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gemini/iss
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kimi/issue
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735bfc7820 |
@@ -197,6 +197,113 @@ def _resolve_backend(requested: str | None) -> str:
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return "ollama"
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def _build_tools_list(use_tools: bool, skip_mcp: bool) -> list:
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"""Build the Agno tools list (toolkit + optional MCP servers).
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Args:
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use_tools: Whether the model supports tool calling.
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skip_mcp: If True, omit MCP tool servers.
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Returns:
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List of Toolkit / MCPTools, possibly empty.
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"""
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if not use_tools:
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logger.info("Tools disabled (model too small for reliable tool calling)")
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return []
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toolkit = create_full_toolkit()
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tools_list: list = [toolkit]
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# Add MCP tool servers (lazy-connected on first arun()).
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# Skipped when skip_mcp=True — MCP's stdio transport uses anyio cancel
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# scopes that conflict with asyncio background task cancellation (#72).
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if not skip_mcp:
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try:
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from timmy.mcp_tools import create_filesystem_mcp_tools, create_gitea_mcp_tools
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gitea_mcp = create_gitea_mcp_tools()
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if gitea_mcp:
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tools_list.append(gitea_mcp)
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fs_mcp = create_filesystem_mcp_tools()
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if fs_mcp:
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tools_list.append(fs_mcp)
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except Exception as exc:
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logger.debug("MCP tools unavailable: %s", exc)
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return tools_list
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def _build_prompt(use_tools: bool, session_id: str) -> str:
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"""Build the full system prompt with optional memory context.
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Args:
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use_tools: Whether tools are enabled (affects prompt tier and context budget).
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session_id: Session identifier for the prompt.
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Returns:
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Complete system prompt string.
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"""
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base_prompt = get_system_prompt(tools_enabled=use_tools, session_id=session_id)
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try:
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from timmy.memory_system import memory_system
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memory_context = memory_system.get_system_context()
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if memory_context:
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# Truncate if too long — smaller budget for small models
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# since the expanded prompt (roster, guardrails) uses more tokens
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max_context = 2000 if not use_tools else 8000
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if len(memory_context) > max_context:
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memory_context = memory_context[:max_context] + "\n... [truncated]"
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return (
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f"{base_prompt}\n\n"
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f"## GROUNDED CONTEXT (verified sources — cite when using)\n\n"
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f"{memory_context}"
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)
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except Exception as exc:
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logger.warning("Failed to load memory context: %s", exc)
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return base_prompt
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def _create_ollama_agent(
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model_name: str,
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db_file: str,
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tools_list: list,
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full_prompt: str,
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use_tools: bool,
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) -> Agent:
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"""Construct the Agno Agent with an Ollama model.
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Args:
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model_name: Resolved Ollama model name.
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db_file: SQLite file for conversation memory.
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tools_list: Pre-built tools list (may be empty).
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full_prompt: Complete system prompt.
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use_tools: Whether tools are enabled.
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Returns:
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Configured Agno Agent.
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"""
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model_kwargs = {}
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if settings.ollama_num_ctx > 0:
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model_kwargs["options"] = {"num_ctx": settings.ollama_num_ctx}
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return Agent(
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name="Agent",
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model=Ollama(id=model_name, host=settings.ollama_url, timeout=300, **model_kwargs),
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db=SqliteDb(db_file=db_file),
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description=full_prompt,
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add_history_to_context=True,
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num_history_runs=20,
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markdown=False,
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tools=tools_list if tools_list else None,
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tool_call_limit=settings.max_agent_steps if use_tools else None,
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telemetry=settings.telemetry_enabled,
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)
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def create_timmy(
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db_file: str = "timmy.db",
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backend: str | None = None,
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@@ -238,16 +345,12 @@ def create_timmy(
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return TimmyAirLLMAgent(model_size=size)
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# Default: Ollama via Agno.
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# Resolve model with automatic pulling and fallback
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model_name, is_fallback = _resolve_model_with_fallback(
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requested_model=None,
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require_vision=False,
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auto_pull=True,
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)
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# If Ollama is completely unreachable, fail loudly.
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# Sovereignty: never silently send data to a cloud API.
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# Use --backend claude explicitly if you want cloud inference.
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if not _check_model_available(model_name):
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logger.error(
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"Ollama unreachable and no local models available. "
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@@ -258,74 +361,9 @@ def create_timmy(
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logger.info("Using fallback model %s (requested was unavailable)", model_name)
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use_tools = _model_supports_tools(model_name)
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# Conditionally include tools — small models get none
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toolkit = create_full_toolkit() if use_tools else None
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if not use_tools:
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logger.info("Tools disabled for model %s (too small for reliable tool calling)", model_name)
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# Build the tools list — Agno accepts a list of Toolkit / MCPTools
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tools_list: list = []
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if toolkit:
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tools_list.append(toolkit)
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# Add MCP tool servers (lazy-connected on first arun()).
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# Skipped when skip_mcp=True — MCP's stdio transport uses anyio cancel
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# scopes that conflict with asyncio background task cancellation (#72).
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if use_tools and not skip_mcp:
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try:
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from timmy.mcp_tools import create_filesystem_mcp_tools, create_gitea_mcp_tools
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gitea_mcp = create_gitea_mcp_tools()
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if gitea_mcp:
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tools_list.append(gitea_mcp)
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fs_mcp = create_filesystem_mcp_tools()
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if fs_mcp:
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tools_list.append(fs_mcp)
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except Exception as exc:
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logger.debug("MCP tools unavailable: %s", exc)
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# Select prompt tier based on tool capability
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base_prompt = get_system_prompt(tools_enabled=use_tools, session_id=session_id)
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# Try to load memory context
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try:
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from timmy.memory_system import memory_system
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memory_context = memory_system.get_system_context()
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if memory_context:
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# Truncate if too long — smaller budget for small models
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# since the expanded prompt (roster, guardrails) uses more tokens
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max_context = 2000 if not use_tools else 8000
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if len(memory_context) > max_context:
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memory_context = memory_context[:max_context] + "\n... [truncated]"
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full_prompt = (
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f"{base_prompt}\n\n"
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f"## GROUNDED CONTEXT (verified sources — cite when using)\n\n"
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f"{memory_context}"
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)
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else:
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full_prompt = base_prompt
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except Exception as exc:
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logger.warning("Failed to load memory context: %s", exc)
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full_prompt = base_prompt
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model_kwargs = {}
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if settings.ollama_num_ctx > 0:
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model_kwargs["options"] = {"num_ctx": settings.ollama_num_ctx}
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agent = Agent(
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name="Agent",
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model=Ollama(id=model_name, host=settings.ollama_url, timeout=300, **model_kwargs),
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db=SqliteDb(db_file=db_file),
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description=full_prompt,
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add_history_to_context=True,
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num_history_runs=20,
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markdown=False,
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tools=tools_list if tools_list else None,
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tool_call_limit=settings.max_agent_steps if use_tools else None,
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telemetry=settings.telemetry_enabled,
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)
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tools_list = _build_tools_list(use_tools, skip_mcp)
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full_prompt = _build_prompt(use_tools, session_id)
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agent = _create_ollama_agent(model_name, db_file, tools_list, full_prompt, use_tools)
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_warmup_model(model_name)
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return agent
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@@ -454,3 +454,127 @@ def test_no_hardcoded_fallback_constants_in_agent():
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assert not hasattr(agent_mod, "VISION_MODEL_FALLBACKS"), (
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"Hardcoded VISION_MODEL_FALLBACKS still exists — use settings.vision_fallback_models"
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)
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# ── _build_tools_list helper ─────────────────────────────────────────────────
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def test_build_tools_list_returns_empty_when_no_tools():
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"""When use_tools=False, _build_tools_list returns an empty list."""
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from timmy.agent import _build_tools_list
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result = _build_tools_list(use_tools=False, skip_mcp=False)
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assert result == []
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def test_build_tools_list_includes_toolkit():
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"""When use_tools=True, _build_tools_list includes the toolkit."""
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mock_toolkit = MagicMock()
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with patch("timmy.agent.create_full_toolkit", return_value=mock_toolkit):
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from timmy.agent import _build_tools_list
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result = _build_tools_list(use_tools=True, skip_mcp=True)
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assert mock_toolkit in result
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def test_build_tools_list_adds_mcp_when_not_skipped():
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"""When skip_mcp=False, _build_tools_list attempts MCP tools."""
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mock_toolkit = MagicMock()
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mock_gitea = MagicMock()
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with (
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patch("timmy.agent.create_full_toolkit", return_value=mock_toolkit),
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patch("timmy.mcp_tools.create_gitea_mcp_tools", return_value=mock_gitea),
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patch("timmy.mcp_tools.create_filesystem_mcp_tools", return_value=None),
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):
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from timmy.agent import _build_tools_list
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result = _build_tools_list(use_tools=True, skip_mcp=False)
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assert mock_toolkit in result
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assert mock_gitea in result
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# ── _build_prompt helper ─────────────────────────────────────────────────────
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def test_build_prompt_returns_base_when_no_memory():
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"""_build_prompt returns base prompt when memory context is empty."""
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with patch("timmy.memory_system.memory_system") as mock_mem:
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mock_mem.get_system_context.return_value = ""
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from timmy.agent import _build_prompt
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result = _build_prompt(use_tools=True, session_id="test")
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assert "Timmy" in result
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def test_build_prompt_appends_memory_context():
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"""_build_prompt appends memory context when available."""
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with patch("timmy.memory_system.memory_system") as mock_mem:
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mock_mem.get_system_context.return_value = "User likes pizza"
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from timmy.agent import _build_prompt
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result = _build_prompt(use_tools=True, session_id="test")
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assert "User likes pizza" in result
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assert "GROUNDED CONTEXT" in result
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def test_build_prompt_truncates_long_memory_for_small_models():
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"""_build_prompt truncates memory for small models (use_tools=False)."""
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long_context = "x" * 5000
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with patch("timmy.memory_system.memory_system") as mock_mem:
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mock_mem.get_system_context.return_value = long_context
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from timmy.agent import _build_prompt
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result = _build_prompt(use_tools=False, session_id="test")
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# Max context is 2000 for small models + truncation marker
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assert "[truncated]" in result
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# ── _create_ollama_agent helper ──────────────────────────────────────────────
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def test_create_ollama_agent_passes_correct_kwargs():
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"""_create_ollama_agent passes the expected kwargs to Agent()."""
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with (
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patch("timmy.agent.Agent") as MockAgent,
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patch("timmy.agent.Ollama"),
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patch("timmy.agent.SqliteDb"),
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):
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from timmy.agent import _create_ollama_agent
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_create_ollama_agent(
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model_name="test-model",
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db_file="test.db",
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tools_list=[MagicMock()],
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full_prompt="Test prompt",
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use_tools=True,
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)
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kwargs = MockAgent.call_args.kwargs
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assert kwargs["description"] == "Test prompt"
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assert kwargs["tools"] is not None
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def test_create_ollama_agent_none_tools_when_empty():
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"""_create_ollama_agent passes tools=None when tools_list is empty."""
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with (
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patch("timmy.agent.Agent") as MockAgent,
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patch("timmy.agent.Ollama"),
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patch("timmy.agent.SqliteDb"),
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):
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from timmy.agent import _create_ollama_agent
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_create_ollama_agent(
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model_name="test-model",
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db_file="test.db",
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tools_list=[],
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full_prompt="Test prompt",
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use_tools=False,
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)
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kwargs = MockAgent.call_args.kwargs
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assert kwargs["tools"] is None
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