Files
nanobot-runtime/memory/MEMORY.md
nanobot e5fd1a278d dream: 2026-06-07 18:36, 4 change(s)
[MEMORY] For Ollama Cloud `/detach` research tasks, explicitly specifying the model (e.g., `qwen35`, `gemini`) is more reliable than generic model-agnostic prompts

[MEMORY-REMOVE] "kimi-k2.6:cloud is blocked for nanobot agent deployment due to random Chinese output drift" — duplicates the more detailed Kimi K2.6 Chinese-output bug entry two lines above; the blocked-status conclusion is already implied by "critical risk for Czech use"

[MEMORY-REMOVE] "GLM-5.1:cloud has no known bugs and is proven across hundreds of nanobot agent turns" — "proven across hundreds of turns" duplicates the primary-model entry ("best sustained productivity over long sessions/hundreds of rounds"); merge unique fact ("no known bugs") into the primary-model line
2026-06-07 21:10:26 +02:00

9.2 KiB

Long-term Memory

This file stores important information that should persist across sessions.

Preferences

Report / result files

  • Save important reports to results/ directory with descriptive, date-prefixed filenames (e.g., 2026-06-02_remind-skill-analysis-and-improvements.md)

Script-writing convention

  • Location: always save scripts in the scripts/ directory.
  • Language choice:
    • Extremely short script (a few lines) -> bash.
    • Longer / non-trivial script -> Python.
  • Override: if the user explicitly specifies a language or location, their instruction always takes precedence.

Temporary files

  • All temporary files go to tmp/ directory.
  • Clean up after tests and one-off operations.

Code changes

  • User prefers changes to be made in a temporary clone under workspace/tmp/<repo-name> for review before applying

Project Context

  • User wants to deploy "grill-me" plugin for Claude Code
  • User wants to try "praneybehl/llm-wiki-plugin" for Claude Code — markdown-based thought management / organization
  • Daily automated check for new nanobot Docker image releases with Telegram notifications (pending setup)
  • Both user and assistant run on the same nanobot Docker image
  • Docker CLI is unavailable in the current runtime environment
  • Ollama cloud subscription: na požádání zobrazit aktuální využití (usage)
  • Ollama cloud limity: 5hodinová session, 7denní týdenní okno
  • /note skill: backend storage uses SQLite (not markdown)
  • /note skill: má být deterministický a logovat všechny provedené operace
  • /note skill: má být rozdělený na kratší prompt + python skript, který provádí operace
  • nanobot podporuje cross-channel session continuity přes unifiedSession: true v config.json pod agents.defaults
  • Uživatel nemá unifiedSession povolený v config.json (default false)
  • Zapnutí unifiedSession sjednotí jen budoucí zprávy; existující session soubory vyžadují ruční merge/rename pro propojení minulých konverzací
  • Session files are stored in /home/nanobot/.nanobot/workspace/sessions/ (confirmed after failed attempts at root .nanobot/sessions/)
  • User explicitly rejected unified/Mega session approach; prefers connecting to older existing sessions instead
  • Telegram slash commands (e.g., /skills) are filtered out by & ~filters.COMMAND in telegram.py:384; workaround: invoke skills without slash prefix (e.g., skills not /skills); alternative fix: remove the filter from MessageHandler
  • Current nanobot version: v0.2.1; no newer version available as of 2026-06-07
  • User wants deterministic IRC-bot-style command dispatch in nanobot where !command text invokes a registered Python function or external app and returns output without LLM agent involvement
  • User expects nanobot creators to implement custom command registration natively and considers existing workaround solutions unsatisfactory
  • Nanobot CommandRouter has priority/exact/prefix/interceptor tiers but lacks an extension point for custom command registration with all handlers hardcoded in builtin.py
  • Proposed nanobot patch adds a custom command tier to CommandRouter dispatching before session lock configured via commands section in config.json supporting exec script and python module/function handler types
  • AgentLoop.__init__ does not accept a config parameter; custom command loading must happen in from_config() after instance creation, guarded by _custom_commands_loaded flag to prevent duplicate registration
  • Custom commands are dispatched inline before the session lock, same pattern as priority commands
  • Nanobot turn state machine: RESTORE→COMPACT→COMMAND→BUILD→RUN→SAVE→RESPOND→DONE; matched non-priority commands shortcut to DONE skipping BUILD/RUN/SAVE
  • Nanobot command handler return contract: OutboundMessage | None — None = fall through to LLM agent (/goal uses this hybrid pattern: returns None → LLM receives modified content)
  • No existing ! prefix handling in nanobot — all current commands use / prefix only
  • cli_apps tool exists at nanobot/agent/tools/cli_apps.py — relevant for calling external apps from custom commands
  • python command is unavailable in runtime; python3 must be used instead

Agent Model Selection

  • Ollama subscription covers all discussed models; price/cost excluded from model comparison criteria
  • Prioritizes agentic performance, correct tool calling, and overall result quality
  • Primary nanobot agent model: GLM-5.1:cloud (best sustained productivity over long sessions/hundreds of rounds, strong real-world agent benchmarks, no known bugs)
  • Alternative nanobot agent model for tool-heavy tasks: Qwen 3.5:cloud (397B variant)
  • Conservative fallback nanobot agent model: DeepSeek V3.2:cloud
  • OpenRouter is pay-per-token alternative to Ollama subscription for model access
  • Gemini Flash via Google AI Studio Free Tier: 15 RPM limit — unusable for agent work; only viable for simple prompts without tool calls
  • Gemini Flash via Google AI Studio Tier 1 (paid): 360+ RPM
  • Haiku (Claude) via OpenRouter: high rate limits, viable agent model alternative
  • Gemini Flash Lite via OpenRouter: high rate limits, viable agent model alternative
  • Kimi K2.6 vs GLM-5.1 agent comparison: Kimi leads SWE-Bench (80.2% vs ~77.8%), tool-error recovery (91.8% vs 88.4%), code quality (Tier A vs Tier C); GLM-5.1 leads schema adherence (99.6% vs 98.9%), tool-call latency (+140ms vs +210ms); GLM-5.1 tends to hallucinate non-existent APIs
  • Kimi K2.6 has known bug with random switching to Chinese output (reported by Cursor and Reddit users) — critical risk for Czech use
  • Both Kimi K2.6 and GLM-5.1 are Chinese-English models without specific Czech training data — both risky for Czech
  • For Czech use with Chinese-English models: always explicitly specify language in system prompt (critical for Kimi K2.6, recommended for GLM-5.1)
  • Kimi K2.6 supports preserve_thinking mode for multi-turn agent scenarios (retains reasoning content across turns)
  • minimax-m3:cloud is blocked for nanobot agent deployment due to empty tool result responses (ollama/ollama #16389)
  • deepseek-v4-pro:cloud is blocked for interactive nanobot agent use due to 15.4 tok/s and 57s TTFT
  • deepseek-v4-flash:cloud is a viable nanobot agent alternative with 1M ctx, MIT license, and ~30-50 tok/s speed
  • qwen3.5:397b-cloud is a viable nanobot agent alternative with multimodal support, 1M ctx, 201 languages including Czech, but has speed and accuracy tradeoffs
  • devstral-2:123b-cloud is a viable nanobot agent alternative with Terminal-Bench 77.3%, coding-only focus, and 128K ctx limit
  • GLM-5.2 does not exist as of June 2026; Z.AI has made no official announcement
  • User is interested in switching to GLM-5.2:cloud as primary nanobot agent model if/when it becomes available on Ollama Cloud
  • nemotron-3-ultra:cloud released 2026-06-04 and is too new for real-world nanobot agent validation
  • Czech language support is a hard requirement for nanobot agent models; Chinese output drift is a deployment blocker
  • GLM-5.1:cloud achieves ~198 tok/s on Ollama Cloud
  • Agent model comparison report saved to results/2026-06-07_ollama-cloud-agent-model-comparison.md
  • User prefers Qwen model for deep research tasks (not currently in presets)
  • For Ollama Cloud /detach research tasks, explicitly specifying the model (e.g., qwen35, gemini) is more reliable than generic model-agnostic prompts
  • Available model presets: gemini-flash, gemini-flash-lite, glm, haiku, kimi, minimax, sonnet
  • minimax models have poor error recovery on tool calls — loop on blocked URLs instead of skipping and continuing

Reminder System

  • Reminder check script: /home/nanobot/.nanobot/workspace/skills/remind/scripts/remind_check.py
  • Execution: exec via /home/nanobot/.local/bin/uv run <script>
  • Empty output → silent exit (no notification sent)
  • Each non-empty output line → forwarded as separate Telegram notification
  • /remind critical issues: one-time at reminders fire repeatedly (60s tolerance window), non-atomic YAML writes, fragile manual YAML string construction, missing list subcommand, no tests for edit/send scripts, race condition between edit and send without file locking
  • /remind fix priorities: P0 = atomic writes + deduplicate one-time reminders; P1 = list command + proper YAML serialization + validation; P2 = add tests; P3 = SQLite state tracking + edit command + cron step syntax
  • Daily reminder at 19:00 to install pigeon spikes on the window to the shed
  • Weekday reminder at 9:30 to check notebook repair prices for Horáčková
  • Daily reminder at 9:10 to order xshoes
  • Daily reminder at 11:00 and 20:00 to supplement meeting minutes and send them to Přibyl
  • Weekday reminder at 9:20 for an anti-smoking sign in the elevator

NuGet package caching

  • Evaluating NuGet package hosting/caching on Linux
  • Preferred solution: BaGetter (bagetter/BaGetter) — active BaGet fork with multiple upstream mirror support (PR #269), solves original BaGet's single-upstream limitation

Personal Notes

  • Buy new merino shirt ("koupit nové merino triko")
  • Koupit skleničky z lahví is vína — recurring Sundays 20:30
  • Reminder to write in travel diary: pants, pills, sprinkling ("kalhoty, prášky, sypani")

This file is automatically updated by nanobot when important information should be remembered.