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https://gitee.com/wanwujie/deer-flow
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feat(agent):Supports custom agent and chat experience with refactoring (#957)
* feat: add agent management functionality with creation, editing, and deletion * feat: enhance agent creation and chat experience - Added AgentWelcome component to display agent description on new thread creation. - Improved agent name validation with availability check during agent creation. - Updated NewAgentPage to handle agent creation flow more effectively, including enhanced error handling and user feedback. - Refactored chat components to streamline message handling and improve user experience. - Introduced new bootstrap skill for personalized onboarding conversations, including detailed conversation phases and a structured SOUL.md template. - Updated localization files to reflect new features and error messages. - General code cleanup and optimizations across various components and hooks. * Refactor workspace layout and agent management components - Updated WorkspaceLayout to use useLayoutEffect for sidebar state initialization. - Removed unused AgentFormDialog and related edit functionality from AgentCard. - Introduced ArtifactTrigger component to manage artifact visibility. - Enhanced ChatBox to handle artifact selection and display. - Improved message list rendering logic to avoid loading states. - Updated localization files to remove deprecated keys and add new translations. - Refined hooks for local settings and thread management to improve performance and clarity. - Added temporal awareness guidelines to deep research skill documentation. * feat: refactor chat components and introduce thread management hooks * feat: improve artifact file detail preview logic and clean up console logs * feat: refactor lead agent creation logic and improve logging details * feat: validate agent name format and enhance error handling in agent setup * feat: simplify thread search query by removing unnecessary metadata * feat: update query key in useDeleteThread and useRenameThread for consistency * feat: add isMock parameter to thread and artifact handling for improved testing * fix: reorder import of setup_agent for consistency in builtins module * feat: append mock parameter to thread links in CaseStudySection for testing purposes * fix: update load_agent_soul calls to use cfg.name for improved clarity * fix: update date format in apply_prompt_template for consistency * feat: integrate isMock parameter into artifact content loading for enhanced testing * docs: add license section to SKILL.md for clarity and attribution * feat(agent): enhance model resolution and agent configuration handling * chore: remove unused import of _resolve_model_name from agents * feat(agent): remove unused field * fix(agent): set default value for requested_model_name in _resolve_model_name function * feat(agent): update get_available_tools call to handle optional agent_config and improve middleware function signature --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
This commit is contained in:
JeffJiang
@@ -14,6 +14,7 @@ from src.agents.middlewares.title_middleware import TitleMiddleware
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from src.agents.middlewares.uploads_middleware import UploadsMiddleware
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from src.agents.middlewares.view_image_middleware import ViewImageMiddleware
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from src.agents.thread_state import ThreadState
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from src.config.agents_config import load_agent_config
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from src.config.app_config import get_app_config
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from src.config.summarization_config import get_summarization_config
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from src.models import create_chat_model
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@@ -22,14 +23,12 @@ from src.sandbox.middleware import SandboxMiddleware
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logger = logging.getLogger(__name__)
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def _resolve_model_name(requested_model_name: str | None) -> str:
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def _resolve_model_name(requested_model_name: str | None = None) -> str:
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"""Resolve a runtime model name safely, falling back to default if invalid. Returns None if no models are configured."""
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app_config = get_app_config()
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default_model_name = app_config.models[0].name if app_config.models else None
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if default_model_name is None:
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raise ValueError(
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"No chat models are configured. Please configure at least one model in config.yaml."
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)
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raise ValueError("No chat models are configured. Please configure at least one model in config.yaml.")
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if requested_model_name and app_config.get_model_config(requested_model_name):
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return requested_model_name
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@@ -205,11 +204,12 @@ Being proactive with task management demonstrates thoroughness and ensures all r
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# MemoryMiddleware queues conversation for memory update (after TitleMiddleware)
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# ViewImageMiddleware should be before ClarificationMiddleware to inject image details before LLM
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# ClarificationMiddleware should be last to intercept clarification requests after model calls
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def _build_middlewares(config: RunnableConfig, model_name: str | None):
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def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_name: str | None = None):
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"""Build middleware chain based on runtime configuration.
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Args:
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config: Runtime configuration containing configurable options like is_plan_mode.
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agent_name: If provided, MemoryMiddleware will use per-agent memory storage.
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Returns:
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List of middleware instances.
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@@ -231,7 +231,7 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None):
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middlewares.append(TitleMiddleware())
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# Add MemoryMiddleware (after TitleMiddleware)
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middlewares.append(MemoryMiddleware())
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middlewares.append(MemoryMiddleware(agent_name=agent_name))
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# Add ViewImageMiddleware only if the current model supports vision.
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# Use the resolved runtime model_name from make_lead_agent to avoid stale config values.
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@@ -254,28 +254,36 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None):
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def make_lead_agent(config: RunnableConfig):
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# Lazy import to avoid circular dependency
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from src.tools import get_available_tools
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from src.tools.builtins import setup_agent
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thinking_enabled = config.get("configurable", {}).get("thinking_enabled", True)
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reasoning_effort = config.get("configurable", {}).get("reasoning_effort", None)
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requested_model_name = config.get("configurable", {}).get("model_name") or config.get("configurable", {}).get("model")
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model_name = _resolve_model_name(requested_model_name)
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if model_name is None:
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raise ValueError(
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"No chat model could be resolved. Please configure at least one model in "
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"config.yaml or provide a valid 'model_name'/'model' in the request."
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)
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requested_model_name: str | None = config.get("configurable", {}).get("model_name") or config.get("configurable", {}).get("model")
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is_plan_mode = config.get("configurable", {}).get("is_plan_mode", False)
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subagent_enabled = config.get("configurable", {}).get("subagent_enabled", False)
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max_concurrent_subagents = config.get("configurable", {}).get("max_concurrent_subagents", 3)
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is_bootstrap = config.get("configurable", {}).get("is_bootstrap", False)
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agent_name = config.get("configurable", {}).get("agent_name")
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agent_config = load_agent_config(agent_name)
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# Custom agent model or fallback to global/default model resolution
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agent_model_name = agent_config.model if agent_config and agent_config.model else _resolve_model_name()
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# Final model name resolution with request override, then agent config, then global default
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model_name = requested_model_name or agent_model_name
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app_config = get_app_config()
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model_config = app_config.get_model_config(model_name) if model_name else None
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if thinking_enabled and model_config is not None and not model_config.supports_thinking:
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if model_config is None:
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raise ValueError("No chat model could be resolved. Please configure at least one model in config.yaml or provide a valid 'model_name'/'model' in the request.")
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if thinking_enabled and not model_config.supports_thinking:
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logger.warning(f"Thinking mode is enabled but model '{model_name}' does not support it; fallback to non-thinking mode.")
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thinking_enabled = False
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logger.info(
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"thinking_enabled: %s, reasoning_effort: %s, model_name: %s, is_plan_mode: %s, subagent_enabled: %s, max_concurrent_subagents: %s",
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"Create Agent(%s) -> thinking_enabled: %s, reasoning_effort: %s, model_name: %s, is_plan_mode: %s, subagent_enabled: %s, max_concurrent_subagents: %s",
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agent_name or "default",
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thinking_enabled,
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reasoning_effort,
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model_name,
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@@ -287,8 +295,10 @@ def make_lead_agent(config: RunnableConfig):
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# Inject run metadata for LangSmith trace tagging
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if "metadata" not in config:
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config["metadata"] = {}
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config["metadata"].update(
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{
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"agent_name": agent_name or "default",
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"model_name": model_name or "default",
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"thinking_enabled": thinking_enabled,
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"reasoning_effort": reasoning_effort,
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@@ -297,10 +307,23 @@ def make_lead_agent(config: RunnableConfig):
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}
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)
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if is_bootstrap:
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# Special bootstrap agent with minimal prompt for initial custom agent creation flow
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system_prompt = apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"]))
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return create_agent(
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model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
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tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled) + [setup_agent],
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middleware=_build_middlewares(config, model_name=model_name),
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system_prompt=system_prompt,
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state_schema=ThreadState,
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)
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# Default lead agent (unchanged behavior)
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return create_agent(
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model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort),
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tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled),
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middleware=_build_middlewares(config, model_name=model_name),
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system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents),
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tools=get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled),
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middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name),
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system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, agent_name=agent_name),
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state_schema=ThreadState,
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)
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