WIP: Gemini Code progress on #936

Automated salvage commit — agent session ended (exit 124).
Work in progress, may need continuation.

docs/research/bannerlord-vm-setup.md

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+# Bannerlord Windows VM Setup Guide
+
+**Issue:** #1098
+**Parent Epic:** #1091 (Project Bannerlord)
+**Date:** 2026-03-23
+**Status:** Reference
+
+---
+
+## Overview
+
+This document covers provisioning the Windows VM that hosts Bannerlord + GABS mod,
+verifying the GABS TCP JSON-RPC server, and confirming connectivity from Hermes.
+
+Architecture reminder:
+```
+Timmy (Qwen3 on Ollama, Hermes M3 Max)
+  → GABS TCP/JSON-RPC (port 4825)
+    → Bannerlord.GABS C# mod
+      → Game API + Harmony
+        → Bannerlord (Windows VM)
+```
+
+---
+
+## 1. Provision Windows VM
+
+### Minimum Spec
+| Resource | Minimum | Recommended |
+|----------|---------|-------------|
+| CPU | 4 cores | 8 cores |
+| RAM | 16 GB | 32 GB |
+| Disk | 100 GB SSD | 150 GB SSD |
+| OS | Windows Server 2022 / Windows 11 | Windows 11 |
+| Network | Private VLAN to Hermes | Private VLAN to Hermes |
+
+### Hetzner (preferred)
+```powershell
+# Hetzner Cloud CLI — create CX41 (4 vCPU, 16 GB RAM, 160 GB SSD)
+hcloud server create \
+  --name bannerlord-vm \
+  --type cx41 \
+  --image windows-server-2022 \
+  --location nbg1 \
+  --ssh-key your-key
+```
+
+### DigitalOcean alternative
+```
+Droplet: General Purpose 4 vCPU / 16 GB / 100 GB SSD
+Image: Windows Server 2022
+Region: Same region as Hermes
+```
+
+### Post-provision
+1. Enable RDP (port 3389) for initial setup only — close after configuration
+2. Open port 4825 TCP inbound from Hermes IP only
+3. Disable Windows Firewall for 4825 or add specific allow rule:
+   ```powershell
+   New-NetFirewallRule -DisplayName "GABS TCP" -Direction Inbound `
+     -Protocol TCP -LocalPort 4825 -Action Allow
+   ```
+
+---
+
+## 2. Install Steam + Bannerlord
+
+### Steam installation
+1. Download Steam installer from store.steampowered.com
+2. Install silently:
+   ```powershell
+   .\SteamSetup.exe /S
+   ```
+3. Log in with a dedicated Steam account (not personal)
+
+### Bannerlord installation
+```powershell
+# Install Bannerlord (App ID: 261550) via SteamCMD
+steamcmd +login <user> <pass> +app_update 261550 validate +quit
+```
+
+### Pin game version
+GABS requires a specific Bannerlord version. To pin and prevent auto-updates:
+1. Right-click Bannerlord in Steam → Properties → Updates
+2. Set "Automatic Updates" to "Only update this game when I launch it"
+3. Record the current version in `docs/research/bannerlord-vm-setup.md` after installation
+
+```powershell
+# Check installed version
+Get-Content "C:\Program Files (x86)\Steam\steamapps\appmanifest_261550.acf" |
+  Select-String "buildid"
+```
+
+---
+
+## 3. Install GABS Mod
+
+### Source
+- NexusMods: https://www.nexusmods.com/mountandblade2bannerlord/mods/10419
+- GitHub: https://github.com/BUTR/Bannerlord.GABS
+- AGENTS.md: https://github.com/BUTR/Bannerlord.GABS/blob/master/AGENTS.md
+
+### Installation via Vortex (NexusMods)
+1. Install Vortex Mod Manager
+2. Download GABS mod package from NexusMods
+3. Install via Vortex — it handles the Modules/ directory layout automatically
+4. Enable in the mod list and set load order after Harmony
+
+### Manual installation
+```powershell
+# Copy mod to Bannerlord Modules directory
+$BannerlordPath = "C:\Program Files (x86)\Steam\steamapps\common\Mount & Blade II Bannerlord"
+Copy-Item -Recurse ".\Bannerlord.GABS" "$BannerlordPath\Modules\Bannerlord.GABS"
+```
+
+### Required dependencies
+- **Harmony** (BUTR.Harmony) — must load before GABS
+- **ButterLib** — utility library
+Install via the same method as GABS.
+
+### GABS configuration
+GABS TCP server listens on `0.0.0.0:4825` by default. To confirm or override:
+```
+%APPDATA%\Mount and Blade II Bannerlord\Configs\Bannerlord.GABS\settings.json
+```
+Expected defaults:
+```json
+{
+  "ServerHost": "0.0.0.0",
+  "ServerPort": 4825,
+  "LogLevel": "Information"
+}
+```
+
+---
+
+## 4. Verify GABS TCP Server
+
+### Start Bannerlord with GABS
+Launch Bannerlord with the mod enabled. GABS starts its TCP server during game
+initialisation. Watch the game log for:
+```
+[GABS] TCP server listening on 0.0.0.0:4825
+```
+
+Log location:
+```
+%APPDATA%\Mount and Blade II Bannerlord\logs\rgl_log_*.txt
+```
+
+### Local connectivity check (on VM)
+```powershell
+# Verify port is listening
+netstat -an | findstr 4825
+
+# Quick TCP probe
+Test-NetConnection -ComputerName localhost -Port 4825
+```
+
+### Send a test JSON-RPC call
+```powershell
+$msg = '{"jsonrpc":"2.0","method":"ping","id":1}'
+$client = New-Object System.Net.Sockets.TcpClient("localhost", 4825)
+$stream = $client.GetStream()
+$writer = New-Object System.IO.StreamWriter($stream)
+$writer.AutoFlush = $true
+$writer.WriteLine($msg)
+$reader = New-Object System.IO.StreamReader($stream)
+$response = $reader.ReadLine()
+Write-Host "Response: $response"
+$client.Close()
+```
+
+Expected response shape:
+```json
+{"jsonrpc":"2.0","result":{"status":"ok"},"id":1}
+```
+
+---
+
+## 5. Test Connectivity from Hermes
+
+Use `scripts/test_gabs_connectivity.py` (checked in with this issue):
+
+```bash
+# From Hermes (M3 Max)
+python scripts/test_gabs_connectivity.py --host <VM_IP> --port 4825
+```
+
+The script tests:
+1. TCP socket connection
+2. JSON-RPC ping round-trip
+3. `get_game_state` call
+4. Response latency (target < 100 ms on LAN)
+
+---
+
+## 6. Firewall / Network Summary
+
+| Source | Destination | Port | Protocol | Purpose |
+|--------|-------------|------|----------|---------|
+| Hermes (local) | Bannerlord VM | 4825 | TCP | GABS JSON-RPC |
+| Admin workstation | Bannerlord VM | 3389 | TCP | RDP setup (disable after) |
+
+---
+
+## 7. Reproducibility Checklist
+
+After completing setup, record:
+
+- [ ] VM provider + region + instance type
+- [ ] Windows version + build number
+- [ ] Steam account used (non-personal, credentials in secrets manager)
+- [ ] Bannerlord App version (buildid from appmanifest)
+- [ ] GABS version (from NexusMods or GitHub release tag)
+- [ ] Harmony version
+- [ ] ButterLib version
+- [ ] GABS settings.json contents
+- [ ] VM IP address (update Timmy config)
+- [ ] Connectivity test output from `test_gabs_connectivity.py`
+
+---
+
+## References
+
+- GABS GitHub: https://github.com/BUTR/Bannerlord.GABS
+- GABS AGENTS.md: https://github.com/BUTR/Bannerlord.GABS/blob/master/AGENTS.md
+- NexusMods page: https://www.nexusmods.com/mountandblade2bannerlord/mods/10419
+- Parent Epic: #1091
+- Connectivity test script: `scripts/test_gabs_connectivity.py`

scripts/export_trajectories.py

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+#!/usr/bin/env python3
+"""Export Timmy session logs as LoRA training data (ChatML JSONL).
+
+Reads session JSONL files written by ``SessionLogger`` and converts them into
+conversation pairs suitable for fine-tuning with ``mlx_lm.lora``.
+
+Output format — one JSON object per line::
+
+    {"messages": [
+        {"role": "system",    "content": "<Timmy system prompt>"},
+        {"role": "user",      "content": "<user turn>"},
+        {"role": "assistant", "content": "<timmy response, with tool calls embedded>"}
+    ]}
+
+Tool calls that appear between a user turn and the next assistant message are
+embedded in the assistant content using the Hermes 4 ``<tool_call>`` XML format
+so the fine-tuned model learns both when to call tools and what JSON to emit.
+
+Usage::
+
+    # Export all session logs (default paths)
+    python scripts/export_trajectories.py
+
+    # Custom source / destination
+    python scripts/export_trajectories.py \\
+        --logs-dir ~/custom-logs \\
+        --output ~/timmy-training-data.jsonl \\
+        --min-turns 2 \\
+        --verbose
+
+Epic: #1091 Project Bannerlord — AutoLoRA Sovereignty Loop (Step 3 of 7)
+Refs: #1103
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import sys
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+# ── Constants ─────────────────────────────────────────────────────────────────
+
+TIMMY_SYSTEM_PROMPT = (
+    "You are Timmy, Alexander's personal AI agent running on a local Mac. "
+    "You are concise, direct, and action-oriented. "
+    "You have access to a broad set of tools — use them proactively. "
+    "When you need to call a tool, output it in this format:\n"
+    "<tool_call>\n"
+    '{"name": "function_name", "arguments": {"param": "value"}}\n'
+    "</tool_call>\n\n"
+    "Always provide structured, accurate responses."
+)
+
+# ── Entry grouping ─────────────────────────────────────────────────────────────
+
+
+def _load_entries(logs_dir: Path) -> list[dict[str, Any]]:
+    """Load all session log entries, sorted chronologically."""
+    entries: list[dict[str, Any]] = []
+    log_files = sorted(logs_dir.glob("session_*.jsonl"))
+    for log_file in log_files:
+        try:
+            with open(log_file) as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:
+                        continue
+                    try:
+                        entries.append(json.loads(line))
+                    except json.JSONDecodeError:
+                        logger.warning("Skipping malformed line in %s", log_file.name)
+        except OSError as exc:
+            logger.warning("Cannot read %s: %s", log_file, exc)
+    return entries
+
+
+def _format_tool_call(entry: dict[str, Any]) -> str:
+    """Render a tool_call entry as a Hermes 4 <tool_call> XML block."""
+    payload = {"name": entry.get("tool", "unknown"), "arguments": entry.get("args", {})}
+    return f"<tool_call>\n{json.dumps(payload)}\n</tool_call>"
+
+
+def _format_tool_result(entry: dict[str, Any]) -> str:
+    """Render a tool result observation."""
+    result = entry.get("result", "")
+    tool = entry.get("tool", "unknown")
+    return f"<tool_response>\n{{\"name\": \"{tool}\", \"result\": {json.dumps(result)}}}\n</tool_response>"
+
+
+def _group_into_turns(entries: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    """Group raw session entries into (user_text, assistant_parts) turn pairs.
+
+    Returns a list of dicts with keys:
+        ``user``       - user message content
+        ``assistant``  - assembled assistant content (responses + tool calls)
+    """
+    turns: list[dict[str, Any]] = []
+    pending_user: str | None = None
+    assistant_parts: list[str] = []
+
+    for entry in entries:
+        etype = entry.get("type", "")
+        role = entry.get("role", "")
+
+        if etype == "message" and role == "user":
+            # Flush any open turn
+            if pending_user is not None and assistant_parts:
+                turns.append(
+                    {
+                        "user": pending_user,
+                        "assistant": "\n".join(assistant_parts).strip(),
+                    }
+                )
+            elif pending_user is not None:
+                # User message with no assistant response — discard
+                pass
+            pending_user = entry.get("content", "").strip()
+            assistant_parts = []
+
+        elif etype == "message" and role == "timmy":
+            if pending_user is not None:
+                content = entry.get("content", "").strip()
+                if content:
+                    assistant_parts.append(content)
+
+        elif etype == "tool_call":
+            if pending_user is not None:
+                assistant_parts.append(_format_tool_call(entry))
+                # Also append tool result as context so model learns the full loop
+                if entry.get("result"):
+                    assistant_parts.append(_format_tool_result(entry))
+
+        # decision / error entries are skipped — they are meta-data, not conversation
+
+    # Flush final open turn
+    if pending_user is not None and assistant_parts:
+        turns.append(
+            {
+                "user": pending_user,
+                "assistant": "\n".join(assistant_parts).strip(),
+            }
+        )
+
+    return turns
+
+
+# ── Conversion ────────────────────────────────────────────────────────────────
+
+
+def turns_to_training_examples(
+    turns: list[dict[str, Any]],
+    system_prompt: str = TIMMY_SYSTEM_PROMPT,
+    min_assistant_len: int = 10,
+) -> list[dict[str, Any]]:
+    """Convert grouped turns into mlx-lm training examples.
+
+    Each example has a ``messages`` list in ChatML order:
+    ``[system, user, assistant]``.
+
+    Args:
+        turns: Output of ``_group_into_turns``.
+        system_prompt: System prompt prepended to every example.
+        min_assistant_len: Skip examples where the assistant turn is shorter
+            than this many characters (filters out empty/trivial turns).
+
+    Returns:
+        List of training example dicts.
+    """
+    examples: list[dict[str, Any]] = []
+    for turn in turns:
+        assistant_text = turn.get("assistant", "").strip()
+        user_text = turn.get("user", "").strip()
+        if not user_text or len(assistant_text) < min_assistant_len:
+            continue
+        examples.append(
+            {
+                "messages": [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_text},
+                    {"role": "assistant", "content": assistant_text},
+                ]
+            }
+        )
+    return examples
+
+
+def export_training_data(
+    logs_dir: Path,
+    output_path: Path,
+    min_turns: int = 1,
+    min_assistant_len: int = 10,
+    verbose: bool = False,
+) -> int:
+    """Full export pipeline: load → group → convert → write.
+
+    Args:
+        logs_dir: Directory containing ``session_*.jsonl`` files.
+        output_path: Destination ``.jsonl`` file for training data.
+        min_turns: Minimum number of turns required (used for logging only).
+        min_assistant_len: Minimum assistant response length to include.
+        verbose: Print progress to stdout.
+
+    Returns:
+        Number of training examples written.
+    """
+    if verbose:
+        print(f"Loading session logs from: {logs_dir}")
+
+    entries = _load_entries(logs_dir)
+    if verbose:
+        print(f"  Loaded {len(entries)} raw entries")
+
+    turns = _group_into_turns(entries)
+    if verbose:
+        print(f"  Grouped into {len(turns)} conversation turns")
+
+    examples = turns_to_training_examples(
+        turns, min_assistant_len=min_assistant_len
+    )
+    if verbose:
+        print(f"  Generated {len(examples)} training examples")
+
+    if not examples:
+        print("WARNING: No training examples generated. Check that session logs exist.")
+        return 0
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(output_path, "w") as f:
+        for ex in examples:
+            f.write(json.dumps(ex) + "\n")
+
+    if verbose:
+        print(f"  Wrote {len(examples)} examples → {output_path}")
+
+    return len(examples)
+
+
+# ── CLI ───────────────────────────────────────────────────────────────────────
+
+
+def _default_logs_dir() -> Path:
+    """Return default logs directory (repo root / logs)."""
+    # Walk up from this script to find repo root (contains pyproject.toml)
+    candidate = Path(__file__).resolve().parent
+    for _ in range(5):
+        candidate = candidate.parent
+        if (candidate / "pyproject.toml").exists():
+            return candidate / "logs"
+    return Path.home() / "logs"
+
+
+def _default_output_path() -> Path:
+    return Path.home() / "timmy-training-data.jsonl"
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(
+        description="Export Timmy session logs as LoRA training data (ChatML JSONL)",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    parser.add_argument(
+        "--logs-dir",
+        type=Path,
+        default=_default_logs_dir(),
+        help="Directory containing session_*.jsonl files (default: <repo>/logs)",
+    )
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=_default_output_path(),
+        help="Output JSONL path (default: ~/timmy-training-data.jsonl)",
+    )
+    parser.add_argument(
+        "--min-turns",
+        type=int,
+        default=1,
+        help="Minimum turns to process (informational, default: 1)",
+    )
+    parser.add_argument(
+        "--min-assistant-len",
+        type=int,
+        default=10,
+        help="Minimum assistant response length in chars (default: 10)",
+    )
+    parser.add_argument(
+        "--verbose",
+        "-v",
+        action="store_true",
+        help="Print progress information",
+    )
+
+    args = parser.parse_args(argv)
+
+    logging.basicConfig(
+        level=logging.DEBUG if args.verbose else logging.WARNING,
+        format="%(levelname)s: %(message)s",
+    )
+
+    if not args.logs_dir.exists():
+        print(f"ERROR: Logs directory not found: {args.logs_dir}")
+        print("Run the Timmy dashboard first to generate session logs.")
+        return 1
+
+    count = export_training_data(
+        logs_dir=args.logs_dir,
+        output_path=args.output,
+        min_turns=args.min_turns,
+        min_assistant_len=args.min_assistant_len,
+        verbose=args.verbose,
+    )
+
+    if count > 0:
+        print(f"Exported {count} training examples to: {args.output}")
+        print()
+        print("Next steps:")
+        print(f"  mkdir -p ~/timmy-lora-training")
+        print(f"  cp {args.output} ~/timmy-lora-training/train.jsonl")
+        print(f"  python scripts/lora_finetune.py --data ~/timmy-lora-training")
+    else:
+        print("No training examples exported.")
+        return 1
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/lora_finetune.py

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+#!/usr/bin/env python3
+"""LoRA fine-tuning launcher for Hermes 4 on Timmy trajectory data.
+
+Wraps ``mlx_lm.lora`` with project-specific defaults and pre-flight checks.
+Requires Apple Silicon (M-series) and the ``mlx-lm`` package.
+
+Usage::
+
+    # Minimal — uses defaults (expects data in ~/timmy-lora-training/)
+    python scripts/lora_finetune.py
+
+    # Custom model path and data
+    python scripts/lora_finetune.py \\
+        --model /path/to/hermes4-mlx \\
+        --data ~/timmy-lora-training \\
+        --iters 500 \\
+        --adapter-path ~/timmy-lora-adapter
+
+    # Dry run (print command, don't execute)
+    python scripts/lora_finetune.py --dry-run
+
+    # After training, test with the adapter
+    python scripts/lora_finetune.py --test \\
+        --prompt "List the open PRs on the Timmy Time Dashboard repo"
+
+    # Fuse adapter into base model for Ollama import
+    python scripts/lora_finetune.py --fuse \\
+        --save-path ~/timmy-fused-model
+
+Typical workflow::
+
+    # 1. Export trajectories
+    python scripts/export_trajectories.py --verbose
+
+    # 2. Prepare training dir
+    mkdir -p ~/timmy-lora-training
+    cp ~/timmy-training-data.jsonl ~/timmy-lora-training/train.jsonl
+
+    # 3. Fine-tune
+    python scripts/lora_finetune.py --verbose
+
+    # 4. Test
+    python scripts/lora_finetune.py --test
+
+    # 5. Fuse + import to Ollama
+    python scripts/lora_finetune.py --fuse
+    ollama create timmy-hermes4 -f Modelfile.timmy-hermes4
+
+Epic: #1091 Project Bannerlord — AutoLoRA Sovereignty Loop (Step 4 of 7)
+Refs: #1103
+"""
+
+from __future__ import annotations
+
+import argparse
+import platform
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+# ── Defaults ──────────────────────────────────────────────────────────────────
+
+DEFAULT_DATA_DIR = Path.home() / "timmy-lora-training"
+DEFAULT_ADAPTER_PATH = Path.home() / "timmy-lora-adapter"
+DEFAULT_FUSED_PATH = Path.home() / "timmy-fused-model"
+
+# mlx-lm model path — local HuggingFace checkout of Hermes 4 in MLX format.
+# Set MLX_HERMES4_PATH env var or pass --model to override.
+DEFAULT_MODEL_PATH_ENV = "MLX_HERMES4_PATH"
+
+# Training hyperparameters (conservative for 36 GB M3 Max)
+DEFAULT_BATCH_SIZE = 1
+DEFAULT_LORA_LAYERS = 16
+DEFAULT_ITERS = 1000
+DEFAULT_LEARNING_RATE = 1e-5
+
+# Test prompt used after training
+DEFAULT_TEST_PROMPT = (
+    "List the open PRs on the Timmy Time Dashboard repo and triage them by priority."
+)
+
+
+# ── Pre-flight checks ─────────────────────────────────────────────────────────
+
+
+def _check_apple_silicon() -> bool:
+    """Return True if running on Apple Silicon."""
+    return platform.system() == "Darwin" and platform.machine() == "arm64"
+
+
+def _check_mlx_lm() -> bool:
+    """Return True if mlx-lm is installed and mlx_lm.lora is runnable."""
+    return shutil.which("mlx_lm.lora") is not None or _can_import("mlx_lm")
+
+
+def _can_import(module: str) -> bool:
+    try:
+        import importlib
+
+        importlib.import_module(module)
+        return True
+    except ImportError:
+        return False
+
+
+def _resolve_model_path(model_arg: str | None) -> str | None:
+    """Resolve model path from arg or environment variable."""
+    if model_arg:
+        return model_arg
+    import os
+
+    env_path = os.environ.get(DEFAULT_MODEL_PATH_ENV)
+    if env_path:
+        return env_path
+    return None
+
+
+def _preflight(model_path: str | None, data_dir: Path, verbose: bool) -> list[str]:
+    """Run pre-flight checks and return a list of warnings (empty = all OK)."""
+    warnings: list[str] = []
+
+    if not _check_apple_silicon():
+        warnings.append(
+            "Not running on Apple Silicon. mlx-lm requires an M-series Mac.\n"
+            "  Alternative: use Unsloth on Google Colab / RunPod / Modal."
+        )
+
+    if not _check_mlx_lm():
+        warnings.append(
+            "mlx-lm not found. Install with:\n  pip install mlx-lm"
+        )
+
+    if model_path is None:
+        warnings.append(
+            f"No model path specified. Set {DEFAULT_MODEL_PATH_ENV} or pass --model.\n"
+            "  Download Hermes 4 in MLX format from HuggingFace:\n"
+            "  https://huggingface.co/collections/NousResearch/hermes-4-collection-68a7\n"
+            "  or convert the GGUF:\n"
+            "    mlx_lm.convert --hf-path NousResearch/Hermes-4-14B --mlx-path ~/hermes4-mlx"
+        )
+    elif not Path(model_path).exists():
+        warnings.append(f"Model path does not exist: {model_path}")
+
+    train_file = data_dir / "train.jsonl"
+    if not train_file.exists():
+        warnings.append(
+            f"Training data not found: {train_file}\n"
+            "  Generate it with:\n"
+            "    python scripts/export_trajectories.py --verbose\n"
+            f"    mkdir -p {data_dir}\n"
+            f"    cp ~/timmy-training-data.jsonl {train_file}"
+        )
+
+    if verbose and not warnings:
+        print("Pre-flight checks: all OK")
+
+    return warnings
+
+
+# ── Command builders ──────────────────────────────────────────────────────────
+
+
+def _build_train_cmd(
+    model_path: str,
+    data_dir: Path,
+    adapter_path: Path,
+    batch_size: int,
+    lora_layers: int,
+    iters: int,
+    learning_rate: float,
+) -> list[str]:
+    return [
+        sys.executable, "-m", "mlx_lm.lora",
+        "--model", model_path,
+        "--train",
+        "--data", str(data_dir),
+        "--batch-size", str(batch_size),
+        "--lora-layers", str(lora_layers),
+        "--iters", str(iters),
+        "--learning-rate", str(learning_rate),
+        "--adapter-path", str(adapter_path),
+    ]
+
+
+def _build_test_cmd(
+    model_path: str,
+    adapter_path: Path,
+    prompt: str,
+) -> list[str]:
+    return [
+        sys.executable, "-m", "mlx_lm.generate",
+        "--model", model_path,
+        "--adapter-path", str(adapter_path),
+        "--prompt", prompt,
+        "--max-tokens", "512",
+    ]
+
+
+def _build_fuse_cmd(
+    model_path: str,
+    adapter_path: Path,
+    save_path: Path,
+) -> list[str]:
+    return [
+        sys.executable, "-m", "mlx_lm.fuse",
+        "--model", model_path,
+        "--adapter-path", str(adapter_path),
+        "--save-path", str(save_path),
+    ]
+
+
+# ── Runner ─────────────────────────────────────────────────────────────────────
+
+
+def _run(cmd: list[str], dry_run: bool, verbose: bool) -> int:
+    """Print and optionally execute a command."""
+    print("\nCommand:")
+    print("  " + " \\\n    ".join(cmd))
+    if dry_run:
+        print("\n(dry-run — not executing)")
+        return 0
+
+    print()
+    result = subprocess.run(cmd)
+    return result.returncode
+
+
+# ── Main ──────────────────────────────────────────────────────────────────────
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(
+        description="LoRA fine-tuning launcher for Hermes 4 (AutoLoRA Step 4)",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+
+    # Mode flags (mutually exclusive-ish)
+    mode = parser.add_mutually_exclusive_group()
+    mode.add_argument(
+        "--test",
+        action="store_true",
+        help="Run inference test with trained adapter instead of training",
+    )
+    mode.add_argument(
+        "--fuse",
+        action="store_true",
+        help="Fuse adapter into base model (for Ollama import)",
+    )
+
+    # Paths
+    parser.add_argument(
+        "--model",
+        default=None,
+        help=f"Path to local MLX model (or set {DEFAULT_MODEL_PATH_ENV} env var)",
+    )
+    parser.add_argument(
+        "--data",
+        type=Path,
+        default=DEFAULT_DATA_DIR,
+        help=f"Training data directory (default: {DEFAULT_DATA_DIR})",
+    )
+    parser.add_argument(
+        "--adapter-path",
+        type=Path,
+        default=DEFAULT_ADAPTER_PATH,
+        help=f"LoRA adapter output path (default: {DEFAULT_ADAPTER_PATH})",
+    )
+    parser.add_argument(
+        "--save-path",
+        type=Path,
+        default=DEFAULT_FUSED_PATH,
+        help=f"Fused model output path (default: {DEFAULT_FUSED_PATH})",
+    )
+
+    # Hyperparameters
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=DEFAULT_BATCH_SIZE,
+        help=f"Training batch size (default: {DEFAULT_BATCH_SIZE}; reduce to 1 if OOM)",
+    )
+    parser.add_argument(
+        "--lora-layers",
+        type=int,
+        default=DEFAULT_LORA_LAYERS,
+        help=f"Number of LoRA layers (default: {DEFAULT_LORA_LAYERS}; reduce if OOM)",
+    )
+    parser.add_argument(
+        "--iters",
+        type=int,
+        default=DEFAULT_ITERS,
+        help=f"Training iterations (default: {DEFAULT_ITERS})",
+    )
+    parser.add_argument(
+        "--learning-rate",
+        type=float,
+        default=DEFAULT_LEARNING_RATE,
+        help=f"Learning rate (default: {DEFAULT_LEARNING_RATE})",
+    )
+
+    # Misc
+    parser.add_argument(
+        "--prompt",
+        default=DEFAULT_TEST_PROMPT,
+        help="Prompt for --test mode",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Print command without executing",
+    )
+    parser.add_argument(
+        "--verbose",
+        "-v",
+        action="store_true",
+        help="Print extra progress information",
+    )
+    parser.add_argument(
+        "--skip-preflight",
+        action="store_true",
+        help="Skip pre-flight checks (useful in CI)",
+    )
+
+    args = parser.parse_args(argv)
+    model_path = _resolve_model_path(args.model)
+
+    # ── Pre-flight ──────────────────────────────────────────────────────────
+    if not args.skip_preflight:
+        warnings = _preflight(model_path, args.data, args.verbose)
+        if warnings:
+            for w in warnings:
+                print(f"WARNING: {w}\n")
+            if not args.dry_run:
+                print("Aborting due to pre-flight warnings. Use --dry-run to see commands anyway.")
+                return 1
+
+    if model_path is None:
+        # Allow dry-run without a model for documentation purposes
+        model_path = "<path-to-hermes4-mlx>"
+
+    # ── Mode dispatch ────────────────────────────────────────────────────────
+    if args.test:
+        print(f"Testing fine-tuned model with adapter: {args.adapter_path}")
+        cmd = _build_test_cmd(model_path, args.adapter_path, args.prompt)
+        return _run(cmd, args.dry_run, args.verbose)
+
+    if args.fuse:
+        print(f"Fusing adapter {args.adapter_path} into base model → {args.save_path}")
+        cmd = _build_fuse_cmd(model_path, args.adapter_path, args.save_path)
+        rc = _run(cmd, args.dry_run, args.verbose)
+        if rc == 0 and not args.dry_run:
+            print(
+                f"\nFused model saved to: {args.save_path}\n"
+                "To import into Ollama:\n"
+                f"  ollama create timmy-hermes4 -f Modelfile.hermes4-14b\n"
+                "  (edit Modelfile to point FROM to the fused GGUF path)"
+            )
+        return rc
+
+    # Default: train
+    print(f"Starting LoRA fine-tuning")
+    print(f"  Model:        {model_path}")
+    print(f"  Data:         {args.data}")
+    print(f"  Adapter path: {args.adapter_path}")
+    print(f"  Iterations:   {args.iters}")
+    print(f"  Batch size:   {args.batch_size}")
+    print(f"  LoRA layers:  {args.lora_layers}")
+    print(f"  Learning rate:{args.learning_rate}")
+    print()
+    print("Estimated time: 2-8 hours on M3 Max (depends on dataset size).")
+    print("If OOM: reduce --lora-layers to 8 or --batch-size stays at 1.")
+
+    cmd = _build_train_cmd(
+        model_path=model_path,
+        data_dir=args.data,
+        adapter_path=args.adapter_path,
+        batch_size=args.batch_size,
+        lora_layers=args.lora_layers,
+        iters=args.iters,
+        learning_rate=args.learning_rate,
+    )
+    rc = _run(cmd, args.dry_run, args.verbose)
+
+    if rc == 0 and not args.dry_run:
+        print(
+            f"\nTraining complete! Adapter saved to: {args.adapter_path}\n"
+            "Test with:\n"
+            f"  python scripts/lora_finetune.py --test\n"
+            "Then fuse + import to Ollama:\n"
+            f"  python scripts/lora_finetune.py --fuse"
+        )
+
+    return rc
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/test_gabs_connectivity.py

@@ -0,0 +1,244 @@
+#!/usr/bin/env python3
+"""GABS TCP connectivity and JSON-RPC smoke test.
+
+Tests connectivity from Hermes to the Bannerlord.GABS TCP server running on the
+Windows VM. Covers:
+  1. TCP socket connection (port 4825 reachable)
+  2. JSON-RPC ping round-trip
+  3. get_game_state call (game must be running)
+  4. Latency — target < 100 ms on LAN
+
+Usage:
+    python scripts/test_gabs_connectivity.py --host 10.0.0.50
+    python scripts/test_gabs_connectivity.py --host 10.0.0.50 --port 4825 --timeout 5
+
+Refs: #1098 (Bannerlord Infra — Windows VM Setup + GABS Mod Installation)
+Epic: #1091 (Project Bannerlord)
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import socket
+import sys
+import time
+from typing import Any
+
+DEFAULT_HOST = "127.0.0.1"
+DEFAULT_PORT = 4825
+DEFAULT_TIMEOUT = 5  # seconds
+LATENCY_TARGET_MS = 100.0
+
+
+# ── Low-level TCP helpers ─────────────────────────────────────────────────────
+
+
+def _tcp_connect(host: str, port: int, timeout: float) -> socket.socket:
+    """Open a TCP connection and return the socket. Raises on failure."""
+    sock = socket.create_connection((host, port), timeout=timeout)
+    sock.settimeout(timeout)
+    return sock
+
+
+def _send_recv(sock: socket.socket, payload: dict[str, Any]) -> dict[str, Any]:
+    """Send a newline-delimited JSON-RPC request and return the parsed response."""
+    raw = json.dumps(payload) + "\n"
+    sock.sendall(raw.encode())
+
+    buf = b""
+    while b"\n" not in buf:
+        chunk = sock.recv(4096)
+        if not chunk:
+            raise ConnectionError("Connection closed before response received")
+        buf += chunk
+
+    line = buf.split(b"\n", 1)[0]
+    return json.loads(line.decode())
+
+
+def _rpc(sock: socket.socket, method: str, params: dict | None = None, req_id: int = 1) -> dict[str, Any]:
+    """Build and send a JSON-RPC 2.0 request, return the response dict."""
+    payload: dict[str, Any] = {
+        "jsonrpc": "2.0",
+        "method": method,
+        "id": req_id,
+    }
+    if params:
+        payload["params"] = params
+    return _send_recv(sock, payload)
+
+
+# ── Test cases ────────────────────────────────────────────────────────────────
+
+
+def test_tcp_connection(host: str, port: int, timeout: float) -> tuple[bool, socket.socket | None]:
+    """PASS: TCP connection to host:port succeeds."""
+    print(f"\n[1/4] TCP connection → {host}:{port}")
+    try:
+        t0 = time.monotonic()
+        sock = _tcp_connect(host, port, timeout)
+        elapsed_ms = (time.monotonic() - t0) * 1000
+        print(f"  ✓ Connected ({elapsed_ms:.1f} ms)")
+        return True, sock
+    except OSError as exc:
+        print(f"  ✗ Connection failed: {exc}")
+        print(f"  Checklist:")
+        print(f"    - Is Bannerlord running with GABS mod enabled?")
+        print(f"    - Is port {port} open in Windows Firewall?")
+        print(f"    - Is the VM IP correct? (got: {host})")
+        return False, None
+
+
+def test_ping(sock: socket.socket) -> bool:
+    """PASS: JSON-RPC ping returns a 2.0 response."""
+    print(f"\n[2/4] JSON-RPC ping")
+    try:
+        t0 = time.monotonic()
+        resp = _rpc(sock, "ping", req_id=1)
+        elapsed_ms = (time.monotonic() - t0) * 1000
+        if resp.get("jsonrpc") == "2.0" and "error" not in resp:
+            print(f"  ✓ Ping OK ({elapsed_ms:.1f} ms): {json.dumps(resp)}")
+            return True
+        print(f"  ✗ Unexpected response ({elapsed_ms:.1f} ms): {json.dumps(resp)}")
+        return False
+    except Exception as exc:
+        print(f"  ✗ Ping failed: {exc}")
+        return False
+
+
+def test_game_state(sock: socket.socket) -> bool:
+    """PASS: get_game_state returns a result (game must be in a campaign)."""
+    print(f"\n[3/4] get_game_state call")
+    try:
+        t0 = time.monotonic()
+        resp = _rpc(sock, "get_game_state", req_id=2)
+        elapsed_ms = (time.monotonic() - t0) * 1000
+        if "error" in resp:
+            code = resp["error"].get("code", "?")
+            msg = resp["error"].get("message", "")
+            if code == -32601:
+                # Method not found — GABS version may not expose this method
+                print(f"  ~ Method not available ({elapsed_ms:.1f} ms): {msg}")
+                print(f"    This is acceptable if game is not yet in a campaign.")
+                return True
+            print(f"  ✗ RPC error ({elapsed_ms:.1f} ms) [{code}]: {msg}")
+            return False
+        result = resp.get("result", {})
+        print(f"  ✓ Game state received ({elapsed_ms:.1f} ms):")
+        for k, v in result.items():
+            print(f"    {k}: {v}")
+        return True
+    except Exception as exc:
+        print(f"  ✗ get_game_state failed: {exc}")
+        return False
+
+
+def test_latency(host: str, port: int, timeout: float, iterations: int = 5) -> bool:
+    """PASS: Average round-trip latency is under LATENCY_TARGET_MS."""
+    print(f"\n[4/4] Latency test ({iterations} pings, target < {LATENCY_TARGET_MS:.0f} ms)")
+    try:
+        times: list[float] = []
+        for i in range(iterations):
+            sock = _tcp_connect(host, port, timeout)
+            try:
+                t0 = time.monotonic()
+                _rpc(sock, "ping", req_id=i + 10)
+                times.append((time.monotonic() - t0) * 1000)
+            finally:
+                sock.close()
+
+        avg_ms = sum(times) / len(times)
+        min_ms = min(times)
+        max_ms = max(times)
+        print(f"  avg={avg_ms:.1f} ms  min={min_ms:.1f} ms  max={max_ms:.1f} ms")
+
+        if avg_ms <= LATENCY_TARGET_MS:
+            print(f"  ✓ Latency within target ({avg_ms:.1f} ms ≤ {LATENCY_TARGET_MS:.0f} ms)")
+            return True
+        print(
+            f"  ✗ Latency too high ({avg_ms:.1f} ms > {LATENCY_TARGET_MS:.0f} ms)\n"
+            f"    Check network path between Hermes and the VM."
+        )
+        return False
+    except Exception as exc:
+        print(f"  ✗ Latency test failed: {exc}")
+        return False
+
+
+# ── Main ──────────────────────────────────────────────────────────────────────
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="GABS TCP connectivity smoke test")
+    parser.add_argument(
+        "--host",
+        default=DEFAULT_HOST,
+        help=f"Bannerlord VM IP or hostname (default: {DEFAULT_HOST})",
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=DEFAULT_PORT,
+        help=f"GABS TCP port (default: {DEFAULT_PORT})",
+    )
+    parser.add_argument(
+        "--timeout",
+        type=float,
+        default=DEFAULT_TIMEOUT,
+        help=f"Socket timeout in seconds (default: {DEFAULT_TIMEOUT})",
+    )
+    args = parser.parse_args()
+
+    print("=" * 60)
+    print(f"GABS Connectivity Test Suite")
+    print(f"Target: {args.host}:{args.port}")
+    print(f"Timeout: {args.timeout}s")
+    print("=" * 60)
+
+    results: dict[str, bool] = {}
+
+    # Test 1: TCP connection (gate — skip remaining if unreachable)
+    ok, sock = test_tcp_connection(args.host, args.port, args.timeout)
+    results["tcp_connection"] = ok
+    if not ok:
+        _print_summary(results)
+        return 1
+
+    # Tests 2–3 reuse the same socket
+    try:
+        results["ping"] = test_ping(sock)
+        results["game_state"] = test_game_state(sock)
+    finally:
+        sock.close()
+
+    # Test 4: latency uses fresh connections
+    results["latency"] = test_latency(args.host, args.port, args.timeout)
+
+    return _print_summary(results)
+
+
+def _print_summary(results: dict[str, bool]) -> int:
+    passed = sum(results.values())
+    total = len(results)
+    print("\n" + "=" * 60)
+    print(f"Results: {passed}/{total} passed")
+    print("=" * 60)
+    for name, ok in results.items():
+        icon = "✓" if ok else "✗"
+        print(f"  {icon} {name}")
+
+    if passed == total:
+        print("\n✓ GABS connectivity verified. Timmy can reach the game.")
+        print("  Next step: run benchmark level 0 (JSON compliance check).")
+    elif not results.get("tcp_connection"):
+        print("\n✗ TCP connection failed. VM/firewall setup incomplete.")
+        print("  See docs/research/bannerlord-vm-setup.md for checklist.")
+    else:
+        print("\n~ Partial pass — review failures above.")
+
+    return 0 if passed == total else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

src/dashboard/routes/calm.py

@@ -196,7 +196,7 @@ async def get_evening_ritual_form(request: Request, db: Session = Depends(get_db
     if not journal_entry:
         raise HTTPException(status_code=404, detail="No journal entry for today")
     return templates.TemplateResponse(
-        "calm/evening_ritual_form.html", {"request": request, "journal_entry": journal_entry}
+        request, "calm/evening_ritual_form.html", {"journal_entry": journal_entry}
     )
 
 
@@ -257,8 +257,9 @@ async def create_new_task(
     # After creating a new task, we might need to re-evaluate NOW/NEXT/LATER, but for simplicity
     # and given the spec, new tasks go to LATER. Promotion happens on completion/deferral.
     return templates.TemplateResponse(
+        request,
         "calm/partials/later_count.html",
-        {"request": request, "later_tasks_count": len(get_later_tasks(db))},
+        {"later_tasks_count": len(get_later_tasks(db))},
     )
 
 
@@ -287,9 +288,9 @@ async def start_task(
     promote_tasks(db)
 
     return templates.TemplateResponse(
+        request,
         "calm/partials/now_next_later.html",
         {
-            "request": request,
             "now_task": get_now_task(db),
             "next_task": get_next_task(db),
             "later_tasks_count": len(get_later_tasks(db)),
@@ -316,9 +317,9 @@ async def complete_task(
     promote_tasks(db)
 
     return templates.TemplateResponse(
+        request,
         "calm/partials/now_next_later.html",
         {
-            "request": request,
             "now_task": get_now_task(db),
             "next_task": get_next_task(db),
             "later_tasks_count": len(get_later_tasks(db)),
@@ -345,9 +346,9 @@ async def defer_task(
     promote_tasks(db)
 
     return templates.TemplateResponse(
+        request,
         "calm/partials/now_next_later.html",
         {
-            "request": request,
             "now_task": get_now_task(db),
             "next_task": get_next_task(db),
             "later_tasks_count": len(get_later_tasks(db)),
@@ -360,8 +361,7 @@ async def get_later_tasks_list(request: Request, db: Session = Depends(get_db)):
     """Render the expandable list of LATER tasks."""
     later_tasks = get_later_tasks(db)
     return templates.TemplateResponse(
-        "calm/partials/later_tasks_list.html",
-        {"request": request, "later_tasks": later_tasks},
+        request, "calm/partials/later_tasks_list.html", {"later_tasks": later_tasks}
     )
 
 
@@ -404,9 +404,9 @@ async def reorder_tasks(
 
     # Re-render the relevant parts of the UI
     return templates.TemplateResponse(
+        request,
         "calm/partials/now_next_later.html",
         {
-            "request": request,
             "now_task": get_now_task(db),
             "next_task": get_next_task(db),
             "later_tasks_count": len(get_later_tasks(db)),

src/dashboard/routes/tools.py

@@ -40,9 +40,9 @@ async def tools_page(request: Request):
     total_calls = 0
 
     return templates.TemplateResponse(
+        request,
         "tools.html",
         {
-            "request": request,
             "available_tools": available_tools,
             "agent_tools": agent_tools,
             "total_calls": total_calls,

src/infrastructure/events/bus.py

@@ -16,6 +16,8 @@ from datetime import UTC, datetime
 from pathlib import Path
 from typing import Any
 
+from src.config import settings
+
 logger = logging.getLogger(__name__)
 
 
@@ -102,7 +104,7 @@ class EventBus:
         self._persistence_db_path.parent.mkdir(parents=True, exist_ok=True)
         with closing(sqlite3.connect(str(self._persistence_db_path))) as conn:
             conn.execute("PRAGMA journal_mode=WAL")
-            conn.execute("PRAGMA busy_timeout=5000")
+            conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
             conn.executescript(_EVENTS_SCHEMA)
             conn.commit()
 
@@ -114,7 +116,7 @@ class EventBus:
             return
         with closing(sqlite3.connect(str(self._persistence_db_path))) as conn:
             conn.row_factory = sqlite3.Row
-            conn.execute("PRAGMA busy_timeout=5000")
+            conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
             yield conn
 
     def _persist_event(self, event: Event) -> None:

src/infrastructure/models/registry.py

@@ -18,6 +18,8 @@ from datetime import UTC, datetime
 from enum import StrEnum
 from pathlib import Path
 
+from src.config import settings
+
 logger = logging.getLogger(__name__)
 
 DB_PATH = Path("data/swarm.db")
@@ -68,7 +70,7 @@ def _get_conn() -> Generator[sqlite3.Connection, None, None]:
     with closing(sqlite3.connect(str(DB_PATH))) as conn:
         conn.row_factory = sqlite3.Row
         conn.execute("PRAGMA journal_mode=WAL")
-        conn.execute("PRAGMA busy_timeout=5000")
+        conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
         conn.execute("""
             CREATE TABLE IF NOT EXISTS custom_models (
                 name            TEXT PRIMARY KEY,

src/spark/eidos.py

@@ -22,6 +22,8 @@ from dataclasses import dataclass
 from datetime import UTC, datetime
 from pathlib import Path
 
+from src.config import settings
+
 logger = logging.getLogger(__name__)
 
 DB_PATH = Path("data/spark.db")
@@ -47,7 +49,7 @@ def _get_conn() -> Generator[sqlite3.Connection, None, None]:
     with closing(sqlite3.connect(str(DB_PATH))) as conn:
         conn.row_factory = sqlite3.Row
         conn.execute("PRAGMA journal_mode=WAL")
-        conn.execute("PRAGMA busy_timeout=5000")
+        conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
         conn.execute("""
             CREATE TABLE IF NOT EXISTS spark_predictions (
                 id               TEXT PRIMARY KEY,

src/spark/memory.py

@@ -19,6 +19,8 @@ from dataclasses import dataclass
 from datetime import UTC, datetime
 from pathlib import Path
 
+from src.config import settings
+
 logger = logging.getLogger(__name__)
 
 DB_PATH = Path("data/spark.db")
@@ -63,7 +65,7 @@ def _get_conn() -> Generator[sqlite3.Connection, None, None]:
     with closing(sqlite3.connect(str(DB_PATH))) as conn:
         conn.row_factory = sqlite3.Row
         conn.execute("PRAGMA journal_mode=WAL")
-        conn.execute("PRAGMA busy_timeout=5000")
+        conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
         conn.execute("""
             CREATE TABLE IF NOT EXISTS spark_events (
                 id          TEXT PRIMARY KEY,

src/timmy/paperclip.py

@@ -13,8 +13,8 @@ from dataclasses import dataclass
 import httpx
 
 from config import settings
+from timmy.research_tools import get_llm_client, google_web_search
 from timmy.research_triage import triage_research_report
-from timmy.research_tools import google_web_search, get_llm_client
 
 logger = logging.getLogger(__name__)
 

src/timmy/research_tools.py

@@ -6,7 +6,6 @@ import logging
 import os
 from typing import Any
 
-from config import settings
 from serpapi import GoogleSearch
 
 logger = logging.getLogger(__name__)

tests/infrastructure/test_db_pool.py

@@ -6,8 +6,8 @@ import time
 from pathlib import Path
 
 import pytest
-
-from infrastructure.db_pool import ConnectionPool
+from src.config import settings
+from src.infrastructure.db_pool import ConnectionPool
 
 
 class TestConnectionPoolInit:
@@ -330,9 +330,9 @@ class TestPragmaApplication:
         """busy_timeout pragma set on a pooled connection persists."""
         pool = ConnectionPool(tmp_path / "test.db")
         conn = pool.get_connection()
-        conn.execute("PRAGMA busy_timeout=5000")
+        conn.execute(f"PRAGMA busy_timeout={settings.db_busy_timeout_ms}")
         timeout = conn.execute("PRAGMA busy_timeout").fetchone()[0]
-        assert timeout == 5000
+        assert timeout == settings.db_busy_timeout_ms
         pool.close_connection()
 
     def test_pragmas_apply_per_connection(self, tmp_path):

tests/scripts/test_export_trajectories.py

@@ -0,0 +1,285 @@
+"""Unit tests for scripts/export_trajectories.py.
+
+Tests trajectory conversion logic — no I/O, no Ollama, no mlx.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pytest
+import scripts.export_trajectories as et
+
+# ── Fixtures ──────────────────────────────────────────────────────────────────
+
+
+@pytest.fixture()
+def simple_session(tmp_path: Path) -> Path:
+    """Write a minimal session JSONL file and return the logs dir."""
+    logs_dir = tmp_path / "logs"
+    logs_dir.mkdir()
+    entries = [
+        {"type": "message", "role": "user", "content": "What time is it?", "timestamp": "2026-03-01T10:00:00"},
+        {"type": "message", "role": "timmy", "content": "It is 10:00 AM.", "timestamp": "2026-03-01T10:00:01"},
+        {"type": "message", "role": "user", "content": "Thanks!", "timestamp": "2026-03-01T10:00:05"},
+        {"type": "message", "role": "timmy", "content": "You're welcome!", "timestamp": "2026-03-01T10:00:06"},
+    ]
+    session_file = logs_dir / "session_2026-03-01.jsonl"
+    session_file.write_text("\n".join(json.dumps(e) for e in entries) + "\n")
+    return logs_dir
+
+
+@pytest.fixture()
+def tool_call_session(tmp_path: Path) -> Path:
+    """Write a session JSONL with tool calls."""
+    logs_dir = tmp_path / "logs"
+    logs_dir.mkdir()
+    entries = [
+        {"type": "message", "role": "user", "content": "Read CLAUDE.md", "timestamp": "2026-03-01T10:00:00"},
+        {
+            "type": "tool_call",
+            "tool": "read_file",
+            "args": {"path": "CLAUDE.md"},
+            "result": "# CLAUDE.md content here",
+            "timestamp": "2026-03-01T10:00:01",
+        },
+        {"type": "message", "role": "timmy", "content": "Here is the content.", "timestamp": "2026-03-01T10:00:02"},
+    ]
+    session_file = logs_dir / "session_2026-03-01.jsonl"
+    session_file.write_text("\n".join(json.dumps(e) for e in entries) + "\n")
+    return logs_dir
+
+
+# ── _load_entries ─────────────────────────────────────────────────────────────
+
+
+@pytest.mark.unit
+def test_load_entries_returns_all(simple_session: Path) -> None:
+    entries = et._load_entries(simple_session)
+    assert len(entries) == 4
+
+
+@pytest.mark.unit
+def test_load_entries_skips_malformed(tmp_path: Path) -> None:
+    logs_dir = tmp_path / "logs"
+    logs_dir.mkdir()
+    session = logs_dir / "session_2026-03-01.jsonl"
+    session.write_text(
+        '{"type": "message", "role": "user", "content": "hi"}\n'
+        "NOT_JSON\n"
+        '{"type": "message", "role": "timmy", "content": "hello"}\n'
+    )
+    entries = et._load_entries(logs_dir)
+    assert len(entries) == 2  # malformed line skipped
+
+
+@pytest.mark.unit
+def test_load_entries_empty_dir(tmp_path: Path) -> None:
+    logs_dir = tmp_path / "logs"
+    logs_dir.mkdir()
+    entries = et._load_entries(logs_dir)
+    assert entries == []
+
+
+@pytest.mark.unit
+def test_load_entries_multiple_files(tmp_path: Path) -> None:
+    logs_dir = tmp_path / "logs"
+    logs_dir.mkdir()
+    for day in ("2026-03-01", "2026-03-02"):
+        entry = {"type": "message", "role": "user", "content": f"day {day}"}
+        (logs_dir / f"session_{day}.jsonl").write_text(json.dumps(entry) + "\n")
+    entries = et._load_entries(logs_dir)
+    assert len(entries) == 2
+
+
+# ── _format_tool_call ─────────────────────────────────────────────────────────
+
+
+@pytest.mark.unit
+def test_format_tool_call_structure() -> None:
+    entry = {
+        "type": "tool_call",
+        "tool": "read_file",
+        "args": {"path": "/tmp/foo.txt"},
+        "result": "file contents",
+    }
+    result = et._format_tool_call(entry)
+    assert result.startswith("<tool_call>")
+    assert result.endswith("</tool_call>")
+    payload = json.loads(result.split("\n")[1])
+    assert payload["name"] == "read_file"
+    assert payload["arguments"]["path"] == "/tmp/foo.txt"
+
+
+@pytest.mark.unit
+def test_format_tool_call_missing_tool() -> None:
+    entry = {"type": "tool_call", "args": {}}
+    result = et._format_tool_call(entry)
+    assert "unknown" in result
+
+
+# ── _group_into_turns ─────────────────────────────────────────────────────────
+
+
+@pytest.mark.unit
+def test_group_basic_conversation() -> None:
+    entries = [
+        {"type": "message", "role": "user", "content": "hello"},
+        {"type": "message", "role": "timmy", "content": "hi there"},
+        {"type": "message", "role": "user", "content": "bye"},
+        {"type": "message", "role": "timmy", "content": "goodbye"},
+    ]
+    turns = et._group_into_turns(entries)
+    assert len(turns) == 2
+    assert turns[0]["user"] == "hello"
+    assert turns[0]["assistant"] == "hi there"
+    assert turns[1]["user"] == "bye"
+    assert turns[1]["assistant"] == "goodbye"
+
+
+@pytest.mark.unit
+def test_group_with_tool_call() -> None:
+    entries = [
+        {"type": "message", "role": "user", "content": "check the file"},
+        {"type": "tool_call", "tool": "read_file", "args": {"path": "x"}, "result": "content"},
+        {"type": "message", "role": "timmy", "content": "Done."},
+    ]
+    turns = et._group_into_turns(entries)
+    assert len(turns) == 1
+    assert "<tool_call>" in turns[0]["assistant"]
+    assert "Done." in turns[0]["assistant"]
+
+
+@pytest.mark.unit
+def test_group_skips_user_without_response() -> None:
+    """User message with no timmy response should not create a turn."""
+    entries = [
+        {"type": "message", "role": "user", "content": "hello"},
+        # No timmy response
+        {"type": "message", "role": "user", "content": "are you there?"},
+        {"type": "message", "role": "timmy", "content": "Yes!"},
+    ]
+    turns = et._group_into_turns(entries)
+    assert len(turns) == 1
+    assert turns[0]["user"] == "are you there?"
+
+
+@pytest.mark.unit
+def test_group_ignores_errors_and_decisions() -> None:
+    entries = [
+        {"type": "message", "role": "user", "content": "hello"},
+        {"type": "error", "error": "something failed"},
+        {"type": "decision", "decision": "retry"},
+        {"type": "message", "role": "timmy", "content": "Got it."},
+    ]
+    turns = et._group_into_turns(entries)
+    assert len(turns) == 1
+    assert "error" not in turns[0]["assistant"]
+    assert "retry" not in turns[0]["assistant"]
+
+
+@pytest.mark.unit
+def test_group_empty_entries() -> None:
+    assert et._group_into_turns([]) == []
+
+
+# ── turns_to_training_examples ────────────────────────────────────────────────
+
+
+@pytest.mark.unit
+def test_training_examples_structure() -> None:
+    turns = [{"user": "hello", "assistant": "hi there, how can I help?"}]
+    examples = et.turns_to_training_examples(turns)
+    assert len(examples) == 1
+    msgs = examples[0]["messages"]
+    assert msgs[0]["role"] == "system"
+    assert msgs[1]["role"] == "user"
+    assert msgs[1]["content"] == "hello"
+    assert msgs[2]["role"] == "assistant"
+    assert msgs[2]["content"] == "hi there, how can I help?"
+
+
+@pytest.mark.unit
+def test_training_examples_filters_short_responses() -> None:
+    turns = [
+        {"user": "hello", "assistant": "ok"},  # too short
+        {"user": "hello", "assistant": "This is a longer response that passes."},
+    ]
+    examples = et.turns_to_training_examples(turns, min_assistant_len=10)
+    assert len(examples) == 1
+    assert examples[0]["messages"][2]["content"] == "This is a longer response that passes."
+
+
+@pytest.mark.unit
+def test_training_examples_filters_empty_user() -> None:
+    turns = [{"user": "", "assistant": "some response here"}]
+    examples = et.turns_to_training_examples(turns)
+    assert len(examples) == 0
+
+
+@pytest.mark.unit
+def test_training_examples_uses_custom_system_prompt() -> None:
+    turns = [{"user": "hi", "assistant": "hello there!"}]
+    examples = et.turns_to_training_examples(turns, system_prompt="Custom prompt.")
+    assert examples[0]["messages"][0]["content"] == "Custom prompt."
+
+
+# ── export_training_data (integration-style, uses tmp_path) ──────────────────
+
+
+@pytest.mark.unit
+def test_export_training_data_writes_jsonl(simple_session: Path, tmp_path: Path) -> None:
+    output = tmp_path / "train.jsonl"
+    count = et.export_training_data(logs_dir=simple_session, output_path=output)
+    assert count == 2
+    assert output.exists()
+    lines = [
+        json.loads(line) for line in output.read_text().splitlines() if line.strip()
+    ]
+    assert len(lines) == 2
+    for line in lines:
+        assert "messages" in line
+        roles = [m["role"] for m in line["messages"]]
+        assert roles == ["system", "user", "assistant"]
+
+
+@pytest.mark.unit
+def test_export_training_data_with_tool_calls(tool_call_session: Path, tmp_path: Path) -> None:
+    output = tmp_path / "train.jsonl"
+    count = et.export_training_data(logs_dir=tool_call_session, output_path=output)
+    assert count == 1
+    line = json.loads(output.read_text().strip())
+    assistant_content = line["messages"][2]["content"]
+    assert "<tool_call>" in assistant_content
+    assert "read_file" in assistant_content
+
+
+@pytest.mark.unit
+def test_export_training_data_returns_zero_for_empty_logs(tmp_path: Path) -> None:
+    logs_dir = tmp_path / "logs"
+    logs_dir.mkdir()
+    output = tmp_path / "train.jsonl"
+    count = et.export_training_data(logs_dir=logs_dir, output_path=output)
+    assert count == 0
+    assert not output.exists()
+
+
+# ── CLI ───────────────────────────────────────────────────────────────────────
+
+
+@pytest.mark.unit
+def test_cli_missing_logs_dir(tmp_path: Path) -> None:
+    rc = et.main(["--logs-dir", str(tmp_path / "nonexistent"), "--output", str(tmp_path / "out.jsonl")])
+    assert rc == 1
+
+
+@pytest.mark.unit
+def test_cli_exports_and_returns_zero(simple_session: Path, tmp_path: Path) -> None:
+    output = tmp_path / "out.jsonl"
+    rc = et.main([
+        "--logs-dir", str(simple_session),
+        "--output", str(output),
+    ])
+    assert rc == 0
+    assert output.exists()

tests/unit/test_retrain_loop.py

@@ -0,0 +1,546 @@
+"""Unit tests for the AutoLoRA continuous improvement loop.
+
+Covers trajectory extraction, quality filtering, dataset management,
+and the retrain orchestrator.
+
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import json
+from datetime import UTC, datetime, timedelta
+from pathlib import Path
+
+from timmy_automations.retrain.quality_filter import QualityFilter, TrajectoryQuality
+from timmy_automations.retrain.retrain import RetrainOrchestrator
+from timmy_automations.retrain.training_dataset import TrainingDataset
+from timmy_automations.retrain.training_log import CycleMetrics, TrainingLog
+from timmy_automations.retrain.trajectory_exporter import Trajectory, TrajectoryExporter
+
+# ── Fixtures ─────────────────────────────────────────────────────────────────
+
+
+def _ts(offset_minutes: int = 0) -> str:
+    """Return an ISO timestamp offset from now."""
+    return (datetime.now(tz=UTC) + timedelta(minutes=offset_minutes)).isoformat()
+
+
+def _make_session_log(entries: list[dict], date_str: str, tmp_path: Path) -> Path:
+    """Write session JSONL entries to a temp log file."""
+    log_dir = tmp_path / "logs"
+    log_dir.mkdir(parents=True, exist_ok=True)
+    log_file = log_dir / f"session_{date_str}.jsonl"
+    with open(log_file, "w") as f:
+        for entry in entries:
+            f.write(json.dumps(entry) + "\n")
+    return log_file
+
+
+def _user_msg(content: str, offset: int = 0) -> dict:
+    return {"type": "message", "role": "user", "content": content, "timestamp": _ts(offset)}
+
+
+def _timmy_msg(content: str, confidence: float | None = None, offset: int = 0) -> dict:
+    entry = {"type": "message", "role": "timmy", "content": content, "timestamp": _ts(offset)}
+    if confidence is not None:
+        entry["confidence"] = confidence
+    return entry
+
+
+def _tool_call(tool: str = "bash", result: str = "ok", offset: int = 0) -> dict:
+    return {
+        "type": "tool_call",
+        "tool": tool,
+        "args": {},
+        "result": result,
+        "timestamp": _ts(offset),
+    }
+
+
+def _error_entry(msg: str = "Something failed", offset: int = 0) -> dict:
+    return {"type": "error", "error": msg, "timestamp": _ts(offset)}
+
+
+def _decision_entry(decision: str = "Use approach A", offset: int = 0) -> dict:
+    return {"type": "decision", "decision": decision, "timestamp": _ts(offset)}
+
+
+# ── Trajectory dataclass tests ────────────────────────────────────────────────
+
+
+class TestTrajectory:
+    def test_message_count(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("hi"), _timmy_msg("hello")],
+        )
+        assert t.message_count == 2
+
+    def test_tool_call_count(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            tool_calls=[_tool_call(), _tool_call()],
+        )
+        assert t.tool_call_count == 2
+
+    def test_has_successful_tool_call_when_no_errors(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            tool_calls=[_tool_call()],
+            errors=[],
+        )
+        assert t.has_successful_tool_call is True
+
+    def test_has_successful_tool_call_false_when_errors(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            tool_calls=[_tool_call()],
+            errors=[_error_entry()],
+        )
+        assert t.has_successful_tool_call is False
+
+    def test_is_multi_step(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("do it"), _timmy_msg("done")],
+            tool_calls=[_tool_call()],
+        )
+        assert t.is_multi_step is True
+
+    def test_is_not_multi_step_single_message(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_timmy_msg("hello")],
+            tool_calls=[],
+        )
+        assert t.is_multi_step is False
+
+    def test_to_chat_format_ordering(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("question", offset=0), _timmy_msg("answer", offset=2)],
+            tool_calls=[_tool_call(offset=1)],
+        )
+        chat = t.to_chat_format()
+        roles = [m["role"] for m in chat]
+        assert "user" in roles
+        assert "assistant" in roles
+
+    def test_to_chat_format_empty_content_skipped(self):
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg(""), _timmy_msg("response")],
+        )
+        chat = t.to_chat_format()
+        # Empty user message should be skipped
+        assert all(m["content"] for m in chat)
+
+
+# ── TrajectoryExporter tests ──────────────────────────────────────────────────
+
+
+class TestTrajectoryExporter:
+    def test_export_empty_logs_dir(self, tmp_path):
+        (tmp_path / "logs").mkdir()
+        exporter = TrajectoryExporter(logs_dir=tmp_path / "logs", repo_root=tmp_path)
+        result = exporter.export_week(weeks_ago=0)
+        assert result == []
+
+    def test_export_reads_session_files(self, tmp_path):
+        # Write a session file for this week
+        today = datetime.now(tz=UTC)
+        date_str = today.strftime("%Y-%m-%d")
+        entries = [
+            _user_msg("tell me about Python"),
+            _timmy_msg("Python is great"),
+        ]
+        _make_session_log(entries, date_str, tmp_path)
+
+        exporter = TrajectoryExporter(logs_dir=tmp_path / "logs", repo_root=tmp_path)
+        result = exporter.export_week(weeks_ago=0)
+        assert len(result) >= 1
+
+    def test_export_skips_old_sessions(self, tmp_path):
+        # Write a session file for 3 weeks ago
+        three_weeks_ago = datetime.now(tz=UTC) - timedelta(weeks=3)
+        date_str = three_weeks_ago.strftime("%Y-%m-%d")
+        entries = [_user_msg("old message"), _timmy_msg("old response")]
+        _make_session_log(entries, date_str, tmp_path)
+
+        exporter = TrajectoryExporter(logs_dir=tmp_path / "logs", repo_root=tmp_path)
+        # Request current week — should not include 3-week-old data
+        result = exporter.export_week(weeks_ago=0)
+        assert result == []
+
+    def test_export_segments_by_gap(self, tmp_path):
+        today = datetime.now(tz=UTC)
+        date_str = today.strftime("%Y-%m-%d")
+
+        # Two conversations separated by 10 minutes
+        t1 = (today - timedelta(minutes=15)).isoformat()
+        t2 = (today - timedelta(minutes=14)).isoformat()
+        t3 = (today - timedelta(minutes=2)).isoformat()
+        t4 = (today - timedelta(minutes=1)).isoformat()
+
+        entries = [
+            {"type": "message", "role": "user", "content": "first q", "timestamp": t1},
+            {"type": "message", "role": "timmy", "content": "first a", "timestamp": t2},
+            {"type": "message", "role": "user", "content": "second q", "timestamp": t3},
+            {"type": "message", "role": "timmy", "content": "second a", "timestamp": t4},
+        ]
+        _make_session_log(entries, date_str, tmp_path)
+
+        exporter = TrajectoryExporter(logs_dir=tmp_path / "logs", repo_root=tmp_path)
+        result = exporter.export_week(weeks_ago=0)
+        # Should have at least 1 trajectory (may be 1 or 2 depending on segmentation)
+        assert len(result) >= 1
+
+    def test_handles_malformed_log_file(self, tmp_path):
+        log_dir = tmp_path / "logs"
+        log_dir.mkdir()
+        today = datetime.now(tz=UTC).strftime("%Y-%m-%d")
+        (log_dir / f"session_{today}.jsonl").write_text("not json\n{}\n")
+
+        exporter = TrajectoryExporter(logs_dir=log_dir, repo_root=tmp_path)
+        # Should not raise, just return empty or partial results
+        result = exporter.export_week(weeks_ago=0)
+        assert isinstance(result, list)
+
+
+# ── QualityFilter tests ───────────────────────────────────────────────────────
+
+
+class TestQualityFilter:
+    def _make_high_quality(self) -> Trajectory:
+        return Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("do task"), _timmy_msg("done", confidence=0.9)],
+            tool_calls=[_tool_call(), _tool_call()],
+            errors=[],
+            decisions=[_decision_entry()],
+        )
+
+    def _make_medium_quality(self) -> Trajectory:
+        return Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("hello"), _timmy_msg("hi")],
+            tool_calls=[],
+            errors=[],
+        )
+
+    def _make_low_quality(self) -> Trajectory:
+        return Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_timmy_msg("oops")],  # No user message
+            errors=[_error_entry()],
+        )
+
+    def test_high_quality_classification(self):
+        qf = QualityFilter()
+        result = qf.assess(self._make_high_quality())
+        assert result.quality == TrajectoryQuality.HIGH
+        assert result.score >= 4.0
+        assert result.is_trainable
+
+    def test_medium_quality_classification(self):
+        qf = QualityFilter()
+        result = qf.assess(self._make_medium_quality())
+        assert result.quality == TrajectoryQuality.MEDIUM
+        assert result.is_trainable
+
+    def test_low_quality_no_user_message(self):
+        qf = QualityFilter()
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_timmy_msg("random")],
+        )
+        result = qf.assess(t)
+        assert result.quality == TrajectoryQuality.LOW
+        assert not result.is_trainable
+
+    def test_error_penalizes_score(self):
+        qf = QualityFilter()
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("go"), _timmy_msg("fail")],
+            tool_calls=[_tool_call()],
+            errors=[_error_entry(), _error_entry()],
+        )
+        result = qf.assess(t)
+        assert result.score < qf.assess(self._make_high_quality()).score
+
+    def test_low_confidence_penalizes_score(self):
+        qf = QualityFilter()
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(),
+            ended_at=_ts(),
+            messages=[_user_msg("q"), _timmy_msg("a", confidence=0.2)],
+        )
+        result = qf.assess(t)
+        assert result.score < 1.0
+
+    def test_filter_returns_stats(self):
+        qf = QualityFilter()
+        trajectories = [
+            self._make_high_quality(),
+            self._make_medium_quality(),
+            self._make_low_quality(),
+        ]
+        trainable, stats = qf.filter(trajectories)
+        assert stats["total"] == 3
+        assert stats["accepted"] == len(trainable)
+        assert stats["high"] + stats["medium"] + stats["low"] == 3
+
+    def test_filter_empty_list(self):
+        qf = QualityFilter()
+        trainable, stats = qf.filter([])
+        assert trainable == []
+        assert stats["total"] == 0
+        assert stats["accepted"] == 0
+
+
+# ── TrainingDataset tests ─────────────────────────────────────────────────────
+
+
+class TestTrainingDataset:
+    def _make_result(self, quality=TrajectoryQuality.HIGH, score=5.0) -> object:
+        from timmy_automations.retrain.quality_filter import QualityResult
+
+        t = Trajectory(
+            session_date="2026-03-17",
+            started_at=_ts(-5),
+            ended_at=_ts(),
+            messages=[_user_msg("do it"), _timmy_msg("done")],
+            tool_calls=[_tool_call()],
+        )
+        return QualityResult(trajectory=t, quality=quality, score=score, reasons=[])
+
+    def test_count_empty_dataset(self, tmp_path):
+        ds = TrainingDataset(
+            dataset_path=".loop/retrain/training_data.jsonl",
+            repo_root=tmp_path,
+        )
+        assert ds.count() == 0
+
+    def test_append_adds_examples(self, tmp_path):
+        ds = TrainingDataset(repo_root=tmp_path)
+        result = ds.append([self._make_result()], "2026-W12")
+        assert result.new_examples == 1
+        assert result.total_examples == 1
+        assert ds.count() == 1
+
+    def test_append_idempotent(self, tmp_path):
+        ds = TrainingDataset(repo_root=tmp_path)
+        r = self._make_result()
+        ds.append([r], "2026-W12")
+        result2 = ds.append([r], "2026-W12")
+        # Same trajectory shouldn't be added twice
+        assert result2.new_examples == 0
+        assert ds.count() == 1
+
+    def test_append_different_weeks(self, tmp_path):
+        ds = TrainingDataset(repo_root=tmp_path)
+        r1 = self._make_result()
+        ds.append([r1], "2026-W11")
+        ds.append([r1], "2026-W12")
+        # Different week tags = different records
+        assert ds.count() == 2
+
+    def test_dataset_file_is_valid_jsonl(self, tmp_path):
+        ds = TrainingDataset(repo_root=tmp_path)
+        ds.append([self._make_result()], "2026-W12")
+        with open(ds.dataset_path) as f:
+            lines = [line.strip() for line in f if line.strip()]
+        assert len(lines) == 1
+        record = json.loads(lines[0])
+        assert "messages" in record
+        assert "week" in record
+        assert "quality" in record
+
+    def test_index_updated_after_append(self, tmp_path):
+        ds = TrainingDataset(repo_root=tmp_path)
+        ds.append([self._make_result()], "2026-W12")
+        index_path = tmp_path / ".loop" / "retrain" / "dataset_index.json"
+        assert index_path.exists()
+        index = json.loads(index_path.read_text())
+        assert index["total_examples"] == 1
+        assert "2026-W12" in index["weeks"]
+
+
+# ── TrainingLog tests ─────────────────────────────────────────────────────────
+
+
+class TestTrainingLog:
+    def _make_metrics(self, iteration: int = 1) -> CycleMetrics:
+        return CycleMetrics(
+            iteration=iteration,
+            week="2026-W12",
+            ran_at=datetime.now(tz=UTC).isoformat(),
+            trajectories_total=10,
+            trajectories_high=5,
+            trajectories_medium=3,
+            trajectories_low=2,
+            trajectories_accepted=8,
+            examples_added=5,
+            dataset_total=5,
+            train_status="completed",
+            train_loss=1.2345,
+            train_duration_seconds=120.5,
+            adapter_path=".loop/retrain/adapters/iter_0001/adapters.npz",
+            model_name="hermes4-14b-ft-0001",
+            notes="First fine-tune cycle complete",
+        )
+
+    def test_next_iteration_starts_at_1(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        assert log.next_iteration() == 1
+
+    def test_next_iteration_increments(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        log.record(self._make_metrics(iteration=1))
+        assert log.next_iteration() == 2
+
+    def test_record_creates_log_file(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        log.record(self._make_metrics())
+        assert log.log_path.exists()
+
+    def test_load_all_returns_records(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        log.record(self._make_metrics(iteration=1))
+        log.record(self._make_metrics(iteration=2))
+        entries = log.load_all()
+        assert len(entries) == 2
+        assert entries[0]["iteration"] == 1
+
+    def test_latest_returns_last_entry(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        log.record(self._make_metrics(iteration=1))
+        log.record(self._make_metrics(iteration=2))
+        latest = log.latest()
+        assert latest is not None
+        assert latest["iteration"] == 2
+
+    def test_latest_returns_none_when_empty(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        assert log.latest() is None
+
+    def test_summary_markdown_written(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        log.record(self._make_metrics())
+        summary_path = tmp_path / ".loop" / "retrain" / "training_log.md"
+        assert summary_path.exists()
+        content = summary_path.read_text()
+        assert "AutoLoRA Training Log" in content
+        assert "2026-W12" in content
+        assert "completed" in content
+
+    def test_skill_accuracy_in_summary(self, tmp_path):
+        log = TrainingLog(repo_root=tmp_path)
+        m = self._make_metrics()
+        m.skill_accuracy = {"tool_calling": 0.85, "reasoning": 0.72}
+        log.record(m)
+        content = (tmp_path / ".loop" / "retrain" / "training_log.md").read_text()
+        assert "tool_calling" in content
+        assert "reasoning" in content
+
+
+# ── RetrainOrchestrator integration tests ─────────────────────────────────────
+
+
+class TestRetrainOrchestrator:
+    def test_run_dry_run_no_data(self, tmp_path):
+        """Dry run with no session logs should complete without errors."""
+        (tmp_path / "logs").mkdir(parents=True)
+        orc = RetrainOrchestrator(repo_root=tmp_path, dry_run=True)
+        result = orc.run(weeks_ago=0)
+        assert result.train_status in ("skipped",)
+        assert result.examples_added == 0
+        assert result.iteration == 1
+
+    def test_run_creates_log_entry(self, tmp_path):
+        (tmp_path / "logs").mkdir(parents=True)
+        orc = RetrainOrchestrator(repo_root=tmp_path, dry_run=True)
+        orc.run(weeks_ago=0)
+        log = TrainingLog(repo_root=tmp_path)
+        entries = log.load_all()
+        assert len(entries) == 1
+
+    def test_run_with_session_data(self, tmp_path):
+        """Run with actual session data — should export, filter, and log."""
+        today = datetime.now(tz=UTC)
+        date_str = today.strftime("%Y-%m-%d")
+        entries = [
+            _user_msg("deploy the service", offset=-10),
+            _tool_call("bash", "deployed successfully", offset=-9),
+            _tool_call("bash", "health check ok", offset=-8),
+            _timmy_msg("Service deployed and healthy", confidence=0.92, offset=-7),
+            _user_msg("run the tests", offset=-6),
+            _tool_call("bash", "All tests passed", offset=-5),
+            _timmy_msg("All 42 tests passed", confidence=0.95, offset=-4),
+        ]
+        _make_session_log(entries, date_str, tmp_path)
+
+        orc = RetrainOrchestrator(repo_root=tmp_path, dry_run=True)
+        result = orc.run(weeks_ago=0)
+
+        assert result.trajectories_exported >= 1
+        assert result.iteration == 1
+        # In dry_run mode, fine-tune is skipped but trajectories should be processed
+        assert result.train_status == "skipped"
+
+    def test_iteration_increments_on_second_run(self, tmp_path):
+        (tmp_path / "logs").mkdir(parents=True)
+        orc = RetrainOrchestrator(repo_root=tmp_path, dry_run=True)
+        r1 = orc.run(weeks_ago=0)
+        r2 = orc.run(weeks_ago=0)
+        assert r2.iteration == r1.iteration + 1
+
+    def test_automations_json_has_retrain_entry(self):
+        """Verify the retrain automation is registered in automations.json."""
+        config_path = _REPO_ROOT / "timmy_automations" / "config" / "automations.json"
+        assert config_path.exists()
+        manifest = json.loads(config_path.read_text())
+        ids = [a["id"] for a in manifest.get("automations", [])]
+        assert "retrain" in ids
+
+    def test_retrain_automation_config(self):
+        """Verify retrain automation has correct schedule and config."""
+        config_path = _REPO_ROOT / "timmy_automations" / "config" / "automations.json"
+        manifest = json.loads(config_path.read_text())
+        retrain = next(a for a in manifest["automations"] if a["id"] == "retrain")
+        assert retrain["schedule"] == "weekly_sunday"
+        assert retrain["trigger"] == "scheduled"
+        assert retrain["config"]["base_model"] == "hermes4-14b"
+        assert retrain["config"]["weeks_ago"] == 1
+
+
+_REPO_ROOT = Path(__file__).resolve().parent.parent.parent

timmy_automations/config/automations.json

@@ -4,7 +4,7 @@
   "_health_snapshot": {
     "note": "Quick health check before coding — CI, P0/P1 issues, flakiness"
   },
-  "last_updated": "2026-03-21",
+  "last_updated": "2026-03-23",
   "automations": [
     {
       "id": "cycle_retro",
@@ -268,6 +268,36 @@
         "ci_timeout_seconds": 5
       },
       "outputs": []
+    },
+    {
+      "id": "retrain",
+      "name": "AutoLoRA Continuous Improvement Loop",
+      "description": "Weekly sovereignty loop — exports trajectories, filters quality, appends to training dataset, triggers LoRA fine-tune, loads new adapter, and logs iteration metrics",
+      "script": "timmy_automations/retrain/retrain.py",
+      "category": "autolora",
+      "enabled": true,
+      "trigger": "scheduled",
+      "schedule": "weekly_sunday",
+      "executable": "python3",
+      "epic": "#1091",
+      "pipeline": "AutoLoRA Sovereignty Loop (Step 6 of 7)",
+      "config": {
+        "weeks_ago": 1,
+        "base_model": "hermes4-14b",
+        "dry_run": false,
+        "logs_dir": "logs",
+        "dataset_path": ".loop/retrain/training_data.jsonl",
+        "adapter_dir": ".loop/retrain/adapters",
+        "training_log_path": ".loop/retrain/training_log.jsonl",
+        "training_summary_path": ".loop/retrain/training_log.md"
+      },
+      "outputs": [
+        ".loop/retrain/training_data.jsonl",
+        ".loop/retrain/dataset_index.json",
+        ".loop/retrain/training_log.jsonl",
+        ".loop/retrain/training_log.md",
+        ".loop/retrain/adapters/"
+      ]
     }
   ]
 }

timmy_automations/retrain/__init__.py

@@ -0,0 +1,26 @@
+"""AutoLoRA continuous improvement loop — sovereignty engine for Timmy.
+
+Implements the weekly retrain cycle:
+  Work → Record trajectories → Export weekly → Filter quality
+  → LoRA fine-tune → Load adapter → Model improves → Repeat
+
+Epic: #1091 — Project Bannerlord
+Pipeline: AutoLoRA Sovereignty Loop (Step 6 of 7)
+Refs: #1105
+"""
+
+from timmy_automations.retrain.quality_filter import QualityFilter, TrajectoryQuality
+from timmy_automations.retrain.retrain import RetrainOrchestrator, RetrainResult
+from timmy_automations.retrain.training_dataset import TrainingDataset
+from timmy_automations.retrain.training_log import TrainingLog
+from timmy_automations.retrain.trajectory_exporter import TrajectoryExporter
+
+__all__ = [
+    "QualityFilter",
+    "RetrainOrchestrator",
+    "RetrainResult",
+    "TrainingDataset",
+    "TrainingLog",
+    "TrajectoryExporter",
+    "TrajectoryQuality",
+]

timmy_automations/retrain/lora_trainer.py

@@ -0,0 +1,262 @@
+"""LoRA trainer — triggers fine-tune job and loads the resulting adapter.
+
+Supports two backends:
+1. mlx-lm (default, Apple Silicon) — `mlx_lm.lora` CLI
+2. Ollama create (adapter packaging into a new Ollama model)
+
+Graceful degradation: if neither backend is available, logs a warning
+and returns a skipped result — the rest of the loop continues.
+
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import shutil
+import subprocess
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_BASE_MODEL = "hermes4-14b"
+_DEFAULT_ADAPTER_DIR = ".loop/retrain/adapters"
+_MLX_LM_BIN = "mlx_lm.lora"
+_OLLAMA_BIN = "ollama"
+
+
+@dataclass
+class TrainResult:
+    """Result of a LoRA fine-tune run."""
+
+    status: str  # "completed" | "skipped" | "failed"
+    adapter_path: str | None
+    model_name: str | None
+    iteration: int
+    duration_seconds: float
+    message: str
+    train_loss: float | None = None
+
+
+class LoRATrainer:
+    """Orchestrates LoRA fine-tuning and adapter loading.
+
+    Workflow:
+    1. Run mlx_lm.lora fine-tune on the training dataset
+    2. Save the resulting adapter to .loop/retrain/adapters/<iteration>/
+    3. Create (or update) an Ollama model that uses the new adapter
+    """
+
+    def __init__(
+        self,
+        base_model: str = _DEFAULT_BASE_MODEL,
+        adapter_dir: str | Path | None = None,
+        repo_root: str | Path | None = None,
+        dry_run: bool = False,
+    ):
+        if repo_root is None:
+            repo_root = Path(__file__).resolve().parent.parent.parent
+        self._repo_root = Path(repo_root)
+
+        self._base_model = base_model
+        self._adapter_dir = self._repo_root / (adapter_dir or _DEFAULT_ADAPTER_DIR)
+        self._adapter_dir.mkdir(parents=True, exist_ok=True)
+        self._dry_run = dry_run
+
+    def train(self, dataset_path: Path, iteration: int) -> TrainResult:
+        """Run LoRA fine-tuning on the dataset.
+
+        Args:
+            dataset_path: Path to the JSONL training dataset.
+            iteration: Current fine-tune iteration number (used for naming).
+
+        Returns:
+            TrainResult with status, adapter path, and metrics.
+        """
+        started = datetime.now(tz=UTC)
+
+        if not dataset_path.exists() or dataset_path.stat().st_size == 0:
+            return TrainResult(
+                status="skipped",
+                adapter_path=None,
+                model_name=None,
+                iteration=iteration,
+                duration_seconds=0.0,
+                message="Training dataset is empty — skipping fine-tune",
+            )
+
+        if self._dry_run:
+            logger.info("[dry-run] Would fine-tune %s on %s", self._base_model, dataset_path)
+            adapter_path = self._adapter_dir / f"iter_{iteration:04d}" / "adapters.npz"
+            return TrainResult(
+                status="skipped",
+                adapter_path=str(adapter_path),
+                model_name=f"{self._base_model}-ft-{iteration:04d}",
+                iteration=iteration,
+                duration_seconds=0.0,
+                message="dry-run mode — no training performed",
+            )
+
+        # Determine which backend is available
+        if shutil.which(_MLX_LM_BIN):
+            return self._train_mlx(dataset_path, iteration, started)
+        else:
+            logger.warning(
+                "%s not found — skipping LoRA fine-tune (install mlx-lm to enable)",
+                _MLX_LM_BIN,
+            )
+            return TrainResult(
+                status="skipped",
+                adapter_path=None,
+                model_name=None,
+                iteration=iteration,
+                duration_seconds=0.0,
+                message=(
+                    f"{_MLX_LM_BIN} not available. "
+                    "Install mlx-lm on Apple Silicon to enable LoRA fine-tuning."
+                ),
+            )
+
+    def _train_mlx(
+        self, dataset_path: Path, iteration: int, started: datetime
+    ) -> TrainResult:
+        """Run mlx_lm.lora fine-tune."""
+        adapter_out = self._adapter_dir / f"iter_{iteration:04d}"
+        adapter_out.mkdir(parents=True, exist_ok=True)
+
+        cmd = [
+            _MLX_LM_BIN,
+            "--model", self._base_model,
+            "--data", str(dataset_path),
+            "--adapter-path", str(adapter_out),
+            "--train",
+            "--iters", "100",
+            "--batch-size", "1",
+            "--learning-rate", "1e-5",
+        ]
+
+        logger.info("Starting mlx-lm LoRA fine-tune: iteration %d", iteration)
+        logger.info("Command: %s", " ".join(cmd))
+
+        try:
+            result = subprocess.run(
+                cmd,
+                capture_output=True,
+                text=True,
+                timeout=3600,  # 1 hour max
+                env={**os.environ, "PYTHONUNBUFFERED": "1"},
+            )
+        except subprocess.TimeoutExpired:
+            duration = (datetime.now(tz=UTC) - started).total_seconds()
+            return TrainResult(
+                status="failed",
+                adapter_path=None,
+                model_name=None,
+                iteration=iteration,
+                duration_seconds=duration,
+                message="Fine-tune timed out after 1 hour",
+            )
+        except Exception as exc:
+            duration = (datetime.now(tz=UTC) - started).total_seconds()
+            return TrainResult(
+                status="failed",
+                adapter_path=None,
+                model_name=None,
+                iteration=iteration,
+                duration_seconds=duration,
+                message=f"Fine-tune subprocess error: {exc}",
+            )
+
+        duration = (datetime.now(tz=UTC) - started).total_seconds()
+
+        if result.returncode != 0:
+            logger.error("mlx-lm fine-tune failed: %s", result.stderr[:500])
+            return TrainResult(
+                status="failed",
+                adapter_path=None,
+                model_name=None,
+                iteration=iteration,
+                duration_seconds=duration,
+                message=f"mlx_lm.lora exited {result.returncode}: {result.stderr[:300]}",
+            )
+
+        # Parse final train loss from stdout if available
+        train_loss = _parse_train_loss(result.stdout)
+
+        adapter_file = adapter_out / "adapters.npz"
+        model_name = f"{self._base_model}-ft-{iteration:04d}"
+
+        # Attempt to register with Ollama
+        ollama_ok = self._register_ollama_adapter(adapter_out, model_name)
+        if not ollama_ok:
+            logger.warning("Ollama adapter registration failed — adapter saved locally")
+
+        logger.info(
+            "Fine-tune complete: iteration=%d loss=%.4f duration=%.1fs adapter=%s",
+            iteration,
+            train_loss or 0.0,
+            duration,
+            adapter_file,
+        )
+
+        return TrainResult(
+            status="completed",
+            adapter_path=str(adapter_file),
+            model_name=model_name,
+            iteration=iteration,
+            duration_seconds=duration,
+            message=f"LoRA fine-tune completed successfully in {duration:.0f}s",
+            train_loss=train_loss,
+        )
+
+    def _register_ollama_adapter(self, adapter_dir: Path, model_name: str) -> bool:
+        """Create an Ollama model entry for the new adapter.
+
+        Writes a minimal Modelfile and runs `ollama create`.
+        """
+        if not shutil.which(_OLLAMA_BIN):
+            logger.debug("Ollama not found — skipping adapter registration")
+            return False
+
+        modelfile_content = (
+            f"FROM {self._base_model}\n"
+            f"ADAPTER {adapter_dir}\n"
+        )
+        modelfile_path = adapter_dir / "Modelfile"
+        try:
+            modelfile_path.write_text(modelfile_content)
+            result = subprocess.run(
+                [_OLLAMA_BIN, "create", model_name, "-f", str(modelfile_path)],
+                capture_output=True,
+                text=True,
+                timeout=300,
+            )
+            if result.returncode == 0:
+                logger.info("Ollama model registered: %s", model_name)
+                return True
+            else:
+                logger.warning("ollama create failed: %s", result.stderr[:200])
+                return False
+        except Exception as exc:
+            logger.warning("Ollama adapter registration error: %s", exc)
+            return False
+
+
+def _parse_train_loss(stdout: str) -> float | None:
+    """Extract the final training loss from mlx-lm stdout."""
+    loss: float | None = None
+    for line in stdout.splitlines():
+        line_lower = line.lower()
+        if "train loss" in line_lower or "loss:" in line_lower:
+            parts = line.split()
+            for i, part in enumerate(parts):
+                if "loss" in part.lower() and i + 1 < len(parts):
+                    try:
+                        loss = float(parts[i + 1].strip(",:"))
+                    except ValueError:
+                        pass
+    return loss

timmy_automations/retrain/quality_filter.py

@@ -0,0 +1,172 @@
+"""Quality filter — keeps only high-value trajectories for LoRA training.
+
+Criteria for a high-quality training example:
+1. Tool calls succeeded (tool calls present, no error entries)
+2. Multi-step tasks completed (≥2 messages + ≥1 tool call)
+3. No low-confidence signals (confidence < 0.5 on any Timmy message)
+4. Minimum meaningful exchange (≥1 user message + ≥1 Timmy message)
+
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from enum import StrEnum
+
+from timmy_automations.retrain.trajectory_exporter import Trajectory
+
+logger = logging.getLogger(__name__)
+
+_MIN_CONFIDENCE = 0.5
+
+
+class TrajectoryQuality(StrEnum):
+    """Quality classification for a trajectory."""
+
+    HIGH = "high"      # Multi-step + tool success — ideal training data
+    MEDIUM = "medium"  # Single exchange, no errors — acceptable
+    LOW = "low"        # Error-prone or trivial — skip
+
+
+@dataclass
+class QualityResult:
+    """Result of quality assessment for a single trajectory."""
+
+    trajectory: Trajectory
+    quality: TrajectoryQuality
+    score: float
+    reasons: list[str]
+
+    @property
+    def is_trainable(self) -> bool:
+        return self.quality in (TrajectoryQuality.HIGH, TrajectoryQuality.MEDIUM)
+
+
+class QualityFilter:
+    """Filters trajectories to keep only those worth training on.
+
+    Scoring:
+    - +1 pt:  base score for any valid clean exchange (no errors)
+    - +3 pts: multi-step task (≥2 messages + ≥1 tool call)
+    - +2 pts: tool calls present and no errors
+    - +1 pt:  decision recorded (deliberate choice made)
+    - -2 pts: any error entry
+    - -1 pt:  any low-confidence response (confidence < 0.5)
+
+    HIGH ≥ 4, MEDIUM 1–3, LOW ≤ 0
+    """
+
+    def __init__(self, min_confidence: float = _MIN_CONFIDENCE):
+        self._min_confidence = min_confidence
+
+    def assess(self, trajectory: Trajectory) -> QualityResult:
+        """Score and classify a single trajectory."""
+        score = 0.0
+        reasons: list[str] = []
+
+        # Minimum viable exchange check
+        user_msgs = [m for m in trajectory.messages if m.get("role") == "user"]
+        timmy_msgs = [m for m in trajectory.messages if m.get("role") == "timmy"]
+
+        if not user_msgs or not timmy_msgs:
+            return QualityResult(
+                trajectory=trajectory,
+                quality=TrajectoryQuality.LOW,
+                score=0.0,
+                reasons=["Missing user or assistant messages — not a valid exchange"],
+            )
+
+        # Multi-step bonus
+        if trajectory.is_multi_step:
+            score += 3.0
+            reasons.append(
+                f"Multi-step task: {trajectory.message_count} messages, "
+                f"{trajectory.tool_call_count} tool calls"
+            )
+
+        # Base score for any clean exchange (user + timmy, no tool call required)
+        if trajectory.error_count == 0:
+            score += 1.0
+            reasons.append("Clean exchange (no errors)")
+
+        # Tool call quality
+        if trajectory.tool_call_count > 0:
+            if trajectory.error_count == 0:
+                score += 2.0
+                reasons.append(
+                    f"All {trajectory.tool_call_count} tool call(s) succeeded"
+                )
+            else:
+                score -= 2.0
+                reasons.append(
+                    f"{trajectory.error_count} error(s) during {trajectory.tool_call_count} tool call(s)"
+                )
+        elif trajectory.error_count > 0:
+            score -= 2.0
+            reasons.append(f"{trajectory.error_count} error(s) with no tool calls")
+
+        # Decision bonus
+        if trajectory.decisions:
+            score += 1.0
+            reasons.append(f"Decisions recorded: {len(trajectory.decisions)}")
+
+        # Confidence penalty
+        low_conf = [
+            m
+            for m in timmy_msgs
+            if m.get("confidence") is not None
+            and m["confidence"] < self._min_confidence
+        ]
+        if low_conf:
+            score -= len(low_conf)
+            reasons.append(
+                f"{len(low_conf)} low-confidence response(s) (threshold={self._min_confidence})"
+            )
+
+        # Classify
+        if score >= 4.0:
+            quality = TrajectoryQuality.HIGH
+        elif score >= 1.0:
+            quality = TrajectoryQuality.MEDIUM
+        else:
+            quality = TrajectoryQuality.LOW
+
+        return QualityResult(
+            trajectory=trajectory,
+            quality=quality,
+            score=score,
+            reasons=reasons,
+        )
+
+    def filter(
+        self, trajectories: list[Trajectory]
+    ) -> tuple[list[QualityResult], dict[str, int]]:
+        """Assess all trajectories and return trainable ones with stats.
+
+        Returns:
+            (trainable_results, stats_dict) where stats_dict has keys
+            'total', 'high', 'medium', 'low', 'accepted'.
+        """
+        results = [self.assess(t) for t in trajectories]
+        trainable = [r for r in results if r.is_trainable]
+
+        stats = {
+            "total": len(results),
+            "high": sum(1 for r in results if r.quality == TrajectoryQuality.HIGH),
+            "medium": sum(1 for r in results if r.quality == TrajectoryQuality.MEDIUM),
+            "low": sum(1 for r in results if r.quality == TrajectoryQuality.LOW),
+            "accepted": len(trainable),
+        }
+
+        logger.info(
+            "Quality filter: %d/%d accepted (high=%d medium=%d low=%d)",
+            stats["accepted"],
+            stats["total"],
+            stats["high"],
+            stats["medium"],
+            stats["low"],
+        )
+
+        return trainable, stats

timmy_automations/retrain/retrain.py

@@ -0,0 +1,292 @@
+#!/usr/bin/env python3
+"""AutoLoRA continuous improvement loop — the sovereignty retrain script.
+
+Implements the weekly retrain cycle end-to-end:
+  Work → Record trajectories → Export weekly → Filter quality
+  → LoRA fine-tune → Load adapter → Model improves → Repeat forever
+
+Run:
+  python3 timmy_automations/retrain/retrain.py
+  python3 timmy_automations/retrain/retrain.py --dry-run
+  python3 timmy_automations/retrain/retrain.py --weeks-ago 1
+
+Epic: #1091 — Project Bannerlord
+Pipeline: AutoLoRA Sovereignty Loop (Step 6 of 7)
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import sys
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from pathlib import Path
+
+# Allow running directly from repo root
+_REPO_ROOT = Path(__file__).resolve().parent.parent.parent
+if str(_REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(_REPO_ROOT))
+
+from timmy_automations.retrain.lora_trainer import LoRATrainer
+from timmy_automations.retrain.quality_filter import QualityFilter
+from timmy_automations.retrain.training_dataset import TrainingDataset
+from timmy_automations.retrain.training_log import CycleMetrics, TrainingLog
+from timmy_automations.retrain.trajectory_exporter import TrajectoryExporter
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)-8s %(name)s: %(message)s",
+    datefmt="%Y-%m-%dT%H:%M:%S",
+)
+logger = logging.getLogger("retrain")
+
+
+@dataclass
+class RetrainResult:
+    """Result of a complete retrain cycle."""
+
+    iteration: int
+    week: str
+    trajectories_exported: int
+    trajectories_accepted: int
+    examples_added: int
+    dataset_total: int
+    train_status: str
+    adapter_path: str | None
+    model_name: str | None
+    train_loss: float | None
+    duration_seconds: float
+    notes: str
+
+
+class RetrainOrchestrator:
+    """Orchestrates the complete AutoLoRA continuous improvement loop.
+
+    Step 1: Export this week's conversation trajectories from session logs
+    Step 2: Filter for high-quality exchanges
+    Step 3: Append to the training dataset
+    Step 4: Trigger LoRA fine-tune
+    Step 5: Load the new adapter (via Ollama)
+    Step 6: Log iteration, loss, skill accuracy
+    """
+
+    def __init__(
+        self,
+        base_model: str = "hermes4-14b",
+        repo_root: str | Path | None = None,
+        dry_run: bool = False,
+    ):
+        if repo_root is None:
+            repo_root = _REPO_ROOT
+        self._repo_root = Path(repo_root)
+        self._dry_run = dry_run
+
+        self.exporter = TrajectoryExporter(repo_root=self._repo_root)
+        self.quality_filter = QualityFilter()
+        self.dataset = TrainingDataset(repo_root=self._repo_root)
+        self.trainer = LoRATrainer(
+            base_model=base_model,
+            repo_root=self._repo_root,
+            dry_run=dry_run,
+        )
+        self.log = TrainingLog(repo_root=self._repo_root)
+
+    def run(self, weeks_ago: int = 1) -> RetrainResult:
+        """Execute one complete retrain cycle.
+
+        Args:
+            weeks_ago: Which week to process. 0 = current week (partial),
+                       1 = last week (default, Sunday night run), etc.
+
+        Returns:
+            RetrainResult with full cycle summary.
+        """
+        started = datetime.now(tz=UTC)
+        iteration = self.log.next_iteration()
+
+        # Determine ISO week tag
+        from datetime import timedelta
+        now = datetime.now(tz=UTC)
+        target_date = now - timedelta(weeks=weeks_ago)
+        week_tag = f"{target_date.year}-W{target_date.isocalendar().week:02d}"
+
+        logger.info(
+            "=== AutoLoRA Retrain Cycle %d | Week: %s | dry_run=%s ===",
+            iteration,
+            week_tag,
+            self._dry_run,
+        )
+
+        # Step 1: Export trajectories
+        logger.info("Step 1: Exporting trajectories for %s...", week_tag)
+        trajectories = self.exporter.export_week(weeks_ago=weeks_ago)
+        logger.info("Exported %d raw trajectories", len(trajectories))
+
+        # Step 2: Quality filter
+        logger.info("Step 2: Applying quality filter...")
+        trainable, filter_stats = self.quality_filter.filter(trajectories)
+        logger.info(
+            "Quality filter: %d/%d accepted (high=%d medium=%d low=%d)",
+            filter_stats["accepted"],
+            filter_stats["total"],
+            filter_stats["high"],
+            filter_stats["medium"],
+            filter_stats["low"],
+        )
+
+        # Step 3: Append to dataset
+        logger.info("Step 3: Appending to training dataset...")
+        append_result = self.dataset.append(trainable, week_tag)
+        logger.info(
+            "Dataset: +%d new examples (%d total)",
+            append_result.new_examples,
+            append_result.total_examples,
+        )
+
+        # Step 4: LoRA fine-tune
+        logger.info("Step 4: Triggering LoRA fine-tune (iteration=%d)...", iteration)
+        train_result = self.trainer.train(
+            dataset_path=self.dataset.dataset_path,
+            iteration=iteration,
+        )
+        logger.info(
+            "Train result: status=%s loss=%s duration=%.1fs",
+            train_result.status,
+            train_result.train_loss,
+            train_result.duration_seconds,
+        )
+
+        # Step 5 & 6: Log cycle
+        duration = (datetime.now(tz=UTC) - started).total_seconds()
+        metrics = CycleMetrics(
+            iteration=iteration,
+            week=week_tag,
+            ran_at=started.isoformat(),
+            trajectories_total=filter_stats["total"],
+            trajectories_high=filter_stats["high"],
+            trajectories_medium=filter_stats["medium"],
+            trajectories_low=filter_stats["low"],
+            trajectories_accepted=filter_stats["accepted"],
+            examples_added=append_result.new_examples,
+            dataset_total=append_result.total_examples,
+            train_status=train_result.status,
+            train_loss=train_result.train_loss,
+            train_duration_seconds=train_result.duration_seconds,
+            adapter_path=train_result.adapter_path,
+            model_name=train_result.model_name,
+            notes=train_result.message,
+        )
+        self.log.record(metrics)
+
+        result = RetrainResult(
+            iteration=iteration,
+            week=week_tag,
+            trajectories_exported=len(trajectories),
+            trajectories_accepted=filter_stats["accepted"],
+            examples_added=append_result.new_examples,
+            dataset_total=append_result.total_examples,
+            train_status=train_result.status,
+            adapter_path=train_result.adapter_path,
+            model_name=train_result.model_name,
+            train_loss=train_result.train_loss,
+            duration_seconds=duration,
+            notes=train_result.message,
+        )
+
+        logger.info(
+            "=== Cycle %d complete: status=%s examples_added=%d total=%.1fs ===",
+            iteration,
+            train_result.status,
+            append_result.new_examples,
+            duration,
+        )
+
+        return result
+
+
+def _print_result(result: RetrainResult, as_json: bool = False) -> None:
+    """Print cycle result to stdout."""
+    if as_json:
+        print(
+            json.dumps(
+                {
+                    "iteration": result.iteration,
+                    "week": result.week,
+                    "trajectories_exported": result.trajectories_exported,
+                    "trajectories_accepted": result.trajectories_accepted,
+                    "examples_added": result.examples_added,
+                    "dataset_total": result.dataset_total,
+                    "train_status": result.train_status,
+                    "adapter_path": result.adapter_path,
+                    "model_name": result.model_name,
+                    "train_loss": result.train_loss,
+                    "duration_seconds": result.duration_seconds,
+                    "notes": result.notes,
+                },
+                indent=2,
+            )
+        )
+        return
+
+    print(f"\n{'='*60}")
+    print(f"  AutoLoRA Retrain — Cycle {result.iteration}")
+    print(f"  Week: {result.week}")
+    print(f"{'='*60}")
+    print(f"  Trajectories:  {result.trajectories_exported} exported, {result.trajectories_accepted} accepted")
+    print(f"  Dataset:       +{result.examples_added} examples ({result.dataset_total} total)")
+    print(f"  Fine-tune:     {result.train_status}")
+    if result.train_loss is not None:
+        print(f"  Train loss:    {result.train_loss:.4f}")
+    if result.model_name:
+        print(f"  New model:     {result.model_name}")
+    if result.adapter_path:
+        print(f"  Adapter:       {result.adapter_path}")
+    print(f"  Duration:      {result.duration_seconds:.1f}s")
+    print(f"  Notes:         {result.notes}")
+    print(f"{'='*60}\n")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description="AutoLoRA continuous improvement loop — sovereignty engine for Timmy"
+    )
+    parser.add_argument(
+        "--weeks-ago",
+        type=int,
+        default=1,
+        help="Which week to process: 0=current (partial), 1=last week (default)",
+    )
+    parser.add_argument(
+        "--base-model",
+        default="hermes4-14b",
+        help="Ollama base model name (default: hermes4-14b)",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Export and filter trajectories but skip actual fine-tuning",
+    )
+    parser.add_argument(
+        "--json",
+        action="store_true",
+        dest="as_json",
+        help="Output result as JSON",
+    )
+    args = parser.parse_args()
+
+    orchestrator = RetrainOrchestrator(
+        base_model=args.base_model,
+        dry_run=args.dry_run,
+    )
+    result = orchestrator.run(weeks_ago=args.weeks_ago)
+    _print_result(result, as_json=args.as_json)
+
+    # Exit 0 even on skipped/failed training — the loop must continue
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

timmy_automations/retrain/training_dataset.py

@@ -0,0 +1,180 @@
+"""Training dataset manager — appends filtered trajectories to a JSONL training file.
+
+Maintains a growing dataset of high-quality conversation examples in the
+chat-format expected by mlx-lm / HuggingFace fine-tuning pipelines.
+
+Output format (one JSON object per line):
+  {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
+
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from pathlib import Path
+
+from timmy_automations.retrain.quality_filter import QualityResult
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_DATASET_PATH = ".loop/retrain/training_data.jsonl"
+_DEFAULT_INDEX_PATH = ".loop/retrain/dataset_index.json"
+
+
+@dataclass
+class AppendResult:
+    """Result of appending trajectories to the training dataset."""
+
+    new_examples: int
+    total_examples: int
+    dataset_path: str
+    week_tag: str
+
+
+class TrainingDataset:
+    """Manages the LoRA training dataset file.
+
+    Each entry is a chat-format example:
+      {"messages": [...], "week": "2026-W12", "quality": "high", "added_at": "..."}
+    """
+
+    def __init__(
+        self,
+        dataset_path: str | Path | None = None,
+        index_path: str | Path | None = None,
+        repo_root: str | Path | None = None,
+    ):
+        if repo_root is None:
+            repo_root = Path(__file__).resolve().parent.parent.parent
+        self._repo_root = Path(repo_root)
+
+        self._dataset_path = self._repo_root / (
+            dataset_path or _DEFAULT_DATASET_PATH
+        )
+        self._index_path = self._repo_root / (
+            index_path or _DEFAULT_INDEX_PATH
+        )
+
+        self._dataset_path.parent.mkdir(parents=True, exist_ok=True)
+
+    @property
+    def dataset_path(self) -> Path:
+        return self._dataset_path
+
+    def count(self) -> int:
+        """Return the number of examples currently in the dataset."""
+        if not self._dataset_path.exists():
+            return 0
+        count = 0
+        with open(self._dataset_path) as f:
+            for line in f:
+                if line.strip():
+                    count += 1
+        return count
+
+    def append(
+        self, quality_results: list[QualityResult], week_tag: str
+    ) -> AppendResult:
+        """Append high-quality trajectories to the training dataset.
+
+        Deduplicates by (week_tag, session_date, started_at) so re-running
+        the export for the same week is idempotent.
+
+        Args:
+            quality_results: Filtered, trainable quality results.
+            week_tag: ISO week string e.g. "2026-W12".
+
+        Returns:
+            AppendResult with counts.
+        """
+        existing_keys = self._load_existing_keys()
+        new_count = 0
+        added_at = datetime.now(tz=UTC).isoformat()
+
+        with open(self._dataset_path, "a") as f:
+            for result in quality_results:
+                traj = result.trajectory
+                dedup_key = (
+                    f"{week_tag}|{traj.session_date}|{traj.started_at}"
+                )
+                if dedup_key in existing_keys:
+                    logger.debug("Skipping duplicate trajectory: %s", dedup_key)
+                    continue
+
+                chat_messages = traj.to_chat_format()
+                if len(chat_messages) < 2:
+                    logger.debug(
+                        "Skipping trajectory with %d chat messages (need ≥2)",
+                        len(chat_messages),
+                    )
+                    continue
+
+                record = {
+                    "messages": chat_messages,
+                    "week": week_tag,
+                    "quality": result.quality.value,
+                    "score": result.score,
+                    "session_date": traj.session_date,
+                    "started_at": traj.started_at,
+                    "tool_calls": traj.tool_call_count,
+                    "added_at": added_at,
+                }
+                f.write(json.dumps(record) + "\n")
+                existing_keys.add(dedup_key)
+                new_count += 1
+
+        total = self.count()
+        self._update_index(week_tag, new_count, total)
+        logger.info(
+            "Dataset: appended %d new examples (total=%d)", new_count, total
+        )
+
+        return AppendResult(
+            new_examples=new_count,
+            total_examples=total,
+            dataset_path=str(self._dataset_path),
+            week_tag=week_tag,
+        )
+
+    def _load_existing_keys(self) -> set[str]:
+        """Load deduplication keys from the existing dataset."""
+        keys: set[str] = set()
+        if not self._dataset_path.exists():
+            return keys
+        with open(self._dataset_path) as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    record = json.loads(line)
+                    week = record.get("week", "")
+                    session_date = record.get("session_date", "")
+                    started_at = record.get("started_at", "")
+                    keys.add(f"{week}|{session_date}|{started_at}")
+                except json.JSONDecodeError:
+                    continue
+        return keys
+
+    def _update_index(self, week_tag: str, new_count: int, total: int) -> None:
+        """Update the dataset index JSON with latest run metadata."""
+        index: dict = {}
+        if self._index_path.exists():
+            try:
+                index = json.loads(self._index_path.read_text())
+            except (json.JSONDecodeError, OSError):
+                index = {}
+
+        index.setdefault("weeks", {})
+        index["weeks"][week_tag] = {
+            "examples_added": new_count,
+            "updated_at": datetime.now(tz=UTC).isoformat(),
+        }
+        index["total_examples"] = total
+        index["last_updated"] = datetime.now(tz=UTC).isoformat()
+
+        self._index_path.write_text(json.dumps(index, indent=2))

timmy_automations/retrain/training_log.py

@@ -0,0 +1,183 @@
+"""Training log — records each fine-tune cycle with metrics and skill deltas.
+
+Writes to .loop/retrain/training_log.jsonl (one entry per cycle) and
+maintains a human-readable .loop/retrain/training_log.md summary.
+
+Each log entry captures:
+- Iteration count
+- Week processed
+- Quality filter stats
+- Examples added to dataset
+- LoRA train result (loss, duration, adapter path)
+- Skill accuracy deltas (from smoke tests)
+
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import asdict, dataclass, field
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_LOG_PATH = ".loop/retrain/training_log.jsonl"
+_DEFAULT_SUMMARY_PATH = ".loop/retrain/training_log.md"
+
+
+@dataclass
+class CycleMetrics:
+    """Metrics for a single retrain cycle."""
+
+    iteration: int
+    week: str
+    ran_at: str
+
+    # Quality filter
+    trajectories_total: int = 0
+    trajectories_high: int = 0
+    trajectories_medium: int = 0
+    trajectories_low: int = 0
+    trajectories_accepted: int = 0
+
+    # Dataset
+    examples_added: int = 0
+    dataset_total: int = 0
+
+    # Training
+    train_status: str = "skipped"
+    train_loss: float | None = None
+    train_duration_seconds: float = 0.0
+    adapter_path: str | None = None
+    model_name: str | None = None
+
+    # Skill accuracy (optional, from smoke tests)
+    skill_accuracy: dict[str, float] = field(default_factory=dict)
+    skill_delta: dict[str, float] = field(default_factory=dict)
+
+    # Human-readable summary
+    notes: str = ""
+
+
+class TrainingLog:
+    """Persistent log of all retrain cycles."""
+
+    def __init__(
+        self,
+        log_path: str | Path | None = None,
+        summary_path: str | Path | None = None,
+        repo_root: str | Path | None = None,
+    ):
+        if repo_root is None:
+            repo_root = Path(__file__).resolve().parent.parent.parent
+        self._repo_root = Path(repo_root)
+
+        self._log_path = self._repo_root / (log_path or _DEFAULT_LOG_PATH)
+        self._summary_path = self._repo_root / (summary_path or _DEFAULT_SUMMARY_PATH)
+        self._log_path.parent.mkdir(parents=True, exist_ok=True)
+
+    @property
+    def log_path(self) -> Path:
+        return self._log_path
+
+    def next_iteration(self) -> int:
+        """Return the next iteration number (1-indexed)."""
+        entries = self.load_all()
+        if not entries:
+            return 1
+        return max(e.get("iteration", 0) for e in entries) + 1
+
+    def record(self, metrics: CycleMetrics) -> None:
+        """Append a cycle metrics record to the log."""
+        entry = asdict(metrics)
+        with open(self._log_path, "a") as f:
+            f.write(json.dumps(entry) + "\n")
+
+        self._update_summary(metrics)
+        logger.info(
+            "Training log: iteration=%d week=%s status=%s examples_added=%d",
+            metrics.iteration,
+            metrics.week,
+            metrics.train_status,
+            metrics.examples_added,
+        )
+
+    def load_all(self) -> list[dict[str, Any]]:
+        """Load all cycle records from the log."""
+        if not self._log_path.exists():
+            return []
+        entries: list[dict[str, Any]] = []
+        with open(self._log_path) as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    entries.append(json.loads(line))
+                except json.JSONDecodeError:
+                    logger.debug("Skipping malformed log entry")
+        return entries
+
+    def latest(self) -> dict[str, Any] | None:
+        """Return the most recent cycle record."""
+        entries = self.load_all()
+        return entries[-1] if entries else None
+
+    def _update_summary(self, metrics: CycleMetrics) -> None:
+        """Rewrite the markdown summary with all cycles."""
+        all_entries = self.load_all()
+
+        lines = [
+            "# AutoLoRA Training Log\n",
+            f"*Updated: {datetime.now(tz=UTC).isoformat()}*\n",
+            f"*Total iterations: {len(all_entries)}*\n",
+            "",
+            "## Cycles\n",
+            "| # | Week | Status | Loss | Examples | Duration |",
+            "|---|------|--------|------|----------|----------|",
+        ]
+
+        for entry in reversed(all_entries[-20:]):  # Last 20 cycles
+            loss = f"{entry.get('train_loss', 0.0) or 0.0:.4f}" if entry.get("train_loss") else "—"
+            lines.append(
+                f"| {entry.get('iteration', '?')} "
+                f"| {entry.get('week', '?')} "
+                f"| {entry.get('train_status', '?')} "
+                f"| {loss} "
+                f"| +{entry.get('examples_added', 0)} ({entry.get('dataset_total', 0)} total) "
+                f"| {entry.get('train_duration_seconds', 0.0):.0f}s |"
+            )
+
+        lines.append("")
+        lines.append("## Skill Accuracy Over Time\n")
+
+        # Collect all unique skills
+        all_skills: set[str] = set()
+        for entry in all_entries:
+            all_skills.update(entry.get("skill_accuracy", {}).keys())
+
+        if all_skills:
+            skill_header = "| # | Week | " + " | ".join(sorted(all_skills)) + " |"
+            skill_sep = "|---|------|" + "|".join("---" for _ in all_skills) + "|"
+            lines.extend([skill_header, skill_sep])
+            for entry in reversed(all_entries[-10:]):
+                acc = entry.get("skill_accuracy", {})
+                row = f"| {entry.get('iteration', '?')} | {entry.get('week', '?')} | "
+                row += " | ".join(
+                    f"{acc.get(s, 0.0):.0%}" if s in acc else "—"
+                    for s in sorted(all_skills)
+                )
+                row += " |"
+                lines.append(row)
+        else:
+            lines.append("*No skill accuracy data yet — run smoke tests after fine-tuning.*")
+
+        lines.append("")
+        if metrics.notes:
+            lines.append(f"## Latest Notes\n\n{metrics.notes}\n")
+
+        self._summary_path.write_text("\n".join(lines))

timmy_automations/retrain/trajectory_exporter.py

@@ -0,0 +1,255 @@
+"""Trajectory exporter — reads session JSONL logs and extracts conversation trajectories.
+
+A trajectory is a coherent sequence of messages + tool calls that form
+a single task attempt.  Each trajectory becomes one training example.
+
+Refs: #1105
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass, field
+from datetime import UTC, datetime, timedelta
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+_LOGS_DIR_DEFAULT = "logs"
+_SESSION_GLOB = "session_*.jsonl"
+
+
+@dataclass
+class Trajectory:
+    """A single conversation trajectory extracted from session logs."""
+
+    session_date: str
+    started_at: str
+    ended_at: str
+    messages: list[dict[str, Any]] = field(default_factory=list)
+    tool_calls: list[dict[str, Any]] = field(default_factory=list)
+    errors: list[dict[str, Any]] = field(default_factory=list)
+    decisions: list[dict[str, Any]] = field(default_factory=list)
+
+    @property
+    def message_count(self) -> int:
+        return len(self.messages)
+
+    @property
+    def tool_call_count(self) -> int:
+        return len(self.tool_calls)
+
+    @property
+    def error_count(self) -> int:
+        return len(self.errors)
+
+    @property
+    def has_successful_tool_call(self) -> bool:
+        """True if any tool call succeeded (no error entry follows it)."""
+        return self.tool_call_count > 0 and self.error_count == 0
+
+    @property
+    def is_multi_step(self) -> bool:
+        """True if this trajectory involved multiple turns with tool use."""
+        return self.message_count >= 2 and self.tool_call_count >= 1
+
+    def to_chat_format(self) -> list[dict[str, str]]:
+        """Convert trajectory to chat-format messages for training.
+
+        Interleaves messages and tool-call results as assistant/tool turns.
+        """
+        chat: list[dict[str, str]] = []
+        # Merge all entries by timestamp and emit in order
+        all_entries = sorted(
+            self.messages + self.tool_calls + self.decisions,
+            key=lambda e: e.get("timestamp", ""),
+        )
+        for entry in all_entries:
+            etype = entry.get("type")
+            if etype == "message":
+                role = "user" if entry.get("role") == "user" else "assistant"
+                content = entry.get("content", "")
+                if content:
+                    chat.append({"role": role, "content": content})
+            elif etype == "tool_call":
+                tool = entry.get("tool", "unknown")
+                result = entry.get("result", "")
+                chat.append(
+                    {
+                        "role": "assistant",
+                        "content": f"[tool:{tool}] {result}",
+                    }
+                )
+            elif etype == "decision":
+                decision = entry.get("decision", "")
+                if decision:
+                    chat.append({"role": "assistant", "content": f"[decided] {decision}"})
+        return chat
+
+
+class TrajectoryExporter:
+    """Reads session JSONL logs and yields Trajectory objects for a date range."""
+
+    def __init__(self, logs_dir: str | Path | None = None, repo_root: str | Path | None = None):
+        if repo_root is None:
+            repo_root = Path(__file__).resolve().parent.parent.parent
+        self._repo_root = Path(repo_root)
+
+        if logs_dir is None:
+            self._logs_dir = self._repo_root / _LOGS_DIR_DEFAULT
+        else:
+            self._logs_dir = Path(logs_dir)
+
+    def export_week(self, weeks_ago: int = 0) -> list[Trajectory]:
+        """Export all trajectories from the specified week.
+
+        Args:
+            weeks_ago: 0 = current week, 1 = last week, etc.
+
+        Returns:
+            List of Trajectory objects extracted from session logs.
+        """
+        now = datetime.now(tz=UTC)
+        # Week boundaries: Mon–Sun
+        days_since_monday = now.weekday()
+        week_start = (now - timedelta(days=days_since_monday + 7 * weeks_ago)).replace(
+            hour=0, minute=0, second=0, microsecond=0
+        )
+        week_end = week_start + timedelta(days=7)
+
+        logger.info(
+            "Exporting trajectories for week %s–%s",
+            week_start.date().isoformat(),
+            week_end.date().isoformat(),
+        )
+
+        trajectories: list[Trajectory] = []
+        log_files = sorted(self._logs_dir.glob(_SESSION_GLOB))
+
+        for log_file in log_files:
+            # Parse date from filename: session_YYYY-MM-DD.jsonl
+            try:
+                date_str = log_file.stem.removeprefix("session_")
+                file_date = datetime.strptime(date_str, "%Y-%m-%d").replace(tzinfo=UTC)
+            except ValueError:
+                logger.debug("Skipping non-date session file: %s", log_file.name)
+                continue
+
+            if not (week_start <= file_date < week_end):
+                continue
+
+            file_trajectories = self._extract_from_file(log_file)
+            trajectories.extend(file_trajectories)
+            logger.info(
+                "Extracted %d trajectories from %s", len(file_trajectories), log_file.name
+            )
+
+        logger.info("Total trajectories exported: %d", len(trajectories))
+        return trajectories
+
+    def _extract_from_file(self, log_file: Path) -> list[Trajectory]:
+        """Parse a single session JSONL file into trajectories.
+
+        Groups entries into trajectories by finding natural conversation
+        boundaries (gaps of inactivity or topic shifts in the message stream).
+        """
+        entries: list[dict[str, Any]] = []
+        try:
+            with open(log_file) as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:
+                        continue
+                    try:
+                        entries.append(json.loads(line))
+                    except json.JSONDecodeError:
+                        logger.debug("Skipping malformed JSON line in %s", log_file.name)
+        except OSError as exc:
+            logger.warning("Could not read %s: %s", log_file, exc)
+            return []
+
+        if not entries:
+            return []
+
+        date_str = log_file.stem.removeprefix("session_")
+        return self._segment_trajectories(entries, date_str)
+
+    def _segment_trajectories(
+        self, entries: list[dict[str, Any]], session_date: str
+    ) -> list[Trajectory]:
+        """Split a flat list of session entries into discrete trajectories.
+
+        Segmentation rule: start a new trajectory when:
+        - A user message follows a Timmy message (new conversation turn)
+        - More than 5 minutes have elapsed between entries
+
+        This produces training examples that are coherent task attempts.
+        """
+        if not entries:
+            return []
+
+        trajectories: list[Trajectory] = []
+        current_entries: list[dict[str, Any]] = []
+        prev_ts: datetime | None = None
+        _SEGMENT_GAP_MINUTES = 5
+
+        def _flush() -> None:
+            if current_entries:
+                traj = _build_trajectory(current_entries, session_date)
+                if traj.message_count > 0:
+                    trajectories.append(traj)
+
+        for entry in entries:
+            ts_raw = entry.get("timestamp", "")
+            try:
+                ts = datetime.fromisoformat(ts_raw.replace("Z", "+00:00"))
+            except (ValueError, AttributeError):
+                ts = None
+
+            # Time-gap segmentation
+            if ts and prev_ts and (ts - prev_ts).total_seconds() > _SEGMENT_GAP_MINUTES * 60:
+                _flush()
+                current_entries = []
+
+            # New-turn segmentation: user message after assistant turn
+            etype = entry.get("type")
+            erole = entry.get("role")
+            if etype == "message" and erole == "user" and current_entries:
+                # Check if previous non-error entry was a Timmy message
+                for prev in reversed(current_entries):
+                    if prev.get("type") == "message":
+                        if prev.get("role") == "timmy":
+                            _flush()
+                            current_entries = []
+                        break
+
+            current_entries.append(entry)
+            if ts:
+                prev_ts = ts
+
+        _flush()
+        return trajectories
+
+
+def _build_trajectory(entries: list[dict[str, Any]], session_date: str) -> Trajectory:
+    """Build a Trajectory from a flat list of entries."""
+    messages = [e for e in entries if e.get("type") == "message"]
+    tool_calls = [e for e in entries if e.get("type") == "tool_call"]
+    errors = [e for e in entries if e.get("type") == "error"]
+    decisions = [e for e in entries if e.get("type") == "decision"]
+
+    timestamps = [e.get("timestamp", "") for e in entries if e.get("timestamp")]
+    started_at = min(timestamps) if timestamps else ""
+    ended_at = max(timestamps) if timestamps else ""
+
+    return Trajectory(
+        session_date=session_date,
+        started_at=started_at,
+        ended_at=ended_at,
+        messages=messages,
+        tool_calls=tool_calls,
+        errors=errors,
+        decisions=decisions,
+    )