Teknium f67063ba81 feat(kanban): generic diagnostics engine for task distress signals (#20332)
* feat(kanban): generic diagnostics engine for task distress signals

Replaces the hallucination-specific ``warnings`` / ``RecoverySection``
surface (shipped in PR #20232) with a reusable diagnostic-rule engine
that covers five distress kinds in v1 and can be extended without
touching UI code. The "something's wrong with this task" signal is
no longer limited to phantom card ids.

Closes the follow-up from #20232 discussion.

New module
----------
``hermes_cli/kanban_diagnostics.py`` — stateless, no-side-effect rule
engine. Each rule is a pure function of
``(task, events, runs, now, config) -> list[Diagnostic]``. Registry
is a simple list; adding a new distress kind is one function + one
import, no UI or API changes required.

v1 rule set
-----------
* ``hallucinated_cards`` (error) — folds the existing
  ``completion_blocked_hallucination`` event into the new surface.
* ``prose_phantom_refs`` (warning) — folds
  ``suspected_hallucinated_references``.
* ``repeated_spawn_failures`` (error → critical at 2x threshold) —
  fires when ``tasks.spawn_failures >= 3``; suggests
  ``hermes -p <profile> doctor`` / ``auth``.
* ``repeated_crashes`` (error → critical) — fires after N consecutive
  ``crashed`` run outcomes with no successful completion between;
  suggests ``hermes kanban log <id>``.
* ``stuck_in_blocked`` (warning) — fires after 24h in ``blocked``
  state with no comments / unblock attempts; suggests commenting.

Every diagnostic carries structured ``actions`` (reclaim, reassign,
unblock, cli_hint, comment, open_docs) that render consistently in
both CLI and dashboard. Suggested actions are highlighted; generic
recovery actions (reclaim / reassign) are available on every kind as
fallbacks.

Diagnostics auto-clear when the underlying failure resolves — a
clean ``completed``/``edited`` event drops hallucination diagnostics,
a successful run drops crash diagnostics, a comment drops
stuck-blocked diagnostics. Audit events persist; the badge goes away.

API
---
``plugin_api.py``:
* ``/board`` now attaches ``diagnostics`` (full list) and
  ``warnings`` (compact summary with ``highest_severity``) per task.
* ``/tasks/{id}`` attaches diagnostics so the drawer's Diagnostics
  section auto-opens on flagged tasks.
* NEW ``/diagnostics`` endpoint — fleet-wide listing, filterable by
  severity, sorted critical-first.

CLI
---
* NEW ``hermes kanban diagnostics [--severity X] [--task id]
  [--json]`` — fleet view or single-task view, matches dashboard rule
  output so CLI users see the same picture.
* ``hermes kanban show <id>`` now renders a Diagnostics section near
  the top with severity markers + suggested actions.

Dashboard
---------
* Card badge is severity-coloured (⚠ amber warning, !! orange error,
  !!! red critical) using ``warnings.highest_severity``.
* Attention strip above the toolbar counts EVERY task with active
  diagnostics (not just hallucinations), severity-coloured, lists
  affected tasks with Open buttons when expanded.
* Drawer's old ``RecoverySection`` replaced with generic
  ``DiagnosticsSection`` rendering a card per active diagnostic:
  title + detail + structured data (task-id chips when payload keys
  look like id lists) + action buttons. Reassign profile picker is
  inline per-diagnostic. Clipboard fallback uses ``.catch()`` for
  environments where writeText rejects.
* Three-rung severity palette; amber for warning, orange for error,
  red for critical. Uses CSS variables so theming is straightforward.

Tests
-----
* NEW ``tests/hermes_cli/test_kanban_diagnostics.py`` — 14 unit tests
  covering each rule's positive/negative/threshold paths, severity
  sorting, broken-rule isolation, and sqlite3.Row integration.
* Dashboard plugin tests extended: ``/diagnostics`` endpoint (empty,
  populated, severity-filtered), ``/board`` exposes both diagnostic
  list and compact summary with ``highest_severity``.
* Existing hallucination-specific test (``test_board_surfaces_
  warnings_field_for_hallucinated_completions``) updated to reflect
  the new contract: warning summary keys by diagnostic kind
  (``hallucinated_cards``) not event kind.

379 kanban-suite tests pass (+16 net from this PR).

Live verification
-----------------
Seeded all 5 diagnostic kinds + one clean + one plain-running task
(7 total) into an isolated HERMES_HOME, spun up the dashboard, and
verified:

* Attention strip: shows ``!! 5 tasks need attention`` in the
  error-severity orange; Show expands to a list of 5 rows ordered
  critical > error > warning.
* Card badges: error tasks render ``!!`` orange, warning tasks
  render ``⚠`` amber, clean and plain-running tasks render no badge.
* Each of the 5 rules opens a correctly-coloured, correctly-styled
  diagnostic card in the drawer with its specific suggested action.
* Live reassign from a diagnostic card flipped
  ``broken-ml-worker → alice`` and the drawer refreshed with the
  new assignee + the same diagnostic still firing (correct:
  spawn_failures counter hasn't reset yet).
* CLI ``hermes kanban diagnostics`` prints all 5 in severity order;
  ``--severity error`` narrows to 3; ``kanban show <id>`` includes
  the Diagnostics block at the top with suggested action hint.

Migration note
--------------
The old ``warnings`` shape (``{count, kinds, latest_at}``) is
preserved on the API but ``kinds`` now keys by diagnostic kind
(``hallucinated_cards``) instead of event kind
(``completion_blocked_hallucination``). ``highest_severity`` is a
new required field. The dashboard was the only consumer and has
been updated in the same commit; external API consumers of the
``warnings`` field will need to update their kind-match logic.

* feat(kanban/diagnostics): lead titles with the actual error text

The generic 'Worker crashed N runs in a row' / 'Worker failed to spawn
N times' titles buried the actual cause in the data section. Operators
had to open logs or expand the diagnostic to see WHY the worker is
stuck — rate-limit vs insufficient quota vs bad auth vs context
overflow vs network blip all looked identical at a glance.

New titles:

  Agent crashed 3x: openai: 429 Too Many Requests - rate limit reached
  Agent crashed 3x: anthropic: 402 insufficient_quota - credit balance
  Agent crashed 3x: provider auth error: 401 Unauthorized
  Agent spawn failed 4x: insufficient_quota: You exceeded your current

Detail keeps the full error snippet (capped at 500 chars + ellipsis
for tracebacks). Title takes the first line capped at 160 chars.
Fallback title if no error recorded stays honest ('no error recorded').

Tests: 4 new cases covering 429/billing/spawn/truncation. 383 total
pass (+4).

Live-verified on dashboard with 6 seeded scenarios
(rate-limit, billing, auth, context, network, spawn-billing) —
each card title leads with the actionable error text.
2026-05-05 13:32:42 -07:00
2026-02-25 11:53:44 -08:00
2026-04-10 00:46:37 -04:00
2026-05-01 16:29:46 +10:00
2026-04-11 15:30:37 -04:00
2026-03-07 13:43:08 -08:00
2026-04-26 05:46:45 -07:00

Hermes Agent

Hermes Agent ☤

Documentation Discord License: MIT Built by Nous Research

The self-improving AI agent built by Nous Research. It's the only agent with a built-in learning loop — it creates skills from experience, improves them during use, nudges itself to persist knowledge, searches its own past conversations, and builds a deepening model of who you are across sessions. Run it on a $5 VPS, a GPU cluster, or serverless infrastructure that costs nearly nothing when idle. It's not tied to your laptop — talk to it from Telegram while it works on a cloud VM.

Use any model you want — Nous Portal, OpenRouter (200+ models), NVIDIA NIM (Nemotron), Xiaomi MiMo, z.ai/GLM, Kimi/Moonshot, MiniMax, Hugging Face, OpenAI, or your own endpoint. Switch with hermes model — no code changes, no lock-in.

A real terminal interfaceFull TUI with multiline editing, slash-command autocomplete, conversation history, interrupt-and-redirect, and streaming tool output.
Lives where you doTelegram, Discord, Slack, WhatsApp, Signal, and CLI — all from a single gateway process. Voice memo transcription, cross-platform conversation continuity.
A closed learning loopAgent-curated memory with periodic nudges. Autonomous skill creation after complex tasks. Skills self-improve during use. FTS5 session search with LLM summarization for cross-session recall. Honcho dialectic user modeling. Compatible with the agentskills.io open standard.
Scheduled automationsBuilt-in cron scheduler with delivery to any platform. Daily reports, nightly backups, weekly audits — all in natural language, running unattended.
Delegates and parallelizesSpawn isolated subagents for parallel workstreams. Write Python scripts that call tools via RPC, collapsing multi-step pipelines into zero-context-cost turns.
Runs anywhere, not just your laptopSix terminal backends — local, Docker, SSH, Daytona, Singularity, and Modal. Daytona and Modal offer serverless persistence — your agent's environment hibernates when idle and wakes on demand, costing nearly nothing between sessions. Run it on a $5 VPS or a GPU cluster.
Research-readyBatch trajectory generation, Atropos RL environments, trajectory compression for training the next generation of tool-calling models.

Quick Install

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

Works on Linux, macOS, WSL2, and Android via Termux. The installer handles the platform-specific setup for you.

Android / Termux: The tested manual path is documented in the Termux guide. On Termux, Hermes installs a curated .[termux] extra because the full .[all] extra currently pulls Android-incompatible voice dependencies.

Windows: Native Windows is not supported. Please install WSL2 and run the command above.

After installation:

source ~/.bashrc    # reload shell (or: source ~/.zshrc)
hermes              # start chatting!

Getting Started

hermes              # Interactive CLI — start a conversation
hermes model        # Choose your LLM provider and model
hermes tools        # Configure which tools are enabled
hermes config set   # Set individual config values
hermes gateway      # Start the messaging gateway (Telegram, Discord, etc.)
hermes setup        # Run the full setup wizard (configures everything at once)
hermes claw migrate # Migrate from OpenClaw (if coming from OpenClaw)
hermes update       # Update to the latest version
hermes doctor       # Diagnose any issues

📖 Full documentation →

CLI vs Messaging Quick Reference

Hermes has two entry points: start the terminal UI with hermes, or run the gateway and talk to it from Telegram, Discord, Slack, WhatsApp, Signal, or Email. Once you're in a conversation, many slash commands are shared across both interfaces.

Action CLI Messaging platforms
Start chatting hermes Run hermes gateway setup + hermes gateway start, then send the bot a message
Start fresh conversation /new or /reset /new or /reset
Change model /model [provider:model] /model [provider:model]
Set a personality /personality [name] /personality [name]
Retry or undo the last turn /retry, /undo /retry, /undo
Compress context / check usage /compress, /usage, /insights [--days N] /compress, /usage, /insights [days]
Browse skills /skills or /<skill-name> /<skill-name>
Interrupt current work Ctrl+C or send a new message /stop or send a new message
Platform-specific status /platforms /status, /sethome

For the full command lists, see the CLI guide and the Messaging Gateway guide.


Documentation

All documentation lives at hermes-agent.nousresearch.com/docs:

Section What's Covered
Quickstart Install → setup → first conversation in 2 minutes
CLI Usage Commands, keybindings, personalities, sessions
Configuration Config file, providers, models, all options
Messaging Gateway Telegram, Discord, Slack, WhatsApp, Signal, Home Assistant
Security Command approval, DM pairing, container isolation
Tools & Toolsets 40+ tools, toolset system, terminal backends
Skills System Procedural memory, Skills Hub, creating skills
Memory Persistent memory, user profiles, best practices
MCP Integration Connect any MCP server for extended capabilities
Cron Scheduling Scheduled tasks with platform delivery
Context Files Project context that shapes every conversation
Architecture Project structure, agent loop, key classes
Contributing Development setup, PR process, code style
CLI Reference All commands and flags
Environment Variables Complete env var reference

Migrating from OpenClaw

If you're coming from OpenClaw, Hermes can automatically import your settings, memories, skills, and API keys.

During first-time setup: The setup wizard (hermes setup) automatically detects ~/.openclaw and offers to migrate before configuration begins.

Anytime after install:

hermes claw migrate              # Interactive migration (full preset)
hermes claw migrate --dry-run    # Preview what would be migrated
hermes claw migrate --preset user-data   # Migrate without secrets
hermes claw migrate --overwrite  # Overwrite existing conflicts

What gets imported:

  • SOUL.md — persona file
  • Memories — MEMORY.md and USER.md entries
  • Skills — user-created skills → ~/.hermes/skills/openclaw-imports/
  • Command allowlist — approval patterns
  • Messaging settings — platform configs, allowed users, working directory
  • API keys — allowlisted secrets (Telegram, OpenRouter, OpenAI, Anthropic, ElevenLabs)
  • TTS assets — workspace audio files
  • Workspace instructions — AGENTS.md (with --workspace-target)

See hermes claw migrate --help for all options, or use the openclaw-migration skill for an interactive agent-guided migration with dry-run previews.


Contributing

We welcome contributions! See the Contributing Guide for development setup, code style, and PR process.

Quick start for contributors — clone and go with setup-hermes.sh:

git clone https://github.com/NousResearch/hermes-agent.git
cd hermes-agent
./setup-hermes.sh     # installs uv, creates venv, installs .[all], symlinks ~/.local/bin/hermes
./hermes              # auto-detects the venv, no need to `source` first

Manual path (equivalent to the above):

curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv venv --python 3.11
source venv/bin/activate
uv pip install -e ".[all,dev]"
scripts/run_tests.sh

RL Training (optional): The RL/Atropos integration (environments/) ships via the atroposlib and tinker dependencies pulled in by .[all,dev] — no submodule setup required.


Community


License

MIT — see LICENSE.

Built by Nous Research.

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