refactor: break up think_once() into _generate_thought() and _finalize_thought()

Extracts the dedup retry loop and post-hook/broadcast pipeline into
focused helpers, reducing think_once() from 118 lines to ~20.

Fixes #513

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

scripts/loop_guard.py

@@ -18,19 +18,13 @@ Exit codes:
 from __future__ import annotations
 
 import json
-import os
 import sys
 import time
-import urllib.request
 from pathlib import Path
 
 REPO_ROOT = Path(__file__).resolve().parent.parent
 QUEUE_FILE = REPO_ROOT / ".loop" / "queue.json"
 IDLE_STATE_FILE = REPO_ROOT / ".loop" / "idle_state.json"
-TOKEN_FILE = Path.home() / ".hermes" / "gitea_token"
-
-GITEA_API = os.environ.get("GITEA_API", "http://localhost:3000/api/v1")
-REPO_SLUG = os.environ.get("REPO_SLUG", "rockachopa/Timmy-time-dashboard")
 
 # Backoff sequence: 60s, 120s, 240s, 600s max
 BACKOFF_BASE = 60
@@ -38,81 +32,19 @@ BACKOFF_MAX = 600
 BACKOFF_MULTIPLIER = 2
 
 
-def _get_token() -> str:
-    """Read Gitea token from env or file."""
-    token = os.environ.get("GITEA_TOKEN", "").strip()
-    if not token and TOKEN_FILE.exists():
-        token = TOKEN_FILE.read_text().strip()
-    return token
-
-
-def _fetch_open_issue_numbers() -> set[int] | None:
-    """Fetch open issue numbers from Gitea. Returns None on failure."""
-    token = _get_token()
-    if not token:
-        return None
-    try:
-        numbers: set[int] = set()
-        page = 1
-        while True:
-            url = (
-                f"{GITEA_API}/repos/{REPO_SLUG}/issues"
-                f"?state=open&type=issues&limit=50&page={page}"
-            )
-            req = urllib.request.Request(url, headers={
-                "Authorization": f"token {token}",
-                "Accept": "application/json",
-            })
-            with urllib.request.urlopen(req, timeout=10) as resp:
-                data = json.loads(resp.read())
-            if not data:
-                break
-            for issue in data:
-                numbers.add(issue["number"])
-            if len(data) < 50:
-                break
-            page += 1
-        return numbers
-    except Exception:
-        return None
-
-
 def load_queue() -> list[dict]:
-    """Load queue.json and return ready items, filtering out closed issues."""
+    """Load queue.json and return ready items."""
     if not QUEUE_FILE.exists():
         return []
     try:
         data = json.loads(QUEUE_FILE.read_text())
-        if not isinstance(data, list):
+        if isinstance(data, list):
-            return []
+            return [item for item in data if item.get("ready")]
-        ready = [item for item in data if item.get("ready")]
+        return []
-        if not ready:
-            return []
-
-        # Filter out issues that are no longer open (auto-hygiene)
-        open_numbers = _fetch_open_issue_numbers()
-        if open_numbers is not None:
-            before = len(ready)
-            ready = [item for item in ready if item.get("issue") in open_numbers]
-            removed = before - len(ready)
-            if removed > 0:
-                print(f"[loop-guard] Filtered {removed} closed issue(s) from queue")
-                # Persist the cleaned queue so stale entries don't recur
-                _save_cleaned_queue(data, open_numbers)
-        return ready
     except (json.JSONDecodeError, OSError):
         return []
 
 
-def _save_cleaned_queue(full_queue: list[dict], open_numbers: set[int]) -> None:
-    """Rewrite queue.json without closed issues."""
-    cleaned = [item for item in full_queue if item.get("issue") in open_numbers]
-    try:
-        QUEUE_FILE.write_text(json.dumps(cleaned, indent=2) + "\n")
-    except OSError:
-        pass
-
-
 def load_idle_state() -> dict:
     """Load persistent idle state."""
     if not IDLE_STATE_FILE.exists():

src/config.py

@@ -254,7 +254,6 @@ class Settings(BaseSettings):
     # When enabled, the agent starts an internal thought loop on server start.
     thinking_enabled: bool = True
     thinking_interval_seconds: int = 300  # 5 minutes between thoughts
-    thinking_timeout_seconds: int = 120  # max wall-clock time per thinking cycle
     thinking_distill_every: int = 10  # distill facts from thoughts every Nth thought
     thinking_issue_every: int = 20  # file Gitea issues from thoughts every Nth thought
     thinking_memory_check_every: int = 50  # check memory status every Nth thought

src/dashboard/app.py

@@ -155,17 +155,7 @@ async def _thinking_scheduler() -> None:
     while True:
         try:
             if settings.thinking_enabled:
-                await asyncio.wait_for(
+                await thinking_engine.think_once()
-                    thinking_engine.think_once(),
-                    timeout=settings.thinking_timeout_seconds,
-                )
-        except TimeoutError:
-            logger.warning(
-                "Thinking cycle timed out after %ds — Ollama may be unresponsive",
-                settings.thinking_timeout_seconds,
-            )
-        except asyncio.CancelledError:
-            raise
         except Exception as exc:
             logger.error("Thinking scheduler error: %s", exc)
 
@@ -185,10 +175,7 @@ async def _loop_qa_scheduler() -> None:
     while True:
         try:
             if settings.loop_qa_enabled:
-                result = await asyncio.wait_for(
+                result = await loop_qa_orchestrator.run_next_test()
-                    loop_qa_orchestrator.run_next_test(),
-                    timeout=settings.thinking_timeout_seconds,
-                )
                 if result:
                     status = "PASS" if result["success"] else "FAIL"
                     logger.info(
@@ -197,13 +184,6 @@ async def _loop_qa_scheduler() -> None:
                         status,
                         result.get("details", "")[:80],
                     )
-        except TimeoutError:
-            logger.warning(
-                "Loop QA test timed out after %ds",
-                settings.thinking_timeout_seconds,
-            )
-        except asyncio.CancelledError:
-            raise
         except Exception as exc:
             logger.error("Loop QA scheduler error: %s", exc)
 

src/dashboard/routes/agents.py

@@ -71,20 +71,21 @@ async def clear_history(request: Request):
     )
 
 
-def _validate_message(message: str) -> str:
+@router.post("/default/chat", response_class=HTMLResponse)
-    """Strip and validate chat input; raise HTTPException on bad input."""
+async def chat_agent(request: Request, message: str = Form(...)):
-    from fastapi import HTTPException
+    """Chat — synchronous response with native Agno tool confirmation."""
-
     message = message.strip()
     if not message:
+        from fastapi import HTTPException
+
         raise HTTPException(status_code=400, detail="Message cannot be empty")
+
     if len(message) > MAX_MESSAGE_LENGTH:
+        from fastapi import HTTPException
+
         raise HTTPException(status_code=422, detail="Message too long")
-    return message
 
-
+    # Record user activity so the thinking engine knows we're not idle
-def _record_user_activity() -> None:
-    """Notify the thinking engine that the user is active."""
     try:
         from timmy.thinking import thinking_engine
 
@@ -92,67 +93,6 @@ def _record_user_activity() -> None:
     except Exception:
         logger.debug("Failed to record user input for thinking engine")
 
-
-def _extract_tool_actions(run_output) -> list[dict]:
-    """If Agno paused the run for tool confirmation, build approval items."""
-    from timmy.approvals import create_item
-
-    tool_actions: list[dict] = []
-    status = getattr(run_output, "status", None)
-    is_paused = status == "PAUSED" or str(status) == "RunStatus.paused"
-
-    if not (is_paused and getattr(run_output, "active_requirements", None)):
-        return tool_actions
-
-    for req in run_output.active_requirements:
-        if not getattr(req, "needs_confirmation", False):
-            continue
-        te = req.tool_execution
-        tool_name = getattr(te, "tool_name", "unknown")
-        tool_args = getattr(te, "tool_args", {}) or {}
-
-        item = create_item(
-            title=f"Dashboard: {tool_name}",
-            description=format_action_description(tool_name, tool_args),
-            proposed_action=json.dumps({"tool": tool_name, "args": tool_args}),
-            impact=get_impact_level(tool_name),
-        )
-        _pending_runs[item.id] = {
-            "run_output": run_output,
-            "requirement": req,
-            "tool_name": tool_name,
-            "tool_args": tool_args,
-        }
-        tool_actions.append(
-            {
-                "approval_id": item.id,
-                "tool_name": tool_name,
-                "description": format_action_description(tool_name, tool_args),
-                "impact": get_impact_level(tool_name),
-            }
-        )
-    return tool_actions
-
-
-def _log_exchange(
-    message: str, response_text: str | None, error_text: str | None, timestamp: str
-) -> None:
-    """Append user message and agent/error reply to the in-memory log."""
-    message_log.append(role="user", content=message, timestamp=timestamp, source="browser")
-    if response_text:
-        message_log.append(
-            role="agent", content=response_text, timestamp=timestamp, source="browser"
-        )
-    elif error_text:
-        message_log.append(role="error", content=error_text, timestamp=timestamp, source="browser")
-
-
-@router.post("/default/chat", response_class=HTMLResponse)
-async def chat_agent(request: Request, message: str = Form(...)):
-    """Chat — synchronous response with native Agno tool confirmation."""
-    message = _validate_message(message)
-    _record_user_activity()
-
     timestamp = datetime.now().strftime("%H:%M:%S")
     response_text = None
     error_text = None
@@ -164,15 +104,54 @@ async def chat_agent(request: Request, message: str = Form(...)):
         error_text = f"Chat error: {exc}"
         run_output = None
 
-    tool_actions: list[dict] = []
+    # Check if Agno paused the run for tool confirmation
+    tool_actions = []
     if run_output is not None:
-        tool_actions = _extract_tool_actions(run_output)
+        status = getattr(run_output, "status", None)
+        is_paused = status == "PAUSED" or str(status) == "RunStatus.paused"
+
+        if is_paused and getattr(run_output, "active_requirements", None):
+            for req in run_output.active_requirements:
+                if getattr(req, "needs_confirmation", False):
+                    te = req.tool_execution
+                    tool_name = getattr(te, "tool_name", "unknown")
+                    tool_args = getattr(te, "tool_args", {}) or {}
+
+                    from timmy.approvals import create_item
+
+                    item = create_item(
+                        title=f"Dashboard: {tool_name}",
+                        description=format_action_description(tool_name, tool_args),
+                        proposed_action=json.dumps({"tool": tool_name, "args": tool_args}),
+                        impact=get_impact_level(tool_name),
+                    )
+                    _pending_runs[item.id] = {
+                        "run_output": run_output,
+                        "requirement": req,
+                        "tool_name": tool_name,
+                        "tool_args": tool_args,
+                    }
+                    tool_actions.append(
+                        {
+                            "approval_id": item.id,
+                            "tool_name": tool_name,
+                            "description": format_action_description(tool_name, tool_args),
+                            "impact": get_impact_level(tool_name),
+                        }
+                    )
+
         raw_content = run_output.content if hasattr(run_output, "content") else ""
         response_text = _clean_response(raw_content or "")
         if not response_text and not tool_actions:
-            response_text = None
+            response_text = None  # let error template show if needed
 
-    _log_exchange(message, response_text, error_text, timestamp)
+    message_log.append(role="user", content=message, timestamp=timestamp, source="browser")
+    if response_text:
+        message_log.append(
+            role="agent", content=response_text, timestamp=timestamp, source="browser"
+        )
+    elif error_text:
+        message_log.append(role="error", content=error_text, timestamp=timestamp, source="browser")
 
     return templates.TemplateResponse(
         request,

src/dashboard/routes/chat_api.py

@@ -31,93 +31,6 @@ _UPLOAD_DIR = str(Path(settings.repo_root) / "data" / "chat-uploads")
 _MAX_UPLOAD_SIZE = 50 * 1024 * 1024  # 50 MB
 
 
-# ── POST /api/chat — helpers ─────────────────────────────────────────────────
-
-
-async def _parse_chat_body(request: Request) -> tuple[dict | None, JSONResponse | None]:
-    """Parse and validate the JSON request body.
-
-    Returns (body, None) on success or (None, error_response) on failure.
-    """
-    content_length = request.headers.get("content-length")
-    if content_length and int(content_length) > settings.chat_api_max_body_bytes:
-        return None, JSONResponse(status_code=413, content={"error": "Request body too large"})
-
-    try:
-        body = await request.json()
-    except Exception as exc:
-        logger.warning("Chat API JSON parse error: %s", exc)
-        return None, JSONResponse(status_code=400, content={"error": "Invalid JSON"})
-
-    messages = body.get("messages")
-    if not messages or not isinstance(messages, list):
-        return None, JSONResponse(status_code=400, content={"error": "messages array is required"})
-
-    return body, None
-
-
-def _extract_user_message(messages: list[dict]) -> str | None:
-    """Return the text of the last user message, or *None* if absent."""
-    for msg in reversed(messages):
-        if msg.get("role") == "user":
-            content = msg.get("content", "")
-            if isinstance(content, list):
-                text_parts = [
-                    p.get("text", "")
-                    for p in content
-                    if isinstance(p, dict) and p.get("type") == "text"
-                ]
-                return " ".join(text_parts).strip() or None
-            text = str(content).strip()
-            return text or None
-    return None
-
-
-def _build_context_prefix() -> str:
-    """Build the system-context preamble injected before the user message."""
-    now = datetime.now()
-    return (
-        f"[System: Current date/time is "
-        f"{now.strftime('%A, %B %d, %Y at %I:%M %p')}]\n"
-        f"[System: Mobile client]\n\n"
-    )
-
-
-def _notify_thinking_engine() -> None:
-    """Record user activity so the thinking engine knows we're not idle."""
-    try:
-        from timmy.thinking import thinking_engine
-
-        thinking_engine.record_user_input()
-    except Exception:
-        logger.debug("Failed to record user input for thinking engine")
-
-
-async def _process_chat(user_msg: str) -> dict | JSONResponse:
-    """Send *user_msg* to the agent, log the exchange, and return a response."""
-    _notify_thinking_engine()
-    timestamp = datetime.now().strftime("%H:%M:%S")
-
-    try:
-        response_text = await agent_chat(
-            _build_context_prefix() + user_msg,
-            session_id="mobile",
-        )
-        message_log.append(role="user", content=user_msg, timestamp=timestamp, source="api")
-        message_log.append(role="agent", content=response_text, timestamp=timestamp, source="api")
-        return {"reply": response_text, "timestamp": timestamp}
-
-    except Exception as exc:
-        error_msg = f"Agent is offline: {exc}"
-        logger.error("api_chat error: %s", exc)
-        message_log.append(role="user", content=user_msg, timestamp=timestamp, source="api")
-        message_log.append(role="error", content=error_msg, timestamp=timestamp, source="api")
-        return JSONResponse(
-            status_code=503,
-            content={"error": error_msg, "timestamp": timestamp},
-        )
-
-
 # ── POST /api/chat ────────────────────────────────────────────────────────────
 
 
@@ -131,15 +44,78 @@ async def api_chat(request: Request):
     Response:
         {"reply": "...", "timestamp": "HH:MM:SS"}
     """
-    body, err = await _parse_chat_body(request)
+    # Enforce request body size limit
-    if err:
+    content_length = request.headers.get("content-length")
-        return err
+    if content_length and int(content_length) > settings.chat_api_max_body_bytes:
+        return JSONResponse(status_code=413, content={"error": "Request body too large"})
 
-    user_msg = _extract_user_message(body["messages"])
+    try:
-    if not user_msg:
+        body = await request.json()
+    except Exception as exc:
+        logger.warning("Chat API JSON parse error: %s", exc)
+        return JSONResponse(status_code=400, content={"error": "Invalid JSON"})
+
+    messages = body.get("messages")
+    if not messages or not isinstance(messages, list):
+        return JSONResponse(status_code=400, content={"error": "messages array is required"})
+
+    # Extract the latest user message text
+    last_user_msg = None
+    for msg in reversed(messages):
+        if msg.get("role") == "user":
+            content = msg.get("content", "")
+            # Handle multimodal content arrays — extract text parts
+            if isinstance(content, list):
+                text_parts = [
+                    p.get("text", "")
+                    for p in content
+                    if isinstance(p, dict) and p.get("type") == "text"
+                ]
+                last_user_msg = " ".join(text_parts).strip()
+            else:
+                last_user_msg = str(content).strip()
+            break
+
+    if not last_user_msg:
         return JSONResponse(status_code=400, content={"error": "No user message found"})
 
-    return await _process_chat(user_msg)
+    # Record user activity so the thinking engine knows we're not idle
+    try:
+        from timmy.thinking import thinking_engine
+
+        thinking_engine.record_user_input()
+    except Exception:
+        logger.debug("Failed to record user input for thinking engine")
+
+    timestamp = datetime.now().strftime("%H:%M:%S")
+
+    try:
+        # Inject context (same pattern as the HTMX chat handler in agents.py)
+        now = datetime.now()
+        context_prefix = (
+            f"[System: Current date/time is "
+            f"{now.strftime('%A, %B %d, %Y at %I:%M %p')}]\n"
+            f"[System: Mobile client]\n\n"
+        )
+        response_text = await agent_chat(
+            context_prefix + last_user_msg,
+            session_id="mobile",
+        )
+
+        message_log.append(role="user", content=last_user_msg, timestamp=timestamp, source="api")
+        message_log.append(role="agent", content=response_text, timestamp=timestamp, source="api")
+
+        return {"reply": response_text, "timestamp": timestamp}
+
+    except Exception as exc:
+        error_msg = f"Agent is offline: {exc}"
+        logger.error("api_chat error: %s", exc)
+        message_log.append(role="user", content=last_user_msg, timestamp=timestamp, source="api")
+        message_log.append(role="error", content=error_msg, timestamp=timestamp, source="api")
+        return JSONResponse(
+            status_code=503,
+            content={"error": error_msg, "timestamp": timestamp},
+        )
 
 
 # ── POST /api/upload ──────────────────────────────────────────────────────────

src/infrastructure/error_capture.py

@@ -100,14 +100,36 @@ def _get_git_context() -> dict:
         return {"branch": "unknown", "commit": "unknown"}
 
 
-def _extract_traceback_info(exc: Exception) -> tuple[str, str, int]:
+def capture_error(
-    """Extract formatted traceback, affected file, and line number.
+    exc: Exception,
+    source: str = "unknown",
+    context: dict | None = None,
+) -> str | None:
+    """Capture an error and optionally create a bug report.
+
+    Args:
+        exc: The exception to capture
+        source: Module/component where the error occurred
+        context: Optional dict of extra context (request path, etc.)
 
     Returns:
-        Tuple of (traceback_string, affected_file, affected_line).
+        Task ID of the created bug report, or None if deduplicated/disabled
     """
+    from config import settings
+
+    if not settings.error_feedback_enabled:
+        return None
+
+    error_hash = _stack_hash(exc)
+
+    if _is_duplicate(error_hash):
+        logger.debug("Duplicate error suppressed: %s (hash=%s)", exc, error_hash)
+        return None
+
+    # Format the stack trace
     tb_str = "".join(traceback.format_exception(type(exc), exc, exc.__traceback__))
 
+    # Extract file/line from traceback
     tb_obj = exc.__traceback__
     affected_file = "unknown"
     affected_line = 0
@@ -117,18 +139,9 @@ def _extract_traceback_info(exc: Exception) -> tuple[str, str, int]:
         affected_file = tb_obj.tb_frame.f_code.co_filename
         affected_line = tb_obj.tb_lineno
 
-    return tb_str, affected_file, affected_line
+    git_ctx = _get_git_context()
 
-
+    # 1. Log to event_log
-def _log_error_event(
-    exc: Exception,
-    source: str,
-    error_hash: str,
-    affected_file: str,
-    affected_line: int,
-    git_ctx: dict,
-) -> None:
-    """Log the captured error to the event log."""
     try:
         from swarm.event_log import EventType, log_event
 
@@ -148,18 +161,8 @@ def _log_error_event(
     except Exception as log_exc:
         logger.debug("Failed to log error event: %s", log_exc)
 
-
+    # 2. Create bug report task
-def _create_bug_report(
+    task_id = None
-    exc: Exception,
-    source: str,
-    context: dict | None,
-    error_hash: str,
-    tb_str: str,
-    affected_file: str,
-    affected_line: int,
-    git_ctx: dict,
-) -> str | None:
-    """Create a bug report task and return the task ID (or None on failure)."""
     try:
         from swarm.task_queue.models import create_task
 
@@ -192,6 +195,7 @@ def _create_bug_report(
         )
         task_id = task.id
 
+        # Log the creation event
         try:
             from swarm.event_log import EventType, log_event
 
@@ -206,16 +210,12 @@ def _create_bug_report(
             )
         except Exception as exc:
             logger.warning("Bug report screenshot error: %s", exc)
-
+            pass
-        return task_id
 
     except Exception as task_exc:
         logger.debug("Failed to create bug report task: %s", task_exc)
-        return None
 
-
+    # 3. Send notification
-def _notify_bug_report(exc: Exception, source: str) -> None:
-    """Send a push notification about the captured error."""
     try:
         from infrastructure.notifications.push import notifier
 
@@ -224,12 +224,11 @@ def _notify_bug_report(exc: Exception, source: str) -> None:
             message=f"{type(exc).__name__} in {source}: {str(exc)[:80]}",
             category="system",
         )
-    except Exception as notify_exc:
+    except Exception as exc:
-        logger.warning("Bug report notification error: %s", notify_exc)
+        logger.warning("Bug report notification error: %s", exc)
+        pass
 
-
+    # 4. Record in session logger (via registered callback)
-def _record_to_session(exc: Exception, source: str) -> None:
-    """Record the error via the registered session callback."""
     if _error_recorder is not None:
         try:
             _error_recorder(
@@ -239,50 +238,4 @@ def _record_to_session(exc: Exception, source: str) -> None:
         except Exception as log_exc:
             logger.warning("Bug report session logging error: %s", log_exc)
 
-
-def capture_error(
-    exc: Exception,
-    source: str = "unknown",
-    context: dict | None = None,
-) -> str | None:
-    """Capture an error and optionally create a bug report.
-
-    Args:
-        exc: The exception to capture
-        source: Module/component where the error occurred
-        context: Optional dict of extra context (request path, etc.)
-
-    Returns:
-        Task ID of the created bug report, or None if deduplicated/disabled
-    """
-    from config import settings
-
-    if not settings.error_feedback_enabled:
-        return None
-
-    error_hash = _stack_hash(exc)
-
-    if _is_duplicate(error_hash):
-        logger.debug("Duplicate error suppressed: %s (hash=%s)", exc, error_hash)
-        return None
-
-    tb_str, affected_file, affected_line = _extract_traceback_info(exc)
-    git_ctx = _get_git_context()
-
-    _log_error_event(exc, source, error_hash, affected_file, affected_line, git_ctx)
-
-    task_id = _create_bug_report(
-        exc,
-        source,
-        context,
-        error_hash,
-        tb_str,
-        affected_file,
-        affected_line,
-        git_ctx,
-    )
-
-    _notify_bug_report(exc, source)
-    _record_to_session(exc, source)
-
     return task_id

src/infrastructure/hands/shell.py

@@ -144,65 +144,6 @@ class ShellHand:
 
         return None
 
-    @staticmethod
-    def _build_run_env(env: dict | None) -> dict:
-        """Merge *env* overrides into a copy of the current environment."""
-        import os
-
-        run_env = os.environ.copy()
-        if env:
-            run_env.update(env)
-        return run_env
-
-    async def _execute_subprocess(
-        self,
-        command: str,
-        effective_timeout: int,
-        cwd: str | None,
-        run_env: dict,
-        start: float,
-    ) -> ShellResult:
-        """Run *command* as a subprocess with timeout enforcement."""
-        proc = await asyncio.create_subprocess_shell(
-            command,
-            stdout=asyncio.subprocess.PIPE,
-            stderr=asyncio.subprocess.PIPE,
-            cwd=cwd,
-            env=run_env,
-        )
-
-        try:
-            stdout_bytes, stderr_bytes = await asyncio.wait_for(
-                proc.communicate(), timeout=effective_timeout
-            )
-        except TimeoutError:
-            proc.kill()
-            await proc.wait()
-            latency = (time.time() - start) * 1000
-            logger.warning("Shell command timed out after %ds: %s", effective_timeout, command)
-            return ShellResult(
-                command=command,
-                success=False,
-                exit_code=-1,
-                error=f"Command timed out after {effective_timeout}s",
-                latency_ms=latency,
-                timed_out=True,
-            )
-
-        latency = (time.time() - start) * 1000
-        exit_code = proc.returncode if proc.returncode is not None else -1
-        stdout = stdout_bytes.decode("utf-8", errors="replace").strip()
-        stderr = stderr_bytes.decode("utf-8", errors="replace").strip()
-
-        return ShellResult(
-            command=command,
-            success=exit_code == 0,
-            exit_code=exit_code,
-            stdout=stdout,
-            stderr=stderr,
-            latency_ms=latency,
-        )
-
     async def run(
         self,
         command: str,
@@ -223,6 +164,7 @@ class ShellHand:
         """
         start = time.time()
 
+        # Validate
         validation_error = self._validate_command(command)
         if validation_error:
             return ShellResult(
@@ -236,8 +178,52 @@ class ShellHand:
         cwd = working_dir or self._working_dir
 
         try:
-            run_env = self._build_run_env(env)
+            import os
-            return await self._execute_subprocess(command, effective_timeout, cwd, run_env, start)
+
+            run_env = os.environ.copy()
+            if env:
+                run_env.update(env)
+
+            proc = await asyncio.create_subprocess_shell(
+                command,
+                stdout=asyncio.subprocess.PIPE,
+                stderr=asyncio.subprocess.PIPE,
+                cwd=cwd,
+                env=run_env,
+            )
+
+            try:
+                stdout_bytes, stderr_bytes = await asyncio.wait_for(
+                    proc.communicate(), timeout=effective_timeout
+                )
+            except TimeoutError:
+                proc.kill()
+                await proc.wait()
+                latency = (time.time() - start) * 1000
+                logger.warning("Shell command timed out after %ds: %s", effective_timeout, command)
+                return ShellResult(
+                    command=command,
+                    success=False,
+                    exit_code=-1,
+                    error=f"Command timed out after {effective_timeout}s",
+                    latency_ms=latency,
+                    timed_out=True,
+                )
+
+            latency = (time.time() - start) * 1000
+            exit_code = proc.returncode if proc.returncode is not None else -1
+            stdout = stdout_bytes.decode("utf-8", errors="replace").strip()
+            stderr = stderr_bytes.decode("utf-8", errors="replace").strip()
+
+            return ShellResult(
+                command=command,
+                success=exit_code == 0,
+                exit_code=exit_code,
+                stdout=stdout,
+                stderr=stderr,
+                latency_ms=latency,
+            )
+
         except Exception as exc:
             latency = (time.time() - start) * 1000
             logger.warning("Shell command failed: %s — %s", command, exc)

src/timmy/__init__.py

@@ -1 +1 @@
-"""Timmy — Core AI agent (Ollama/Grok/Claude backends, CLI, prompts)."""
+"""Timmy — Core AI agent (Ollama/AirLLM backends, CLI, prompts)."""

src/timmy/agent.py

@@ -26,12 +26,12 @@ from timmy.prompts import get_system_prompt
 from timmy.tools import create_full_toolkit
 
 if TYPE_CHECKING:
-    from timmy.backends import ClaudeBackend, GrokBackend
+    from timmy.backends import ClaudeBackend, GrokBackend, TimmyAirLLMAgent
 
 logger = logging.getLogger(__name__)
 
 # Union type for callers that want to hint the return type.
-TimmyAgent = Union[Agent, "GrokBackend", "ClaudeBackend"]
+TimmyAgent = Union[Agent, "TimmyAirLLMAgent", "GrokBackend", "ClaudeBackend"]
 
 # Models known to be too small for reliable tool calling.
 # These hallucinate tool calls as text, invoke tools randomly,
@@ -172,34 +172,107 @@ def _warmup_model(model_name: str) -> bool:
 
 
 def _resolve_backend(requested: str | None) -> str:
-    """Return the backend name to use.
+    """Return the backend name to use, resolving 'auto' and explicit overrides.
 
-    Priority (highest -> lowest):
+    Priority (highest → lowest):
       1. CLI flag passed directly to create_timmy()
       2. TIMMY_MODEL_BACKEND env var / .env setting
-      3. 'ollama' (safe default -- no surprises)
+      3. 'ollama' (safe default — no surprises)
+
+    'auto' triggers Apple Silicon detection: uses AirLLM if both
+    is_apple_silicon() and airllm_available() return True.
     """
     if requested is not None:
         return requested
 
-    return settings.timmy_model_backend  # "ollama" | "grok" | "claude"
+    configured = settings.timmy_model_backend  # "ollama" | "airllm" | "grok" | "claude" | "auto"
+    if configured != "auto":
+        return configured
+
+    # "auto" path — lazy import to keep startup fast and tests clean.
+    from timmy.backends import airllm_available, is_apple_silicon
+
+    if is_apple_silicon() and airllm_available():
+        return "airllm"
+    return "ollama"
 
 
-def _build_tools_list(use_tools: bool, skip_mcp: bool, model_name: str) -> list:
+def create_timmy(
-    """Assemble the tools list based on model capability and MCP flags.
+    db_file: str = "timmy.db",
+    backend: str | None = None,
+    model_size: str | None = None,
+    *,
+    skip_mcp: bool = False,
+    session_id: str = "unknown",
+) -> TimmyAgent:
+    """Instantiate the agent — Ollama or AirLLM, same public interface.
 
-    Returns a list of Toolkit / MCPTools objects, or an empty list.
+    Args:
+        db_file:    SQLite file for Agno conversation memory (Ollama path only).
+        backend:    "ollama" | "airllm" | "auto" | None (reads config/env).
+        model_size: AirLLM size — "8b" | "70b" | "405b" | None (reads config).
+        skip_mcp:   If True, omit MCP tool servers (Gitea, filesystem).
+                    Use for background tasks (thinking, QA) where MCP's
+                    stdio cancel-scope lifecycle conflicts with asyncio
+                    task cancellation.
+
+    Returns an Agno Agent or backend-specific agent — all expose
+    print_response(message, stream).
     """
+    resolved = _resolve_backend(backend)
+    size = model_size or "70b"
+
+    if resolved == "claude":
+        from timmy.backends import ClaudeBackend
+
+        return ClaudeBackend()
+
+    if resolved == "grok":
+        from timmy.backends import GrokBackend
+
+        return GrokBackend()
+
+    if resolved == "airllm":
+        from timmy.backends import TimmyAirLLMAgent
+
+        return TimmyAirLLMAgent(model_size=size)
+
+    # Default: Ollama via Agno.
+    # Resolve model with automatic pulling and fallback
+    model_name, is_fallback = _resolve_model_with_fallback(
+        requested_model=None,
+        require_vision=False,
+        auto_pull=True,
+    )
+
+    # If Ollama is completely unreachable, fail loudly.
+    # Sovereignty: never silently send data to a cloud API.
+    # Use --backend claude explicitly if you want cloud inference.
+    if not _check_model_available(model_name):
+        logger.error(
+            "Ollama unreachable and no local models available. "
+            "Start Ollama with 'ollama serve' or use --backend claude explicitly."
+        )
+
+    if is_fallback:
+        logger.info("Using fallback model %s (requested was unavailable)", model_name)
+
+    use_tools = _model_supports_tools(model_name)
+
+    # Conditionally include tools — small models get none
+    toolkit = create_full_toolkit() if use_tools else None
     if not use_tools:
         logger.info("Tools disabled for model %s (too small for reliable tool calling)", model_name)
-        return []
 
-    tools_list: list = [create_full_toolkit()]
+    # Build the tools list — Agno accepts a list of Toolkit / MCPTools
+    tools_list: list = []
+    if toolkit:
+        tools_list.append(toolkit)
 
     # Add MCP tool servers (lazy-connected on first arun()).
     # Skipped when skip_mcp=True — MCP's stdio transport uses anyio cancel
     # scopes that conflict with asyncio background task cancellation (#72).
-    if not skip_mcp:
+    if use_tools and not skip_mcp:
         try:
             from timmy.mcp_tools import create_filesystem_mcp_tools, create_gitea_mcp_tools
 
@@ -213,46 +286,34 @@ def _build_tools_list(use_tools: bool, skip_mcp: bool, model_name: str) -> list:
         except Exception as exc:
             logger.debug("MCP tools unavailable: %s", exc)
 
-    return tools_list
+    # Select prompt tier based on tool capability
-
-
-def _build_prompt(use_tools: bool, session_id: str) -> str:
-    """Build the full system prompt with optional memory context."""
     base_prompt = get_system_prompt(tools_enabled=use_tools, session_id=session_id)
 
+    # Try to load memory context
     try:
         from timmy.memory_system import memory_system
 
         memory_context = memory_system.get_system_context()
         if memory_context:
-            # Smaller budget for small models — expanded prompt uses more tokens
+            # Truncate if too long — smaller budget for small models
+            # since the expanded prompt (roster, guardrails) uses more tokens
             max_context = 2000 if not use_tools else 8000
             if len(memory_context) > max_context:
                 memory_context = memory_context[:max_context] + "\n... [truncated]"
-            return (
+            full_prompt = (
                 f"{base_prompt}\n\n"
                 f"## GROUNDED CONTEXT (verified sources — cite when using)\n\n"
                 f"{memory_context}"
             )
+        else:
+            full_prompt = base_prompt
     except Exception as exc:
         logger.warning("Failed to load memory context: %s", exc)
+        full_prompt = base_prompt
 
-    return base_prompt
-
-
-def _create_ollama_agent(
-    *,
-    db_file: str,
-    model_name: str,
-    tools_list: list,
-    full_prompt: str,
-    use_tools: bool,
-) -> Agent:
-    """Construct the Agno Agent with Ollama backend and warm up the model."""
     model_kwargs = {}
     if settings.ollama_num_ctx > 0:
         model_kwargs["options"] = {"num_ctx": settings.ollama_num_ctx}
-
     agent = Agent(
         name="Agent",
         model=Ollama(id=model_name, host=settings.ollama_url, timeout=300, **model_kwargs),
@@ -269,67 +330,6 @@ def _create_ollama_agent(
     return agent
 
 
-def create_timmy(
-    db_file: str = "timmy.db",
-    backend: str | None = None,
-    *,
-    skip_mcp: bool = False,
-    session_id: str = "unknown",
-) -> TimmyAgent:
-    """Instantiate the agent — Ollama, Grok, or Claude.
-
-    Args:
-        db_file:    SQLite file for Agno conversation memory (Ollama path only).
-        backend:    "ollama" | "grok" | "claude" | None (reads config/env).
-        skip_mcp:   If True, omit MCP tool servers (Gitea, filesystem).
-                    Use for background tasks (thinking, QA) where MCP's
-                    stdio cancel-scope lifecycle conflicts with asyncio
-                    task cancellation.
-
-    Returns an Agno Agent or backend-specific agent — all expose
-    print_response(message, stream).
-    """
-    resolved = _resolve_backend(backend)
-
-    if resolved == "claude":
-        from timmy.backends import ClaudeBackend
-
-        return ClaudeBackend()
-
-    if resolved == "grok":
-        from timmy.backends import GrokBackend
-
-        return GrokBackend()
-
-    # Default: Ollama via Agno.
-    model_name, is_fallback = _resolve_model_with_fallback(
-        requested_model=None,
-        require_vision=False,
-        auto_pull=True,
-    )
-
-    if not _check_model_available(model_name):
-        logger.error(
-            "Ollama unreachable and no local models available. "
-            "Start Ollama with 'ollama serve' or use --backend claude explicitly."
-        )
-
-    if is_fallback:
-        logger.info("Using fallback model %s (requested was unavailable)", model_name)
-
-    use_tools = _model_supports_tools(model_name)
-    tools_list = _build_tools_list(use_tools, skip_mcp, model_name)
-    full_prompt = _build_prompt(use_tools, session_id)
-
-    return _create_ollama_agent(
-        db_file=db_file,
-        model_name=model_name,
-        tools_list=tools_list,
-        full_prompt=full_prompt,
-        use_tools=use_tools,
-    )
-
-
 class TimmyWithMemory:
     """Agent wrapper with explicit three-tier memory management."""
 

src/timmy/backends.py

@@ -1,10 +1,11 @@
-"""LLM backends — Grok (xAI) and Claude (Anthropic).
+"""LLM backends — AirLLM (local big models), Grok (xAI), and Claude (Anthropic).
 
 Provides drop-in replacements for the Agno Agent that expose the same
 run(message, stream) → RunResult interface used by the dashboard and the
 print_response(message, stream) interface used by the CLI.
 
 Backends:
+  - TimmyAirLLMAgent: Local 8B/70B/405B via AirLLM (Apple Silicon or PyTorch)
   - GrokBackend: xAI Grok API via OpenAI-compatible SDK (opt-in premium)
   - ClaudeBackend: Anthropic Claude API — lightweight cloud fallback
 
@@ -15,11 +16,21 @@ import logging
 import platform
 import time
 from dataclasses import dataclass
+from typing import Literal
 
 from timmy.prompts import get_system_prompt
 
 logger = logging.getLogger(__name__)
 
+# HuggingFace model IDs for each supported size.
+_AIRLLM_MODELS: dict[str, str] = {
+    "8b": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+    "70b": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+    "405b": "meta-llama/Meta-Llama-3.1-405B-Instruct",
+}
+
+ModelSize = Literal["8b", "70b", "405b"]
+
 
 @dataclass
 class RunResult:
@@ -34,6 +45,108 @@ def is_apple_silicon() -> bool:
     return platform.system() == "Darwin" and platform.machine() == "arm64"
 
 
+def airllm_available() -> bool:
+    """Return True when the airllm package is importable."""
+    try:
+        import airllm  # noqa: F401
+
+        return True
+    except ImportError:
+        return False
+
+
+class TimmyAirLLMAgent:
+    """Thin AirLLM wrapper compatible with both dashboard and CLI call sites.
+
+    Exposes:
+      run(message, stream)           → RunResult(content=...)  [dashboard]
+      print_response(message, stream) → None                   [CLI]
+
+    Maintains a rolling 10-turn in-memory history so Timmy remembers the
+    conversation within a session — no SQLite needed at this layer.
+    """
+
+    def __init__(self, model_size: str = "70b") -> None:
+        model_id = _AIRLLM_MODELS.get(model_size)
+        if model_id is None:
+            raise ValueError(
+                f"Unknown model size {model_size!r}. Choose from: {list(_AIRLLM_MODELS)}"
+            )
+
+        if is_apple_silicon():
+            from airllm import AirLLMMLX  # type: ignore[import]
+
+            self._model = AirLLMMLX(model_id)
+        else:
+            from airllm import AutoModel  # type: ignore[import]
+
+            self._model = AutoModel.from_pretrained(model_id)
+
+        self._history: list[str] = []
+        self._model_size = model_size
+
+    # ── public interface (mirrors Agno Agent) ────────────────────────────────
+
+    def run(self, message: str, *, stream: bool = False) -> RunResult:
+        """Run inference and return a structured result (matches Agno Agent.run()).
+
+        `stream` is accepted for API compatibility; AirLLM always generates
+        the full output in one pass.
+        """
+        prompt = self._build_prompt(message)
+
+        input_tokens = self._model.tokenizer(
+            [prompt],
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+            max_length=2048,
+        )
+        output = self._model.generate(
+            **input_tokens,
+            max_new_tokens=512,
+            use_cache=True,
+            do_sample=True,
+            temperature=0.7,
+        )
+
+        # Decode only the newly generated tokens, not the prompt.
+        input_len = input_tokens["input_ids"].shape[1]
+        response = self._model.tokenizer.decode(
+            output[0][input_len:], skip_special_tokens=True
+        ).strip()
+
+        self._history.append(f"User: {message}")
+        self._history.append(f"Timmy: {response}")
+
+        return RunResult(content=response)
+
+    def print_response(self, message: str, *, stream: bool = True) -> None:
+        """Run inference and render the response to stdout (CLI interface)."""
+        result = self.run(message, stream=stream)
+        self._render(result.content)
+
+    # ── private helpers ──────────────────────────────────────────────────────
+
+    def _build_prompt(self, message: str) -> str:
+        context = get_system_prompt(tools_enabled=False, session_id="airllm") + "\n\n"
+        # Include the last 10 turns (5 exchanges) for continuity.
+        if self._history:
+            context += "\n".join(self._history[-10:]) + "\n\n"
+        return context + f"User: {message}\nTimmy:"
+
+    @staticmethod
+    def _render(text: str) -> None:
+        """Print response with rich markdown when available, plain text otherwise."""
+        try:
+            from rich.console import Console
+            from rich.markdown import Markdown
+
+            Console().print(Markdown(text))
+        except ImportError:
+            print(text)
+
+
 # ── Grok (xAI) Backend ─────────────────────────────────────────────────────
 # Premium cloud augmentation — opt-in only, never the default path.
 
@@ -74,7 +187,7 @@ class GrokBackend:
     Uses the OpenAI-compatible SDK to connect to xAI's API.
     Only activated when GROK_ENABLED=true and XAI_API_KEY is set.
 
-    Exposes the same interface as Agno Agent:
+    Exposes the same interface as TimmyAirLLMAgent and Agno Agent:
       run(message, stream)           → RunResult  [dashboard]
       print_response(message, stream) → None       [CLI]
       health_check()                 → dict        [monitoring]
@@ -324,7 +437,8 @@ CLAUDE_MODELS: dict[str, str] = {
 class ClaudeBackend:
     """Anthropic Claude backend — cloud fallback when local models are offline.
 
-    Uses the official Anthropic SDK.  Same interface as GrokBackend:
+    Uses the official Anthropic SDK.  Same interface as GrokBackend and
+    TimmyAirLLMAgent:
       run(message, stream)           → RunResult  [dashboard]
       print_response(message, stream) → None       [CLI]
       health_check()                 → dict        [monitoring]

src/timmy/cli.py

@@ -22,13 +22,13 @@ _BACKEND_OPTION = typer.Option(
     None,
     "--backend",
     "-b",
-    help="Inference backend: 'ollama' (default) | 'grok' | 'claude'",
+    help="Inference backend: 'ollama' (default) | 'airllm' | 'auto'",
 )
 _MODEL_SIZE_OPTION = typer.Option(
     None,
     "--model-size",
     "-s",
-    help="Model size (reserved for future use).",
+    help="AirLLM model size when --backend airllm: '8b' | '70b' | '405b'",
 )
 
 

src/timmy/memory_system.py

@@ -98,73 +98,6 @@ def _get_table_columns(conn: sqlite3.Connection, table_name: str) -> set[str]:
     return {row[1] for row in cursor.fetchall()}
 
 
-def _migrate_episodes(conn: sqlite3.Connection) -> None:
-    """Migrate episodes table rows into the unified memories table."""
-    logger.info("Migration: Converting episodes table to memories")
-    try:
-        cols = _get_table_columns(conn, "episodes")
-        context_type_col = "context_type" if "context_type" in cols else "'conversation'"
-
-        conn.execute(f"""
-            INSERT INTO memories (
-                id, content, memory_type, source, embedding,
-                metadata, agent_id, task_id, session_id,
-                created_at, access_count, last_accessed
-            )
-            SELECT
-                id, content,
-                COALESCE({context_type_col}, 'conversation'),
-                COALESCE(source, 'agent'),
-                embedding,
-                metadata, agent_id, task_id, session_id,
-                COALESCE(timestamp, datetime('now')), 0, NULL
-            FROM episodes
-        """)
-        conn.execute("DROP TABLE episodes")
-        logger.info("Migration: Migrated episodes to memories")
-    except sqlite3.Error as exc:
-        logger.warning("Migration: Failed to migrate episodes: %s", exc)
-
-
-def _migrate_chunks(conn: sqlite3.Connection) -> None:
-    """Migrate chunks table rows into the unified memories table."""
-    logger.info("Migration: Converting chunks table to memories")
-    try:
-        cols = _get_table_columns(conn, "chunks")
-
-        id_col = "id" if "id" in cols else "CAST(rowid AS TEXT)"
-        content_col = "content" if "content" in cols else "text"
-        source_col = (
-            "filepath" if "filepath" in cols else ("source" if "source" in cols else "'vault'")
-        )
-        embedding_col = "embedding" if "embedding" in cols else "NULL"
-        created_col = "created_at" if "created_at" in cols else "datetime('now')"
-
-        conn.execute(f"""
-            INSERT INTO memories (
-                id, content, memory_type, source, embedding,
-                created_at, access_count
-            )
-            SELECT
-                {id_col}, {content_col}, 'vault_chunk', {source_col},
-                {embedding_col}, {created_col}, 0
-            FROM chunks
-        """)
-        conn.execute("DROP TABLE chunks")
-        logger.info("Migration: Migrated chunks to memories")
-    except sqlite3.Error as exc:
-        logger.warning("Migration: Failed to migrate chunks: %s", exc)
-
-
-def _drop_legacy_table(conn: sqlite3.Connection, table: str) -> None:
-    """Drop a legacy table if it exists."""
-    try:
-        conn.execute(f"DROP TABLE {table}")  # noqa: S608
-        logger.info("Migration: Dropped old %s table", table)
-    except sqlite3.Error as exc:
-        logger.warning("Migration: Failed to drop %s: %s", table, exc)
-
-
 def _migrate_schema(conn: sqlite3.Connection) -> None:
     """Migrate from old three-table schema to unified memories table.
 
@@ -177,16 +110,78 @@ def _migrate_schema(conn: sqlite3.Connection) -> None:
     tables = {row[0] for row in cursor.fetchall()}
 
     has_memories = "memories" in tables
+    has_episodes = "episodes" in tables
+    has_chunks = "chunks" in tables
+    has_facts = "facts" in tables
 
-    if not has_memories and (tables & {"episodes", "chunks", "facts"}):
+    # Check if we need to migrate (old schema exists)
+    if not has_memories and (has_episodes or has_chunks or has_facts):
         logger.info("Migration: Creating unified memories table")
+        # Schema will be created by _ensure_schema above
 
-    if "episodes" in tables and has_memories:
+    # Migrate episodes -> memories
-        _migrate_episodes(conn)
+    if has_episodes and has_memories:
-    if "chunks" in tables and has_memories:
+        logger.info("Migration: Converting episodes table to memories")
-        _migrate_chunks(conn)
+        try:
-    if "facts" in tables:
+            cols = _get_table_columns(conn, "episodes")
-        _drop_legacy_table(conn, "facts")
+            context_type_col = "context_type" if "context_type" in cols else "'conversation'"
+
+            conn.execute(f"""
+                INSERT INTO memories (
+                    id, content, memory_type, source, embedding,
+                    metadata, agent_id, task_id, session_id,
+                    created_at, access_count, last_accessed
+                )
+                SELECT 
+                    id, content, 
+                    COALESCE({context_type_col}, 'conversation'),
+                    COALESCE(source, 'agent'),
+                    embedding,
+                    metadata, agent_id, task_id, session_id,
+                    COALESCE(timestamp, datetime('now')), 0, NULL
+                FROM episodes
+            """)
+            conn.execute("DROP TABLE episodes")
+            logger.info("Migration: Migrated episodes to memories")
+        except sqlite3.Error as exc:
+            logger.warning("Migration: Failed to migrate episodes: %s", exc)
+
+    # Migrate chunks -> memories as vault_chunk
+    if has_chunks and has_memories:
+        logger.info("Migration: Converting chunks table to memories")
+        try:
+            cols = _get_table_columns(conn, "chunks")
+
+            id_col = "id" if "id" in cols else "CAST(rowid AS TEXT)"
+            content_col = "content" if "content" in cols else "text"
+            source_col = (
+                "filepath" if "filepath" in cols else ("source" if "source" in cols else "'vault'")
+            )
+            embedding_col = "embedding" if "embedding" in cols else "NULL"
+            created_col = "created_at" if "created_at" in cols else "datetime('now')"
+
+            conn.execute(f"""
+                INSERT INTO memories (
+                    id, content, memory_type, source, embedding,
+                    created_at, access_count
+                )
+                SELECT 
+                    {id_col}, {content_col}, 'vault_chunk', {source_col},
+                    {embedding_col}, {created_col}, 0
+                FROM chunks
+            """)
+            conn.execute("DROP TABLE chunks")
+            logger.info("Migration: Migrated chunks to memories")
+        except sqlite3.Error as exc:
+            logger.warning("Migration: Failed to migrate chunks: %s", exc)
+
+    # Drop old tables
+    if has_facts:
+        try:
+            conn.execute("DROP TABLE facts")
+            logger.info("Migration: Dropped old facts table")
+        except sqlite3.Error as exc:
+            logger.warning("Migration: Failed to drop facts: %s", exc)
 
     conn.commit()
 
@@ -303,86 +298,6 @@ def store_memory(
     return entry
 
 
-def _build_memory_filter(
-    context_type: str | None,
-    agent_id: str | None,
-    session_id: str | None,
-) -> tuple[str, list]:
-    """Build WHERE clause and params for memory queries."""
-    conditions: list[str] = []
-    params: list = []
-
-    if context_type:
-        conditions.append("memory_type = ?")
-        params.append(context_type)
-    if agent_id:
-        conditions.append("agent_id = ?")
-        params.append(agent_id)
-    if session_id:
-        conditions.append("session_id = ?")
-        params.append(session_id)
-
-    where_clause = "WHERE " + " AND ".join(conditions) if conditions else ""
-    return where_clause, params
-
-
-def _fetch_memory_candidates(
-    where_clause: str, params: list, candidate_limit: int
-) -> list[sqlite3.Row]:
-    """Fetch candidate memory rows from the database."""
-    query_sql = f"""
-        SELECT * FROM memories
-        {where_clause}
-        ORDER BY created_at DESC
-        LIMIT ?
-    """
-    params.append(candidate_limit)
-
-    with get_connection() as conn:
-        return conn.execute(query_sql, params).fetchall()
-
-
-def _row_to_entry(row: sqlite3.Row) -> MemoryEntry:
-    """Convert a database row to a MemoryEntry."""
-    return MemoryEntry(
-        id=row["id"],
-        content=row["content"],
-        source=row["source"],
-        context_type=row["memory_type"],  # DB column -> API field
-        agent_id=row["agent_id"],
-        task_id=row["task_id"],
-        session_id=row["session_id"],
-        metadata=json.loads(row["metadata"]) if row["metadata"] else None,
-        embedding=json.loads(row["embedding"]) if row["embedding"] else None,
-        timestamp=row["created_at"],
-    )
-
-
-def _score_and_rank(
-    rows: list[sqlite3.Row],
-    query: str,
-    query_embedding: list[float],
-    min_relevance: float,
-    limit: int,
-) -> list[MemoryEntry]:
-    """Score candidates by similarity and return top results."""
-    results = []
-    for row in rows:
-        entry = _row_to_entry(row)
-
-        if entry.embedding:
-            score = cosine_similarity(query_embedding, entry.embedding)
-        else:
-            score = _keyword_overlap(query, entry.content)
-
-        entry.relevance_score = score
-        if score >= min_relevance:
-            results.append(entry)
-
-    results.sort(key=lambda x: x.relevance_score or 0, reverse=True)
-    return results[:limit]
-
-
 def search_memories(
     query: str,
     limit: int = 10,
@@ -405,9 +320,66 @@ def search_memories(
         List of MemoryEntry objects sorted by relevance
     """
     query_embedding = embed_text(query)
-    where_clause, params = _build_memory_filter(context_type, agent_id, session_id)
+
-    rows = _fetch_memory_candidates(where_clause, params, limit * 3)
+    # Build query with filters
-    return _score_and_rank(rows, query, query_embedding, min_relevance, limit)
+    conditions = []
+    params = []
+
+    if context_type:
+        conditions.append("memory_type = ?")
+        params.append(context_type)
+    if agent_id:
+        conditions.append("agent_id = ?")
+        params.append(agent_id)
+    if session_id:
+        conditions.append("session_id = ?")
+        params.append(session_id)
+
+    where_clause = "WHERE " + " AND ".join(conditions) if conditions else ""
+
+    # Fetch candidates (we'll do in-memory similarity for now)
+    query_sql = f"""
+        SELECT * FROM memories
+        {where_clause}
+        ORDER BY created_at DESC
+        LIMIT ?
+    """
+    params.append(limit * 3)  # Get more candidates for ranking
+
+    with get_connection() as conn:
+        rows = conn.execute(query_sql, params).fetchall()
+
+    # Compute similarity scores
+    results = []
+    for row in rows:
+        entry = MemoryEntry(
+            id=row["id"],
+            content=row["content"],
+            source=row["source"],
+            context_type=row["memory_type"],  # DB column -> API field
+            agent_id=row["agent_id"],
+            task_id=row["task_id"],
+            session_id=row["session_id"],
+            metadata=json.loads(row["metadata"]) if row["metadata"] else None,
+            embedding=json.loads(row["embedding"]) if row["embedding"] else None,
+            timestamp=row["created_at"],
+        )
+
+        if entry.embedding:
+            score = cosine_similarity(query_embedding, entry.embedding)
+            entry.relevance_score = score
+            if score >= min_relevance:
+                results.append(entry)
+        else:
+            # Fallback: check for keyword overlap
+            score = _keyword_overlap(query, entry.content)
+            entry.relevance_score = score
+            if score >= min_relevance:
+                results.append(entry)
+
+    # Sort by relevance and return top results
+    results.sort(key=lambda x: x.relevance_score or 0, reverse=True)
+    return results[:limit]
 
 
 def delete_memory(memory_id: str) -> bool:

src/timmy/session_logger.py

@@ -323,75 +323,6 @@ def session_history(query: str, role: str = "", limit: int = 10) -> str:
 _LOW_CONFIDENCE_THRESHOLD = 0.5
 
 
-def _categorize_entries(
-    entries: list[dict],
-) -> tuple[list[dict], list[dict], list[dict], list[dict]]:
-    """Split session entries into messages, errors, timmy msgs, user msgs."""
-    messages = [e for e in entries if e.get("type") == "message"]
-    errors = [e for e in entries if e.get("type") == "error"]
-    timmy_msgs = [e for e in messages if e.get("role") == "timmy"]
-    user_msgs = [e for e in messages if e.get("role") == "user"]
-    return messages, errors, timmy_msgs, user_msgs
-
-
-def _find_low_confidence(timmy_msgs: list[dict]) -> list[dict]:
-    """Return Timmy responses below the confidence threshold."""
-    return [
-        m
-        for m in timmy_msgs
-        if m.get("confidence") is not None and m["confidence"] < _LOW_CONFIDENCE_THRESHOLD
-    ]
-
-
-def _find_repeated_topics(user_msgs: list[dict], top_n: int = 5) -> list[tuple[str, int]]:
-    """Identify frequently mentioned words in user messages."""
-    topic_counts: dict[str, int] = {}
-    for m in user_msgs:
-        for word in (m.get("content") or "").lower().split():
-            cleaned = word.strip(".,!?\"'()[]")
-            if len(cleaned) > 3:
-                topic_counts[cleaned] = topic_counts.get(cleaned, 0) + 1
-    return sorted(
-        ((w, c) for w, c in topic_counts.items() if c >= 3),
-        key=lambda x: x[1],
-        reverse=True,
-    )[:top_n]
-
-
-def _format_reflection_section(
-    title: str,
-    items: list[dict],
-    formatter: object,
-    empty_msg: str,
-) -> list[str]:
-    """Format a titled section with items, or an empty-state message."""
-    if items:
-        lines = [f"### {title} ({len(items)})"]
-        for item in items[:5]:
-            lines.append(formatter(item))  # type: ignore[operator]
-        lines.append("")
-        return lines
-    return [f"### {title}\n{empty_msg}\n"]
-
-
-def _build_insights(
-    low_conf: list[dict],
-    errors: list[dict],
-    repeated: list[tuple[str, int]],
-) -> list[str]:
-    """Generate actionable insight bullets from analysis results."""
-    insights: list[str] = []
-    if low_conf:
-        insights.append("Consider studying topics where confidence was low.")
-    if errors:
-        insights.append("Review error patterns for recurring infrastructure issues.")
-    if repeated:
-        insights.append(
-            f'User frequently asks about "{repeated[0][0]}" — consider deepening knowledge here.'
-        )
-    return insights or ["Conversations look healthy. Keep up the good work."]
-
-
 def self_reflect(limit: int = 30) -> str:
     """Review recent conversations and reflect on Timmy's own behavior.
 
@@ -412,12 +343,35 @@ def self_reflect(limit: int = 30) -> str:
     if not entries:
         return "No conversation history to reflect on yet."
 
-    _messages, errors, timmy_msgs, user_msgs = _categorize_entries(entries)
+    # Categorize entries
-    low_conf = _find_low_confidence(timmy_msgs)
+    messages = [e for e in entries if e.get("type") == "message"]
-    repeated = _find_repeated_topics(user_msgs)
+    errors = [e for e in entries if e.get("type") == "error"]
+    timmy_msgs = [e for e in messages if e.get("role") == "timmy"]
+    user_msgs = [e for e in messages if e.get("role") == "user"]
+
+    # 1. Low-confidence responses
+    low_conf = [
+        m
+        for m in timmy_msgs
+        if m.get("confidence") is not None and m["confidence"] < _LOW_CONFIDENCE_THRESHOLD
+    ]
+
+    # 2. Identify repeated user topics (simple word frequency)
+    topic_counts: dict[str, int] = {}
+    for m in user_msgs:
+        for word in (m.get("content") or "").lower().split():
+            cleaned = word.strip(".,!?\"'()[]")
+            if len(cleaned) > 3:
+                topic_counts[cleaned] = topic_counts.get(cleaned, 0) + 1
+    repeated = sorted(
+        ((w, c) for w, c in topic_counts.items() if c >= 3),
+        key=lambda x: x[1],
+        reverse=True,
+    )[:5]
 
     # Build reflection report
     sections: list[str] = ["## Self-Reflection Report\n"]
+
     sections.append(
         f"Reviewed {len(entries)} recent entries: "
         f"{len(user_msgs)} user messages, "
@@ -425,27 +379,32 @@ def self_reflect(limit: int = 30) -> str:
         f"{len(errors)} errors.\n"
     )
 
-    sections.extend(
+    # Low confidence
-        _format_reflection_section(
+    if low_conf:
-            "Low-Confidence Responses",
+        sections.append(f"### Low-Confidence Responses ({len(low_conf)})")
-            low_conf,
+        for m in low_conf[:5]:
-            lambda m: (
+            ts = (m.get("timestamp") or "?")[:19]
-                f"- [{(m.get('timestamp') or '?')[:19]}] "
+            conf = m.get("confidence", 0)
-                f"confidence={m.get('confidence', 0):.0%}: "
+            text = (m.get("content") or "")[:120]
-                f"{(m.get('content') or '')[:120]}"
+            sections.append(f"- [{ts}] confidence={conf:.0%}: {text}")
-            ),
+        sections.append("")
-            "None found — all responses above threshold.",
+    else:
+        sections.append(
+            "### Low-Confidence Responses\nNone found — all responses above threshold.\n"
         )
-    )
-    sections.extend(
-        _format_reflection_section(
-            "Errors",
-            errors,
-            lambda e: f"- [{(e.get('timestamp') or '?')[:19]}] {(e.get('error') or '')[:120]}",
-            "No errors recorded.",
-        )
-    )
 
+    # Errors
+    if errors:
+        sections.append(f"### Errors ({len(errors)})")
+        for e in errors[:5]:
+            ts = (e.get("timestamp") or "?")[:19]
+            err = (e.get("error") or "")[:120]
+            sections.append(f"- [{ts}] {err}")
+        sections.append("")
+    else:
+        sections.append("### Errors\nNo errors recorded.\n")
+
+    # Repeated topics
     if repeated:
         sections.append("### Recurring Topics")
         for word, count in repeated:
@@ -454,8 +413,22 @@ def self_reflect(limit: int = 30) -> str:
     else:
         sections.append("### Recurring Topics\nNo strong patterns detected.\n")
 
+    # Actionable summary
+    insights: list[str] = []
+    if low_conf:
+        insights.append("Consider studying topics where confidence was low.")
+    if errors:
+        insights.append("Review error patterns for recurring infrastructure issues.")
+    if repeated:
+        top_topic = repeated[0][0]
+        insights.append(
+            f'User frequently asks about "{top_topic}" — consider deepening knowledge here.'
+        )
+    if not insights:
+        insights.append("Conversations look healthy. Keep up the good work.")
+
     sections.append("### Insights")
-    for insight in _build_insights(low_conf, errors, repeated):
+    for insight in insights:
         sections.append(f"- {insight}")
 
     return "\n".join(sections)

src/timmy/thinking.py

@@ -232,29 +232,58 @@ class ThinkingEngine:
             return False  # Disabled — never idle
         return datetime.now(UTC) - self._last_input_time > timedelta(minutes=timeout)
 
-    def _build_thinking_context(self) -> tuple[str, str, list["Thought"]]:
+    async def think_once(self, prompt: str | None = None) -> Thought | None:
-        """Assemble the context needed for a thinking cycle.
+        """Execute one thinking cycle.
+
+        Args:
+            prompt: Optional custom seed prompt. When provided, overrides
+                    the random seed selection and uses "prompted" as the
+                    seed type — useful for journal prompts from the CLI.
+
+        1. Gather a seed context (or use the custom prompt)
+        2. Build a prompt with continuity from recent thoughts
+        3. Call the agent
+        4. Store the thought
+        5. Log the event and broadcast via WebSocket
+        """
+        if not settings.thinking_enabled:
+            return None
+
+        # Skip idle periods — don't count internal processing as thoughts
+        if not prompt and self._is_idle():
+            logger.debug(
+                "Thinking paused — no user input for %d minutes",
+                settings.thinking_idle_timeout_minutes,
+            )
+            return None
+
+        content, seed_type = await self._generate_thought(prompt)
+        if not content:
+            return None
+
+        thought = self._store_thought(content, seed_type)
+        self._last_thought_id = thought.id
+
+        await self._finalize_thought(thought)
+        return thought
+
+    async def _generate_thought(self, prompt: str | None = None) -> tuple[str | None, str]:
+        """Generate novel thought content via the dedup retry loop.
+
+        Gathers context, builds the LLM prompt, calls the agent, and
+        retries with a fresh seed if the result is too similar to recent
+        thoughts.
 
         Returns:
-            (memory_context, system_context, recent_thoughts)
+            A (content, seed_type) tuple. *content* is ``None`` when the
+            cycle should be skipped (agent failure, empty response, or
+            all retries exhausted).
         """
         memory_context = self._load_memory_context()
         system_context = self._gather_system_snapshot()
         recent_thoughts = self.get_recent_thoughts(limit=5)
-        return memory_context, system_context, recent_thoughts
 
-    async def _generate_novel_thought(
+        content: str | None = None
-        self,
-        prompt: str | None,
-        memory_context: str,
-        system_context: str,
-        recent_thoughts: list["Thought"],
-    ) -> tuple[str | None, str]:
-        """Run the dedup-retry loop to produce a novel thought.
-
-        Returns:
-            (content, seed_type) — content is None if no novel thought produced.
-        """
         seed_type: str = "freeform"
 
         for attempt in range(self._MAX_DEDUP_RETRIES + 1):
@@ -287,7 +316,7 @@ class ThinkingEngine:
 
             # Dedup: reject thoughts too similar to recent ones
             if not self._is_too_similar(content, recent_thoughts):
-                return content, seed_type  # Good — novel thought
+                break  # Good — novel thought
 
             if attempt < self._MAX_DEDUP_RETRIES:
                 logger.info(
@@ -295,6 +324,7 @@ class ThinkingEngine:
                     attempt + 1,
                     self._MAX_DEDUP_RETRIES + 1,
                 )
+                content = None  # Will retry
             else:
                 logger.warning(
                     "Thought still repetitive after %d retries, discarding",
@@ -302,10 +332,10 @@ class ThinkingEngine:
                 )
                 return None, seed_type
 
-        return None, seed_type
+        return content, seed_type
 
-    async def _process_thinking_result(self, thought: "Thought") -> None:
+    async def _finalize_thought(self, thought: Thought) -> None:
-        """Run all post-hooks after a thought is stored."""
+        """Run post-hooks, log, journal, and broadcast a stored thought."""
         self._maybe_check_memory()
         await self._maybe_distill()
         await self._maybe_file_issues()
@@ -316,54 +346,12 @@ class ThinkingEngine:
         self._write_journal(thought)
         await self._broadcast(thought)
 
-    async def think_once(self, prompt: str | None = None) -> Thought | None:
-        """Execute one thinking cycle.
-
-        Args:
-            prompt: Optional custom seed prompt. When provided, overrides
-                    the random seed selection and uses "prompted" as the
-                    seed type — useful for journal prompts from the CLI.
-
-        1. Gather a seed context (or use the custom prompt)
-        2. Build a prompt with continuity from recent thoughts
-        3. Call the agent
-        4. Store the thought
-        5. Log the event and broadcast via WebSocket
-        """
-        if not settings.thinking_enabled:
-            return None
-
-        # Skip idle periods — don't count internal processing as thoughts
-        if not prompt and self._is_idle():
-            logger.debug(
-                "Thinking paused — no user input for %d minutes",
-                settings.thinking_idle_timeout_minutes,
-            )
-            return None
-
-        memory_context, system_context, recent_thoughts = self._build_thinking_context()
-
-        content, seed_type = await self._generate_novel_thought(
-            prompt,
-            memory_context,
-            system_context,
-            recent_thoughts,
-        )
-        if not content:
-            return None
-
-        thought = self._store_thought(content, seed_type)
-        self._last_thought_id = thought.id
-
-        await self._process_thinking_result(thought)
-
         logger.info(
             "Thought [%s] (%s): %s",
             thought.id[:8],
-            seed_type,
+            thought.seed_type,
             thought.content[:80],
         )
-        return thought
 
     def get_recent_thoughts(self, limit: int = 20) -> list[Thought]:
         """Retrieve the most recent thoughts."""
@@ -772,80 +760,6 @@ class ThinkingEngine:
         except Exception as exc:
             logger.debug("Thought issue filing skipped: %s", exc)
 
-    # ── System snapshot helpers ────────────────────────────────────────────
-
-    def _snap_thought_count(self, now: datetime) -> str | None:
-        """Return today's thought count, or *None* on failure."""
-        try:
-            today_start = now.replace(hour=0, minute=0, second=0, microsecond=0)
-            with _get_conn(self._db_path) as conn:
-                count = conn.execute(
-                    "SELECT COUNT(*) as c FROM thoughts WHERE created_at >= ?",
-                    (today_start.isoformat(),),
-                ).fetchone()["c"]
-            return f"Thoughts today: {count}"
-        except Exception as exc:
-            logger.debug("Thought count query failed: %s", exc)
-            return None
-
-    def _snap_chat_activity(self) -> list[str]:
-        """Return chat-activity lines (in-memory, no I/O)."""
-        try:
-            from infrastructure.chat_store import message_log
-
-            messages = message_log.all()
-            if messages:
-                last = messages[-1]
-                return [
-                    f"Chat messages this session: {len(messages)}",
-                    f'Last chat ({last.role}): "{last.content[:80]}"',
-                ]
-            return ["No chat messages this session"]
-        except Exception as exc:
-            logger.debug("Chat activity query failed: %s", exc)
-            return []
-
-    def _snap_task_queue(self) -> str | None:
-        """Return a one-line task queue summary, or *None*."""
-        try:
-            from swarm.task_queue.models import get_task_summary_for_briefing
-
-            s = get_task_summary_for_briefing()
-            running, pending = s.get("running", 0), s.get("pending_approval", 0)
-            done, failed = s.get("completed", 0), s.get("failed", 0)
-            if running or pending or done or failed:
-                return (
-                    f"Tasks: {running} running, {pending} pending, "
-                    f"{done} completed, {failed} failed"
-                )
-        except Exception as exc:
-            logger.debug("Task queue query failed: %s", exc)
-        return None
-
-    def _snap_workspace(self) -> list[str]:
-        """Return workspace-update lines (file-based Hermes comms)."""
-        try:
-            from timmy.workspace import workspace_monitor
-
-            updates = workspace_monitor.get_pending_updates()
-            lines: list[str] = []
-            new_corr = updates.get("new_correspondence")
-            if new_corr:
-                line_count = len([ln for ln in new_corr.splitlines() if ln.strip()])
-                lines.append(
-                    f"Workspace: {line_count} new correspondence entries (latest from: Hermes)"
-                )
-            new_inbox = updates.get("new_inbox_files", [])
-            if new_inbox:
-                files_str = ", ".join(new_inbox[:5])
-                if len(new_inbox) > 5:
-                    files_str += f", ... (+{len(new_inbox) - 5} more)"
-                lines.append(f"Workspace: {len(new_inbox)} new inbox files: {files_str}")
-            return lines
-        except Exception as exc:
-            logger.debug("Workspace check failed: %s", exc)
-            return []
-
     def _gather_system_snapshot(self) -> str:
         """Gather lightweight real system state for grounding thoughts in reality.
 
@@ -853,24 +767,83 @@ class ThinkingEngine:
         recent chat activity, and task queue status. Never crashes — every
         section is independently try/excepted.
         """
+        parts: list[str] = []
+
+        # Current local time
         now = datetime.now().astimezone()
         tz = now.strftime("%Z") or "UTC"
-
+        parts.append(
-        parts: list[str] = [
             f"Local time: {now.strftime('%I:%M %p').lstrip('0')} {tz}, {now.strftime('%A %B %d')}"
-        ]
+        )
 
-        thought_line = self._snap_thought_count(now)
+        # Thought count today (cheap DB query)
-        if thought_line:
+        try:
-            parts.append(thought_line)
+            today_start = now.replace(hour=0, minute=0, second=0, microsecond=0)
+            with _get_conn(self._db_path) as conn:
+                count = conn.execute(
+                    "SELECT COUNT(*) as c FROM thoughts WHERE created_at >= ?",
+                    (today_start.isoformat(),),
+                ).fetchone()["c"]
+            parts.append(f"Thoughts today: {count}")
+        except Exception as exc:
+            logger.debug("Thought count query failed: %s", exc)
+            pass
 
-        parts.extend(self._snap_chat_activity())
+        # Recent chat activity (in-memory, no I/O)
+        try:
+            from infrastructure.chat_store import message_log
 
-        task_line = self._snap_task_queue()
+            messages = message_log.all()
-        if task_line:
+            if messages:
-            parts.append(task_line)
+                parts.append(f"Chat messages this session: {len(messages)}")
+                last = messages[-1]
+                parts.append(f'Last chat ({last.role}): "{last.content[:80]}"')
+            else:
+                parts.append("No chat messages this session")
+        except Exception as exc:
+            logger.debug("Chat activity query failed: %s", exc)
+            pass
 
-        parts.extend(self._snap_workspace())
+        # Task queue (lightweight DB query)
+        try:
+            from swarm.task_queue.models import get_task_summary_for_briefing
+
+            summary = get_task_summary_for_briefing()
+            running = summary.get("running", 0)
+            pending = summary.get("pending_approval", 0)
+            done = summary.get("completed", 0)
+            failed = summary.get("failed", 0)
+            if running or pending or done or failed:
+                parts.append(
+                    f"Tasks: {running} running, {pending} pending, "
+                    f"{done} completed, {failed} failed"
+                )
+        except Exception as exc:
+            logger.debug("Task queue query failed: %s", exc)
+            pass
+
+        # Workspace updates (file-based communication with Hermes)
+        try:
+            from timmy.workspace import workspace_monitor
+
+            updates = workspace_monitor.get_pending_updates()
+            new_corr = updates.get("new_correspondence")
+            new_inbox = updates.get("new_inbox_files", [])
+
+            if new_corr:
+                # Count entries (assuming each entry starts with a timestamp or header)
+                line_count = len([line for line in new_corr.splitlines() if line.strip()])
+                parts.append(
+                    f"Workspace: {line_count} new correspondence entries (latest from: Hermes)"
+                )
+            if new_inbox:
+                files_str = ", ".join(new_inbox[:5])
+                if len(new_inbox) > 5:
+                    files_str += f", ... (+{len(new_inbox) - 5} more)"
+                parts.append(f"Workspace: {len(new_inbox)} new inbox files: {files_str}")
+        except Exception as exc:
+            logger.debug("Workspace check failed: %s", exc)
+            pass
 
         return "\n".join(parts) if parts else ""
 
@@ -1139,37 +1112,21 @@ class ThinkingEngine:
             lines.append(f"- [{thought.seed_type}] {snippet}")
         return "\n".join(lines)
 
-    _thinking_agent = None  # cached agent — avoids per-call resource leaks (#525)
-
     async def _call_agent(self, prompt: str) -> str:
         """Call Timmy's agent to generate a thought.
 
-        Reuses a cached agent with skip_mcp=True to avoid the cancel-scope
+        Creates a lightweight agent with skip_mcp=True to avoid the cancel-scope
         errors that occur when MCP stdio transports are spawned inside asyncio
-        background tasks (#72) and to prevent per-call resource leaks (httpx
+        background tasks (#72).  The thinking engine doesn't need Gitea or
-        clients, SQLite connections, model warmups) that caused the thinking
+        filesystem tools — it only needs the LLM.
-        loop to die every ~10 min (#525).
-
-        Individual calls are capped at 120 s so a hung Ollama never blocks
-        the scheduler indefinitely.
 
         Strips ``<think>`` tags from reasoning models (qwen3, etc.) so that
         downstream parsers (fact distillation, issue filing) receive clean text.
         """
-        import asyncio
+        from timmy.agent import create_timmy
-
-        if self._thinking_agent is None:
-            from timmy.agent import create_timmy
-
-            self._thinking_agent = create_timmy(skip_mcp=True)
-
-        try:
-            async with asyncio.timeout(120):
-                run = await self._thinking_agent.arun(prompt, stream=False)
-        except TimeoutError:
-            logger.warning("Thinking LLM call timed out after 120 s")
-            return ""
 
+        agent = create_timmy(skip_mcp=True)
+        run = await agent.arun(prompt, stream=False)
         raw = run.content if hasattr(run, "content") else str(run)
         return _THINK_TAG_RE.sub("", raw) if raw else raw
 

src/timmy/tools_intro/__init__.py

@@ -26,7 +26,7 @@ def get_system_info() -> dict[str, Any]:
         - python_version: Python version
         - platform: OS platform
         - model: Current Ollama model (queried from API)
-        - model_backend: Configured backend (ollama/grok/claude)
+        - model_backend: Configured backend (ollama/airllm/grok)
         - ollama_url: Ollama host URL
         - repo_root: Repository root path
         - grok_enabled: Whether GROK is enabled
@@ -127,48 +127,54 @@ def check_ollama_health() -> dict[str, Any]:
     return result
 
 
-def _hot_memory_info(repo_root: Path) -> dict[str, Any]:
+def get_memory_status() -> dict[str, Any]:
-    """Tier 1: Hot memory (MEMORY.md) status."""
+    """Get the status of Timmy's memory system.
+
+    Returns:
+        Dict with memory tier information
+    """
+    from config import settings
+
+    repo_root = Path(settings.repo_root)
+
+    # Check tier 1: Hot memory
     memory_md = repo_root / "MEMORY.md"
     tier1_exists = memory_md.exists()
     tier1_content = ""
     if tier1_exists:
-        tier1_content = memory_md.read_text()[:500]
+        tier1_content = memory_md.read_text()[:500]  # First 500 chars
 
-    info: dict[str, Any] = {
+    # Check tier 2: Vault
+    vault_path = repo_root / "memory" / "self"
+    tier2_exists = vault_path.exists()
+    tier2_files = []
+    if tier2_exists:
+        tier2_files = [f.name for f in vault_path.iterdir() if f.is_file()]
+
+    tier1_info: dict[str, Any] = {
         "exists": tier1_exists,
         "path": str(memory_md),
         "preview": " ".join(tier1_content[:200].split()) if tier1_content else None,
     }
     if tier1_exists:
         lines = memory_md.read_text().splitlines()
-        info["line_count"] = len(lines)
+        tier1_info["line_count"] = len(lines)
-        info["sections"] = [ln.lstrip("# ").strip() for ln in lines if ln.startswith("## ")]
+        tier1_info["sections"] = [ln.lstrip("# ").strip() for ln in lines if ln.startswith("## ")]
-    return info
-
-
-def _vault_info(repo_root: Path) -> dict[str, Any]:
-    """Tier 2: Vault (memory/ directory tree) status."""
-    vault_path = repo_root / "memory" / "self"
-    tier2_exists = vault_path.exists()
-    tier2_files = [f.name for f in vault_path.iterdir() if f.is_file()] if tier2_exists else []
 
+    # Vault — scan all subdirs under memory/
     vault_root = repo_root / "memory"
-    info: dict[str, Any] = {
+    vault_info: dict[str, Any] = {
         "exists": tier2_exists,
         "path": str(vault_path),
         "file_count": len(tier2_files),
         "files": tier2_files[:10],
     }
     if vault_root.exists():
-        info["directories"] = [d.name for d in vault_root.iterdir() if d.is_dir()]
+        vault_info["directories"] = [d.name for d in vault_root.iterdir() if d.is_dir()]
-        info["total_markdown_files"] = sum(1 for _ in vault_root.rglob("*.md"))
+        vault_info["total_markdown_files"] = sum(1 for _ in vault_root.rglob("*.md"))
-    return info
 
-
+    # Tier 3: Semantic memory row count
-def _semantic_memory_info(repo_root: Path) -> dict[str, Any]:
+    tier3_info: dict[str, Any] = {"available": False}
-    """Tier 3: Semantic memory (vector DB) status."""
-    info: dict[str, Any] = {"available": False}
     try:
         sem_db = repo_root / "data" / "memory.db"
         if sem_db.exists():
@@ -178,16 +184,14 @@ def _semantic_memory_info(repo_root: Path) -> dict[str, Any]:
                 ).fetchone()
                 if row and row[0]:
                     count = conn.execute("SELECT COUNT(*) FROM chunks").fetchone()
-                    info["available"] = True
+                    tier3_info["available"] = True
-                    info["vector_count"] = count[0] if count else 0
+                    tier3_info["vector_count"] = count[0] if count else 0
     except Exception as exc:
         logger.debug("Memory status query failed: %s", exc)
-    return info
+        pass
 
-
+    # Self-coding journal stats
-def _journal_info(repo_root: Path) -> dict[str, Any]:
+    journal_info: dict[str, Any] = {"available": False}
-    """Self-coding journal statistics."""
-    info: dict[str, Any] = {"available": False}
     try:
         journal_db = repo_root / "data" / "self_coding.db"
         if journal_db.exists():
@@ -199,7 +203,7 @@ def _journal_info(repo_root: Path) -> dict[str, Any]:
                 if rows:
                     counts = {r["outcome"]: r["cnt"] for r in rows}
                     total = sum(counts.values())
-                    info = {
+                    journal_info = {
                         "available": True,
                         "total_attempts": total,
                         "successes": counts.get("success", 0),
@@ -208,24 +212,13 @@ def _journal_info(repo_root: Path) -> dict[str, Any]:
                     }
     except Exception as exc:
         logger.debug("Journal stats query failed: %s", exc)
-    return info
+        pass
-
-
-def get_memory_status() -> dict[str, Any]:
-    """Get the status of Timmy's memory system.
-
-    Returns:
-        Dict with memory tier information
-    """
-    from config import settings
-
-    repo_root = Path(settings.repo_root)
 
     return {
-        "tier1_hot_memory": _hot_memory_info(repo_root),
+        "tier1_hot_memory": tier1_info,
-        "tier2_vault": _vault_info(repo_root),
+        "tier2_vault": vault_info,
-        "tier3_semantic": _semantic_memory_info(repo_root),
+        "tier3_semantic": tier3_info,
-        "self_coding_journal": _journal_info(repo_root),
+        "self_coding_journal": journal_info,
     }
 
 

tests/conftest.py

@@ -18,6 +18,7 @@ except ImportError:
 # agno is a core dependency (always installed) — do NOT stub it, or its
 # internal import chains break under xdist parallel workers.
 for _mod in [
+    "airllm",
     "mcp",
     "mcp.client",
     "mcp.client.stdio",

tests/dashboard/test_mobile_scenarios.py

@@ -10,10 +10,12 @@ Categories:
   M3xx  iOS keyboard & zoom prevention
   M4xx  HTMX robustness (double-submit, sync)
   M5xx  Safe-area / notch support
+  M6xx  AirLLM backend interface contract
 """
 
 import re
 from pathlib import Path
+from unittest.mock import AsyncMock, MagicMock, patch
 
 # ── helpers ───────────────────────────────────────────────────────────────────
 
@@ -204,3 +206,147 @@ def test_M505_dvh_units_used():
     """Dynamic viewport height (dvh) accounts for collapsing browser chrome."""
     css = _css()
     assert "dvh" in css
+
+
+# ── M6xx — AirLLM backend interface contract ──────────────────────────────────
+
+
+def test_M601_airllm_agent_has_run_method():
+    """TimmyAirLLMAgent must expose run() so the dashboard route can call it."""
+    from timmy.backends import TimmyAirLLMAgent
+
+    assert hasattr(TimmyAirLLMAgent, "run"), (
+        "TimmyAirLLMAgent is missing run() — dashboard will fail with AirLLM backend"
+    )
+
+
+def test_M602_airllm_run_returns_content_attribute():
+    """run() must return an object with a .content attribute (Agno RunResponse compat)."""
+    with patch("timmy.backends.is_apple_silicon", return_value=False):
+        from timmy.backends import TimmyAirLLMAgent
+
+        agent = TimmyAirLLMAgent(model_size="8b")
+
+    mock_model = MagicMock()
+    mock_tokenizer = MagicMock()
+    input_ids_mock = MagicMock()
+    input_ids_mock.shape = [1, 5]
+    mock_tokenizer.return_value = {"input_ids": input_ids_mock}
+    mock_tokenizer.decode.return_value = "Sir, affirmative."
+    mock_model.tokenizer = mock_tokenizer
+    mock_model.generate.return_value = [list(range(10))]
+    agent._model = mock_model
+
+    result = agent.run("test")
+    assert hasattr(result, "content"), "run() result must have a .content attribute"
+    assert isinstance(result.content, str)
+
+
+def test_M603_airllm_run_updates_history():
+    """run() must update _history so multi-turn context is preserved."""
+    with patch("timmy.backends.is_apple_silicon", return_value=False):
+        from timmy.backends import TimmyAirLLMAgent
+
+        agent = TimmyAirLLMAgent(model_size="8b")
+
+    mock_model = MagicMock()
+    mock_tokenizer = MagicMock()
+    input_ids_mock = MagicMock()
+    input_ids_mock.shape = [1, 5]
+    mock_tokenizer.return_value = {"input_ids": input_ids_mock}
+    mock_tokenizer.decode.return_value = "Acknowledged."
+    mock_model.tokenizer = mock_tokenizer
+    mock_model.generate.return_value = [list(range(10))]
+    agent._model = mock_model
+
+    assert len(agent._history) == 0
+    agent.run("hello")
+    assert len(agent._history) == 2
+    assert any("hello" in h for h in agent._history)
+
+
+def test_M604_airllm_print_response_delegates_to_run():
+    """print_response must use run() so both interfaces share one inference path."""
+    with patch("timmy.backends.is_apple_silicon", return_value=False):
+        from timmy.backends import RunResult, TimmyAirLLMAgent
+
+        agent = TimmyAirLLMAgent(model_size="8b")
+
+    with (
+        patch.object(agent, "run", return_value=RunResult(content="ok")) as mock_run,
+        patch.object(agent, "_render"),
+    ):
+        agent.print_response("hello", stream=True)
+
+    mock_run.assert_called_once_with("hello", stream=True)
+
+
+def test_M605_health_status_passes_model_to_template(client):
+    """Health status partial must receive the configured model name, not a hardcoded string."""
+    from config import settings
+
+    with patch(
+        "dashboard.routes.health.check_ollama",
+        new_callable=AsyncMock,
+        return_value=True,
+    ):
+        response = client.get("/health/status")
+    # Model name should come from settings, not be hardcoded
+    assert response.status_code == 200
+    model_short = settings.ollama_model.split(":")[0]
+    assert model_short in response.text
+
+
+# ── M7xx — XSS prevention ─────────────────────────────────────────────────────
+
+
+def _mobile_html() -> str:
+    """Read the mobile template source."""
+    path = Path(__file__).parent.parent.parent / "src" / "dashboard" / "templates" / "mobile.html"
+    return path.read_text()
+
+
+def _swarm_live_html() -> str:
+    """Read the swarm live template source."""
+    path = (
+        Path(__file__).parent.parent.parent / "src" / "dashboard" / "templates" / "swarm_live.html"
+    )
+    return path.read_text()
+
+
+def test_M701_mobile_chat_no_raw_message_interpolation():
+    """mobile.html must not interpolate ${message} directly into innerHTML — XSS risk."""
+    html = _mobile_html()
+    # The vulnerable pattern is `${message}` inside a template literal assigned to innerHTML
+    # After the fix, message must only appear via textContent assignment
+    assert "textContent = message" in html or "textContent=message" in html, (
+        "mobile.html still uses innerHTML + ${message} interpolation — XSS vulnerability"
+    )
+
+
+def test_M702_mobile_chat_user_input_not_in_innerhtml_template_literal():
+    """${message} must not appear inside a backtick string that is assigned to innerHTML."""
+    html = _mobile_html()
+    # Find all innerHTML += `...` blocks and verify none contain ${message}
+    blocks = re.findall(r"innerHTML\s*\+=?\s*`([^`]*)`", html, re.DOTALL)
+    for block in blocks:
+        assert "${message}" not in block, (
+            "innerHTML template literal still contains ${message} — XSS vulnerability"
+        )
+
+
+def test_M703_swarm_live_agent_name_not_interpolated_in_innerhtml():
+    """swarm_live.html must not put ${agent.name} inside innerHTML template literals."""
+    html = _swarm_live_html()
+    blocks = re.findall(r"innerHTML\s*=\s*agents\.map\([^;]+\)\.join\([^)]*\)", html, re.DOTALL)
+    assert len(blocks) == 0, (
+        "swarm_live.html still uses innerHTML=agents.map(…) with interpolated agent data — XSS vulnerability"
+    )
+
+
+def test_M704_swarm_live_uses_textcontent_for_agent_data():
+    """swarm_live.html must use textContent (not innerHTML) to set agent name/description."""
+    html = _swarm_live_html()
+    assert "textContent" in html, (
+        "swarm_live.html does not use textContent — agent data may be raw-interpolated into DOM"
+    )

tests/infrastructure/test_error_capture.py

@@ -5,14 +5,9 @@ from datetime import UTC, datetime, timedelta
 from unittest.mock import patch
 
 from infrastructure.error_capture import (
-    _create_bug_report,
     _dedup_cache,
-    _extract_traceback_info,
     _get_git_context,
     _is_duplicate,
-    _log_error_event,
-    _notify_bug_report,
-    _record_to_session,
     _stack_hash,
     capture_error,
 )
@@ -198,91 +193,3 @@ class TestCaptureError:
 
     def teardown_method(self):
         _dedup_cache.clear()
-
-
-class TestExtractTracebackInfo:
-    """Test _extract_traceback_info helper."""
-
-    def test_returns_three_tuple(self):
-        try:
-            raise ValueError("extract test")
-        except ValueError as e:
-            tb_str, affected_file, affected_line = _extract_traceback_info(e)
-        assert "ValueError" in tb_str
-        assert "extract test" in tb_str
-        assert affected_file.endswith(".py")
-        assert affected_line > 0
-
-    def test_file_points_to_raise_site(self):
-        try:
-            _make_exception()
-        except ValueError as e:
-            _, affected_file, _ = _extract_traceback_info(e)
-        assert "test_error_capture" in affected_file
-
-
-class TestLogErrorEvent:
-    """Test _log_error_event helper."""
-
-    def test_does_not_crash_on_missing_deps(self):
-        try:
-            raise RuntimeError("log test")
-        except RuntimeError as e:
-            _log_error_event(e, "test", "abc123", "file.py", 42, {"branch": "main"})
-
-
-class TestCreateBugReport:
-    """Test _create_bug_report helper."""
-
-    def test_does_not_crash_on_missing_deps(self):
-        try:
-            raise RuntimeError("report test")
-        except RuntimeError as e:
-            result = _create_bug_report(
-                e, "test", None, "abc123", "traceback...", "file.py", 42, {}
-            )
-        # May return None if swarm deps unavailable — that's fine
-        assert result is None or isinstance(result, str)
-
-    def test_with_context(self):
-        try:
-            raise RuntimeError("ctx test")
-        except RuntimeError as e:
-            result = _create_bug_report(e, "test", {"path": "/api"}, "abc", "tb", "f.py", 1, {})
-        assert result is None or isinstance(result, str)
-
-
-class TestNotifyBugReport:
-    """Test _notify_bug_report helper."""
-
-    def test_does_not_crash(self):
-        try:
-            raise RuntimeError("notify test")
-        except RuntimeError as e:
-            _notify_bug_report(e, "test")
-
-
-class TestRecordToSession:
-    """Test _record_to_session helper."""
-
-    def test_does_not_crash_without_recorder(self):
-        try:
-            raise RuntimeError("session test")
-        except RuntimeError as e:
-            _record_to_session(e, "test")
-
-    def test_calls_registered_recorder(self):
-        from infrastructure.error_capture import register_error_recorder
-
-        calls = []
-        register_error_recorder(lambda **kwargs: calls.append(kwargs))
-        try:
-            try:
-                raise RuntimeError("callback test")
-            except RuntimeError as e:
-                _record_to_session(e, "test_source")
-            assert len(calls) == 1
-            assert "RuntimeError" in calls[0]["error"]
-            assert calls[0]["context"] == "test_source"
-        finally:
-            register_error_recorder(None)

tests/timmy/test_agent.py

@@ -81,6 +81,7 @@ def test_create_timmy_respects_custom_ollama_url():
         mock_settings.ollama_url = custom_url
         mock_settings.ollama_num_ctx = 4096
         mock_settings.timmy_model_backend = "ollama"
+        mock_settings.airllm_model_size = "70b"
 
         from timmy.agent import create_timmy
 
@@ -90,6 +91,33 @@ def test_create_timmy_respects_custom_ollama_url():
         assert kwargs["host"] == custom_url
 
 
+# ── AirLLM path ──────────────────────────────────────────────────────────────
+
+
+def test_create_timmy_airllm_returns_airllm_agent():
+    """backend='airllm' must return a TimmyAirLLMAgent, not an Agno Agent."""
+    with patch("timmy.backends.is_apple_silicon", return_value=False):
+        from timmy.agent import create_timmy
+        from timmy.backends import TimmyAirLLMAgent
+
+        result = create_timmy(backend="airllm", model_size="8b")
+
+    assert isinstance(result, TimmyAirLLMAgent)
+
+
+def test_create_timmy_airllm_does_not_call_agno_agent():
+    """When using the airllm backend, Agno Agent should never be instantiated."""
+    with (
+        patch("timmy.agent.Agent") as MockAgent,
+        patch("timmy.backends.is_apple_silicon", return_value=False),
+    ):
+        from timmy.agent import create_timmy
+
+        create_timmy(backend="airllm", model_size="8b")
+
+    MockAgent.assert_not_called()
+
+
 def test_create_timmy_explicit_ollama_ignores_autodetect():
     """backend='ollama' must always use Ollama, even on Apple Silicon."""
     with (
@@ -113,6 +141,7 @@ def test_create_timmy_explicit_ollama_ignores_autodetect():
 def test_resolve_backend_explicit_takes_priority():
     from timmy.agent import _resolve_backend
 
+    assert _resolve_backend("airllm") == "airllm"
     assert _resolve_backend("ollama") == "ollama"
 
 
@@ -123,6 +152,39 @@ def test_resolve_backend_defaults_to_ollama_without_config():
     assert _resolve_backend(None) == "ollama"
 
 
+def test_resolve_backend_auto_uses_airllm_on_apple_silicon():
+    """'auto' on Apple Silicon with airllm stubbed → 'airllm'."""
+    with (
+        patch("timmy.backends.is_apple_silicon", return_value=True),
+        patch("timmy.agent.settings") as mock_settings,
+    ):
+        mock_settings.timmy_model_backend = "auto"
+        mock_settings.airllm_model_size = "70b"
+        mock_settings.ollama_model = "llama3.2"
+
+        from timmy.agent import _resolve_backend
+
+        assert _resolve_backend(None) == "airllm"
+
+
+def test_resolve_backend_auto_falls_back_on_non_apple():
+    """'auto' on non-Apple Silicon → 'ollama'."""
+    with (
+        patch("timmy.backends.is_apple_silicon", return_value=False),
+        patch("timmy.agent.settings") as mock_settings,
+    ):
+        mock_settings.timmy_model_backend = "auto"
+        mock_settings.airllm_model_size = "70b"
+        mock_settings.ollama_model = "llama3.2"
+
+        from timmy.agent import _resolve_backend
+
+        assert _resolve_backend(None) == "ollama"
+
+
+# ── _model_supports_tools ────────────────────────────────────────────────────
+
+
 def test_model_supports_tools_llama32_returns_false():
     """llama3.2 (3B) is too small for reliable tool calling."""
     from timmy.agent import _model_supports_tools
@@ -197,6 +259,7 @@ def test_create_timmy_includes_tools_for_large_model():
         mock_settings.ollama_url = "http://localhost:11434"
         mock_settings.ollama_num_ctx = 4096
         mock_settings.timmy_model_backend = "ollama"
+        mock_settings.airllm_model_size = "70b"
         mock_settings.telemetry_enabled = False
 
         from timmy.agent import create_timmy
@@ -381,150 +444,6 @@ def test_get_effective_ollama_model_walks_fallback_chain():
         assert result == "fb-2"
 
 
-# ── _build_tools_list ─────────────────────────────────────────────────────
-
-
-def test_build_tools_list_empty_when_tools_disabled():
-    """Small models get an empty tools list."""
-    from timmy.agent import _build_tools_list
-
-    result = _build_tools_list(use_tools=False, skip_mcp=False, model_name="llama3.2")
-    assert result == []
-
-
-def test_build_tools_list_includes_toolkit_when_enabled():
-    """Tool-capable models get the full toolkit."""
-    mock_toolkit = MagicMock()
-    with patch("timmy.agent.create_full_toolkit", return_value=mock_toolkit):
-        from timmy.agent import _build_tools_list
-
-        result = _build_tools_list(use_tools=True, skip_mcp=True, model_name="llama3.1")
-        assert mock_toolkit in result
-
-
-def test_build_tools_list_skips_mcp_when_flagged():
-    """skip_mcp=True must not call MCP factories."""
-    mock_toolkit = MagicMock()
-    with (
-        patch("timmy.agent.create_full_toolkit", return_value=mock_toolkit),
-        patch("timmy.mcp_tools.create_gitea_mcp_tools") as mock_gitea,
-        patch("timmy.mcp_tools.create_filesystem_mcp_tools") as mock_fs,
-    ):
-        from timmy.agent import _build_tools_list
-
-        _build_tools_list(use_tools=True, skip_mcp=True, model_name="llama3.1")
-        mock_gitea.assert_not_called()
-        mock_fs.assert_not_called()
-
-
-def test_build_tools_list_includes_mcp_when_not_skipped():
-    """skip_mcp=False should attempt MCP tool creation."""
-    mock_toolkit = MagicMock()
-    with (
-        patch("timmy.agent.create_full_toolkit", return_value=mock_toolkit),
-        patch("timmy.mcp_tools.create_gitea_mcp_tools", return_value=None) as mock_gitea,
-        patch("timmy.mcp_tools.create_filesystem_mcp_tools", return_value=None) as mock_fs,
-    ):
-        from timmy.agent import _build_tools_list
-
-        _build_tools_list(use_tools=True, skip_mcp=False, model_name="llama3.1")
-        mock_gitea.assert_called_once()
-        mock_fs.assert_called_once()
-
-
-# ── _build_prompt ─────────────────────────────────────────────────────────
-
-
-def test_build_prompt_includes_base_prompt():
-    """Prompt should always contain the base system prompt."""
-    from timmy.agent import _build_prompt
-
-    result = _build_prompt(use_tools=False, session_id="test")
-    assert "Timmy" in result
-
-
-def test_build_prompt_appends_memory_context():
-    """Memory context should be appended when available."""
-    mock_memory = MagicMock()
-    mock_memory.get_system_context.return_value = "User prefers dark mode."
-    with patch("timmy.memory_system.memory_system", mock_memory):
-        from timmy.agent import _build_prompt
-
-        result = _build_prompt(use_tools=True, session_id="test")
-        assert "GROUNDED CONTEXT" in result
-        assert "dark mode" in result
-
-
-def test_build_prompt_truncates_long_memory():
-    """Long memory context should be truncated."""
-    mock_memory = MagicMock()
-    mock_memory.get_system_context.return_value = "x" * 10000
-    with patch("timmy.memory_system.memory_system", mock_memory):
-        from timmy.agent import _build_prompt
-
-        result = _build_prompt(use_tools=False, session_id="test")
-        assert "[truncated]" in result
-
-
-def test_build_prompt_survives_memory_failure():
-    """Prompt should fall back to base when memory fails."""
-    mock_memory = MagicMock()
-    mock_memory.get_system_context.side_effect = RuntimeError("db locked")
-    with patch("timmy.memory_system.memory_system", mock_memory):
-        from timmy.agent import _build_prompt
-
-        result = _build_prompt(use_tools=True, session_id="test")
-        assert "Timmy" in result
-        # Memory context should NOT be appended (the db locked error was caught)
-        assert "db locked" not in result
-
-
-# ── _create_ollama_agent ──────────────────────────────────────────────────
-
-
-def test_create_ollama_agent_passes_correct_kwargs():
-    """_create_ollama_agent must pass the expected kwargs to Agent."""
-    with (
-        patch("timmy.agent.Agent") as MockAgent,
-        patch("timmy.agent.Ollama"),
-        patch("timmy.agent.SqliteDb"),
-        patch("timmy.agent._warmup_model", return_value=True),
-    ):
-        from timmy.agent import _create_ollama_agent
-
-        _create_ollama_agent(
-            db_file="test.db",
-            model_name="llama3.1",
-            tools_list=[MagicMock()],
-            full_prompt="test prompt",
-            use_tools=True,
-        )
-        kwargs = MockAgent.call_args.kwargs
-        assert kwargs["description"] == "test prompt"
-        assert kwargs["markdown"] is False
-
-
-def test_create_ollama_agent_none_tools_when_empty():
-    """Empty tools_list should pass tools=None to Agent."""
-    with (
-        patch("timmy.agent.Agent") as MockAgent,
-        patch("timmy.agent.Ollama"),
-        patch("timmy.agent.SqliteDb"),
-        patch("timmy.agent._warmup_model", return_value=True),
-    ):
-        from timmy.agent import _create_ollama_agent
-
-        _create_ollama_agent(
-            db_file="test.db",
-            model_name="llama3.2",
-            tools_list=[],
-            full_prompt="test prompt",
-            use_tools=False,
-        )
-        kwargs = MockAgent.call_args.kwargs
-        assert kwargs["tools"] is None
-
-
 def test_no_hardcoded_fallback_constants_in_agent():
     """agent.py must not define module-level DEFAULT_MODEL_FALLBACKS."""
     import timmy.agent as agent_mod

tests/timmy/test_backends.py

@@ -1,7 +1,10 @@
-"""Tests for src/timmy/backends.py — backend helpers and classes."""
+"""Tests for src/timmy/backends.py — AirLLM wrapper and helpers."""
 
+import sys
 from unittest.mock import MagicMock, patch
 
+import pytest
+
 # ── is_apple_silicon ──────────────────────────────────────────────────────────
 
 
@@ -35,6 +38,183 @@ def test_is_apple_silicon_false_on_intel_mac():
         assert is_apple_silicon() is False
 
 
+# ── airllm_available ─────────────────────────────────────────────────────────
+
+
+def test_airllm_available_true_when_stub_in_sys_modules():
+    # conftest already stubs 'airllm' — importable → True.
+    from timmy.backends import airllm_available
+
+    assert airllm_available() is True
+
+
+def test_airllm_available_false_when_not_importable():
+    # Temporarily remove the stub to simulate airllm not installed.
+    saved = sys.modules.pop("airllm", None)
+    try:
+        from timmy.backends import airllm_available
+
+        assert airllm_available() is False
+    finally:
+        if saved is not None:
+            sys.modules["airllm"] = saved
+
+
+# ── TimmyAirLLMAgent construction ────────────────────────────────────────────
+
+
+def test_airllm_agent_raises_on_unknown_size():
+    from timmy.backends import TimmyAirLLMAgent
+
+    with pytest.raises(ValueError, match="Unknown model size"):
+        TimmyAirLLMAgent(model_size="3b")
+
+
+def test_airllm_agent_uses_automodel_on_non_apple():
+    """Non-Apple-Silicon path uses AutoModel.from_pretrained."""
+    with patch("timmy.backends.is_apple_silicon", return_value=False):
+        from timmy.backends import TimmyAirLLMAgent
+
+        TimmyAirLLMAgent(model_size="8b")
+    # sys.modules["airllm"] is a MagicMock; AutoModel.from_pretrained was called.
+    assert sys.modules["airllm"].AutoModel.from_pretrained.called
+
+
+def test_airllm_agent_uses_mlx_on_apple_silicon():
+    """Apple Silicon path uses AirLLMMLX, not AutoModel."""
+    with patch("timmy.backends.is_apple_silicon", return_value=True):
+        from timmy.backends import TimmyAirLLMAgent
+
+        TimmyAirLLMAgent(model_size="8b")
+    assert sys.modules["airllm"].AirLLMMLX.called
+
+
+def test_airllm_agent_resolves_correct_model_id_for_70b():
+    with patch("timmy.backends.is_apple_silicon", return_value=False):
+        from timmy.backends import _AIRLLM_MODELS, TimmyAirLLMAgent
+
+        TimmyAirLLMAgent(model_size="70b")
+    sys.modules["airllm"].AutoModel.from_pretrained.assert_called_with(_AIRLLM_MODELS["70b"])
+
+
+# ── TimmyAirLLMAgent.print_response ──────────────────────────────────────────
+
+
+def _make_agent(model_size: str = "8b") -> "TimmyAirLLMAgent":  # noqa: F821
+    """Helper: create an agent with a fully mocked underlying model."""
+    with patch("timmy.backends.is_apple_silicon", return_value=False):
+        from timmy.backends import TimmyAirLLMAgent
+
+        agent = TimmyAirLLMAgent(model_size=model_size)
+
+    # Replace the underlying model with a clean mock that returns predictable output.
+    mock_model = MagicMock()
+    mock_tokenizer = MagicMock()
+    # tokenizer() returns a dict-like object with an "input_ids" tensor mock.
+    input_ids_mock = MagicMock()
+    input_ids_mock.shape = [1, 10]  # shape[1] = prompt token count = 10
+    token_dict = {"input_ids": input_ids_mock}
+    mock_tokenizer.return_value = token_dict
+    # generate() returns a list of token sequences.
+    mock_tokenizer.decode.return_value = "Sir, affirmative."
+    mock_model.tokenizer = mock_tokenizer
+    mock_model.generate.return_value = [list(range(15))]  # 15 tokens total
+    agent._model = mock_model
+    return agent
+
+
+def test_print_response_calls_generate():
+    agent = _make_agent()
+    agent.print_response("What is sovereignty?", stream=True)
+    agent._model.generate.assert_called_once()
+
+
+def test_print_response_decodes_only_generated_tokens():
+    agent = _make_agent()
+    agent.print_response("Hello", stream=False)
+    # decode should be called with tokens starting at index 10 (prompt length).
+    decode_call = agent._model.tokenizer.decode.call_args
+    token_slice = decode_call[0][0]
+    assert list(token_slice) == list(range(10, 15))
+
+
+def test_print_response_updates_history():
+    agent = _make_agent()
+    agent.print_response("First message")
+    assert any("First message" in turn for turn in agent._history)
+    assert any("Timmy:" in turn for turn in agent._history)
+
+
+def test_print_response_history_included_in_second_prompt():
+    agent = _make_agent()
+    agent.print_response("First")
+    # Build the prompt for the second call — history should appear.
+    prompt = agent._build_prompt("Second")
+    assert "First" in prompt
+    assert "Second" in prompt
+
+
+def test_print_response_stream_flag_accepted():
+    """stream=False should not raise — it's accepted for API compatibility."""
+    agent = _make_agent()
+    agent.print_response("hello", stream=False)  # no error
+
+
+# ── Prompt formatting tests ────────────────────────────────────────────────
+
+
+def test_airllm_prompt_contains_formatted_model_name():
+    """AirLLM prompt should have actual model name, not literal {model_name}."""
+    with (
+        patch("timmy.backends.is_apple_silicon", return_value=False),
+        patch("config.settings") as mock_settings,
+    ):
+        mock_settings.ollama_model = "llama3.2:3b"
+        from timmy.backends import TimmyAirLLMAgent
+
+        agent = TimmyAirLLMAgent(model_size="8b")
+        prompt = agent._build_prompt("test message")
+
+    # Should contain the actual model name, not the placeholder
+    assert "{model_name}" not in prompt
+    assert "llama3.2:3b" in prompt
+
+
+def test_airllm_prompt_gets_lite_tier():
+    """AirLLM should get LITE tier prompt (tools_enabled=False)."""
+    with (
+        patch("timmy.backends.is_apple_silicon", return_value=False),
+        patch("config.settings") as mock_settings,
+    ):
+        mock_settings.ollama_model = "test-model"
+        from timmy.backends import TimmyAirLLMAgent
+
+        agent = TimmyAirLLMAgent(model_size="8b")
+        prompt = agent._build_prompt("test message")
+
+    # LITE tier should NOT have TOOL USAGE section
+    assert "TOOL USAGE" not in prompt
+    # LITE tier should have the basic rules
+    assert "Be brief by default" in prompt
+
+
+def test_airllm_prompt_contains_session_id():
+    """AirLLM prompt should have session_id formatted, not placeholder."""
+    with (
+        patch("timmy.backends.is_apple_silicon", return_value=False),
+        patch("config.settings") as mock_settings,
+    ):
+        mock_settings.ollama_model = "test-model"
+        from timmy.backends import TimmyAirLLMAgent
+
+        agent = TimmyAirLLMAgent(model_size="8b")
+        prompt = agent._build_prompt("test message")
+
+    # Should contain the session_id, not the placeholder
+    assert '{session_id}"' not in prompt
+    assert 'session "airllm"' in prompt
+
+
 # ── ClaudeBackend ─────────────────────────────────────────────────────────
 
 

tests/timmy/test_cli.py

@@ -107,7 +107,19 @@ def test_chat_new_session_uses_unique_id():
 
 
 def test_chat_passes_backend_option():
-    pass
+    """chat --backend airllm must forward the backend to create_timmy."""
+    mock_run_output = MagicMock()
+    mock_run_output.content = "OK"
+    mock_run_output.status = "COMPLETED"
+    mock_run_output.active_requirements = []
+
+    mock_timmy = MagicMock()
+    mock_timmy.run.return_value = mock_run_output
+
+    with patch("timmy.cli.create_timmy", return_value=mock_timmy) as mock_create:
+        runner.invoke(app, ["chat", "test", "--backend", "airllm"])
+
+    mock_create.assert_called_once_with(backend="airllm", model_size=None, session_id="cli")
 
 
 def test_chat_cleans_response():

tests/timmy/test_thinking.py

@@ -250,6 +250,99 @@ def test_continuity_includes_recent(tmp_path):
 # ---------------------------------------------------------------------------
 
 
+# ---------------------------------------------------------------------------
+# _generate_thought helper
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_generate_thought_returns_content_and_seed_type(tmp_path):
+    """_generate_thought should return (content, seed_type) on success."""
+    from timmy.thinking import SEED_TYPES
+
+    engine = _make_engine(tmp_path)
+
+    with patch.object(engine, "_call_agent", return_value="A novel idea."):
+        content, seed_type = await engine._generate_thought()
+
+    assert content == "A novel idea."
+    assert seed_type in SEED_TYPES
+
+
+@pytest.mark.asyncio
+async def test_generate_thought_with_prompt(tmp_path):
+    """_generate_thought(prompt=...) should use 'prompted' seed type."""
+    engine = _make_engine(tmp_path)
+
+    with patch.object(engine, "_call_agent", return_value="A prompted idea."):
+        content, seed_type = await engine._generate_thought(prompt="Reflect on joy")
+
+    assert content == "A prompted idea."
+    assert seed_type == "prompted"
+
+
+@pytest.mark.asyncio
+async def test_generate_thought_returns_none_on_agent_failure(tmp_path):
+    """_generate_thought should return (None, ...) when the agent fails."""
+    engine = _make_engine(tmp_path)
+
+    with patch.object(engine, "_call_agent", side_effect=Exception("Ollama down")):
+        content, seed_type = await engine._generate_thought()
+
+    assert content is None
+
+
+@pytest.mark.asyncio
+async def test_generate_thought_returns_none_on_empty(tmp_path):
+    """_generate_thought should return (None, ...) when agent returns empty."""
+    engine = _make_engine(tmp_path)
+
+    with patch.object(engine, "_call_agent", return_value="   "):
+        content, seed_type = await engine._generate_thought()
+
+    assert content is None
+
+
+# ---------------------------------------------------------------------------
+# _finalize_thought helper
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_finalize_thought_calls_all_hooks(tmp_path):
+    """_finalize_thought should call all post-hooks, log, journal, and broadcast."""
+    engine = _make_engine(tmp_path)
+    thought = engine._store_thought("Test finalize.", "freeform")
+
+    with (
+        patch.object(engine, "_maybe_check_memory") as m_mem,
+        patch.object(engine, "_maybe_distill", new_callable=AsyncMock) as m_distill,
+        patch.object(engine, "_maybe_file_issues", new_callable=AsyncMock) as m_issues,
+        patch.object(engine, "_check_workspace", new_callable=AsyncMock) as m_ws,
+        patch.object(engine, "_maybe_check_memory_status") as m_status,
+        patch.object(engine, "_update_memory") as m_update,
+        patch.object(engine, "_log_event") as m_log,
+        patch.object(engine, "_write_journal") as m_journal,
+        patch.object(engine, "_broadcast", new_callable=AsyncMock) as m_broadcast,
+    ):
+        await engine._finalize_thought(thought)
+
+    m_mem.assert_called_once()
+    m_distill.assert_awaited_once()
+    m_issues.assert_awaited_once()
+    m_ws.assert_awaited_once()
+    m_status.assert_called_once()
+    m_update.assert_called_once_with(thought)
+    m_log.assert_called_once_with(thought)
+    m_journal.assert_called_once_with(thought)
+    m_broadcast.assert_awaited_once_with(thought)
+
+
+# ---------------------------------------------------------------------------
+# think_once (async)
+# ---------------------------------------------------------------------------
+
+
 @pytest.mark.asyncio
 async def test_think_once_stores_thought(tmp_path):
     """think_once should store a thought in the DB."""