refactor: break up create_timmy() into testable helpers

Extract three focused helpers from the 131-line create_timmy():
- _build_tools_list(): assembles toolkit + optional MCP servers
- _build_prompt(): builds system prompt with memory context
- _create_ollama_agent(): constructs the Agno Agent

create_timmy() now delegates to these helpers, making each
concern independently testable. Added 8 unit tests for the
new helpers.

Fixes #512

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

src/timmy/agent.py

@@ -197,6 +197,113 @@ def _resolve_backend(requested: str | None) -> str:
     return "ollama"
 
 
+def _build_tools_list(use_tools: bool, skip_mcp: bool) -> list:
+    """Build the Agno tools list (toolkit + optional MCP servers).
+
+    Args:
+        use_tools: Whether the model supports tool calling.
+        skip_mcp:  If True, omit MCP tool servers.
+
+    Returns:
+        List of Toolkit / MCPTools, possibly empty.
+    """
+    if not use_tools:
+        logger.info("Tools disabled (model too small for reliable tool calling)")
+        return []
+
+    toolkit = create_full_toolkit()
+    tools_list: list = [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:
+        try:
+            from timmy.mcp_tools import create_filesystem_mcp_tools, create_gitea_mcp_tools
+
+            gitea_mcp = create_gitea_mcp_tools()
+            if gitea_mcp:
+                tools_list.append(gitea_mcp)
+
+            fs_mcp = create_filesystem_mcp_tools()
+            if fs_mcp:
+                tools_list.append(fs_mcp)
+        except Exception as exc:
+            logger.debug("MCP tools unavailable: %s", exc)
+
+    return tools_list
+
+
+def _build_prompt(use_tools: bool, session_id: str) -> str:
+    """Build the full system prompt with optional memory context.
+
+    Args:
+        use_tools:  Whether tools are enabled (affects prompt tier and context budget).
+        session_id: Session identifier for the prompt.
+
+    Returns:
+        Complete system prompt string.
+    """
+    base_prompt = get_system_prompt(tools_enabled=use_tools, session_id=session_id)
+
+    try:
+        from timmy.memory_system import memory_system
+
+        memory_context = memory_system.get_system_context()
+        if memory_context:
+            # 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 (
+                f"{base_prompt}\n\n"
+                f"## GROUNDED CONTEXT (verified sources — cite when using)\n\n"
+                f"{memory_context}"
+            )
+    except Exception as exc:
+        logger.warning("Failed to load memory context: %s", exc)
+
+    return base_prompt
+
+
+def _create_ollama_agent(
+    model_name: str,
+    db_file: str,
+    tools_list: list,
+    full_prompt: str,
+    use_tools: bool,
+) -> Agent:
+    """Construct the Agno Agent with an Ollama model.
+
+    Args:
+        model_name: Resolved Ollama model name.
+        db_file:    SQLite file for conversation memory.
+        tools_list: Pre-built tools list (may be empty).
+        full_prompt: Complete system prompt.
+        use_tools:  Whether tools are enabled.
+
+    Returns:
+        Configured Agno Agent.
+    """
+    model_kwargs = {}
+    if settings.ollama_num_ctx > 0:
+        model_kwargs["options"] = {"num_ctx": settings.ollama_num_ctx}
+
+    return Agent(
+        name="Agent",
+        model=Ollama(id=model_name, host=settings.ollama_url, timeout=300, **model_kwargs),
+        db=SqliteDb(db_file=db_file),
+        description=full_prompt,
+        add_history_to_context=True,
+        num_history_runs=20,
+        markdown=False,
+        tools=tools_list if tools_list else None,
+        tool_call_limit=settings.max_agent_steps if use_tools else None,
+        telemetry=settings.telemetry_enabled,
+    )
+
+
 def create_timmy(
     db_file: str = "timmy.db",
     backend: str | None = None,
@@ -238,16 +345,12 @@ def create_timmy(
         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. "
@@ -258,74 +361,9 @@ def create_timmy(
         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)
-
-    # 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 use_tools and not skip_mcp:
-        try:
-            from timmy.mcp_tools import create_filesystem_mcp_tools, create_gitea_mcp_tools
-
-            gitea_mcp = create_gitea_mcp_tools()
-            if gitea_mcp:
-                tools_list.append(gitea_mcp)
-
-            fs_mcp = create_filesystem_mcp_tools()
-            if fs_mcp:
-                tools_list.append(fs_mcp)
-        except Exception as exc:
-            logger.debug("MCP tools unavailable: %s", exc)
-
-    # Select prompt tier based on tool capability
-    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:
-            # 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]"
-            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
-
-    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),
-        db=SqliteDb(db_file=db_file),
-        description=full_prompt,
-        add_history_to_context=True,
-        num_history_runs=20,
-        markdown=False,
-        tools=tools_list if tools_list else None,
-        tool_call_limit=settings.max_agent_steps if use_tools else None,
-        telemetry=settings.telemetry_enabled,
-    )
+    tools_list = _build_tools_list(use_tools, skip_mcp)
+    full_prompt = _build_prompt(use_tools, session_id)
+    agent = _create_ollama_agent(model_name, db_file, tools_list, full_prompt, use_tools)
     _warmup_model(model_name)
     return agent
 

src/timmy/thinking.py

@@ -232,6 +232,90 @@ 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"]]:
+        """Assemble the context needed for a thinking cycle.
+
+        Returns:
+            (memory_context, system_context, recent_thoughts)
+        """
+        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(
+        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):
+            if prompt:
+                seed_type = "prompted"
+                seed_context = f"Journal prompt: {prompt}"
+            else:
+                seed_type, seed_context = self._gather_seed()
+
+            continuity = self._build_continuity_context()
+
+            full_prompt = _THINKING_PROMPT.format(
+                memory_context=memory_context,
+                system_context=system_context,
+                seed_context=seed_context,
+                continuity_context=continuity,
+            )
+
+            try:
+                raw = await self._call_agent(full_prompt)
+            except Exception as exc:
+                logger.warning("Thinking cycle failed (Ollama likely down): %s", exc)
+                return None, seed_type
+
+            if not raw or not raw.strip():
+                logger.debug("Thinking cycle produced empty response, skipping")
+                return None, seed_type
+
+            content = raw.strip()
+
+            # Dedup: reject thoughts too similar to recent ones
+            if not self._is_too_similar(content, recent_thoughts):
+                return content, seed_type  # Good — novel thought
+
+            if attempt < self._MAX_DEDUP_RETRIES:
+                logger.info(
+                    "Thought too similar to recent (attempt %d/%d), retrying with new seed",
+                    attempt + 1,
+                    self._MAX_DEDUP_RETRIES + 1,
+                )
+            else:
+                logger.warning(
+                    "Thought still repetitive after %d retries, discarding",
+                    self._MAX_DEDUP_RETRIES + 1,
+                )
+                return None, seed_type
+
+        return None, seed_type
+
+    async def _process_thinking_result(self, thought: "Thought") -> None:
+        """Run all post-hooks after a thought is stored."""
+        self._maybe_check_memory()
+        await self._maybe_distill()
+        await self._maybe_file_issues()
+        await self._check_workspace()
+        self._maybe_check_memory_status()
+        self._update_memory(thought)
+        self._log_event(thought)
+        self._write_journal(thought)
+        await self._broadcast(thought)
+
     async def think_once(self, prompt: str | None = None) -> Thought | None:
         """Execute one thinking cycle.
 
@@ -257,91 +341,21 @@ class ThinkingEngine:
             )
             return None
 
-        memory_context = self._load_memory_context()
-        system_context = self._gather_system_snapshot()
-        recent_thoughts = self.get_recent_thoughts(limit=5)
-
-        content: str | None = None
-        seed_type: str = "freeform"
-
-        for attempt in range(self._MAX_DEDUP_RETRIES + 1):
-            if prompt:
-                seed_type = "prompted"
-                seed_context = f"Journal prompt: {prompt}"
-            else:
-                seed_type, seed_context = self._gather_seed()
-
-            continuity = self._build_continuity_context()
-
-            full_prompt = _THINKING_PROMPT.format(
-                memory_context=memory_context,
-                system_context=system_context,
-                seed_context=seed_context,
-                continuity_context=continuity,
-            )
-
-            try:
-                raw = await self._call_agent(full_prompt)
-            except Exception as exc:
-                logger.warning("Thinking cycle failed (Ollama likely down): %s", exc)
-                return None
-
-            if not raw or not raw.strip():
-                logger.debug("Thinking cycle produced empty response, skipping")
-                return None
-
-            content = raw.strip()
-
-            # Dedup: reject thoughts too similar to recent ones
-            if not self._is_too_similar(content, recent_thoughts):
-                break  # Good — novel thought
-
-            if attempt < self._MAX_DEDUP_RETRIES:
-                logger.info(
-                    "Thought too similar to recent (attempt %d/%d), retrying with new seed",
-                    attempt + 1,
-                    self._MAX_DEDUP_RETRIES + 1,
-                )
-                content = None  # Will retry
-            else:
-                logger.warning(
-                    "Thought still repetitive after %d retries, discarding",
-                    self._MAX_DEDUP_RETRIES + 1,
-                )
-                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
 
-        # Post-hook: check memory status periodically
-        self._maybe_check_memory()
-
-        # Post-hook: distill facts from recent thoughts periodically
-        await self._maybe_distill()
-
-        # Post-hook: file Gitea issues for actionable observations
-        await self._maybe_file_issues()
-
-        # Post-hook: check workspace for new messages from Hermes
-        await self._check_workspace()
-
-        # Post-hook: proactive memory status audit
-        self._maybe_check_memory_status()
-
-        # Post-hook: update MEMORY.md with latest reflection
-        self._update_memory(thought)
-
-        # Log to swarm event system
-        self._log_event(thought)
-
-        # Append to daily journal file
-        self._write_journal(thought)
-
-        # Broadcast to WebSocket clients
-        await self._broadcast(thought)
+        await self._process_thinking_result(thought)
 
         logger.info(
             "Thought [%s] (%s): %s",

tests/timmy/test_agent.py

@@ -454,3 +454,127 @@ def test_no_hardcoded_fallback_constants_in_agent():
     assert not hasattr(agent_mod, "VISION_MODEL_FALLBACKS"), (
         "Hardcoded VISION_MODEL_FALLBACKS still exists — use settings.vision_fallback_models"
     )
+
+
+# ── _build_tools_list helper ─────────────────────────────────────────────────
+
+
+def test_build_tools_list_returns_empty_when_no_tools():
+    """When use_tools=False, _build_tools_list returns an empty list."""
+    from timmy.agent import _build_tools_list
+
+    result = _build_tools_list(use_tools=False, skip_mcp=False)
+    assert result == []
+
+
+def test_build_tools_list_includes_toolkit():
+    """When use_tools=True, _build_tools_list includes the 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)
+
+    assert mock_toolkit in result
+
+
+def test_build_tools_list_adds_mcp_when_not_skipped():
+    """When skip_mcp=False, _build_tools_list attempts MCP tools."""
+    mock_toolkit = MagicMock()
+    mock_gitea = MagicMock()
+    with (
+        patch("timmy.agent.create_full_toolkit", return_value=mock_toolkit),
+        patch("timmy.mcp_tools.create_gitea_mcp_tools", return_value=mock_gitea),
+        patch("timmy.mcp_tools.create_filesystem_mcp_tools", return_value=None),
+    ):
+        from timmy.agent import _build_tools_list
+
+        result = _build_tools_list(use_tools=True, skip_mcp=False)
+
+    assert mock_toolkit in result
+    assert mock_gitea in result
+
+
+# ── _build_prompt helper ─────────────────────────────────────────────────────
+
+
+def test_build_prompt_returns_base_when_no_memory():
+    """_build_prompt returns base prompt when memory context is empty."""
+    with patch("timmy.memory_system.memory_system") as mock_mem:
+        mock_mem.get_system_context.return_value = ""
+        from timmy.agent import _build_prompt
+
+        result = _build_prompt(use_tools=True, session_id="test")
+
+    assert "Timmy" in result
+
+
+def test_build_prompt_appends_memory_context():
+    """_build_prompt appends memory context when available."""
+    with patch("timmy.memory_system.memory_system") as mock_mem:
+        mock_mem.get_system_context.return_value = "User likes pizza"
+        from timmy.agent import _build_prompt
+
+        result = _build_prompt(use_tools=True, session_id="test")
+
+    assert "User likes pizza" in result
+    assert "GROUNDED CONTEXT" in result
+
+
+def test_build_prompt_truncates_long_memory_for_small_models():
+    """_build_prompt truncates memory for small models (use_tools=False)."""
+    long_context = "x" * 5000
+    with patch("timmy.memory_system.memory_system") as mock_mem:
+        mock_mem.get_system_context.return_value = long_context
+        from timmy.agent import _build_prompt
+
+        result = _build_prompt(use_tools=False, session_id="test")
+
+    # Max context is 2000 for small models + truncation marker
+    assert "[truncated]" in result
+
+
+# ── _create_ollama_agent helper ──────────────────────────────────────────────
+
+
+def test_create_ollama_agent_passes_correct_kwargs():
+    """_create_ollama_agent passes the expected kwargs to Agent()."""
+    with (
+        patch("timmy.agent.Agent") as MockAgent,
+        patch("timmy.agent.Ollama"),
+        patch("timmy.agent.SqliteDb"),
+    ):
+        from timmy.agent import _create_ollama_agent
+
+        _create_ollama_agent(
+            model_name="test-model",
+            db_file="test.db",
+            tools_list=[MagicMock()],
+            full_prompt="Test prompt",
+            use_tools=True,
+        )
+
+        kwargs = MockAgent.call_args.kwargs
+        assert kwargs["description"] == "Test prompt"
+        assert kwargs["tools"] is not None
+
+
+def test_create_ollama_agent_none_tools_when_empty():
+    """_create_ollama_agent passes tools=None when tools_list is empty."""
+    with (
+        patch("timmy.agent.Agent") as MockAgent,
+        patch("timmy.agent.Ollama"),
+        patch("timmy.agent.SqliteDb"),
+    ):
+        from timmy.agent import _create_ollama_agent
+
+        _create_ollama_agent(
+            model_name="test-model",
+            db_file="test.db",
+            tools_list=[],
+            full_prompt="Test prompt",
+            use_tools=False,
+        )
+
+        kwargs = MockAgent.call_args.kwargs
+        assert kwargs["tools"] is None