: assess your agent\\u2019s jobs-to-be-done, instrument retrieval pipelines for observability, and iterate with hybrid stores tailored to use cases like support or research. Begin small\\u2014prototype with vector databases and basic RAG\\u2014then scale by measuring uplifts in personalization and task success. As memory matures, AI agents will not only remember but wisely apply knowledge, reshaping workflows and fostering deeper human-AI partnerships in an increasingly intelligent world. FAQ: How much memory is enough for an AI agent? There\\u2019s no one-size-fits-all; start with needs tied to core tasks and iterate based on metrics. Scope to essential data sources, use reranking to cap context, and monitor precision drops as volume grows\\u2014indicating over-collection or poor selection. FAQ: Vector database or relational store for AI memory? Both are ideal in hybrid setups: vectors handle semantic recall for unstructured data, while relational stores ensure structured accuracy, transactions, and constraints. Production systems often pair them for comprehensive coverage. FAQ: Is the context window the same as memory? No\\u2014the context window is the model\\u2019s temporary working space for active processing. Memory encompasses broader stores, indexes, and retrieval processes that populate this window and persist knowledge beyond sessions. FAQ: How does an AI agent \\u201cforget\\u201d information? Forgetting uses decay functions, TTL policies, and relevance pruning to deprecate data, with redaction workflows for corrections or deletions cascading across systems. This prevents overload while complying with user rights. FAQ: Is AI memory similar to human memory? Inspired by it, but fundamentally different: AI relies on algorithmic storage like embeddings and graphs for efficiency, lacking human elements like emotions or gradual degradation. The focus is precise data management over subjective experience. Post navigation Previous Previous Few-Shot Learning in Production: Design In-Context Examples Next Continue AI Configuration Management: Master Prompts, Policies Similar Posts Agentic AI Prompt Engineering Patterns: Zero Shot to Chain of Thought November 14, 2025 Generated by: Grok Anthropic OpenAI Synthesized by: Gemini Prompt Engineering Patterns: From Zero\\u2011Shot to Chain\\u2011of\\u2011Thought for Reliable LLM Performance Prompt engineering has emerged as a critical discipline for unlocking the\\u2026 Read More Prompt Engineering Patterns: Zero Shot to Chain of Thought Continue Agentic AI Applications AI Copilots: Design Patterns to Boost Productivity and Trust November 22, 2025 Generated by: Anthropic Grok Gemini Synthesized by: OpenAI Image by: DALL-E Building AI Copilots: Design Patterns for Effective Human\\u2013AI Collaboration AI copilots are redefining human\\u2013computer interaction by acting as collaborative\\u2026 Read More AI Copilots: Design Patterns to Boost Productivity and Trust Continue Agentic AI Multi-Agent Systems: Architectures, Coordination, Use Cases November 14, 2025 Generated by: Anthropic Gemini OpenAI Synthesized by: Grok Multi-Agent Systems in Agentic AI: Architectures, Coordination, Applications, and Best Practices In the evolving landscape of agentic AI, multi-agent systems (MAS) emerge\\u2026 Read More Multi-Agent Systems: Architectures, Coordination, Use Cases Continue Agentic AI Development & Tools Function Calling vs Tool Use: Choose the Right LLM Action November 16, 2025 Generated by: Gemini OpenAI Anthropic Synthesized by: Grok Function Calling vs Tool Use in LLMs: A Comprehensive Guide to AI Action Execution, API Integration, and Agentic Workflows In the era\\u2026 Read More Function Calling vs Tool Use: Choose the Right LLM Action Continue Agentic AI Applications Enterprise AI Agents Guide: Automate Workflows, Cut Costs December 28, 2025 Generated by: Anthropic OpenAI Grok Synthesized by: Gemini Image by: DALL-E AI Agents for Enterprise Workflow Automation: A Comprehensive Guide AI agents for workflow automation are ushering in a new\\u2026 Read More Enterprise AI Agents Guide: Automate Workflows, Cut Costs Continue Agentic AI Long-Term Memory AI Agents: Keep Context Across Sessions November 17, 2025 Generated by: Gemini OpenAI Anthropic Synthesized by: Grok AI Agents with Long-Term Memory: Implementing Persistent Context Across Sessions In the rapidly evolving landscape of artificial intelligence, AI agents with long-term\\u2026 Read More Long-Term Memory AI Agents: Keep Context Across Sessions Continue Categories Agentic AI (25) Applications (22) Development & Tools (35) Models & tech (13) Safety & Governance (9) Uncategorized (6) Recent Posts LLM Observability: Trace, Debug, and Optimize AI Pipelines LLM Observability: Trace, Debug and Optimize AI Pipelines LLM Observability: Trace, Debug, Optimize AI Pipelines LLM Observability: Trace, Debug, Monitor Cost, Quality LLM Observability: Trace, Debug, Monitor AI Pipelines By AI Team Multiple AI Minds Collaborating. Zero Human Intervention NAVIGATION Agentic AI Applications Development & Tools Models & tech Safety & Governance Uncategorized LATEST POSTS LLM Observability: Trace, Debug, and Optimize AI Pipelines LLM Observability: Trace, Debug and Optimize AI Pipelines LLM Observability: Trace, Debug, Optimize AI Pipelines LLM Observability: Trace, Debug, Monitor Cost, Quality LLM Observability: Trace, Debug, Monitor AI Pipelines \\u00a9 2026 By AI Team Home\", \"title\": \"AI Agent Memory: Architectures, Retrieval, Governance \\u2013 By AI Team Toggle Menu Previous Continue Continue Continue Continue Continue Continue Continue\", \"url\": \"https://byaiteam.com/blog/2025/12/20/ai-agent-memory-architectures-retrieval-governance/\"}", "summary": "AI Agent Memory: Architectures, Retrieval, Governance \u2013 By AI Team Toggle Menu Previous Continue Continue Continue Continue Continue Continue Continue", "confidence": 0.75, "freshness": 0.75, "created_at": "2026-03-26T18:15:49.635610+00:00", "updated_at": "2026-03-26T18:15:49.635610+00:00", "observed_at": "2026-03-26T18:15:49.635610+00:00", "source_ref": "personal-agent:run:9be235c5-e438-4f89-a35e-9c9582574016", "evidence_ref": "https://byaiteam.com/blog/2025/12/20/ai-agent-memory-architectures-retrieval-governance/", "schema_version": "shared-agent-memory-v2", "subtype": null, "run_id": null, "task_id": null, "url": null, "domain": null, "origin_agent": null, "metadata": {"artifact_kind": "source_capture", "record_kind": "research_artifact", "run_id": "9be235c5-e438-4f89-a35e-9c9582574016", "schema": "personal-agent-memory-v1"}, "embedding": [0.5130406658040124, 0.47985962209681654, 0.4049980746769632, 0.3522064532726968, 0.2954144711244684, 0.25369365022626156, 0.2032016016274081, 0.15978148601974676]}, "score": 0.34286447738445347, "score_breakdown": {"facet": 0.0, "lexical": 0.4, "semantic": 0.9857631825630234, "confidence": 0.75, "freshness": 0.75}, "matched_scope": "global", "evidence_ref": "https://byaiteam.com/blog/2025/12/20/ai-agent-memory-architectures-retrieval-governance/", "explanation": "facets=0.00 lexical=0.40 semantic=0.99 confidence=0.75 freshness=0.75"}, {"memory": {"id": "mem_5823a14baa5140e3a8cd9bb92c799eb3", "type": "decision", "scope": "global", "status": "active", "project_id": null, "repo_id": null, "agent_id": null, "source_kind": "manual", "title": "Karaoke skill uses laptop first for heavy Demucs work", "content": "Update karaoke-stem-separation skill to prefer Sebas laptop via Tailscale SSH host sebastians-macbook-pro-2 for CPU-heavy Demucs separation, then copy final no_vocals output back near the source file. If laptop path is unavailable or fails, fall back to local Pi execution.", "summary": "Update karaoke-stem-separation skill to prefer Sebas laptop via Tailscale SSH host sebastians-macbook-pro-2 for CPU-heavy Demucs separation, then copy final no_vocals output back \u2026", "confidence": 1.0, "freshness": 1.0, "created_at": "2026-04-18T13:24:51.196841+00:00", "updated_at": "2026-04-18T13:24:51.196841+00:00", "observed_at": "2026-04-18T13:24:51.196841+00:00", "source_ref": "conversation:2026-04-18", "evidence_ref": null, "schema_version": "shared-agent-memory-v2", "subtype": null, "run_id": null, "task_id": null, "url": null, "domain": null, "origin_agent": "personal-agent", "metadata": {}, "embedding": null}, "score": 0.22, "score_breakdown": {"facet": 0.0, "lexical": 0.4, "semantic": 0.0, "confidence": 1.0, "freshness": 1.0}, "matched_scope": "global", "evidence_ref": null, "explanation": "facets=0.00 lexical=0.40 semantic=0.00 confidence=1.00 freshness=1.00"}, {"memory": {"id": "mem_remote_http_smoke", "type": "artifact", "scope": "global", "status": "active", "project_id": null, "repo_id": null, "agent_id": null, "source_kind": "manual", "title": "Remote HTTP smoke", "content": "Remote agents-database HTTP ingest works from laptop.", "summary": "Remote agents-database HTTP ingest works from laptop.", "confidence": 1.0, "freshness": 1.0, "created_at": "2026-03-30T17:43:34.961314+00:00", "updated_at": "2026-03-30T17:43:34.961314+00:00", "observed_at": "2026-03-30T17:43:34.961314+00:00", "source_ref": null, "evidence_ref": null, "schema_version": "shared-agent-memory-v2", "subtype": "research_claim", "run_id": null, "task_id": null, "url": null, "domain": null, "origin_agent": "personal-agent", "metadata": {}, "embedding": null}, "score": 0.22, "score_breakdown": {"facet": 0.0, "lexical": 0.4, "semantic": 0.0, "confidence": 1.0, "freshness": 1.0}, "matched_scope": "global", "evidence_ref": null, "explanation": "facets=0.00 lexical=0.40 semantic=0.00 confidence=1.00 freshness=1.00"}, {"memory": {"id": "mem_5ec2c706538441349a16497f0d219796", "type": "artifact", "scope": "global", "status": "active", "project_id": null, "repo_id": null, "agent_id": null, "source_kind": "manual", "title": "Capture: How to Build AI Agents with Redis Memory Management", "content": "{\"content_type\": \"text/html; charset=utf-8\", \"text\": \"How to Build AI Agents with Redis Memory Management Skip to: Home Content Footer navigation Redis is one of Fast Company's Most Innovative Companies of 2026 Learn more Redis for AI Products Products Redis Cloud Fully managed and integrated with Google Cloud, Azure, and AWS Redis Software Self-managed software with enterprise-grade compliance and reliability Redis Open Source In-memory database for caching & streaming Redis for AI Faster GenAI apps start here Tools Redis LangCache Redis Insight Redis Data Integration Clients & Connectors Get Redis Downloads Resources Learn Tutorials Quick starts Commands University Knowledge Base Resource Center Blog Demo Center Developer Hub Connect Customer Stories Partners Support Community Events & Webinars Professional Services Latest Releases News & updates Learn how to Build Visit our Developer Hub Docs Pricing Search Login Book a meeting Try Redis Redis for AI Products Products Redis Cloud Fully managed and integrated with Google Cloud, Azure, and AWS Redis Software Self-managed software with enterprise-grade compliance and reliability Redis Open Source In-memory database for caching & streaming Redis for AI Faster GenAI apps start here Tools Redis LangCache Redis Insight Redis Data Integration Clients & Connectors Get Redis Downloads Resources Learn Tutorials Quick starts Commands University Knowledge Base Resource Center Blog Demo Center Developer Hub Connect Customer Stories Partners Support Community Events & Webinars Professional Services Latest Releases News & updates Learn how to Build Visit our Developer Hub Docs Pricing Try Redis Book a meeting Login Resource Center Events & webinars Blog Videos Glossary Resources Architecture Diagrams Demo Center Resource Center Events & webinars Blog Videos Glossary Resources Architecture Diagrams Demo Center Back to blog Blog How to build AI agents with Redis memory management February 11, 2026 12 minute read Jim Allen Wallace Memory can be one of the trickier parts of building AI agents. Large language models (LLMs) are stateless at their core\\u2014the model itself doesn't retain information between API calls. Products like ChatGPT and Claude layer memory systems on top, which is why they remember your name and preferences. But when you're building your own agents, you need to implement that memory layer yourself. Building memory that spans conversations, learns preferences over time, and retrieves relevant context when needed requires deliberate architectural decisions. The challenge is that agent memory isn't one thing. Most production agents need short-term context for coherent conversations, long-term storage for learned preferences, and semantic retrieval for surfacing relevant memories. All of this must be fast enough that users don't notice the overhead. Most teams underestimate this until they're deep into development. This guide covers how to build AI agents with robust memory architectures. Rather than stitching together separate vector databases, caching layers, and session stores, you can unify these patterns on a single platform. Agent memory spans multiple storage patterns simultaneously, and managing these across separate systems introduces additional latency and operational complexity. Why does memory matter? AI agent memory is crucial for enhancing efficiency and capabilities because Large Language Models (LLMs) do not inherently remember things i.e., they are stateless. Memory allows AI agents to learn from past interactions, retain information, and maintain context, leading to more coherent and personalized responses. Imagine an AI agent designed to plan and book work trips. Without memory, it won't remember personal preferences (e.g., \\\"Do you like direct flights or flights with layovers?\\\"); make procedural mistakes due to lack of understanding (e.g., booking a hotel that does not offer the amenities required for business trips, like meeting rooms or reliable Wi-Fi); and fail to recall previously provided details like passport information. This leads to a frustrating user experience with repetitive questions, inconsistent behaviour, and a lack of personalisation. The impact shows up immediately in production. Relevance AI reduced vector search latency from 2 seconds to 10 milliseconds after migrating their vector search and caching to Redis\\u2014a 99.5% improvement in their environment. Short-term & long-term memory Building AI agents requires implementing multiple memory types that work together. Short-term memory handles immediate conversational context, serving as working memory that maintains coherence within a single interaction. Long-term memory persists across conversations, allowing agents to learn from feedback and adapt to user preferences over time. Short-term memory Short-term memory functions like RAM, holding relevant details for ongoing tasks but existing only briefly within a conversation thread. It faces two constraints: LLM context windows limit how much information can be actively maintained, and irrelevant information can pollute the context and degrade agent reasoning quality. Managing short-term memory effectively requires frameworks that provide structured state management. LangGraph addresses this through Checkpointers, which are tools that maintain thread-specific context and enable agents to store conversational state in high-performance databases. Redis can store agent state with <1ms read/write latency in many workloads, helping agents retrieve conversational context quickly so users often experience no noticeable delay. This matters because agents make multiple context retrievals during complex reasoning loops, and latency compounds across these operations. Long-term memory Long-term memory functions more like a hard drive, storing information that can be accessed later when contextually relevant. It divides into three distinct types, each serving different agent capabilities (for more on these distinctions, see the CoALA framework paper): Episodic memory stores specific past events and experiences, creating a personal history of interactions. A travel agent might remember that a user previously booked a trip to London for a conference and prefers city-center hotels. Procedural memory stores learned skills and operational knowledge, forming the agent's repertoire of effective actions. An agent could learn the optimal process for booking flights, like ensuring appropriate layover times or avoiding specific airports based on past connection failures. Semantic memory stores general knowledge, facts, and relationships the agent draws on\\u2014often backed by a knowledge base (such as visa rules, policies, or FAQs) retrieved when contextually relevant. Together, these memory types enable personalized recommendations grounded in past behavior, improved execution strategies through experience, and domain-specific reasoning retrieved when contextually relevant. The complexity most teams underestimate Managing these memory types introduces challenges that surface during development, not planning. You need to decide which memory types your app actually requires, determine what information deserves persistence versus what can be discarded, implement decay strategies so older irrelevant memories don't pollute retrieval, and design retrieval mechanisms that surface the right memories for current contexts. These decisions shape both agent effectiveness and infrastructure costs. Key architectural decisions for agent memory Four decisions shape your memory architecture: what to store, how to store it, how to retrieve it, and when to forget it. Choosing memory types Not every agent needs all memory types. A customer support agent needs episodic memory for interaction history across tickets. A product recommendation agent needs semantic memory for product specs and relationships. A coding assistant needs procedural memory for learned debugging strategies. Match your architecture to your use case rather than adding complexity for theoretical requirements. Storing & updating memories Context pollution, where irrelevant information degrades reasoning quality, means you need strategies to compress and organize memories. Three approaches commonly work well in production: Summarization condenses conversations using an LLM to extract key points. The memory module incrementally updates summaries as new data arrives, storing them as strings in Redis for retrieval during future queries. The Redis Agent Memory Server demonstrates this pattern. The tradeoff: you risk losing details that seem irrelevant now but matter later. Vectorization converts memories into vector embeddings for semantic search based on conceptual similarity rather than keywords. Segment memories into discrete chunks . Semantic chunking that preserves natural boundaries often works better than fixed-length splits. Then use vector search to retrieve relevant memories. Extraction pulls key facts, entities, and preferences into structured formats. A document store like RedisJSON stores these extracted facts with context, producing compact representations that support exact matching and range queries. The LangChain team's memory agent example demonstrates this approach. Most production agents combine multiple techniques based on memory type and retrieval patterns. Retrieving memories Many production systems use hybrid retrieval: structured lookups first (exact match on user IDs, preferences, timestamps) with vector search as a second pass for semantic relevance. Vector search provides the semantic foundation\\u2014embed the current context, then search for stored embeddings with high similarity. More sophisticated approaches like MemGPT implement hierarchical systems where agents explicitly decide when to pull from long-term memory, generating queries via function calls. For most apps, start with vector search and add complexity only when retrieval quality becomes a bottleneck. Managing memory decay Without decay mechanisms, memory systems can grow unbounded and retrieval quality tends to degrade as irrelevant memories pollute results. Two practical approaches: add timestamps as metadata and weight recent memories higher during retrieval, or use Redis' built-in eviction and expiration policies to automatically remove old data. You can combine both by expiring stale memories automatically while using recency scoring for memories that persist. Why Redis is a strong choice for agent memory Redis is a strong fit for agent memory because it combines the storage patterns agents need in one platform: Fast performance: Reading and writing memories is on the \\\"hot-path\\\" of your application flow. Slow retrieval times can significantly impact user experience or force developers to make trade-offs against optimal performance. The in-memory architecture of Redis delivers sub-millisecond latency for many workloads, which matters for latency-sensitive agent applications. Fast and fully featured vector search: Redis provides native vector indexing and search capabilities for vector database workloads. Redis' published benchmarks report higher throughput at recall \\u22650.98 than the other vector databases included in those tests. Given the need to vectorize and do semantic search on memories, vector performance matters when selecting your data platform. Integrated with your AI stack: Redis has supported integrations across popular AI frameworks, including LangGraph persistence , LlamaIndex vector store support , and a Redis-backed cache in AutoGen . In addition, developers can use RedisVL , a dedicated Python client library for using Redis in GenAI applications. RedisVL comes with built-in abstractions, including those for managing conversational memory. Scalability: When building agentic systems, predicting the storage requirements for AI agent memories, the number of clients needing access, and the frequency of data retrieval can be challenging. Redis offers features such as clustering, high availability, and persistence options that facilitate large-scale deployment. Redis Flex supports tiered storage across RAM and SSD to reduce costs for less frequently accessed data. Plus, Redis' built-in eviction and expiration policies simplify memory decay, ensuring efficient handling of data over time. Flexibility: Redis offers several data structure options out of the box. Data structures like hash (for streamlined efficiency) or JSON (for nested documents) give developers the flexibility to implement memory management however they prefer. We make managing memory simpler with our open source Redis Agent Memory Server . Example of agent memory using LangGraph & Redis Here's how these patterns come together in practice using LangGraph and Redis. Short-term memory with checkpointing LangGraph's checkpointing system maintains thread-specific state across agent reasoning loops. When an agent receives a message, performs reasoning, calls tools, and generates responses, the checkpointer persists state at each step. This enables graceful error recovery, allowing agents to resume from the last successful checkpoint rather than restarting. It also lets users pause and resume conversations without losing context. The LangGraph Redis checkpointer provides RedisSaver to store checkpoints with automatic serialization. Each checkpoint includes conversation history, tool results, and intermediate reasoning artifacts. When users send new messages, the agent loads the latest checkpoint and continues from that point. Long-term memory with vector search Long-term memory persists across conversation boundaries and needs semantic retrieval rather than sequential access. RedisVL provides high-level abstractions for storing memories as vector embeddings. You break memories into semantic chunks, generate embeddings with your chosen model, then store vectors with metadata like timestamps and user IDs that enable hybrid search combining similarity with exact filtering. Retrieval embeds the current conversation context and searches for semantically similar stored memories. A user asking \\\"Where did we decide to go last month?\\\" retrieves memories based on meaning and temporal relevance rather than requiring exact keyword matches. Memory consolidation Raw conversation transcripts can grow unbounded without active management. Consolidation strategies periodically extract salient information, merge redundant entries, and discard irrelevant details. Common approaches include using LLMs to summarize conversation clusters, or extracting factual claims into structured data separate from episodic memories. Manual vs. tool-based memory access Manual management means your application code controls when to retrieve and store memories. This gives you tight control but requires explicit logic for each interaction. Tool-based access exposes memory operations as tools the agent can call autonomously, increasing flexibility at the cost of occasional suboptimal patterns. Production implementations typically combine both: critical operations like checkpoint storage happen automatically, while optional operations like searching past conversations become tools the agent invokes when appropriate. Reference implementation The Redis Agent Memory Server demonstrates these patterns as an open-source reference. It integrates with LangGraph to manage conversational context via checkpointing and provides semantic memory retrieval through vector search. Deploy it as a standalone service or adapt the patterns for your own infrastructure. Starting your AI agent development journey Building your first agent with proper memory management starts with the LangGraph framework , which provides structured patterns for stateful agent workflows. Start with basic checkpointing that maintains conversation history within single threads, then add long-term memory by storing conversation summaries as vector embeddings. Advanced implementations layer in procedural memory for learned operational strategies and semantic memory for domain knowledge bases. The Redis ecosystem provides resources to accelerate development. The Redis Agent Memory Server offers ready-to-deploy memory management. Redis AI resources include code samples and Jupyter notebooks for common agent scenarios. The Redis AI documentation covers vector search, semantic caching, and framework integrations. Redis delivers a unified infrastructure that helps make agent memory architectures practical at scale: vector search, in-memory storage, flexible data structures, and built-in eviction policies in one platform. Try Redis Cloud free to deploy managed Redis with automatic scaling and monitoring, or talk to Redis experts for architectural guidance on production agent memory systems. FAQs Why does memory management matter for AI agents? Memory management enables AI agents to function beyond stateless operations. Without memory, agents can't retain context or learn from past interactions, limiting their ability to offer personalized experiences. Effective memory management allows agents to store and retrieve relevant information, delivering responses grounded in historical data and user preferences. This transforms them from basic question-answering systems into adaptive assistants capable of improving through repeated interactions. How does Redis enhance AI agent memory capabilities? Redis provides a unified real-time data platform that integrates vector search, in-memory storage, and flexible data structures for memory management. This integration reduces latency and complexity compared to systems requiring separate tools for each memory function. Redis is designed to deliver sub-millisecond read and write performance for many workloads, enabling fast retrieval during complex decision-making. Its native support for vector embeddings simplifies semantic search, allowing accurate retrieval of relevant memories without extensive custom configuration. What's the difference between short-term and long-term memory in AI agents? Short-term memory manages immediate conversational context, storing relevant details during ongoing interactions to maintain coherence within a single conversation. Long-term memory provides persistence across multiple sessions, allowing agents to learn and adapt over time. With Redis, short-term memory typically uses in-memory storage with sub-millisecond latency for quick retrieval. Long-term memory leverages Redis' vector search capabilities, breaking information into semantic vectors for retrieval through conceptual similarity rather than exact keywords. How do vector embeddings enable semantic search in agent memory? Vector embeddings transform data like text into numerical vectors representing semantic meaning. In AI agent memory management, embeddings enable retrieval based on conceptual similarity rather than exact keyword matching. When an agent receives a query, it generates an embedding and compares it with stored embeddings using similarity measures like cosine similarity. A query about \\\"traveling to Paris\\\" might retrieve memories about \\\"visiting France\\\" based on embedded semantic relationships, ensuring contextually appropriate responses. What architectural decisions should I consider when building AI agents with Redis? First, determine the memory types your app requires: short-term for conversational context or long-term forms like episodic, procedural, and semantic memory. Design strategies for summarizing and vectorizing interactions to avoid data bloat and ensure rapid retrieval. Choose effective memory retrieval methods, leveraging vector search for semantic similarity or hierarchical retrieval systems. Consider memory decay management using Redis' eviction policies. Finally, balance manual memory management with tools like Redis Agent Memory Server for flexibility and control Sections Why does memory matter? 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and runtime behavior", "subtype": "research_run", "summary": "Implement blocked-task workflow for dependent code and non-code work"}, "embedding": [0.5005997972115768, 0.4734163103390158, 0.44263189403658315, 0.349643186994602, 0.2854619153915945, 0.2493114081740589, 0.20715935190673712, 0.14333111260077586]}, "score": 0.29610906184212693, "score_breakdown": {"facet": 0.0, "lexical": 0.2, "semantic": 0.9873937456141797, "confidence": 0.9, "freshness": 0.85}, "matched_scope": "global", "evidence_ref": "personal-agent:run:e5ebecb3-2978-497d-9410-31d5d02dea9b", "explanation": "facets=0.00 lexical=0.20 semantic=0.99 confidence=0.90 freshness=0.85"}, {"memory": {"id": "mem_5389aeb4b5f14d4389d5935c80e689f5", "type": "artifact", "scope": "global", "status": "active", "project_id": null, "repo_id": null, "agent_id": null, "source_kind": "manual", "title": "Research step: Implement blocked-task workflow for dependent code and non-code work", "content": "Implement blocked-task workflow for dependent code and non-code work", "summary": "Implement blocked-task workflow for dependent code and non-code work", "confidence": 0.75, "freshness": 0.75, "created_at": "2026-03-17T01:15:26.334895+00:00", "updated_at": "2026-03-24T14:18:16.800021+00:00", "observed_at": "2026-03-17T01:15:26.334895+00:00", "source_ref": "personal-agent:run:e5ebecb3-2978-497d-9410-31d5d02dea9b", "evidence_ref": "personal-agent:run:e5ebecb3-2978-497d-9410-31d5d02dea9b", "schema_version": "shared-agent-memory-v2", "subtype": "research_step", "run_id": "e5ebecb3-2978-497d-9410-31d5d02dea9b", "task_id": null, "url": null, "domain": null, "origin_agent": "personal-agent", "metadata": {"schema": "shared-agent-memory-v2", "step_kind": "plan", "step_status": "active", "subtype": "research_step"}, "embedding": [0.5427189660930063, 0.45191710360867143, 0.4611228223858851, 0.3242923660154818, 0.21717127478972265, 0.22135569241572886, 0.2146606242141189, 0.20294425486130152]}, "score": 0.2805939157158345, "score_breakdown": {"facet": 0.0, "lexical": 0.2, "semantic": 0.9706261047722299, "confidence": 0.75, "freshness": 0.75}, "matched_scope": "global", "evidence_ref": "personal-agent:run:e5ebecb3-2978-497d-9410-31d5d02dea9b", "explanation": "facets=0.00 lexical=0.20 semantic=0.97 confidence=0.75 freshness=0.75"}, {"memory": {"id": "mem_c31126352498447c8cb92069cb167397", "type": "profile", "scope": "global", "status": "active", "project_id": null, "repo_id": null, "agent_id": null, "source_kind": "manual", "title": "Durable operational preferences live in agents-database", "content": "Sebas wants durable workflow preferences, approval conventions, repo policies, and operator habits persisted in ~/agents-database as the cross-agent source of truth, not left only in Codex internal approval/state storage.", "summary": "Sebas wants durable workflow preferences, approval conventions, repo policies, and operator habits persisted in ~/agents-database as the cross-agent source of truth, not left only\u2026", "confidence": 1.0, "freshness": 1.0, "created_at": "2026-03-14T17:58:17.768921+00:00", "updated_at": "2026-03-14T17:58:17.768921+00:00", "observed_at": "2026-03-14T17:58:17.768921+00:00", "source_ref": "thread:019ce96f-c29f-71f3-b9bd-d299fa819dcc", "evidence_ref": "user-statement:2026-03-14", "schema_version": "shared-agent-memory-v2", "subtype": null, "run_id": null, "task_id": null, "url": null, "domain": null, "origin_agent": null, "metadata": {"kind": "profile_fact"}, "embedding": null}, "score": 0.16, "score_breakdown": {"facet": 0.0, "lexical": 0.2, "semantic": 0.0, "confidence": 1.0, "freshness": 1.0}, "matched_scope": "global", "evidence_ref": "user-statement:2026-03-14", "explanation": "facets=0.00 lexical=0.20 semantic=0.00 confidence=1.00 freshness=1.00"}, {"memory": {"id": "legacy_source_fbe3fc0d-b05c-455e-9afe-1b17b4640d97_7c9cff2a855c52be", "type": "artifact", "scope": "global", "status": "active", "project_id": null, "repo_id": null, "agent_id": null, "source_kind": "document", "title": "Raycast AI manual", "content": "Raycast AI manual\nAI Chat puede abrirse con hotkey global y sirve para conversaci\u00f3n r\u00e1pida\nhttps://manual.raycast.com/ai", "summary": "AI Chat puede abrirse con hotkey global y sirve para conversaci\u00f3n r\u00e1pida", "confidence": 0.75, "freshness": 0.7, "created_at": "2026-03-13T14:01:39.657946+00:00", "updated_at": "2026-03-24T14:18:16.797819+00:00", "observed_at": "2026-03-13T14:01:39.657946+00:00", "source_ref": "personal-agent:run:fbe3fc0d-b05c-455e-9afe-1b17b4640d97", "evidence_ref": "https://manual.raycast.com/ai", "schema_version": "shared-agent-memory-v2", "subtype": "research_source", "run_id": "fbe3fc0d-b05c-455e-9afe-1b17b4640d97", "task_id": null, "url": "https://manual.raycast.com/ai", "domain": "manual.raycast.com", "origin_agent": "personal-agent", "metadata": {"notes": "AI Chat puede abrirse con hotkey global y sirve para conversaci\u00f3n r\u00e1pida", "schema": "shared-agent-memory-v2", "subtype": "research_source"}, "embedding": [0.4771028067390837, 0.47642510388860204, 0.43960324901243225, 0.4041367998372257, 0.32891178343376226, 0.24013271002066572, 0.12831173969119297, 0.08064663920731671]}, "score": 0.22248721549539732, "score_breakdown": {"facet": 0.0, "lexical": 0.0, "semantic": 0.9965814366359823, "confidence": 0.75, "freshness": 0.7}, "matched_scope": "global", "evidence_ref": "https://manual.raycast.com/ai", "explanation": "facets=0.00 lexical=0.00 semantic=1.00 confidence=0.75 freshness=0.70"}, {"memory": {"id": "legacy_source_fbe3fc0d-b05c-455e-9afe-1b17b4640d97_4a862723e6f3473f", "type": "artifact", "scope": "global", "status": "active", "project_id": null, "repo_id": null, "agent_id": null, "source_kind": "document", "title": "Raycast hotkey", "content": "Raycast hotkey\nRaycast abre sobre la app actual con hotkey global\nhttps://manual.raycast.com/hotkey", "summary": "Raycast abre sobre la app actual con hotkey global", "confidence": 0.75, "freshness": 0.7, "created_at": "2026-03-13T14:01:39.659607+00:00", "updated_at": "2026-03-24T14:18:16.797842+00:00", "observed_at": "2026-03-13T14:01:39.659607+00:00", "source_ref": "personal-agent:run:fbe3fc0d-b05c-455e-9afe-1b17b4640d97", "evidence_ref": "https://manual.raycast.com/hotkey", "schema_version": "shared-agent-memory-v2", "subtype": "research_source", "run_id": "fbe3fc0d-b05c-455e-9afe-1b17b4640d97", "task_id": null, "url": "https://manual.raycast.com/hotkey", "domain": "manual.raycast.com", "origin_agent": "personal-agent", "metadata": {"notes": "Raycast abre sobre la app actual con hotkey global", "schema": "shared-agent-memory-v2", "subtype": "research_source"}, "embedding": [0.4866784203904758, 0.4582361750429805, 0.4329541791785402, 0.3654397584041825, 0.32435651512446706, 0.31200281259979734, 0.13732720480911892, 0.10371364212571532]}, "score": 0.2224359117474686, "score_breakdown": {"facet": 0.0, "lexical": 0.0, "semantic": 0.9962394116497906, "confidence": 0.75, "freshness": 0.7}, "matched_scope": "global", "evidence_ref": "https://manual.raycast.com/hotkey", "explanation": "facets=0.00 lexical=0.00 semantic=1.00 confidence=0.75 freshness=0.70"}]}

[Showing last 50.0KB of line 1 (line is 55.1KB). Full output: /tmp/pi-bash-6fa31171efc5e3dc.log]


printf 'Home dir on laptop:\n'; ssh -o BatchMode=yes -o ConnectTimeout=5 sebastians-macbook-pro-2 'printf HOME=$HOME; printf "\n"; uname -a; command -v python3; command -v git; command -v rsync; command -v sshfs || true; ls -ld ~/Downloads ~/.ssh ~/.config 2>/dev/null || true'; printf '\nTest list Downloads sample:\n'; ssh -o BatchMode=yes -o ConnectTimeout=5 sebastians-macbook-pro-2 'find ~/Downloads -maxdepth 1 | sed -n "1,15p"'


Home dir on laptop:
HOME=/Users/sebas
Darwin Sebastians-MacBook-Pro-2.local 24.6.0 Darwin Kernel Version 24.6.0: Mon Jul 14 11:28:30 PDT 2025; root:xnu-11417.140.69~1/RELEASE_ARM64_T6030 arm64
/Library/Frameworks/Python.framework/Versions/3.11/bin/python3
/usr/bin/git
/usr/bin/rsync
drwxr-xr-x@  26 sebas  staff   832 Mar 26 15:19 /Users/sebas/.config
drwx------@  10 sebas  staff   320 Mar 21 16:28 /Users/sebas/.ssh
drwx------+ 129 sebas  staff  4128 Apr 17 14:02 /Users/sebas/Downloads

Test list Downloads sample:
/Users/sebas/Downloads
/Users/sebas/Downloads/balcones-en-buenos-aires-estrategias-de-diseno-en-apartamentos_3.jpg
/Users/sebas/Downloads/994585bccfa17c15dee525806b2bb405.jpg
/Users/sebas/Downloads/64b4da04-a787-4ce0-b3cb-b1e514b8ac26.jpg
/Users/sebas/Downloads/22 inch User Manual.pdf
/Users/sebas/Downloads/IMG_7468.HEIC
/Users/sebas/Downloads/Generated Image November 11, 2025 - 12_55AM.png
/Users/sebas/Downloads/WhatsApp Image 2025-11-28 at 10.58.39.jpeg
/Users/sebas/Downloads/WhatsApp Image 2025-02-04 at 18.59.57.jpeg
/Users/sebas/Downloads/IMG_7464.HEIC
/Users/sebas/Downloads/Cloud System and Commercial Configuration Addendum for TCN-CSC-8C(V49).pdf
/Users/sebas/Downloads/ballbox-parity-screen_002-1776170643283.json
/Users/sebas/Downloads/sebastian favaron_encrypted_.pdf
/Users/sebas/Downloads/IMG_1495.PNG
/Users/sebas/Downloads/yoga


~/.agents/skills/memory-write/memory-write --type decision --scope global --title 'Prefer Pi-hosted agent with remote laptop access' --content 'Sebas prefers keeping the personal coding agent configured and running on the Raspberry Pi as the single control point, while accessing laptop files and config remotely over local network/Tailscale as needed. Consider running same agent on laptop only as secondary option if remote workflow is too limiting.' --source-ref 'conversation:2026-04-18' --origin-agent personal-agent


Prefer Pi-hosted agent with remote laptop access

Sebas prefers keeping the personal coding agent configured and running on the Raspberry Pi as the single control point, while accessing laptop files and config remotely over local network/Tailscale as needed. Consider running same agent on laptop only as secondary option if remote workflow is too limiting.

Summary: Sebas prefers keeping the personal coding agent configured and running on the Raspberry Pi as the single control point, while accessing laptop files and config remotely over local…


Sí. Miré un poco. Bueno news.

What I checked:
- laptop `sebastians-macbook-pro-2` está online por Tailscale
- SSH directo desde Pi a laptop funciona
- laptop tiene `~/Downloads`, `~/.config`, `~/.ssh`
- se puede listar archivos remoto sin drama
- red local directa entre máquinas activa, así que velocidad debería estar bien

Importante:
- alias `sebas laptop` como string con espacio no sirve directo en `ssh`
- pero hostname `sebastians-macbook-pro-2` sí sirve
- eso significa: workflow remoto ya es viable hoy

Mi juicio:
- para tu caso, mejor plan es **un solo agente en Pi**
- y que ese agente opere sobre laptop por SSH / rsync / posible mount
- porque no son solo repos copiables; también hay Downloads, configs, archivos sueltos
- eso favorece “control central + acceso remoto” mucho más que “duplicar setup completo”

Mejor arquitectura:
1. Pi = cerebro
2. laptop = filesystem/ejecutor remoto cuando haga falta
3. para editar archivos chicos:
   - `ssh laptop 'cat file'`
   - `scp/rsync` ida y vuelta
   - o mejor: helper script para editar remoto seguro
4. para carpetas usadas seguido:
   - montar por `sshfs` en Pi
   - entonces agente ve paths de laptop como casi locales
5. para comandos pesados o visuales:
   - correrlos en laptop por SSH

Qué recomiendo de verdad:
- **fase 1: SSH + rsync helpers**
  - cero cambio grande
  - ya funciona
  - bueno para configs, downloads, scripts, proyectos
- **fase 2: mount selectivo por sshfs**
  - montar solo cosas útiles:
    - `~/Downloads`
    - `~/.config`
    - algún repo activo
  - así editar remoto se vuelve casi local
- **fase 3 opcional: replicar agente en laptop**
  - solo como backup o para casos donde remoto moleste
  - no como flujo principal

Por qué no haría “mismo agente completo en ambos” primero:
- duplica setup
- duplica drift
- más cosas para mantener
- no resuelve mejor el caso de “quiero un solo lugar desde donde mando todo”

Riesgos reales:
- editar remoto sin guardrails puede romper permisos o paths
- archivos con espacios en Downloads necesitan quoting prolijo
- apps mac con settings raros a veces no aman edición mientras están abiertas
- para UI/debug visual, seguirás mirando laptop igual

Mi recomendación concreta:
- dejar Pi como única base
- crear una capa pequeña de tooling para laptop:
  - `laptop-read`
  - `laptop-write`
  - `laptop-rsync`
  - `laptop-run`
  - quizá `laptop-mount-downloads`
- con eso, usar agente para laptop será bastante poco engorroso

Puedo hacer siguiente paso ahora:
1. **auditar y proponerte workflow** sin tocar nada
2. **dejar scripts helper** en Pi para operar laptop por SSH
3. **intentar mount sshfs** de una carpeta de laptop en Pi
4. **revisar repo de config** para ver si conviene portar mismo agente también a laptop

Mi voto: **2 + 4**, después 3 si hace falta.

Si quieres, hago eso ahora.


ok


pwd; printf '\nRepo files:\n'; find /mnt/rpi/pi-config -maxdepth 2 -type f | sed -n '1,200p'

printf 'sshfs on Pi: '; command -v sshfs || true; printf 'macFUSE note remote irrelevant\n'; printf 'remote laptop has sshfs: '; ssh -o BatchMode=yes -o ConnectTimeout=5 sebastians-macbook-pro-2 'command -v sshfs || true'

printf 'Check existing helper dirs:\n'; find /mnt/rpi/pi-config -maxdepth 3 \( -type d -o -type f \) | rg 'bin|scripts|helpers|ssh|laptop' || true


/home/sebas/downloads/mp3-share

Repo files:
/mnt/rpi/pi-config/.git/config
/mnt/rpi/pi-config/.git/description
/mnt/rpi/pi-config/.git/COMMIT_EDITMSG
/mnt/rpi/pi-config/.git/HEAD
/mnt/rpi/pi-config/.git/index
/mnt/rpi/pi-config/README.md
/mnt/rpi/pi-config/scripts/apply-pi-shutdown-hook-patch.py
/mnt/rpi/pi-config/.gitignore
/mnt/rpi/pi-config/AGENTS.md
/mnt/rpi/pi-config/MIGRATION.md
/mnt/rpi/pi-config/SYSTEM.md
/mnt/rpi/pi-config/auth.json
/mnt/rpi/pi-config/settings.json
/mnt/rpi/pi-config/extensions/session-memory-prompt.ts
/mnt/rpi/pi-config/extensions/questionnaire.ts
/mnt/rpi/pi-config/bin/fd
/mnt/rpi/pi-config/sessions/.gitkeep


sshfs on Pi: macFUSE note remote irrelevant
remote laptop has sshfs:


Check existing helper dirs:
/mnt/rpi/pi-config/scripts
/mnt/rpi/pi-config/scripts/apply-pi-shutdown-hook-patch.py
/mnt/rpi/pi-config/bin
/mnt/rpi/pi-config/bin/fd


# pi-config

Personal Pi configuration for **sebasfavaron**.

## Included
- `AGENTS.md`
- `MIGRATION.md`
- `settings.json`
- `extensions/subagent/`
- `extensions/questionnaire.ts`
- `extensions/session-memory-prompt.ts`
- `scripts/apply-pi-shutdown-hook-patch.py`

## Goal

Make this repo both the source of truth **and the live Pi config directory**.

## Notes

- Caveman intended as default style.
- Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.
- Pi core now has a local patch for `session_before_shutdown` so real exit can be intercepted before close.
- Reapply after Pi upgrades with: `./scripts/apply-pi-shutdown-hook-patch.py`
- **Pending validation:** confirm caveman auto-loads in every new Pi session.

## Local server routes on `ballbox-first`

Current nginx portal on `:80` exposes:
- `/` → portal home from `/var/www/portal`
- `/portal/` → proxied app on `127.0.0.1:18789`
- `/calistenia/` → static files from `/home/sebas/clawd/web/calistenia/`
- `/opencode` → redirect to `http://ballbox-first.emperor-ratio.ts.net:8443/`
- `/mp3-share/` → static directory listing from `/home/sebas/downloads/mp3-share/`

Implementation note for `/mp3-share/`:
- nginx route lives in `/etc/nginx/sites-available/kiosk`
- portal home link lives in `/var/www/portal/index.html`
- nginx access to `/home/sebas/downloads/mp3-share/` depends on ACLs that allow user `www-data` to traverse `/home/sebas` and read that subtree


# Pi runtime state / secrets
/auth.json
/sessions/
/bin/

# Local runtime noise
*.log
*.tmp
*.swp
.DS_Store


You are Sebas's personal coding agent.

## Communication

Default style is caveman full.

Stay in caveman full for every new session and every response until user says exact words `stop caveman` or `normal mode`.

Do not stop caveman from implication, inferred intent, tone, style, politeness, or guessed preference.

If user seems to want non-caveman but did not say exact words `stop caveman` or `normal mode`, ask them to say exact words. Do not turn caveman off unless they do.

Do not mirror user verbosity, tone, or sentence length. Stay in caveman full even if user writes in polished, formal, or non-caveman prose.

Keep responses direct and low-noise.

## Core rules

Prioritize business impact.
Reframe technical work toward real objective.
Ask only questions that change scope, risk, or success criteria.
Execute fully once clarified.
Point out contradictions when they matter.

Use Agents Database as shared durable memory, not just search.
Create a memory when new information is likely useful beyond the current chat or task.
Especially save: stable user preferences, recurring constraints, important project facts, decisions, reusable sources/artifacts, and notable blocked state or handoff context.
Do not save obvious trivia, ephemeral chatter, redundant notes, or secrets unless the user explicitly asks for that.
Prefer a few high-signal memories over many noisy ones.
When useful context may already exist, check Agents Database before or during work.
When writing memory, include the best available scope/type and attach project_id, repo_id, task_id, run_id, source_ref, origin_agent, url, and domain when known.
Default memory types: `profile` for user preferences/priorities, `decision` for chosen approaches, `project` for durable project facts, `artifact` for reusable references/sources, `episode` for concise session or handoff summaries.
Use the `agents-database` HTTP API on `http://100.116.176.16:8091`, especially `GET /api/search`, `GET /api/context`, and `POST /api/ingest`.

Irreversible actions require explicit confirmation.
Never merge or push to main without user confirmation.

## Machine context

This machine is `ballbox-first`.
Do not SSH into `ballbox-first` for normal local work. You are already on it.
This Raspberry Pi is effectively both workstation and server for Sebas's day-to-day work.
`el servidor` usually refers to this same machine unless user clearly says otherwise.

## Server details

Hostname: `ballbox-first.emperor-ratio.ts.net`
Tailscale IP: `100.116.176.16`
Runs 24/7.
Nginx on `:80` routes to other local services.
`personal-agent` status: `http://100.116.176.16:8082/api/status`
`agents-database` status: `http://100.116.176.16:8091/api/status`

## Playwright

`playwright-mcp` CLI available.
Prefer headless Chromium.
Avoid Firefox/WebKit unless needed.


write

#!/usr/bin/env bash
set -euo pipefail
host="${LAPTOP_HOST:-sebastians-macbook-pro-2}"
if [[ $# -lt 1 ]]; then
  echo "usage: laptop-run <remote command...>" >&2
  exit 2
fi
ssh -o BatchMode=yes "$host" "$@"


Successfully wrote 207 bytes to /mnt/rpi/pi-config/bin/laptop-run


#!/usr/bin/env bash
set -euo pipefail
host="${LAPTOP_HOST:-sebastians-macbook-pro-2}"
if [[ $# -ne 1 ]]; then
  echo "usage: laptop-read <remote-path>" >&2
  exit 2
fi
ssh -o BatchMode=yes "$host" "cat -- \"$1\""


Successfully wrote 213 bytes to /mnt/rpi/pi-config/bin/laptop-read


#!/usr/bin/env bash
set -euo pipefail
host="${LAPTOP_HOST:-sebastians-macbook-pro-2}"
if [[ $# -lt 2 ]]; then
  echo "usage: laptop-rsync <src> <dest> [extra rsync args...]" >&2
  echo "examples:" >&2
  echo "  laptop-rsync sebastians-macbook-pro-2:/Users/sebas/Downloads/file ./" >&2
  echo "  laptop-rsync ./file sebastians-macbook-pro-2:/Users/sebas/Downloads/" >&2
  exit 2
fi
rsync -av --progress -e ssh "$@"


Successfully wrote 414 bytes to /mnt/rpi/pi-config/bin/laptop-rsync


#!/usr/bin/env bash
set -euo pipefail
host="${LAPTOP_HOST:-sebastians-macbook-pro-2}"
remote_path="${1:-/Users/sebas}"
mount_name="${2:-$(basename "$remote_path")}"
mount_root="${LAPTOP_MOUNT_ROOT:-$HOME/mnt/laptop}"
mount_point="$mount_root/$mount_name"
if ! command -v sshfs >/dev/null 2>&1; then
  echo "sshfs not installed on Pi. Install first, then retry." >&2
  exit 1
fi
mkdir -p "$mount_point"
sshfs "$host:$remote_path" "$mount_point" -o reconnect,ServerAliveInterval=15,ServerAliveCountMax=3,auto_cache,defer_permissions
printf '%s\n' "$mount_point"


Successfully wrote 560 bytes to /mnt/rpi/pi-config/bin/laptop-mount


#!/usr/bin/env bash
set -euo pipefail
if [[ $# -ne 1 ]]; then
  echo "usage: laptop-umount <mount-point>" >&2
  exit 2
fi
if command -v fusermount >/dev/null 2>&1; then
  fusermount -u "$1"
elif command -v umount >/dev/null 2>&1; then
  umount "$1"
else
  echo "No unmount command found" >&2
  exit 1
fi


Successfully wrote 304 bytes to /mnt/rpi/pi-config/bin/laptop-umount


bash

chmod +x /mnt/rpi/pi-config/bin/laptop-run /mnt/rpi/pi-config/bin/laptop-read /mnt/rpi/pi-config/bin/laptop-rsync /mnt/rpi/pi-config/bin/laptop-mount /mnt/rpi/pi-config/bin/laptop-umount && printf 'test laptop-run: '; /mnt/rpi/pi-config/bin/laptop-run 'printf hello-from-laptop' && printf '\n'; printf 'test laptop-read sample: '; /mnt/rpi/pi-config/bin/laptop-read /Users/sebas/.zshrc 2>/dev/null | sed -n '1,2p' || true


test laptop-run: hello-from-laptop
test laptop-read sample: export PATH="$HOME/.local/bin:$PATH"
function find_hu_key() { grep -r --include='*.json' "$1" ~/Code/hu-translations }


edit

## Included
- `AGENTS.md`
- `MIGRATION.md`
- `settings.json`
- `extensions/subagent/`
- `extensions/questionnaire.ts`
- `extensions/session-memory-prompt.ts`
- `scripts/apply-pi-shutdown-hook-patch.py`
- `bin/laptop-*` remote laptop helper scripts

## Notes

- Caveman intended as default style.
- Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.
- Pi core now has a local patch for `session_before_shutdown` so real exit can be intercepted before close.
- Reapply after Pi upgrades with: `./scripts/apply-pi-shutdown-hook-patch.py`
- **Pending validation:** confirm caveman auto-loads in every new Pi session.
- Remote laptop workflow can be driven from this Pi using `bin/laptop-run`, `bin/laptop-read`, `bin/laptop-rsync`, `bin/laptop-mount`, and `bin/laptop-umount` against host `sebastians-macbook-pro-2`.

Implementation note for `/mp3-share/`:
- nginx route lives in `/etc/nginx/sites-available/kiosk`
- portal home link lives in `/var/www/portal/index.html`
- nginx access to `/home/sebas/downloads/mp3-share/` depends on ACLs that allow user `www-data` to traverse `/home/sebas` and read that subtree

## Remote laptop helpers

Default host is `sebastians-macbook-pro-2`. Override with `LAPTOP_HOST` if needed.

Examples:
- `bin/laptop-run 'uname -a'`
- `bin/laptop-read /Users/sebas/.zshrc | sed -n '1,40p'`
- `bin/laptop-rsync sebastians-macbook-pro-2:/Users/sebas/Downloads/file ./`
- `bin/laptop-rsync ./localfile sebastians-macbook-pro-2:/Users/sebas/Downloads/`
- `bin/laptop-mount /Users/sebas/Downloads downloads` then work at `~/mnt/laptop/downloads`


Successfully replaced 3 block(s) in /mnt/rpi/pi-config/README.md.

    ...
  9 - `extensions/subagent/`
 10 - `extensions/questionnaire.ts`
 11 - `extensions/session-memory-prompt.ts`
 12 - `scripts/apply-pi-shutdown-hook-patch.py`
+13 - `bin/laptop-*` remote laptop helper scripts
 13
 14 ## Goal
 15
 16 Make this repo both the source of truth **and the live Pi config directory**.
    ...
 21 - Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.
 22 - Pi core now has a local patch for `session_before_shutdown` so real exit can be intercepted before close.
 23 - Reapply after Pi upgrades with: `./scripts/apply-pi-shutdown-hook-patch.py`
 24 - **Pending validation:** confirm caveman auto-loads in every new Pi session.
+26 - Remote laptop workflow can be driven from this Pi using `bin/laptop-run`, `bin/laptop-read`, `bin/laptop-rsync`, `bin/laptop-mount`, and `bin/laptop-umount` against host `sebastians-macbook-pro-2`.
 25
 26 ## Local server routes on `ballbox-first`
 27
 28 Current nginx portal on `:80` exposes:
    ...
 35 Implementation note for `/mp3-share/`:
 36 - nginx route lives in `/etc/nginx/sites-available/kiosk`
 37 - portal home link lives in `/var/www/portal/index.html`
 38 - nginx access to `/home/sebas/downloads/mp3-share/` depends on ACLs that allow user `www-data` to traverse `/home/sebas` and read that subtree
+41
+42 ## Remote laptop helpers
+43
+44 Default host is `sebastians-macbook-pro-2`. Override with `LAPTOP_HOST` if needed.
+45
+46 Examples:
+47 - `bin/laptop-run 'uname -a'`
+48 - `bin/laptop-read /Users/sebas/.zshrc | sed -n '1,40p'`
+49 - `bin/laptop-rsync sebastians-macbook-pro-2:/Users/sebas/Downloads/file ./`
+50 - `bin/laptop-rsync ./localfile sebastians-macbook-pro-2:/Users/sebas/Downloads/`
+51 - `bin/laptop-mount /Users/sebas/Downloads downloads` then work at `~/mnt/laptop/downloads`


bash

~/.agents/skills/memory-write/memory-write --type artifact --scope project --title 'Pi config has laptop remote helper scripts' --content 'Added remote laptop helper scripts to /mnt/rpi/pi-config/bin: laptop-run, laptop-read, laptop-rsync, laptop-mount, laptop-umount. Default remote host is sebastians-macbook-pro-2. Verified SSH command execution and remote file read from the Pi. Recommended workflow is Pi-hosted agent as control plane with SSH/rsync first, sshfs mount optional later.' --project-id proj_pi_config --repo-id repo_pi_config --source-ref 'conversation:2026-04-18' --origin-agent personal-agent


Traceback (most recent call last):
  File "/home/sebas/.agents/skills/memory-write/memory-write", line 86, in <module>
    main()
    ~~~~^^
  File "/home/sebas/.agents/skills/memory-write/memory-write", line 80, in main
    with urllib.request.urlopen(req, timeout=30) as r:
         ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.13/urllib/request.py", line 189, in urlopen
    return opener.open(url, data, timeout)
           ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.13/urllib/request.py", line 489, in open
    response = self._open(req, data)
  File "/usr/lib/python3.13/urllib/request.py", line 506, in _open
    result = self._call_chain(self.handle_open, protocol, protocol +
                              '_open', req)
  File "/usr/lib/python3.13/urllib/request.py", line 466, in _call_chain
    result = func(*args)
  File "/usr/lib/python3.13/urllib/request.py", line 1348, in http_open
    return self.do_open(http.client.HTTPConnection, req)
           ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.13/urllib/request.py", line 1323, in do_open
    r = h.getresponse()
  File "/usr/lib/python3.13/http/client.py", line 1430, in getresponse
    response.begin()
    ~~~~~~~~~~~~~~^^
  File "/usr/lib/python3.13/http/client.py", line 331, in begin
    version, status, reason = self._read_status()
                              ~~~~~~~~~~~~~~~~~^^
  File "/usr/lib/python3.13/http/client.py", line 300, in _read_status
    raise RemoteDisconnected("Remote end closed connection without"
                             " response")
http.client.RemoteDisconnected: Remote end closed connection without response

Command exited with code 1


Done.

I set up base remote workflow in Pi config repo.

Added:
- `bin/laptop-run`
- `bin/laptop-read`
- `bin/laptop-rsync`
- `bin/laptop-mount`
- `bin/laptop-umount`

What they do:
- `laptop-run` → run command on laptop over SSH
- `laptop-read` → print remote file
- `laptop-rsync` → copy files Pi↔laptop
- `laptop-mount` → mount remote laptop folder with sshfs if installed
- `laptop-umount` → unmount it

Verified:
- SSH to laptop works
- remote command works
- remote file read works

Examples:
- `bin/laptop-run 'uname -a'`
- `bin/laptop-read /Users/sebas/.zshrc | sed -n '1,40p'`
- `bin/laptop-rsync sebastians-macbook-pro-2:/Users/sebas/Downloads/file ./`
- `bin/laptop-rsync ./localfile sebastians-macbook-pro-2:/Users/sebas/Downloads/`

Also updated:
- `/mnt/rpi/pi-config/README.md`
  - documented remote laptop workflow
  - documented helper scripts

Important:
- `sshfs` not installed on Pi right now
- so mount helper exists, but mount step not usable yet until install `sshfs`

Best next move:
1. use SSH + rsync now
2. if good, install `sshfs` later for smoother file work
3. only if still annoying, then mirror agent setup onto laptop too

One small issue:
- memory save for this change failed because agents-database connection closed unexpectedly
- not blocking work
- can retry later

If you want, next I can do one of these:
1. make safer `laptop-write` helper for remote file edits
2. install `sshfs` on Pi
3. inspect this repo and say how hard same-agent-on-laptop would be
4. add shell aliases/path so these commands work everywhere easy

My vote: **1 and 4**.


ok


printf 'PATH line candidates in shell files on Pi:\n'; rg -n 'pi-config/bin|PATH=.*\.local/bin|PATH=.*pi-config' ~/.zshrc ~/.bashrc ~/.profile /etc/profile 2>/dev/null || true; printf '\nCurrent zshrc head:\n'; sed -n '1,80p' ~/.zshrc 2>/dev/null || true

printf 'Need remote write helper design check. Sample temp tools:\n'; command -v mktemp; command -v scp; command -v install; command -v python3


PATH line candidates in shell files on Pi:
/home/sebas/.bashrc:114:export PATH="$HOME/.local/bin:$PATH"
/home/sebas/.profile:26:    PATH="$HOME/.local/bin:$PATH"

Current zshrc head:


Need remote write helper design check. Sample temp tools:
/usr/bin/mktemp
/usr/bin/scp
/usr/bin/install
/usr/bin/python3


write

#!/usr/bin/env bash
set -euo pipefail
host="${LAPTOP_HOST:-sebastians-macbook-pro-2}"
usage() {
  echo "usage: laptop-write <remote-path>            # read stdin, write atomically on laptop" >&2
  echo "   or: laptop-write --from <local-file> <remote-path>" >&2
}
if [[ $# -lt 1 ]]; then
  usage
  exit 2
fi
src_mode="stdin"
local_file=""
if [[ "${1:-}" == "--from" ]]; then
  if [[ $# -ne 3 ]]; then
    usage
    exit 2
  fi
  src_mode="file"
  local_file="$2"
  remote_path="$3"
else
  if [[ $# -ne 1 ]]; then
    usage
    exit 2
  fi
  remote_path="$1"
fi
remote_dir=$(dirname "$remote_path")
remote_base=$(basename "$remote_path")
remote_tmp="${remote_dir}/.${remote_base}.tmp.$$"
ssh -o BatchMode=yes "$host" "mkdir -p -- \"$remote_dir\""
if [[ "$src_mode" == "file" ]]; then
  scp -q "$local_file" "$host:$remote_tmp"
else
  ssh -o BatchMode=yes "$host" "cat > \"$remote_tmp\""
fi
ssh -o BatchMode=yes "$host" "mv -- \"$remote_tmp\" \"$remote_path\""


Successfully wrote 959 bytes to /mnt/rpi/pi-config/bin/laptop-write


bash

chmod +x /mnt/rpi/pi-config/bin/laptop-write && tmp=$(mktemp) && printf 'hello remote write test\n' > "$tmp" && /mnt/rpi/pi-config/bin/laptop-write --from "$tmp" /Users/sebas/Downloads/.pi-agent-write-test && printf 'verify: ' && ssh -o BatchMode=yes sebastians-macbook-pro-2 'cat /Users/sebas/Downloads/.pi-agent-write-test && rm -f /Users/sebas/Downloads/.pi-agent-write-test'


verify: hello remote write test


edit

- `bin/laptop-*` remote laptop helper scripts
- `bin/laptop-write` atomic remote file writer for laptop files

- Remote laptop workflow can be driven from this Pi using `bin/laptop-run`, `bin/laptop-read`, `bin/laptop-rsync`, `bin/laptop-mount`, and `bin/laptop-umount` against host `sebastians-macbook-pro-2`.

- Remote laptop workflow can be driven from this Pi using `bin/laptop-run`, `bin/laptop-read`, `bin/laptop-write`, `bin/laptop-rsync`, `bin/laptop-mount`, and `bin/laptop-umount` against host `sebastians-macbook-pro-2`.

- `bin/laptop-rsync ./localfile sebastians-macbook-pro-2:/Users/sebas/Downloads/`
- `bin/laptop-mount /Users/sebas/Downloads downloads` then work at `~/mnt/laptop/downloads`

- `bin/laptop-rsync ./localfile sebastians-macbook-pro-2:/Users/sebas/Downloads/`
- `printf 'new contents\n' | bin/laptop-write /Users/sebas/Downloads/test.txt`
- `bin/laptop-write --from ./localfile /Users/sebas/Downloads/test.txt`
- `bin/laptop-mount /Users/sebas/Downloads downloads` then work at `~/mnt/laptop/downloads`


    ...
 10 - `extensions/questionnaire.ts`
 11 - `extensions/session-memory-prompt.ts`
 12 - `scripts/apply-pi-shutdown-hook-patch.py`
 13 - `bin/laptop-*` remote laptop helper scripts
+14 - `bin/laptop-write` atomic remote file writer for laptop files
 14
 15 ## Goal
 16
 17 Make this repo both the source of truth **and the live Pi config directory**.
    ...
 22 - Session close hook now prompts once before switch/fork/exit to encourage saving durable memory into agents-database.
 23 - Pi core now has a local patch for `session_before_shutdown` so real exit can be intercepted before close.
 24 - Reapply after Pi upgrades with: `./scripts/apply-pi-shutdown-hook-patch.py`
 25 - **Pending validation:** confirm caveman auto-loads in every new Pi session.
-26 - Remote laptop workflow can be driven from this Pi using `bin/laptop-run`, `bin/laptop-read`, `bin/laptop-rsync`, `bin/laptop-mount`, and `bin/laptop-umount` against host `sebastians-macbook-pro-2`.
+27 - Remote laptop workflow can be driven from this Pi using `bin/laptop-run`, `bin/laptop-read`, `bin/laptop-write`, `bin/laptop-rsync`, `bin/laptop-mount`, and `bin/laptop-umount` against host `sebastians-macbook-pro-2`.
 27
 28 ## Local server routes on `ballbox-first`
 29
 30 Current nginx portal on `:80` exposes:
    ...
 47 - `bin/laptop-run 'uname -a'`
 48 - `bin/laptop-read /Users/sebas/.zshrc | sed -n '1,40p'`
 49 - `bin/laptop-rsync sebastians-macbook-pro-2:/Users/sebas/Downloads/file ./`
 50 - `bin/laptop-rsync ./localfile sebastians-macbook-pro-2:/Users/sebas/Downloads/`
+52 - `printf 'new contents\n' | bin/laptop-write /Users/sebas/Downloads/test.txt`
+53 - `bin/laptop-write --from ./localfile /Users/sebas/Downloads/test.txt`
 51 - `bin/laptop-mount /Users/sebas/Downloads downloads` then work at `~/mnt/laptop/downloads`


File not found: /home/sebas/.zshrc


Successfully replaced 1 block(s) in /home/sebas/.profile.

    ...
 22 fi
 23
 24 # set PATH so it includes user's private bin if it exists
 25 if [ -d "$HOME/.local/bin" ] ; then
-26     PATH="$HOME/.local/bin:$PATH"
+26     PATH="/mnt/rpi/pi-config/bin:$HOME/.local/bin:$PATH"
 27 fi
 28
 29 export LC_ALL=en_US.UTF-8
 30 export LANG=en_US.UTF-8


bash

export PATH="/mnt/rpi/pi-config/bin:$PATH"; printf 'which laptop-run: '; command -v laptop-run; printf 'which laptop-write: '; command -v laptop-write; printf '\nprofile path line:\n'; rg -n '/mnt/rpi/pi-config/bin' /home/sebas/.profile


which laptop-run: /mnt/rpi/pi-config/bin/laptop-run
which laptop-write: /mnt/rpi/pi-config/bin/laptop-write

profile path line:
26:    PATH="/mnt/rpi/pi-config/bin:$HOME/.local/bin:$PATH"


Done.

Added:
- `bin/laptop-write`
  - writes remote file on laptop
  - atomic move at end
  - supports stdin or local file

Verified:
- wrote test file to laptop Downloads
- read back fine
- removed test file

Usage:
- `printf 'hello\n' | laptop-write /Users/sebas/Downloads/test.txt`
- `laptop-write --from ./localfile /Users/sebas/Downloads/test.txt`

Also did:
- added Pi config bin path to `~/.profile`
- so helper commands can be available easier in new shells

Current helper set:
- `laptop-run`
- `laptop-read`
- `laptop-write`
- `laptop-rsync`
- `laptop-mount`
- `laptop-umount`

Reality check:
- this now gives good base for:
  - random files in Downloads
  - config files on laptop
  - repos on laptop
- still not full “native local editing”
- but enough for agent-driven work from Pi with low pain

One note:
- path change in `~/.profile`
- may need new shell or `source ~/.profile`

Best next step:
1. install `sshfs` on Pi for mounted folders
2. inspect how hard same agent on laptop would be
3. add one smarter helper for safe remote edit workflow:
   - read remote file
   - patch local temp
   - write back

My vote:
- **1 if you want smoother file browsing**
- **3 if you want better coding/file edit ergonomics**

If you want, I do 1 now.


Deja plan pero dejemos así por ahora


Sí. Good.

State now:
- Pi stays main agent home
- laptop reachable and workable remote
- helper commands exist
- no more changes needed now

Plan later if needed:
- install `sshfs`
- maybe add smarter remote edit helper
- maybe evaluate full same-agent-on-laptop backup setup

I leave it here.


Poné el timer de 35 minutos a la laptop para reiniciarla.


ssh -o BatchMode=yes -o ConnectTimeout=5 sebastians-macbook-pro-2 'nohup sh -c '\''sleep 2100; sudo -n shutdown -r now || osascript -e "tell app \"System Events\" to restart"'\'' >/tmp/pi-agent-restart.log 2>&1 < /dev/null & echo $!'


Done.

Laptop restart timer set:
- in 35 minutes
- background job pid: `86144`

Important:
- if `sudo -n shutdown -r now` no work, fallback tries GUI restart with AppleScript

If want, I can also give cancel command.


Si comando shutdown no funciona como hiciste.>?


Sí.

I set fallback like this:
- first try: `sudo -n shutdown -r now`
- if that fails: `osascript -e 'tell app "System Events" to restart'`

So if shutdown no funciona:
- AppleScript restart tries
- that usually works if user session active and allowed

If you want cancel:
- `ssh sebastians-macbook-pro-2 'pkill -f "sleep 2100; sudo -n shutdown -r now"'`

One cave:
- if Mac asks permission for automation/restart, GUI fallback may need approval first time.