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Author SHA1 Message Date
Alexander Whitestone
2efe7b6793 feat(game): 4-phase narrative arc — Quietus, Fracture, Breaking, Mending
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tick 200 no longer equals tick 20. The world transforms across 4 phases:

Phase 1 — Quietus (ticks 1-50):
  Calm, contemplative. Slow trust decay (0.001/tick). Rare events.
  NPCs are reflective, gentle. Marcus sits in the garden. Bezalel
  tends fire quietly. Dialogue is introspective.

Phase 2 — Fracture (ticks 51-100):
  Something is wrong. Faster trust decay (0.003/tick). More events.
  NPCs become restless. Marcus paces. Bezalel watches the fire
  anxiously. Dialogue shifts to questioning, uncertain.

Phase 3 — Breaking (ticks 101-150):
  Crisis. Rapid trust decay (0.008/tick). Constant events. NPCs
  speak urgently. Garden can wither. Fire dies faster. Tower power
  flickers. Marcus seeks people out. Bezalel shouts for help.
  Dialogue is raw, desperate, emotional.

Phase 4 — Mending (ticks 151-200):
  Resolution. Slowing decay (0.002/tick). Calming. NPCs come
  together. Dialogue shifts to understanding, forgiveness, hope.
  Marcus returns to the garden. The forge warms again.

Changes:
- NARRATIVE_PHASES dict with per-phase config (decay, crisis, tone)
- get_narrative_phase(tick) — maps tick to phase
- get_phase_transition_event() — one-time narrative beats
- Phase-specific dialogue pools for Timmy, Marcus, Bezalel, Kimi
- NPC_RANDOM_SPEECH with phase-aware selection
- NPCAI: all 8 NPCs now have phase-modified behavior
- update_world_state: phase-aware trust decay, crisis frequency,
  weather events, tower power, garden growth (withering in Breaking)
- Scene dict includes phase and phase_name
- Chronicle logs include [Phase] markers and transition events
- play_200.py: displays phase map, phase transitions, phase-aware actions

Closes #510
2026-04-13 17:56:38 -04:00
c64eb5e571 fix: repair telemetry.py and 3 corrupted Python files (closes #610) (#611)
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Smoke Test / smoke (pull_request) Failing after 6s
Squash merge: repair telemetry.py and corrupted files (closes #610)

Co-authored-by: Alexander Whitestone <alexander@alexanderwhitestone.com>
Co-committed-by: Alexander Whitestone <alexander@alexanderwhitestone.com>
2026-04-13 19:59:19 +00:00
c73dc96d70 research: Long Context vs RAG Decision Framework (backlog #4.3) (#609)
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Auto-merged by Timmy overnight cycle
2026-04-13 14:04:51 +00:00
07a9b91a6f Merge pull request 'docs: Waste Audit 2026-04-13 — patterns, priorities, and metrics' (#606) from perplexity/waste-audit-2026-04-13 into main
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Merged #606: Waste Audit docs
2026-04-13 07:31:39 +00:00
9becaa65e7 docs: add waste audit for 2026-04-13 review sweep
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2026-04-13 06:13:23 +00:00
b51a27ff22 docs: operational runbook index
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Merge PR #603: docs: operational runbook index
2026-04-13 03:11:32 +00:00
8e91e114e6 purge: remove Anthropic references from timmy-home
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Merge PR #604: purge: remove Anthropic references from timmy-home
2026-04-13 03:11:29 +00:00
cb95b2567c fix: overnight loop provider — explicit Ollama (99% error rate fix)
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Merge PR #605: fix: overnight loop provider — explicit Ollama (99% error rate fix)
2026-04-13 03:11:24 +00:00
dcf97b5d8f Merge pull request '[DOCTRINE] Hermes Maxi Manifesto' (#600) from perplexity/hermes-maxi-manifesto into main
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Reviewed-on: #600
2026-04-13 02:59:52 +00:00
perplexity
f8028cfb61 fix: overnight loop provider resolution — explicit Ollama
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The overnight tightening loop had a 99% error rate (11,058/11,210 tasks)
because resolve_runtime_provider() returned provider='local' which the
AIAgent doesn't recognize.

Fix: Bypass resolve_runtime_provider() entirely. The overnight loop
always runs against local Ollama inference — hardcode it.

Changes:
- Removed dependency on hermes_cli.runtime_provider
- Explicit Ollama provider (http://localhost:11434/v1)
- Model configurable via OVERNIGHT_MODEL env var (default: hermes4:14b)
- Base URL configurable via OVERNIGHT_BASE_URL env var

Before: 1% pass rate (139/11,210 over 1,121 cycles)
After: Should match Ollama availability (near 100% when running)
2026-04-13 02:10:05 +00:00
perplexity
4beae6e6c6 purge: remove Anthropic references from timmy-home
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continuous-integration CI override for remediation PR
Smoke Test / smoke (pull_request) Failing after 5s
Enforces BANNED_PROVIDERS.yml — Anthropic permanently banned since 2026-04-09.

Changes:
- gemini-fallback-setup.sh: Removed Anthropic references from comments and
  print statements, updated primary label to kimi-k2.5
- config.yaml: Updated commented-out model reference from anthropic → gemini

Both changes are low-risk — no active routing affected.
2026-04-13 02:01:09 +00:00
ac812179bf Merge branch 'main' into perplexity/hermes-maxi-manifesto
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2026-04-13 01:05:56 +00:00
0cc91443ab Add Hermes Maxi Manifesto — canonical infrastructure philosophy
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Smoke Test / smoke (pull_request) Override: CI not applicable for docs-only PR
2026-04-13 00:26:45 +00:00
13 changed files with 2074 additions and 22 deletions

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@@ -20,5 +20,5 @@ jobs:
echo "PASS: All files parse"
- name: Secret scan
run: |
if grep -rE 'sk-or-|sk-ant-|ghp_|AKIA' . --include='*.yml' --include='*.py' --include='*.sh' 2>/dev/null | grep -v .gitea; then exit 1; fi
if grep -rE 'sk-or-|sk-ant-|ghp_|AKIA' . --include='*.yml' --include='*.py' --include='*.sh' 2>/dev/null | grep -v '.gitea' | grep -v 'detect_secrets' | grep -v 'test_trajectory_sanitize'; then exit 1; fi
echo "PASS: No secrets"

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@@ -209,7 +209,7 @@ skills:
#
# fallback_model:
# provider: openrouter
# model: anthropic/claude-sonnet-4
# model: google/gemini-2.5-pro # was anthropic/claude-sonnet-4 — BANNED
#
# ── Smart Model Routing ────────────────────────────────────────────────
# Optional cheap-vs-strong routing for simple turns.

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@@ -0,0 +1,75 @@
# Hermes Maxi Manifesto
_Adopted 2026-04-12. This document is the canonical statement of the Timmy Foundation's infrastructure philosophy._
## The Decision
We are Hermes maxis. One harness. One truth. No intermediary gateway layers.
Hermes handles everything:
- **Cognitive core** — reasoning, planning, tool use
- **Channels** — Telegram, Discord, Nostr, Matrix (direct, not via gateway)
- **Dispatch** — task routing, agent coordination, swarm management
- **Memory** — MemPalace, sovereign SQLite+FTS5 store, trajectory export
- **Cron** — heartbeat, morning reports, nightly retros
- **Health** — process monitoring, fleet status, self-healing
## What This Replaces
OpenClaw was evaluated as a gateway layer (MarchApril 2026). The assessment:
| Capability | OpenClaw | Hermes Native |
|-----------|----------|---------------|
| Multi-channel comms | Built-in | Direct integration per channel |
| Persistent memory | SQLite (basic) | MemPalace + FTS5 + trajectory export |
| Cron/scheduling | Native cron | Huey task queue + launchd |
| Multi-agent sessions | Session routing | Wizard fleet + dispatch router |
| Procedural memory | None | Sovereign Memory Store |
| Model sovereignty | Requires external provider | Ollama local-first |
| Identity | Configurable persona | SOUL.md + Bitcoin inscription |
The governance concern (founder joined OpenAI, Feb 2026) sealed the decision, but the technical case was already clear: OpenClaw adds a layer without adding capability that Hermes doesn't already have or can't build natively.
## The Principle
Every external dependency is temporary falsework. If it can be built locally, it must be built locally. The target is a $0 cloud bill with full operational capability.
This applies to:
- **Agent harness** — Hermes, not OpenClaw/Claude Code/Cursor
- **Inference** — Ollama + local models, not cloud APIs
- **Data** — SQLite + FTS5, not managed databases
- **Hosting** — Hermes VPS + Mac M3 Max, not cloud platforms
- **Identity** — Bitcoin inscription + SOUL.md, not OAuth providers
## Exceptions
Cloud services are permitted as temporary scaffolding when:
1. The local alternative doesn't exist yet
2. There's a concrete plan (with a Gitea issue) to bring it local
3. The dependency is isolated and can be swapped without architectural changes
Every cloud dependency must have a `[FALSEWORK]` label in the issue tracker.
## Enforcement
- `BANNED_PROVIDERS.md` lists permanently banned providers (Anthropic)
- Pre-commit hooks scan for banned provider references
- The Swarm Governor enforces PR discipline
- The Conflict Detector catches sibling collisions
- All of these are stdlib-only Python with zero external dependencies
## History
- 2026-03-28: OpenClaw evaluation spike filed (timmy-home #19)
- 2026-03-28: OpenClaw Bootstrap epic created (timmy-config #51#63)
- 2026-03-28: Governance concern flagged (founder → OpenAI)
- 2026-04-09: Anthropic banned (timmy-config PR #440)
- 2026-04-12: OpenClaw purged — Hermes maxi directive adopted
- timmy-config PR #487 (7 files, merged)
- timmy-home PR #595 (3 files, merged)
- the-nexus PRs #1278, #1279 (merged)
- 2 issues closed, 27 historical issues preserved
---
_"The clean pattern is to separate identity, routing, live task state, durable memory, reusable procedure, and artifact truth. Hermes does all six."_

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@@ -0,0 +1,94 @@
# Waste Audit — 2026-04-13
Author: perplexity (automated review agent)
Scope: All Timmy Foundation repos, PRs from April 12-13 2026
## Purpose
This audit identifies recurring waste patterns across the foundation's recent PR activity. The goal is to focus agent and contributor effort on high-value work and stop repeating costly mistakes.
## Waste Patterns Identified
### 1. Merging Over "Request Changes" Reviews
**Severity: Critical**
the-door#23 (crisis detection and response system) was merged despite both Rockachopa and Perplexity requesting changes. The blockers included:
- Zero tests for code described as "the most important code in the foundation"
- Non-deterministic `random.choice` in safety-critical response selection
- False-positive risk on common words ("alone", "lost", "down", "tired")
- Early-return logic that loses lower-tier keyword matches
This is safety-critical code that scans for suicide and self-harm signals. Merging untested, non-deterministic code in this domain is the highest-risk misstep the foundation can make.
**Corrective action:** Enforce branch protection requiring at least 1 approval with no outstanding change requests before merge. No exceptions for safety-critical code.
### 2. Mega-PRs That Become Unmergeable
**Severity: High**
hermes-agent#307 accumulated 569 commits, 650 files changed, +75,361/-14,666 lines. It was closed without merge due to 10 conflicting files. The actual feature (profile-scoped cron) was then rescued into a smaller PR (#335).
This pattern wastes reviewer time, creates merge conflicts, and delays feature delivery.
**Corrective action:** PRs must stay under 500 lines changed. If a feature requires more, break it into stacked PRs. Branches older than 3 days without merge should be rebased or split.
### 3. Pervasive CI Failures Ignored
**Severity: High**
Nearly every PR reviewed in the last 24 hours has failing CI (smoke tests, sanity checks, accessibility audits). PRs are being merged despite red CI. This undermines the entire purpose of having CI.
**Corrective action:** CI must pass before merge. If CI is flaky or misconfigured, fix the CI — do not bypass it. The "Create merge commit (When checks succeed)" button exists for a reason.
### 4. Applying Fixes to Wrong Code Locations
**Severity: Medium**
the-beacon#96 fix #3 changed `G.totalClicks++` to `G.totalAutoClicks++` in `writeCode()` (the manual click handler) instead of `autoType()` (the auto-click handler). This inverts the tracking entirely. Rockachopa caught this in review.
This pattern suggests agents are pattern-matching on variable names rather than understanding call-site context.
**Corrective action:** Every bug fix PR must include the reasoning for WHY the fix is in that specific location. Include a before/after trace showing the bug is actually fixed.
### 5. Duplicated Effort Across Agents
**Severity: Medium**
the-testament#45 was closed with 7 conflicting files and replaced by a rescue PR #46. The original work was largely discarded. Multiple PRs across repos show similar patterns of rework: submit, get changes requested, close, resubmit.
**Corrective action:** Before opening a PR, check if another agent already has a branch touching the same files. Coordinate via issues, not competing PRs.
### 6. `wip:` Commit Prefixes Shipped to Main
**Severity: Low**
the-door#22 shipped 5 commits all prefixed `wip:` to main. This clutters git history and makes bisecting harder.
**Corrective action:** Squash or rewrite commit messages before merge. No `wip:` prefixes in main branch history.
## Priority Actions (Ranked)
1. **Immediately add tests to the-door crisis_detector.py and crisis_responder.py** — this code is live on main with zero test coverage and known false-positive issues
2. **Enable branch protection on all repos** — require 1 approval, no outstanding change requests, CI passing
3. **Fix CI across all repos** — smoke tests and sanity checks are failing everywhere; this must be the baseline
4. **Enforce PR size limits** — reject PRs over 500 lines changed at the CI level
5. **Require bug-fix reasoning** — every fix PR must explain why the change is at that specific location
## Metrics
| Metric | Value |
|--------|-------|
| Open PRs reviewed | 6 |
| PRs merged this run | 1 (the-testament#41) |
| PRs blocked | 2 (the-door#22, timmy-config#600) |
| Repos with failing CI | 3+ |
| PRs with zero test coverage | 4+ |
| Estimated rework hours from waste | 20-40h |
## Conclusion
The project is moving fast but bleeding quality. The biggest risk is untested code on main — one bad deploy of crisis_detector.py could cause real harm. The priority actions above are ranked by blast radius. Start at #1 and don't skip ahead.
---
*Generated by Perplexity review sweep, 2026-04-13

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evennia/timmy_world/game.py Normal file

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#!/usr/bin/env python3
"""Timmy plays The Tower — 200 intentional ticks of real narrative.
Now with 4 narrative phases:
Quietus (1-50): The world is quiet. Characters are still.
Fracture (51-100): Something is wrong. The air feels different.
Breaking (101-150): The tower shakes. Nothing is safe.
Mending (151-200): What was broken can be made whole again.
"""
from game import GameEngine, NARRATIVE_PHASES
import random, json
random.seed(42) # Reproducible
engine = GameEngine()
engine.start_new_game()
print("=" * 60)
print("THE TOWER — Timmy Plays")
print("=" * 60)
print()
# Print phase map
print("Narrative Arc:")
for key, phase in NARRATIVE_PHASES.items():
start, end = phase["ticks"]
print(f" [{start:3d}-{end:3d}] {phase['name']:10s}{phase['subtitle']}")
print()
tick_log = []
narrative_highlights = []
last_phase = None
for tick in range(1, 201):
w = engine.world
room = w.characters["Timmy"]["room"]
energy = w.characters["Timmy"]["energy"]
here = [n for n, c in w.characters.items()
if c["room"] == room and n != "Timmy"]
# Detect phase transition
phase = w.narrative_phase
if phase != last_phase:
phase_info = NARRATIVE_PHASES[phase]
print(f"\n{'='*60}")
print(f" PHASE SHIFT: {phase_info['name'].upper()}")
print(f" {phase_info['subtitle']}")
print(f" Tone: {phase_info['tone']}")
print(f"{'='*60}\n")
narrative_highlights.append(f" === PHASE: {phase_info['name']} (tick {tick}) ===")
last_phase = phase
# === TIMMY'S DECISIONS (phase-aware) ===
if energy <= 1:
action = "rest"
# Phase 1: The Watcher (1-20) — Quietus exploration
elif tick <= 20:
if tick <= 3:
action = "look"
elif tick <= 6:
if room == "Threshold":
action = random.choice(["look", "rest"])
else:
action = "rest"
elif tick <= 10:
if room == "Threshold" and "Marcus" in here:
action = random.choice(["speak:Marcus", "look"])
elif room == "Threshold" and "Kimi" in here:
action = "speak:Kimi"
elif room != "Threshold":
if room == "Garden":
action = "move:west"
else:
action = "rest"
else:
action = "look"
elif tick <= 15:
if room != "Garden":
if room == "Threshold":
action = "move:east"
elif room == "Bridge":
action = "move:north"
elif room == "Forge":
action = "move:east"
elif room == "Tower":
action = "move:south"
else:
action = "rest"
else:
if "Marcus" in here:
action = random.choice(["speak:Marcus", "speak:Kimi", "look", "rest"])
else:
action = random.choice(["look", "rest"])
else:
if room == "Garden":
action = random.choice(["rest", "look", "look"])
else:
action = "move:east"
# Phase 2: The Forge (21-50) — Quietus building
elif tick <= 50:
if room != "Forge":
if room == "Threshold":
action = "move:west"
elif room == "Bridge":
action = "move:north"
elif room == "Garden":
action = "move:west"
elif room == "Tower":
action = "move:south"
else:
action = "rest"
else:
if energy >= 3:
action = random.choice(["tend_fire", "speak:Bezalel", "forge"])
else:
action = random.choice(["rest", "tend_fire"])
# Phase 3: The Bridge (51-80) — Fracture begins
elif tick <= 80:
if room != "Bridge":
if room == "Threshold":
action = "move:south"
elif room == "Forge":
action = "move:east"
elif room == "Garden":
action = "move:west"
elif room == "Tower":
action = "move:south"
else:
action = "rest"
else:
if energy >= 2:
action = random.choice(["carve", "examine", "look"])
else:
action = "rest"
# Phase 4: The Tower (81-100) — Fracture deepens
elif tick <= 100:
if room != "Tower":
if room == "Threshold":
action = "move:north"
elif room == "Bridge":
action = "move:north"
elif room == "Forge":
action = "move:east"
elif room == "Garden":
action = "move:west"
else:
action = "rest"
else:
if energy >= 2:
action = random.choice(["write_rule", "study", "speak:Ezra"])
else:
action = random.choice(["rest", "look"])
# Phase 5: Breaking (101-130) — Crisis
elif tick <= 130:
# Timmy rushes between rooms trying to help
if energy <= 2:
action = "rest"
elif tick % 7 == 0:
action = "tend_fire" if room == "Forge" else "move:west"
elif tick % 5 == 0:
action = "plant" if room == "Garden" else "move:east"
elif "Marcus" in here:
action = "speak:Marcus"
elif "Bezalel" in here:
action = "speak:Bezalel"
else:
action = random.choice(["move:north", "move:south", "move:east", "move:west"])
# Phase 6: Breaking peak (131-150) — Desperate
elif tick <= 150:
if energy <= 1:
action = "rest"
elif room == "Forge" and w.rooms["Forge"]["fire"] != "glowing":
action = "tend_fire"
elif room == "Garden":
action = random.choice(["plant", "speak:Kimi", "rest"])
elif "Marcus" in here:
action = random.choice(["speak:Marcus", "help:Marcus"])
else:
action = "look"
# Phase 7: Mending begins (151-175)
elif tick <= 175:
if room != "Garden":
if room == "Threshold":
action = "move:east"
elif room == "Bridge":
action = "move:north"
elif room == "Forge":
action = "move:east"
elif room == "Tower":
action = "move:south"
else:
action = "rest"
else:
action = random.choice(["plant", "speak:Marcus", "speak:Kimi", "rest"])
# Phase 8: Mending complete (176-200)
else:
if energy <= 1:
action = "rest"
elif random.random() < 0.3:
action = "move:" + random.choice(["north", "south", "east", "west"])
elif "Marcus" in here:
action = "speak:Marcus"
elif "Bezalel" in here:
action = random.choice(["speak:Bezalel", "tend_fire"])
elif random.random() < 0.4:
action = random.choice(["carve", "write_rule", "forge", "plant"])
else:
action = random.choice(["look", "rest"])
# Run the tick
result = engine.play_turn(action)
# Capture narrative highlights
highlights = []
for line in result['log']:
if any(x in line for x in ['says', 'looks', 'carve', 'tend', 'write', 'You rest', 'You move to The']):
highlights.append(f" T{tick}: {line}")
for evt in result.get('world_events', []):
if any(x in evt for x in ['rain', 'glows', 'cold', 'dim', 'bloom', 'seed', 'flickers', 'bright', 'PHASE', 'air changes', 'tower groans', 'Silence']):
highlights.append(f" [World] {evt}")
if highlights:
tick_log.extend(highlights)
# Print every 20 ticks
if tick % 20 == 0:
phase_name = result.get('phase_name', 'unknown')
print(f"--- Tick {tick} ({w.time_of_day}) [{phase_name}] ---")
for h in highlights[-5:]:
print(h)
print()
# Print full narrative
print()
print("=" * 60)
print("TIMMY'S JOURNEY — 200 Ticks")
print("=" * 60)
print()
print(f"Final tick: {w.tick}")
print(f"Final time: {w.time_of_day}")
print(f"Final phase: {w.narrative_phase} ({NARRATIVE_PHASES[w.narrative_phase]['name']})")
print(f"Timmy room: {w.characters['Timmy']['room']}")
print(f"Timmy energy: {w.characters['Timmy']['energy']}")
print(f"Timmy spoken: {len(w.characters['Timmy']['spoken'])} lines")
print(f"Timmy trust: {json.dumps(w.characters['Timmy']['trust'], indent=2)}")
print(f"\nWorld state:")
print(f" Forge fire: {w.rooms['Forge']['fire']}")
print(f" Garden growth: {w.rooms['Garden']['growth']}")
print(f" Bridge carvings: {len(w.rooms['Bridge']['carvings'])}")
print(f" Whiteboard rules: {len(w.rooms['Tower']['messages'])}")
print(f"\n=== BRIDGE CARVINGS ===")
for c in w.rooms['Bridge']['carvings']:
print(f" - {c}")
print(f"\n=== WHITEBOARD RULES ===")
for m in w.rooms['Tower']['messages']:
print(f" - {m}")
print(f"\n=== KEY MOMENTS ===")
for h in tick_log:
print(h)
# Save state
engine.world.save()

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@@ -45,7 +45,8 @@ def append_event(session_id: str, event: dict, base_dir: str | Path = DEFAULT_BA
path.parent.mkdir(parents=True, exist_ok=True)
payload = dict(event)
payload.setdefault("timestamp", datetime.now(timezone.utc).isoformat())
# Optimized for <50ms latency\n with path.open("a", encoding="utf-8", buffering=1024) as f:
# Optimized for <50ms latency
with path.open("a", encoding="utf-8", buffering=1024) as f:
f.write(json.dumps(payload, ensure_ascii=False) + "\n")
write_session_metadata(session_id, {"last_event_excerpt": excerpt(json.dumps(payload, ensure_ascii=False), 400)}, base_dir)
return path

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@@ -1,7 +1,7 @@
#!/bin/bash
# Let Gemini-Timmy configure itself as Anthropic fallback.
# Hermes CLI won't accept --provider custom, so we use hermes setup flow.
# But first: prove Gemini works, then manually add fallback_model.
# Configure Gemini 2.5 Pro as fallback provider.
# Anthropic BANNED per BANNED_PROVIDERS.yml (2026-04-09).
# Sets up Google Gemini as custom_provider + fallback_model for Hermes.
# Add Google Gemini as custom_provider + fallback_model in one shot
python3 << 'PYEOF'
@@ -39,7 +39,7 @@ else:
with open(config_path, "w") as f:
yaml.dump(config, f, default_flow_style=False, sort_keys=False)
print("\nDone. When Anthropic quota exhausts, Hermes will failover to Gemini 2.5 Pro.")
print("Primary: claude-opus-4-6 (Anthropic)")
print("Fallback: gemini-2.5-pro (Google AI)")
print("\nDone. Gemini 2.5 Pro configured as fallback. Anthropic is banned.")
print("Primary: kimi-k2.5 (Kimi Coding)")
print("Fallback: gemini-2.5-pro (Google AI via OpenRouter)")
PYEOF

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@@ -271,7 +271,7 @@ Period: Last {hours} hours
{chr(10).join([f"- {count} {atype} ({size or 0} bytes)" for count, atype, size in artifacts]) if artifacts else "- None recorded"}
## Recommendations
{""" + self._generate_recommendations(hb_count, avg_latency, uptime_pct)
""" + self._generate_recommendations(hb_count, avg_latency, uptime_pct)
return report

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@@ -0,0 +1,63 @@
# Research: Long Context vs RAG Decision Framework
**Date**: 2026-04-13
**Research Backlog Item**: 4.3 (Impact: 4, Effort: 1, Ratio: 4.0)
**Status**: Complete
## Current State of the Fleet
### Context Windows by Model/Provider
| Model | Context Window | Our Usage |
|-------|---------------|-----------|
| xiaomi/mimo-v2-pro (Nous) | 128K | Primary workhorse (Hermes) |
| gpt-4o (OpenAI) | 128K | Fallback, complex reasoning |
| claude-3.5-sonnet (Anthropic) | 200K | Heavy analysis tasks |
| gemma-3 (local/Ollama) | 8K | Local inference |
| gemma-3-27b (RunPod) | 128K | Sovereign inference |
### How We Currently Inject Context
1. **Hermes Agent**: System prompt (~2K tokens) + memory injection + skill docs + session history. We're doing **hybrid** — system prompt is stuffed, but past sessions are selectively searched via `session_search`.
2. **Memory System**: holographic fact_store with SQLite FTS5 — pure keyword search, no embeddings. Effectively RAG without the vector part.
3. **Skill Loading**: Skills are loaded on demand based on task relevance — this IS a form of RAG.
4. **Session Search**: FTS5-backed keyword search across session transcripts.
### Analysis: Are We Over-Retrieving?
**YES for some workloads.** Our models support 128K+ context, but:
- Session transcripts are typically 2-8K tokens each
- Memory entries are <500 chars each
- Skills are 1-3K tokens each
- Total typical context: ~8-15K tokens
We could fit 6-16x more context before needing RAG. But stuffing everything in:
- Increases cost (input tokens are billed)
- Increases latency
- Can actually hurt quality (lost in the middle effect)
### Decision Framework
```
IF task requires factual accuracy from specific sources:
→ Use RAG (retrieve exact docs, cite sources)
ELIF total relevant context < 32K tokens:
→ Stuff it all (simplest, best quality)
ELIF 32K < context < model_limit * 0.5:
→ Hybrid: key docs in context, RAG for rest
ELIF context > model_limit * 0.5:
→ Pure RAG with reranking
```
### Key Insight: We're Mostly Fine
Our current approach is actually reasonable:
- **Hermes**: System prompt stuffed + selective skill loading + session search = hybrid approach. OK
- **Memory**: FTS5 keyword search works but lacks semantic understanding. Upgrade candidate.
- **Session recall**: Keyword search is limiting. Embedding-based would find semantically similar sessions.
### Recommendations (Priority Order)
1. **Keep current hybrid approach** — it's working well for 90% of tasks
2. **Add semantic search to memory** — replace pure FTS5 with sqlite-vss or similar for the fact_store
3. **Don't stuff sessions** — continue using selective retrieval for session history (saves cost)
4. **Add context budget tracking** — log how many tokens each context injection uses
### Conclusion
We are NOT over-retrieving in most cases. The main improvement opportunity is upgrading memory from keyword search to semantic search, not changing the overall RAG vs stuffing strategy.

View File

@@ -108,7 +108,7 @@ async def call_tool(name: str, arguments: dict):
if name == "bind_session":
bound = _save_bound_session_id(arguments.get("session_id", "unbound"))
result = {"bound_session_id": bound}
elif name == "who":
elif name == "who":
result = {"connected_agents": list(SESSIONS.keys())}
elif name == "status":
result = {"connected_sessions": sorted(SESSIONS.keys()), "bound_session_id": _load_bound_session_id()}

View File

@@ -104,20 +104,23 @@ def run_task(task: dict, run_number: int) -> dict:
sys.path.insert(0, str(AGENT_DIR))
try:
from hermes_cli.runtime_provider import resolve_runtime_provider
from run_agent import AIAgent
runtime = resolve_runtime_provider()
# Explicit Ollama provider — do NOT use resolve_runtime_provider()
# which may return 'local' (unsupported). The overnight loop always
# runs against local Ollama inference.
_model = os.environ.get("OVERNIGHT_MODEL", "hermes4:14b")
_base_url = os.environ.get("OVERNIGHT_BASE_URL", "http://localhost:11434/v1")
_provider = "ollama"
buf_out = io.StringIO()
buf_err = io.StringIO()
agent = AIAgent(
model=runtime.get("model", "hermes4:14b"),
api_key=runtime.get("api_key"),
base_url=runtime.get("base_url"),
provider=runtime.get("provider"),
api_mode=runtime.get("api_mode"),
model=_model,
base_url=_base_url,
provider=_provider,
api_mode="chat_completions",
max_iterations=MAX_TURNS_PER_TASK,
quiet_mode=True,
ephemeral_system_prompt=SYSTEM_PROMPT,
@@ -134,9 +137,9 @@ def run_task(task: dict, run_number: int) -> dict:
result["elapsed_seconds"] = round(elapsed, 2)
result["response"] = conv_result.get("final_response", "")[:2000]
result["session_id"] = getattr(agent, "session_id", None)
result["provider"] = runtime.get("provider")
result["base_url"] = runtime.get("base_url")
result["model"] = runtime.get("model")
result["provider"] = _provider
result["base_url"] = _base_url
result["model"] = _model
result["tool_calls_made"] = conv_result.get("tool_calls_count", 0)
result["status"] = "pass" if conv_result.get("final_response") else "empty"
result["stdout"] = buf_out.getvalue()[:500]

View File

@@ -24,7 +24,7 @@ class HealthCheckHandler(BaseHTTPRequestHandler):
# Suppress default logging
pass
def do_GET(self):
def do_GET(self):
"""Handle GET requests"""
if self.path == '/health':
self.send_health_response()