package vip.mate.agent; // PR-0b: DashScope imports moved with the construction code into DashScopeChatModelBuilder. import com.alibaba.cloud.ai.graph.CompiledGraph; import com.alibaba.cloud.ai.graph.CompileConfig; import com.alibaba.cloud.ai.graph.KeyStrategy; import com.alibaba.cloud.ai.graph.KeyStrategyFactory; import com.alibaba.cloud.ai.graph.StateGraph; import com.alibaba.cloud.ai.graph.action.AsyncEdgeAction; import com.alibaba.cloud.ai.graph.action.AsyncNodeAction; import lombok.RequiredArgsConstructor; import lombok.extern.slf4j.Slf4j; import org.springframework.ai.chat.client.ChatClient; import org.springframework.ai.chat.model.ChatModel; import org.springframework.ai.tool.ToolCallback; import org.springframework.retry.support.RetryTemplate; import org.springframework.stereotype.Component; import vip.mate.agent.graph.StateGraphReActAgent; import vip.mate.agent.graph.NodeStreamingChatHelper; import vip.mate.agent.graph.executor.ToolExecutionExecutor; import vip.mate.agent.graph.edge.ObservationDispatcher; import vip.mate.agent.graph.edge.ReasoningDispatcher; import vip.mate.agent.graph.lifecycle.ReActLifecycleListener; import vip.mate.agent.graph.node.*; import vip.mate.agent.graph.state.MateClawStateAccessor; import vip.mate.agent.graph.observation.ObservationProcessor; import vip.mate.agent.graph.plan.StateGraphPlanExecuteAgent; import vip.mate.agent.graph.plan.edge.PlanGenerationDispatcher; import vip.mate.agent.graph.plan.edge.StepProgressDispatcher; import vip.mate.agent.graph.plan.node.*; import vip.mate.agent.graph.plan.state.PlanStateKeys; import vip.mate.agent.graph.state.MateClawStateKeys; import vip.mate.agent.binding.service.AgentBindingService; import vip.mate.agent.model.AgentEntity; import org.springframework.beans.factory.annotation.Autowired; import vip.mate.config.GraphObservationProperties; import vip.mate.config.ReasoningRetentionProperties; import vip.mate.exception.MateClawException; import vip.mate.llm.chatmodel.HttpTimeouts; import vip.mate.llm.chatmodel.OpenAiCompatibleChatModelBuilder; import vip.mate.llm.chatmodel.ProviderGenerateKwargs; import vip.mate.llm.chatmodel.ReasoningEffortResolver; import vip.mate.llm.model.ModelConfigEntity; import vip.mate.llm.model.ModelFamily; import vip.mate.llm.model.ModelProtocol; import vip.mate.agent.context.PrefixBudgetPlan; import vip.mate.agent.context.PrefixBudgetPlanner; import vip.mate.agent.context.TokenEstimator; import vip.mate.llm.model.ModelProviderEntity; import vip.mate.llm.probe.ModelContextWindowResolver; import vip.mate.llm.routing.ProviderModelRef; import vip.mate.llm.routing.ProviderRouter; import vip.mate.llm.service.ModelConfigService; import vip.mate.llm.service.ModelProviderService; import vip.mate.planning.service.PlanningService; import vip.mate.skill.runtime.SkillCatalogRenderer; import vip.mate.skill.service.SkillService; import vip.mate.system.service.SystemSettingService; import vip.mate.tool.ToolRegistry; import vip.mate.tool.disclosure.ToolUsageRecencyTracker; import vip.mate.tool.mcp.runtime.McpProgressContext; import vip.mate.memory.spi.MemoryManager; import vip.mate.workspace.document.WorkspaceFileService; import vip.mate.tool.guard.service.ToolGuardService; import vip.mate.workspace.conversation.ConversationService; import vip.mate.approval.ApprovalWorkflowService; import vip.mate.channel.web.ChatStreamTracker; import vip.mate.team.service.TeamContextBuilder; import vip.mate.team.service.TeamPlanBridge; import vip.mate.wiki.service.WikiContextService; import java.lang.reflect.Field; import java.nio.file.Path; import java.nio.file.Paths; import java.util.ArrayList; import java.util.LinkedHashMap; import java.util.List; import java.util.Map; import java.util.Set; /** * Agent 图构建器 *

* 纯构建器，不做执行。从 AgentService 中提取出所有 Agent 实例构建逻辑， * 包括模型创建、图编译、prompt 增强等。 * * @author MateClaw Team */ @Slf4j @Component @RequiredArgsConstructor public class AgentGraphBuilder { private final ToolRegistry toolRegistry; private final AgentBindingService agentBindingService; private final SkillService skillService; private final vip.mate.skill.runtime.SkillRuntimeService skillRuntimeService; private final vip.mate.tool.disclosure.ToolDisclosureService toolDisclosureService; private final vip.mate.agent.progress.ProgressLedgerService progressLedgerService; /** Escape hatch: when false, the load_skill meta tool is not advertised. */ @org.springframework.beans.factory.annotation.Value( "${mateclaw.skill.disclosure.load-skill-tool.enabled:true}") private boolean loadSkillToolEnabled; /** Escape hatch: when false, the final answer is sent verbatim without Markdown normalization. */ @org.springframework.beans.factory.annotation.Value( "${mate.agent.markdown-normalize-enabled:true}") private boolean markdownNormalizeEnabled; private final ConversationService conversationService; private final TeamContextBuilder teamContextBuilder; private final TeamPlanBridge teamPlanBridge; private final ModelConfigService modelConfigService; private final ModelProviderService modelProviderService; private final ModelContextWindowResolver contextWindowResolver; private final PrefixBudgetPlanner prefixBudgetPlanner; private final ToolUsageRecencyTracker toolUsageRecencyTracker; private final McpProgressContext progressContext; private final vip.mate.llm.service.ModelCapabilityService modelCapabilityService; private final ProviderRouter providerRouter; private final PlanningService planningService; private final ToolGuardService toolGuardService; private final vip.mate.tool.guard.service.ToolGuardConfigService toolGuardConfigService; private final ApprovalWorkflowService approvalService; private final ChatStreamTracker streamTracker; private final SystemSettingService systemSettingService; // PR-0b: dashScopeChatModel + dashScopeConnectionProperties live on DashScopeChatModelBuilder now. private final RetryTemplate retryTemplate; private final GraphObservationProperties graphObservationProperties; private final vip.mate.config.ToolTimeoutProperties toolTimeoutProperties; private final MemoryManager memoryManager; private final WorkspaceFileService workspaceFileService; private final vip.mate.agent.context.ConversationWindowManager conversationWindowManager; private final vip.mate.llm.chatgpt.ChatGPTResponsesClient chatGPTResponsesClient; private final WikiContextService wikiContextService; private final vip.mate.workspace.core.service.WorkspaceService workspaceService; private final vip.mate.llm.cache.AnthropicCacheOptionsFactory anthropicCacheOptionsFactory; private final vip.mate.llm.cache.LlmCacheMetricsAggregator llmCacheMetricsAggregator; private final vip.mate.agent.graph.executor.ToolResultStorage toolResultStorage; private final vip.mate.tool.ToolConcurrencyRegistry toolConcurrencyRegistry; private final vip.mate.i18n.I18nService i18nService; private final vip.mate.llm.failover.ProviderHealthTracker providerHealthTracker; private final vip.mate.llm.chatmodel.ProviderChatModelFactory chatModelFactory; private final vip.mate.llm.failover.AvailableProviderPool providerPool; private final vip.mate.tool.document.GeneratedFileCache generatedFileCache; /** DashScope-specific construction lives here; only called for the built-in-search log. */ private final vip.mate.llm.chatmodel.DashScopeChatModelBuilder dashScopeBuilder; private final vip.mate.llm.routing.MultimodalRouter multimodalRouter; private final vip.mate.llm.routing.MediaCaptionService mediaCaptionService; private final vip.mate.goal.service.GoalService goalService; private final vip.mate.goal.service.GoalEvaluationService goalEvaluationService; private final vip.mate.goal.service.GoalFollowupService goalFollowupService; private final vip.mate.goal.config.GoalProperties goalProperties; /** C4: per-conversation environment notification registry, injected into ReasoningNode. */ private final vip.mate.agent.runtime.RunningConversationRegistry runningConversationRegistry; /** * Auto-grant resolver wired into the executor so an active * {@code mate_approval_grant} row can skip {@code createPending()} for matching * tool calls. Together with {@link #workspaceLookupCache}, these two deps form * the auto-grant entry point; the executor's null-guard turns the feature off * cleanly if either is missing. */ private final vip.mate.approval.grant.service.ApprovalGrantResolver approvalGrantResolver; /** Conversation→workspaceId lookup cache; see {@link #approvalGrantResolver}. */ private final vip.mate.approval.grant.WorkspaceLookupCache workspaceLookupCache; /** * Optional audit pipeline. Setter injection (rather than a constructor * parameter) keeps existing constructor-based wiring + tests intact. * When present, the executor receives it so child-agent denied-tool * attempts can be recorded. */ private vip.mate.audit.service.AuditEventService auditEventService; @Autowired(required = false) public void setAuditEventService(vip.mate.audit.service.AuditEventService s) { this.auditEventService = s; } /** * Reasoning retention policy for ReAct turns. Setter injection so the * {@code @RequiredArgsConstructor} signature stays stable for the unit * constructions across the test suite; null in those, where the agent's own * default (keep every iteration) applies. */ private ReasoningRetentionProperties reasoningRetentionProperties; @Autowired(required = false) public void setReasoningRetentionProperties(ReasoningRetentionProperties p) { this.reasoningRetentionProperties = p; } /** * Optional per-step delegation dependencies for the Plan-Execute graph. * Setter injection (like {@link #auditEventService}) breaks the * {@code AgentService ⇆ AgentGraphBuilder} construction cycle. Null when not * wired (legacy / test) — per-step delegation is then simply disabled. */ private AgentService agentService; // @Lazy on the injection point: inject a lazy-resolution proxy so the // AgentService ⇆ AgentGraphBuilder cycle is broken at bean-creation time // (the real bean is resolved on first use, when the graph is built). @org.springframework.beans.factory.annotation.Autowired(required = false) public void setAgentService(@org.springframework.context.annotation.Lazy AgentService agentService) { this.agentService = agentService; } private vip.mate.tool.builtin.DelegateAgentTool delegateAgentTool; @org.springframework.beans.factory.annotation.Autowired(required = false) public void setDelegateAgentTool( @org.springframework.context.annotation.Lazy vip.mate.tool.builtin.DelegateAgentTool delegateAgentTool) { this.delegateAgentTool = delegateAgentTool; } /** * 根据 AgentEntity 构建完整的 Agent 实例（沿用 Agent / 全局默认模型）。 */ public BaseAgent build(AgentEntity entity) { return build(entity, null, null); } /** * Resolve the model the runtime should use, honouring the precedence * conversation pin > Agent model override > global default. * A conversation pin that no longer resolves to an enabled model (the model * was disabled or deleted after it was picked) silently degrades to the * Agent / global default rather than failing the chat. */ private ModelConfigEntity resolveRuntimeBaseModel(String modelProvider, String modelName, String agentModelName) { if (modelProvider != null && !modelProvider.isBlank() && modelName != null && !modelName.isBlank()) { ModelConfigEntity pinned = modelConfigService.findEnabledModel(modelProvider, modelName); if (pinned != null) { return pinned; } log.info("Conversation model pin {}/{} is no longer an enabled model — " + "falling back to the Agent / global default", modelProvider, modelName); } return modelConfigService.resolveModel(agentModelName); } /** * True iff the caller passed a complete (provider, model) pin AND that * pair resolves to an enabled model row. Used by {@link #build} to decide * whether the explicit pick should bypass capability-driven routing. */ private boolean pinResolvesToEnabledModel(String modelProvider, String modelName) { if (modelProvider == null || modelProvider.isBlank() || modelName == null || modelName.isBlank()) { return false; } try { return modelConfigService.findEnabledModel(modelProvider, modelName) != null; } catch (Exception e) { return false; } } /** * True when the Agent declared its own modelName and that name resolved to * a real enabled row (rather than silently falling back to the system default). */ private boolean agentModelOverrideResolved(AgentEntity entity, ModelConfigEntity resolved) { if (entity == null || resolved == null) return false; String agentModelName = entity.getModelName(); if (agentModelName == null || agentModelName.isBlank()) return false; return agentModelName.equalsIgnoreCase(resolved.getModelName()); } /** * 根据 AgentEntity 构建完整的 Agent 实例。 * *

{@code modelProvider} / {@code modelName} carry an optional * per-conversation model pin; when both are blank the build falls back to * the Agent's model override, then the global default.

*/ public BaseAgent build(AgentEntity entity, String modelProvider, String modelName) { AgentToolSet toolSet = toolRegistry.getEnabledToolSet(); // Move 6 — Permission flattening at build time. // // Two layers of tool filtering exist in MateClaw: // (1) Build-time filter (HERE) — decides which tools the model SEES // in the tool list. Computed once per agent build; stable for // the agent's lifecycle unless bindings change. // (2) Runtime guard (ToolGuardService.evaluate) — decides which // tools the model can CALL. Runs on every invocation; checks // workspace boundaries, sensitive paths, credential exposure, // shell command patterns, and approval workflows. All dynamic // (depends on tool arguments, not just tool name). // // The build-time filter previously ran as 4 separate passes // (deny → allow → deny → exclude). Move 6 consolidates them into // a single deny-set + a single allow-set, applied in two passes: // denied = global denied ∪ skill-discovery denied ∪ {load_skill if disabled} // allowed = agent's bound tools (null = global default) Set deniedTools = new java.util.LinkedHashSet<>( toolGuardConfigService.getDeniedTools()); deniedTools.addAll(agentBindingService.getSkillDiscoveryDeniedTools(entity.getId())); if (!loadSkillToolEnabled) { deniedTools.add("load_skill"); } toolSet = toolSet.withDeniedToolsFiltered(deniedTools); // RFC-090 §14.2 — single entry point that merges: // (a) tools expanded from bound skills' active features, and // (b) directly bound atomic tools (the Advanced bypass, §9.2 调整 B). // Three-state semantics: null = no agent-level restriction (use // global default); non-null (possibly empty) = explicit allowlist. Set boundTools = agentBindingService.getEffectiveToolNames(entity.getId()); toolSet = toolSet.withAllowedToolsOnly(boundTools); // null = 全局默认 // Resolve the base model with the precedence: per-conversation pin > // per-Agent model override > global default. resolveRuntimeBaseModel // looks up enabled-only models and silently degrades an unmatched pin / // override to the global default, preserving the legacy behaviour for // Agents and conversations without an explicit choice. ModelConfigEntity globalDefault; boolean explicitPinHonoured; boolean agentOverrideHonoured; try { explicitPinHonoured = pinResolvesToEnabledModel(modelProvider, modelName); globalDefault = resolveRuntimeBaseModel(modelProvider, modelName, entity.getModelName()); agentOverrideHonoured = !explicitPinHonoured && agentModelOverrideResolved(entity, globalDefault); } catch (Exception e) { throw new MateClawException("err.agent.no_default_model", "无法构建 Agent：请先在「设置 → 模型」中配置并启用默认模型"); } ModelConfigEntity runtimeModel; if (explicitPinHonoured || agentOverrideHonoured) { // The caller (admin UI / chat console) handed us a concrete // (provider, model) pin and it points to an enabled row. Honour // it verbatim — running providerRouter.selectPrimary here would // silently swap to a different model whenever a bound skill // advertised a capability gap, which is exactly the "I switched // model but the agent kept using the old one" surface. The // diagnostic below still surfaces capability gaps in the logs // so operators can see if the pinned model misses a need. runtimeModel = globalDefault; } else { try { runtimeModel = providerRouter.selectPrimary(entity.getId(), globalDefault); if (runtimeModel == null) runtimeModel = globalDefault; } catch (Exception e) { log.debug("[ProviderRouter] primary selection failed, falling back to global default: {}", e.getMessage()); runtimeModel = globalDefault; } } // Even after the upgrade, log a WARN when the chosen primary // still doesn't satisfy needs (e.g. no preferred provider was // capable). The diagnostic is observability-only. try { providerRouter.diagnosePrimary(entity.getId(), runtimeModel); } catch (Exception e) { log.debug("[ProviderRouter] diagnostic failed: {}", e.getMessage()); } ModelProviderEntity provider; try { provider = modelProviderService.getProviderConfig(runtimeModel.getProvider()); } catch (Exception e) { throw new MateClawException("err.agent.model_not_configured", "模型 " + runtimeModel.getModelName() + " 的 Provider（" + runtimeModel.getProvider() + "）未配置，请检查模型设置"); } // Safety net: getDefaultModel() already skips unconfigured providers, but guard here // too so a stale cached model doesn't silently proceed to a broken API call. if (!modelProviderService.isProviderConfigured(provider.getProviderId())) { String reason = modelProviderService.getProviderUnavailableReason(provider.getProviderId()); log.warn("Runtime model {}/{} provider not configured ({}); trying fallback", runtimeModel.getProvider(), runtimeModel.getModelName(), reason); ModelConfigEntity fallback = findFirstAvailableChatModel(); if (fallback == null) { throw new MateClawException("err.agent.no_configured_model", "默认模型 Provider「" + runtimeModel.getProvider() + "」未配置（" + reason + "），且找不到其他已配置的 Provider，请先在「设置 → 模型」中完成配置"); } runtimeModel = fallback; try { provider = modelProviderService.getProviderConfig(runtimeModel.getProvider()); } catch (Exception e) { throw new MateClawException("err.agent.model_not_configured", "备用模型 " + runtimeModel.getModelName() + " 的 Provider（" + runtimeModel.getProvider() + "）获取失败"); } } ModelProtocol protocol = ModelProtocol.fromChatModel(provider.getChatModel()); // Effective context window: explicit config > local-server probe > null // (downstream keeps its global-default fallback). Without probing, a // local 8k/16k model with maxInputTokens unset budgets against the // 128k global default and the first oversized request fails outright. Integer effectiveMaxInputTokens = contextWindowResolver.resolveMaxInputTokens(provider, runtimeModel); // 内置搜索检测（DashScope / Kimi），但不再移除 WebSearchTool — 两者协同而非互斥 boolean builtinSearchEnabled = false; Map providerKwargs = modelProviderService.readProviderGenerateKwargs(provider); if (protocol == ModelProtocol.DASHSCOPE_NATIVE) { builtinSearchEnabled = dashScopeBuilder.isBuiltinSearchEnabled(runtimeModel, provider); } else if (OpenAiCompatibleChatModelBuilder.isKimiProvider(provider) && Boolean.TRUE.equals(ProviderGenerateKwargs.findOptionValue(providerKwargs, "enableSearch"))) { builtinSearchEnabled = true; } if (builtinSearchEnabled) { // Phase 2: 不再移除 search 工具，改为在 prompt 中设定优先级引导 // 内置搜索作为首选，search 工具作为补充/兜底 log.info("内置搜索已开启 (provider={})，search 工具保留作为补充通道", provider.getProviderId()); } // Default 100 if DB row leaves max_iterations null. Negative or zero is an // explicit opt-in to "no soft cap" — ObservationDispatcher already treats // maxIterations<=0 as "do not enforce", so the agent runs until the LLM // emits a final answer (or returnDirect short-circuits). Positive values // are clamped to the hard ceiling so a misconfigured row can't skip the // safety net unintentionally. int rawMaxIter = entity.getMaxIterations() != null ? entity.getMaxIterations() : 100; int maxIter; if (rawMaxIter <= 0) { maxIter = 0; log.info("Agent {} max_iterations={} → unlimited soft cap (LLM controls termination)", entity.getId(), rawMaxIter); } else { maxIter = Math.min(rawMaxIter, BaseAgent.MAX_ITERATIONS_HARD_CEILING); if (maxIter != rawMaxIter) { log.warn("Agent {} max_iterations={} clamped to {} (1..{})", entity.getId(), rawMaxIter, maxIter, BaseAgent.MAX_ITERATIONS_HARD_CEILING); } } // Prefix injection budget: optional blocks (memory / wiki / skill // catalog / extension catalog / ledger) share a token budget scaled // to the model's effective window. The agent's own prompt and the // tool schemas are never truncated — they are subtracted from the // budget so the optional blocks absorb the squeeze. int basePromptTokens = TokenEstimator.estimateTokens(entity.getSystemPrompt()); int toolSchemaTokens = TokenEstimator.estimateToolsTokens(toolSet.callbacks()); PrefixBudgetPlan prefixBudgetPlan = prefixBudgetPlanner.plan( effectiveMaxInputTokens, basePromptTokens, toolSchemaTokens); if (basePromptTokens > prefixBudgetPlan.effectiveMaxTokens() / 2) { log.warn("Agent {} 的身份 prompt 约 {} tokens,已超过模型有效窗口 {} 的一半——" + "系统不会截断用户自写的身份 prompt,请自行精简,否则小上下文模型可能无法响应", entity.getId(), basePromptTokens, prefixBudgetPlan.effectiveMaxTokens()); } String enhancedPrompt = buildEnhancedPrompt(entity, builtinSearchEnabled, prefixBudgetPlan.memoryTokens()); // Runtime skill-catalog renderer — captures this agent's bound skills, // effective tool allowlist, model window and workspace; invoked each // turn by the reasoning / step-execution nodes with the skills loaded // so far this run so load_skill pins float to the top of the catalog. SkillCatalogRenderer skillCatalogRenderer = buildSkillCatalogRenderer( entity, boundTools, effectiveMaxInputTokens); // Progressive tool catalog is shared by both agent types. ReAct applies // the split in ReasoningNode; Plan-Execute receives a separate advertised // set for StepExecutionNode while its executor retains the full scoped // set, so tool_call can recover a deferred tool in the same action round. Set autoDemotedTools = Set.of(); if (prefixBudgetPlan.enabled()) { autoDemotedTools = toolDisclosureService.computeAutoDemotions( toolSet, prefixBudgetPlan.toolSchemaBudgetTokens()); } String extensionCatalog = toolDisclosureService.renderExtensionCatalog( toolSet, effectiveMaxInputTokens, autoDemotedTools); if (extensionCatalog != null && !extensionCatalog.isBlank()) { enhancedPrompt = enhancedPrompt + extensionCatalog; } // 当前仅支持 DashScope 和 OpenAI-compatible，其他协议直接拒绝 if (!supportsStateGraph(protocol)) { throw new MateClawException("err.agent.protocol_not_supported", "当前不支持协议 " + protocol.getId() + "，请切换到 DashScope 或 OpenAI-compatible 模型"); } BaseAgent agent; boolean toolCallingEnabled; if ("plan_execute".equals(entity.getAgentType())) { agent = buildPlanExecuteAgent(toolSet, runtimeModel, maxIter, entity.getId(), skillCatalogRenderer, autoDemotedTools); toolCallingEnabled = true; log.info("Built StateGraph Plan-Execute agent: {} (maxIterations={}, tools={}, protocol={})", entity.getName(), maxIter, toolSet.size(), protocol.getId()); } else { agent = buildReActAgent(toolSet, runtimeModel, maxIter, entity.getId(), skillCatalogRenderer, prefixBudgetPlan, autoDemotedTools); // StateGraph 路径下工具调用由 ActionNode 控制，始终启用 toolCallingEnabled = true; log.info("Built StateGraph ReAct agent: {} (maxIterations={}, tools={}, protocol={})", entity.getName(), maxIter, toolSet.size(), protocol.getId()); } // 设置通用属性 agent.agentId = String.valueOf(entity.getId()); agent.agentName = entity.getName(); agent.systemPrompt = enhancedPrompt; agent.maxIterations = maxIter; agent.modelName = runtimeModel.getModelName(); agent.modelCapabilities = modelCapabilityService.resolve( runtimeModel.getModelName(), runtimeModel.getModalities()); agent.runtimeProviderId = provider != null ? provider.getProviderId() : ""; agent.runtimeModelConfig = runtimeModel; agent.toolSet = toolSet; // RFC 48 — wire the goal lookup so buildInitialState can inject // ACTIVE_GOAL. The node itself stays inert until goalProperties.enabled // flips true, but tests need findActiveByConversation to work even // when the runtime path is disabled. agent.goalService = goalService; agent.multimodalRouter = multimodalRouter; agent.mediaCaptionService = mediaCaptionService; agent.userLocale = resolveLocale(); agent.temperature = runtimeModel.getTemperature(); agent.maxTokens = runtimeModel.getMaxTokens(); agent.maxInputTokens = effectiveMaxInputTokens; agent.topP = runtimeModel.getTopP(); agent.toolCallingEnabled = toolCallingEnabled; // Agent-level override takes priority; a relative override is resolved // under the workspace basePath so admins can express agent directories // relative to the workspace root (matching the UI hint). String workspaceBase = null; if (entity.getWorkspaceId() != null) { try { var workspace = workspaceService.getById(entity.getWorkspaceId()); if (workspace != null) { workspaceBase = workspace.getBasePath(); } } catch (Exception e) { log.warn("Failed to lookup workspace basePath for agent {}: {}", entity.getName(), e.getMessage()); } } String resolvedBase; try { resolvedBase = resolveAgentBasePath(entity.getWorkspaceBasePath(), workspaceBase); } catch (IllegalArgumentException e) { // Override violates the workspace-scoping rule (e.g. admin tried to // set an absolute path outside the workspace root). Fall back to // inheriting the workspace basePath so chat stays available, but // surface the violation in logs so the admin can fix it. log.warn("Agent {} workspaceBasePath override rejected, falling back to workspace: {}", entity.getName(), e.getMessage()); resolvedBase = workspaceBase; } if (resolvedBase != null && !resolvedBase.isBlank()) { agent.workspaceBasePath = resolvedBase; boolean fromOverride = entity.getWorkspaceBasePath() != null && !entity.getWorkspaceBasePath().isBlank() && resolvedBase.equals(entity.getWorkspaceBasePath()); log.info("Agent {} basePath = {} (source: {})", entity.getName(), resolvedBase, fromOverride ? "agent-override" : "workspace"); } log.info("Built agent instance: {} (type={}, protocol={}, tools={}, toolCallingEnabled={})", entity.getName(), entity.getAgentType(), protocol.getId(), toolSet.size(), agent.toolCallingEnabled); return agent; } // ==================== Agent 构建方法 ==================== StateGraphReActAgent buildReActAgent(AgentToolSet toolSet, ModelConfigEntity runtimeModel, int maxIter) { return buildReActAgent(toolSet, runtimeModel, maxIter, null); } StateGraphReActAgent buildReActAgent(AgentToolSet toolSet, ModelConfigEntity runtimeModel, int maxIter, Long agentId) { return buildReActAgent(toolSet, runtimeModel, maxIter, agentId, null); } StateGraphReActAgent buildReActAgent(AgentToolSet toolSet, ModelConfigEntity runtimeModel, int maxIter, Long agentId, SkillCatalogRenderer skillCatalogRenderer) { return buildReActAgent(toolSet, runtimeModel, maxIter, agentId, skillCatalogRenderer, null, Set.of()); } StateGraphReActAgent buildReActAgent(AgentToolSet toolSet, ModelConfigEntity runtimeModel, int maxIter, Long agentId, SkillCatalogRenderer skillCatalogRenderer, PrefixBudgetPlan prefixBudgetPlan, Set autoDemotedTools) { ChatModel chatModel = buildRuntimeChatModel(runtimeModel); ChatClient chatClient = ChatClient.create(chatModel); String reasoningEffort = resolveReasoningEffortForModel(runtimeModel); CompiledGraph compiledGraph = buildReActGraph(toolSet, chatModel, maxIter, reasoningEffort, runtimeModel, agentId, skillCatalogRenderer, prefixBudgetPlan, autoDemotedTools); StateGraphReActAgent agent = new StateGraphReActAgent(chatClient, conversationService, compiledGraph, chatModel, conversationWindowManager, toolSet); if (reasoningRetentionProperties != null) { agent.setPersistEveryIterationReasoning(reasoningRetentionProperties.persistsEveryIteration()); } return agent; } StateGraphPlanExecuteAgent buildPlanExecuteAgent(AgentToolSet toolSet, ModelConfigEntity runtimeModel, int maxIter) { return buildPlanExecuteAgent(toolSet, runtimeModel, maxIter, null); } StateGraphPlanExecuteAgent buildPlanExecuteAgent(AgentToolSet toolSet, ModelConfigEntity runtimeModel, int maxIter, Long agentId) { return buildPlanExecuteAgent(toolSet, runtimeModel, maxIter, agentId, null); } StateGraphPlanExecuteAgent buildPlanExecuteAgent(AgentToolSet toolSet, ModelConfigEntity runtimeModel, int maxIter, Long agentId, SkillCatalogRenderer skillCatalogRenderer) { return buildPlanExecuteAgent(toolSet, runtimeModel, maxIter, agentId, skillCatalogRenderer, Set.of()); } StateGraphPlanExecuteAgent buildPlanExecuteAgent(AgentToolSet toolSet, ModelConfigEntity runtimeModel, int maxIter, Long agentId, SkillCatalogRenderer skillCatalogRenderer, Set autoDemotedTools) { ChatModel chatModel = buildRuntimeChatModel(runtimeModel); ChatClient chatClient = ChatClient.create(chatModel); String reasoningEffort = resolveReasoningEffortForModel(runtimeModel); CompiledGraph graph = buildPlanExecuteGraph(toolSet, chatModel, maxIter, reasoningEffort, runtimeModel, agentId, skillCatalogRenderer, autoDemotedTools); return new StateGraphPlanExecuteAgent(chatClient, conversationService, graph, planningService, chatModel, conversationWindowManager, toolSet); } CompiledGraph buildPlanExecuteGraph(AgentToolSet toolSet, ChatModel chatModel, int maxIterations, String reasoningEffort) { return buildPlanExecuteGraph(toolSet, chatModel, maxIterations, reasoningEffort, null, null); } CompiledGraph buildPlanExecuteGraph(AgentToolSet toolSet, ChatModel chatModel, int maxIterations, String reasoningEffort, ModelConfigEntity primaryModelConfig) { return buildPlanExecuteGraph(toolSet, chatModel, maxIterations, reasoningEffort, primaryModelConfig, null); } CompiledGraph buildPlanExecuteGraph(AgentToolSet toolSet, ChatModel chatModel, int maxIterations, String reasoningEffort, ModelConfigEntity primaryModelConfig, Long agentId) { return buildPlanExecuteGraph(toolSet, chatModel, maxIterations, reasoningEffort, primaryModelConfig, agentId, null); } CompiledGraph buildPlanExecuteGraph(AgentToolSet toolSet, ChatModel chatModel, int maxIterations, String reasoningEffort, ModelConfigEntity primaryModelConfig, Long agentId, SkillCatalogRenderer skillCatalogRenderer) { return buildPlanExecuteGraph(toolSet, chatModel, maxIterations, reasoningEffort, primaryModelConfig, agentId, skillCatalogRenderer, Set.of()); } CompiledGraph buildPlanExecuteGraph(AgentToolSet toolSet, ChatModel chatModel, int maxIterations, String reasoningEffort, ModelConfigEntity primaryModelConfig, Long agentId, SkillCatalogRenderer skillCatalogRenderer, Set autoDemotedTools) { try { List fallbackChain = buildFallbackChain(primaryModelConfig, agentId); NodeStreamingChatHelper streamingHelper = new NodeStreamingChatHelper( streamTracker, fallbackChain, llmCacheMetricsAggregator, providerHealthTracker, primaryModelConfig != null ? primaryModelConfig.getProvider() : null, providerPool); if (primaryModelConfig != null) { // Feed "prompt too long" rejections back into the window resolver // so the next turn budgets against the server-reported limit. streamingHelper.setContextLimitObserver(errorMessage -> contextWindowResolver.noteContextLimitError( primaryModelConfig.getProvider(), primaryModelConfig.getModelName(), errorMessage)); } streamingHelper.setStreamIdleTimeoutSec( resolveStreamIdleTimeoutSeconds(primaryModelConfig)); ToolExecutionExecutor executor = new ToolExecutionExecutor( toolSet, toolGuardService, approvalService, streamTracker, toolTimeoutProperties, toolResultStorage, toolConcurrencyRegistry, workspaceLookupCache, approvalGrantResolver); // Issue #46: enable skill-aware "Tool not found" hint so when the // LLM mis-calls a skill name as a tool, the response tells it // the right invocation pattern instead of a dead-end error. executor.setSkillRuntimeService(skillRuntimeService); executor.setUsageRecencyTracker(toolUsageRecencyTracker); executor.setProgressContext(progressContext); // Optional: route child-agent denied-tool audit events through // the audit pipeline. Null when audit is not wired (legacy / test). if (auditEventService != null) { executor.setAuditEventService(auditEventService); } PlanGenerationNode planGenerationNode = new PlanGenerationNode(chatModel, planningService, streamingHelper, conversationWindowManager, toolSet, goalService, goalProperties, agentService); // Team hand-off: a lead-of-team plan agent parks multi-step plans on // the team task board instead of the serial delegation pipeline. planGenerationNode.setTeamPlanBridge(teamPlanBridge); List advertisedCallbacks = toolDisclosureService .split(toolSet, Set.of(), autoDemotedTools).activeCallbacks(); AgentToolSet advertisedToolSet = AgentToolSet.fromCallbacks( toolSet.toolBeans(), advertisedCallbacks); StepExecutionNode stepExecutionNode = new StepExecutionNode(chatModel, advertisedToolSet, executor, planningService, streamTracker, reasoningEffort, streamingHelper, conversationWindowManager, skillCatalogRenderer); // Per-step delegation: route a step assigned to a specialist agent // through DelegateAgentTool (null when delegation deps aren't wired). stepExecutionNode.setDelegateAgentTool(delegateAgentTool); PlanSummaryNode planSummaryNode = new PlanSummaryNode(chatModel, planningService, streamingHelper); DirectAnswerNode directAnswerNode = new DirectAnswerNode(); KeyStrategyFactory keyStrategyFactory = KeyStrategy.builder() // 共享键 .addStrategy(MateClawStateKeys.PENDING_EVENTS, KeyStrategy.APPEND) .addStrategy(MateClawStateKeys.CURRENT_PHASE, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.SYSTEM_PROMPT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.CONVERSATION_ID, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.TRACE_ID, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.AGENT_ID, KeyStrategy.REPLACE) // 会话消息（复用 ReAct 的 MESSAGES key，APPEND 策略） .addStrategy(MateClawStateKeys.MESSAGES, KeyStrategy.APPEND) // Plan 特有键 .addStrategy(PlanStateKeys.GOAL, KeyStrategy.REPLACE) .addStrategy(PlanStateKeys.PLAN_ID, KeyStrategy.REPLACE) .addStrategy(PlanStateKeys.PLAN_STEPS, KeyStrategy.REPLACE) .addStrategy(PlanStateKeys.PLAN_VALID, KeyStrategy.REPLACE) .addStrategy(PlanStateKeys.NEEDS_PLANNING, KeyStrategy.REPLACE) .addStrategy(PlanStateKeys.CURRENT_STEP_INDEX, KeyStrategy.REPLACE) .addStrategy(PlanStateKeys.CURRENT_STEP_TITLE, KeyStrategy.REPLACE) .addStrategy(PlanStateKeys.CURRENT_STEP_RESULT, KeyStrategy.REPLACE) .addStrategy(PlanStateKeys.COMPLETED_RESULTS, KeyStrategy.APPEND) .addStrategy(PlanStateKeys.PLAN_REPLAN_COUNT, KeyStrategy.REPLACE) .addStrategy(PlanStateKeys.FINAL_SUMMARY, KeyStrategy.REPLACE) .addStrategy(PlanStateKeys.DIRECT_ANSWER, KeyStrategy.REPLACE) // 工作上下文（REPLACE 策略，每次重新生成） .addStrategy(PlanStateKeys.WORKING_CONTEXT, KeyStrategy.REPLACE) // Thinking 键 .addStrategy(PlanStateKeys.FINAL_SUMMARY_THINKING, KeyStrategy.REPLACE) .addStrategy(PlanStateKeys.CURRENT_STEP_THINKING, KeyStrategy.REPLACE) .addStrategy(PlanStateKeys.PLAN_THINKING, KeyStrategy.REPLACE) // 流式防重键 .addStrategy(MateClawStateKeys.CONTENT_STREAMED, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.THINKING_STREAMED, KeyStrategy.REPLACE) // 流式内容暂存（AWAITING_APPROVAL 路径持久化使用） .addStrategy(MateClawStateKeys.STREAMED_CONTENT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.STREAMED_THINKING, KeyStrategy.REPLACE) // 请求者身份（审批身份校验使用） .addStrategy(MateClawStateKeys.REQUESTER_ID, KeyStrategy.REPLACE) // 审批重放键 .addStrategy(MateClawStateKeys.FORCED_TOOL_CALL, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.PRE_APPROVED_TOOL_CALL, KeyStrategy.REPLACE) // RFC-063r §2.5: ChatOrigin must survive every node merge so // sub-graph nodes (StepExecutionNode + DelegateAgentTool's // child agents) can read the originating channel binding. // Without explicit REPLACE the framework's merge drops it // on multi-iteration paths — root cause of the channel-binding // flakiness reported on first deployment. .addStrategy(MateClawStateKeys.CHAT_ORIGIN, KeyStrategy.REPLACE) // Caught by StateKeyRegistrationCoverageTest — these state keys // were silently unregistered before the post-deploy audit. // WORKSPACE_BASE_PATH: written by buildInitialState; sub-graph // tools read it via WorkspacePathGuard. // STOP_REQUESTED: external cancel flag checked by every node. // RETURN_DIRECT_TRIGGERED / DIRECT_TOOL_OUTPUTS (RFC-052): // Plan-Execute itself doesn't trigger returnDirect, but // DelegateAgentTool sub-agents could; register defensively. .addStrategy(MateClawStateKeys.WORKSPACE_BASE_PATH, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.STOP_REQUESTED, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.RETURN_DIRECT_TRIGGERED, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.DIRECT_TOOL_OUTPUTS, KeyStrategy.REPLACE) // Token Usage .addStrategy(MateClawStateKeys.PROMPT_TOKENS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.COMPLETION_TOKENS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.CACHE_READ_TOKENS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.CACHE_WRITE_TOKENS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.REASONING_TOKENS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.LLM_CALL_COUNT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.RUNTIME_MODEL_NAME, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.RUNTIME_PROVIDER_ID, KeyStrategy.REPLACE) // SourceEvidenceLedger: ActionNode 把每轮 ToolResponse 抽取出的 // (sourcePaths, sourceSymbols, failedPaths) merge 进这个 ledger， // 后续 ReasoningNode / FinalAnswerNode 调 validateAnswer 校验 // 模型引用是否有真实证据。漏注册时框架在多 node merge 时会偶发 // 丢这个键，evidence_insufficient 检查会"静默地不生效" —— // StateKeyRegistrationCoverageTest 专门兜这条。 .addStrategy(MateClawStateKeys.SOURCE_EVIDENCE_LEDGER, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.ACTION_EXECUTION_LEDGER, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.ACTION_COMPLETION_REQUIRED, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.ACTION_COMPLETION_RETRY_COUNT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.CONTINUE_REASONING, KeyStrategy.REPLACE) // Multimodal sidecar routing decision for the current turn. .addStrategy(MateClawStateKeys.ROUTING_DECISION, KeyStrategy.REPLACE) // RFC 48 — persistent goal state keys must be registered in // BOTH graph KeyStrategyFactory blocks. The architecture // coverage test only checks "appears somewhere"; the // GoalStateKeyDoubleRegistrationTest below verifies the // double registration explicitly. .addStrategy(MateClawStateKeys.ACTIVE_GOAL, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_EVALUATION_RESULT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_FOLLOWUP_INJECTED, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_FOLLOWUP_PROMPT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_EVALUATED_THIS_RUN, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_FOLLOWUP_COUNT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_ACCOUNTED_LLM_CALL_COUNT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_HARD_CONTINUATION_COUNT, KeyStrategy.REPLACE) // Skill progressive disclosure — pinned skills loaded this // run. Registered in BOTH graphs so the read-merge-write in // ActionNode is not dropped on multi-node merges. .addStrategy(MateClawStateKeys.LOADED_SKILLS, KeyStrategy.REPLACE) // Tool progressive disclosure — extensions enabled this run. // Registered in BOTH graphs for the same merge-safety reason. .addStrategy(MateClawStateKeys.ENABLED_EXTENSION_TOOLS, KeyStrategy.REPLACE) // Tool-call loop guard counters + one-shot post-mutation // verification reminder flag. Read-merge-write by // ObservationNode; registered in BOTH graphs so the // counters survive multi-node merges. .addStrategy(MateClawStateKeys.TOOL_LOOP_STATS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.MUTATION_REMINDER_INJECTED, KeyStrategy.REPLACE) .build(); // Graph 拓扑： // START → PLAN_GENERATION → (PlanGenerationDispatcher) // ├→ DIRECT_ANSWER_NODE → END // └→ STEP_EXECUTION → (StepProgressDispatcher) // ├→ STEP_EXECUTION (loop) // ├→ PLAN_GENERATION (re-plan on step failure, bounded by PLAN_REPLAN_COUNT) // └→ PLAN_SUMMARY → (active goal?) // ├→ GOAL_EVALUATION → (followup?) // │ ├→ PLAN_GENERATION (re-plan) // │ └→ END // └→ END GoalEvaluationNode goalEvalNode = new GoalEvaluationNode( goalEvaluationService, goalFollowupService, goalService, goalProperties, conversationWindowManager, conversationService, vip.mate.goal.service.GraphFlavor.PLAN_EXECUTE); StateGraph graph = new StateGraph("plan-execute-agent", keyStrategyFactory) .addNode(PlanStateKeys.PLAN_GENERATION_NODE, AsyncNodeAction.node_async(planGenerationNode)) .addNode(PlanStateKeys.STEP_EXECUTION_NODE, AsyncNodeAction.node_async(stepExecutionNode)) .addNode(PlanStateKeys.PLAN_SUMMARY_NODE, AsyncNodeAction.node_async(planSummaryNode)) .addNode(PlanStateKeys.DIRECT_ANSWER_NODE, AsyncNodeAction.node_async(directAnswerNode)) .addNode(MateClawStateKeys.GOAL_EVALUATION_NODE, AsyncNodeAction.node_async(goalEvalNode)) .addEdge(StateGraph.START, PlanStateKeys.PLAN_GENERATION_NODE) .addConditionalEdges(PlanStateKeys.PLAN_GENERATION_NODE, AsyncEdgeAction.edge_async(new PlanGenerationDispatcher()), Map.of( PlanStateKeys.STEP_EXECUTION_NODE, PlanStateKeys.STEP_EXECUTION_NODE, PlanStateKeys.DIRECT_ANSWER_NODE, PlanStateKeys.DIRECT_ANSWER_NODE, // Board-delegated plan settled: step results were // rebuilt from team tasks — summarize directly. PlanStateKeys.PLAN_SUMMARY_NODE, PlanStateKeys.PLAN_SUMMARY_NODE)) .addConditionalEdges(PlanStateKeys.STEP_EXECUTION_NODE, AsyncEdgeAction.edge_async(new StepProgressDispatcher()), Map.of( PlanStateKeys.STEP_EXECUTION_NODE, PlanStateKeys.STEP_EXECUTION_NODE, PlanStateKeys.PLAN_SUMMARY_NODE, PlanStateKeys.PLAN_SUMMARY_NODE, // Step-failure recovery: re-plan the remaining work // (StepProgressDispatcher returns this on phase=plan_replan). PlanStateKeys.PLAN_GENERATION_NODE, PlanStateKeys.PLAN_GENERATION_NODE, StateGraph.END, StateGraph.END)) .addConditionalEdges(PlanStateKeys.PLAN_SUMMARY_NODE, AsyncEdgeAction.edge_async(state -> { MateClawStateAccessor a = new MateClawStateAccessor(state); // Same-turn activation: fall back to a DB lookup (gated on the // feature flag) so a goal the agent set THIS turn is evaluated now, // not only from the next message. See GoalEvaluationNode.resolveActiveGoal. boolean hasGoal = goalProperties.isEnabled() && GoalEvaluationNode.resolveActiveGoal(state, goalService).isPresent(); boolean already = a.goalEvaluatedThisRun(); return (hasGoal && !already) ? MateClawStateKeys.GOAL_EVALUATION_NODE : StateGraph.END; }), Map.of( MateClawStateKeys.GOAL_EVALUATION_NODE, MateClawStateKeys.GOAL_EVALUATION_NODE, StateGraph.END, StateGraph.END)) .addConditionalEdges(MateClawStateKeys.GOAL_EVALUATION_NODE, AsyncEdgeAction.edge_async(new vip.mate.agent.graph.edge.GoalEvaluationDispatcher( PlanStateKeys.PLAN_GENERATION_NODE, StateGraph.END)), Map.of( PlanStateKeys.PLAN_GENERATION_NODE, PlanStateKeys.PLAN_GENERATION_NODE, StateGraph.END, StateGraph.END)) // DIRECT_ANSWER_NODE handles trivial requests that bypass the // multi-step plan. For active goals, the direct answer is still // a turn — without this edge, turns_used / score / completion // would never tick on plan-execute conversations whose every // reply happened to be simple enough to short-circuit through // the direct path. Mirror PLAN_SUMMARY_NODE's gate so non-goal // turns still go straight to END (no goal node invocation). .addConditionalEdges(PlanStateKeys.DIRECT_ANSWER_NODE, AsyncEdgeAction.edge_async(state -> { MateClawStateAccessor a = new MateClawStateAccessor(state); // Same-turn activation: fall back to a DB lookup (gated on the // feature flag) so a goal the agent set THIS turn is evaluated now, // not only from the next message. See GoalEvaluationNode.resolveActiveGoal. boolean hasGoal = goalProperties.isEnabled() && GoalEvaluationNode.resolveActiveGoal(state, goalService).isPresent(); boolean already = a.goalEvaluatedThisRun(); return (hasGoal && !already) ? MateClawStateKeys.GOAL_EVALUATION_NODE : StateGraph.END; }), Map.of( MateClawStateKeys.GOAL_EVALUATION_NODE, MateClawStateKeys.GOAL_EVALUATION_NODE, StateGraph.END, StateGraph.END)); return graph.compile(CompileConfig.builder() .recursionLimit(frameworkRecursionLimit()) .build()); } catch (Exception e) { throw new MateClawException("err.agent.plan_compile_failed", "Plan-Execute StateGraph 编译失败: " + e.getMessage()); } } /** * Hard ceiling for the underlying graph framework's recursion guard. *

* The framework treats "recursion limit reached" as a normal completion — * it emits a {@code done} signal with no exception and no log. That makes * it indistinguishable from a real final answer downstream, and is the * mechanism by which a turn can silently stop mid-execution and persist * only whatever partial content the accumulator happened to hold. *

* To avoid that class of bug, the recursion limit must be sized so it can * never trip before the soft cap (ObservationDispatcher → * LimitExceededNode), which is the only path that produces a proper * {@code finish_reason} and human-facing message. Sized for the maximum * effective soft cap (DB hard ceiling + thinking-mode bonus) multiplied * by 4 (each iteration is worst-case reasoning + summarizing + action + * observation) plus a 100-step buffer for phase nodes, approval replays * and tool-result chunking. Decoupled from the per-agent value so a small * {@code max_iterations} can never accidentally re-introduce the silent * killer. *

* The base segment budget is further multiplied to cover goal-driven "hard * continuations" — each grants a fresh full iteration budget after a * max-iterations turn (see {@code GoalEvaluationNode}). One run can perform * up to {@link vip.mate.goal.config.GoalProperties#MAX_HARD_CONTINUATIONS_CEILING} of them, so * the ceiling is sized for {@code (1 + CEILING)} segments to keep the * recursion guard from tripping before the soft caps do. */ private static int frameworkRecursionLimit() { int perSegment = (BaseAgent.MAX_ITERATIONS_HARD_CEILING + 5) * 4; return perSegment * (1 + vip.mate.goal.config.GoalProperties.MAX_HARD_CONTINUATIONS_CEILING) + 100; } static long resolveStreamIdleTimeoutSeconds(ModelConfigEntity modelConfig) { Integer override = modelConfig != null ? modelConfig.getRequestTimeoutSeconds() : null; return HttpTimeouts.resolveStreamIdleTimeout(override).toSeconds(); } CompiledGraph buildReActGraph(AgentToolSet toolSet, ChatModel chatModel, int maxIterations, String reasoningEffort) { return buildReActGraph(toolSet, chatModel, maxIterations, reasoningEffort, null, null); } CompiledGraph buildReActGraph(AgentToolSet toolSet, ChatModel chatModel, int maxIterations, String reasoningEffort, ModelConfigEntity primaryModelConfig) { return buildReActGraph(toolSet, chatModel, maxIterations, reasoningEffort, primaryModelConfig, null); } CompiledGraph buildReActGraph(AgentToolSet toolSet, ChatModel chatModel, int maxIterations, String reasoningEffort, ModelConfigEntity primaryModelConfig, Long agentId) { return buildReActGraph(toolSet, chatModel, maxIterations, reasoningEffort, primaryModelConfig, agentId, null); } CompiledGraph buildReActGraph(AgentToolSet toolSet, ChatModel chatModel, int maxIterations, String reasoningEffort, ModelConfigEntity primaryModelConfig, Long agentId, SkillCatalogRenderer skillCatalogRenderer) { return buildReActGraph(toolSet, chatModel, maxIterations, reasoningEffort, primaryModelConfig, agentId, skillCatalogRenderer, null, Set.of()); } CompiledGraph buildReActGraph(AgentToolSet toolSet, ChatModel chatModel, int maxIterations, String reasoningEffort, ModelConfigEntity primaryModelConfig, Long agentId, SkillCatalogRenderer skillCatalogRenderer, PrefixBudgetPlan prefixBudgetPlan, Set autoDemotedTools) { try { List fallbackChain = buildFallbackChain(primaryModelConfig, agentId); NodeStreamingChatHelper streamingHelper = new NodeStreamingChatHelper( streamTracker, fallbackChain, llmCacheMetricsAggregator, providerHealthTracker, primaryModelConfig != null ? primaryModelConfig.getProvider() : null, providerPool); if (primaryModelConfig != null) { // Feed "prompt too long" rejections back into the window resolver // so the next turn budgets against the server-reported limit. streamingHelper.setContextLimitObserver(errorMessage -> contextWindowResolver.noteContextLimitError( primaryModelConfig.getProvider(), primaryModelConfig.getModelName(), errorMessage)); } streamingHelper.setStreamIdleTimeoutSec( resolveStreamIdleTimeoutSeconds(primaryModelConfig)); ToolExecutionExecutor executor = new ToolExecutionExecutor( toolSet, toolGuardService, approvalService, streamTracker, toolTimeoutProperties, toolResultStorage, toolConcurrencyRegistry, workspaceLookupCache, approvalGrantResolver); // Issue #46: enable skill-aware "Tool not found" hint so when the // LLM mis-calls a skill name as a tool, the response tells it // the right invocation pattern instead of a dead-end error. executor.setSkillRuntimeService(skillRuntimeService); executor.setUsageRecencyTracker(toolUsageRecencyTracker); executor.setProgressContext(progressContext); // Optional: route child-agent denied-tool audit events through // the audit pipeline. Null when audit is not wired (legacy / test). if (auditEventService != null) { executor.setAuditEventService(auditEventService); } // PR-1.2 (RFC-049 L1-B): propagate the bound model's capability so ReasoningNode // can gate the ThinkingLevelHolder override explicitly, rather than inferring // capability from reasoningEffort == null. boolean supportsReasoningEffort = primaryModelConfig != null && ModelFamily.detect(primaryModelConfig.getModelName()).supportsReasoningEffort(); // Honor the model's configured output cap. Passing 0 here made the // node fall back to its 16384 default, so the user-configured // maxTokens never took effect and strict local servers (vLLM's // max_model_len pre-check) rejected the request outright. int configuredMaxOutputTokens = (primaryModelConfig != null && primaryModelConfig.getMaxTokens() != null && primaryModelConfig.getMaxTokens() > 0) ? primaryModelConfig.getMaxTokens() : 0; ReasoningNode reasoningNode = new ReasoningNode(chatModel, toolSet, reasoningEffort, supportsReasoningEffort, streamingHelper, conversationWindowManager, streamTracker, configuredMaxOutputTokens, wikiContextService, skillCatalogRenderer, toolDisclosureService, progressLedgerService); reasoningNode.setPrefixBudgetPlan(prefixBudgetPlan); reasoningNode.setAutoDemotedTools(autoDemotedTools); // C4: wire the environment-notification registry so ReasoningNode // can drain pending MCP/skill events and inject them as a SystemMessage. reasoningNode.setRunningConversationRegistry(runningConversationRegistry); // Live team-board snapshot for leads, injected per turn as a meta // user message; no-op for agents outside any team. reasoningNode.setTeamContextBuilder(teamContextBuilder); ActionNode actionNode = new ActionNode(executor, streamTracker); // B2/B5: wire optional collaborators so ActionNode can pin skill // constraints and auto-record tool completions into ProgressLedger. // Setter injection keeps the existing constructor signature stable // for tests that build ActionNode directly. actionNode.setSkillRuntimeService(skillRuntimeService); actionNode.setProgressLedgerService(progressLedgerService); ObservationProcessor observationProcessor = new ObservationProcessor(graphObservationProperties); ObservationNode observationNode = new ObservationNode(observationProcessor, streamTracker); SummarizingNode summarizingNode = new SummarizingNode(chatModel, streamingHelper, streamTracker); LimitExceededNode limitExceededNode = new LimitExceededNode( chatModel, observationProcessor, streamingHelper, i18nService, progressLedgerService); FinalAnswerNode finalAnswerNode = new FinalAnswerNode(generatedFileCache, markdownNormalizeEnabled); KeyStrategyFactory keyStrategyFactory = KeyStrategy.builder() // 输入字段 .addStrategy(MateClawStateKeys.USER_MESSAGE, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.CONVERSATION_ID, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.SYSTEM_PROMPT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.AGENT_ID, KeyStrategy.REPLACE) // 消息列表（追加策略） .addStrategy(MateClawStateKeys.MESSAGES, KeyStrategy.APPEND) // 迭代控制 .addStrategy(MateClawStateKeys.CURRENT_ITERATION, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.ITERATION_REFUND_COUNT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.MAX_ITERATIONS, KeyStrategy.REPLACE) // 工具调用 .addStrategy(MateClawStateKeys.TOOL_CALLS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.TOOL_RESULTS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.TOOL_CALL_COUNT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.LLM_CALL_COUNT, KeyStrategy.REPLACE) // 控制流 .addStrategy(MateClawStateKeys.FINAL_ANSWER, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.NEEDS_TOOL_CALL, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.ERROR, KeyStrategy.REPLACE) // 观察历史（REPLACE 策略，由 ObservationNode 手动累加，SummarizingNode 可清空） .addStrategy(MateClawStateKeys.OBSERVATION_HISTORY, KeyStrategy.REPLACE) // Summarizing .addStrategy(MateClawStateKeys.SUMMARIZED_CONTEXT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.FINAL_ANSWER_DRAFT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.LONG_FORM_DRAFT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.SHOULD_SUMMARIZE, KeyStrategy.REPLACE) // 终止控制 .addStrategy(MateClawStateKeys.FINISH_REASON, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.LIMIT_EXCEEDED, KeyStrategy.REPLACE) // 统计与追踪 .addStrategy(MateClawStateKeys.ERROR_COUNT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.TRACE_ID, KeyStrategy.REPLACE) // 事件流 .addStrategy(MateClawStateKeys.PENDING_EVENTS, KeyStrategy.APPEND) .addStrategy(MateClawStateKeys.CURRENT_PHASE, KeyStrategy.REPLACE) // Thinking .addStrategy(MateClawStateKeys.FINAL_THINKING, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.CURRENT_THINKING, KeyStrategy.REPLACE) // 流式防重 .addStrategy(MateClawStateKeys.CONTENT_STREAMED, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.THINKING_STREAMED, KeyStrategy.REPLACE) // 审批控制 .addStrategy(MateClawStateKeys.AWAITING_APPROVAL, KeyStrategy.REPLACE) // 流式内容暂存（AWAITING_APPROVAL 路径持久化使用） .addStrategy(MateClawStateKeys.STREAMED_CONTENT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.STREAMED_THINKING, KeyStrategy.REPLACE) // 请求者身份（审批身份校验使用） .addStrategy(MateClawStateKeys.REQUESTER_ID, KeyStrategy.REPLACE) // 审批重放 .addStrategy(MateClawStateKeys.FORCED_TOOL_CALL, KeyStrategy.REPLACE) // RFC-063r §2.5: ChatOrigin must survive every node merge so // ActionNode (and DelegateAgentTool's child agents) can read // the originating channel binding across multi-iteration ReAct // loops. Without explicit REPLACE the framework's merge drops // it after the first node transition — root cause of the // channel-binding flakiness reported on first deployment. .addStrategy(MateClawStateKeys.CHAT_ORIGIN, KeyStrategy.REPLACE) // Caught by StateKeyRegistrationCoverageTest — silently // unregistered before the audit. WORKSPACE_BASE_PATH from // initial state; STOP_REQUESTED is the external cancel flag; // RETURN_DIRECT_TRIGGERED / DIRECT_TOOL_OUTPUTS are RFC-052 // returnDirect short-circuit signals consumed by ObservationDispatcher. .addStrategy(MateClawStateKeys.WORKSPACE_BASE_PATH, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.STOP_REQUESTED, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.RETURN_DIRECT_TRIGGERED, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.DIRECT_TOOL_OUTPUTS, KeyStrategy.REPLACE) // Token Usage .addStrategy(MateClawStateKeys.PROMPT_TOKENS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.COMPLETION_TOKENS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.CACHE_READ_TOKENS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.CACHE_WRITE_TOKENS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.REASONING_TOKENS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.RUNTIME_MODEL_NAME, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.RUNTIME_PROVIDER_ID, KeyStrategy.REPLACE) // SourceEvidenceLedger: ActionNode 把每轮 ToolResponse 抽取出的 // (sourcePaths, sourceSymbols, failedPaths) merge 进这个 ledger， // 后续 ReasoningNode / FinalAnswerNode 调 validateAnswer 校验 // 模型引用是否有真实证据。漏注册时框架在多 node merge 时会偶发 // 丢这个键，evidence_insufficient 检查会"静默地不生效" —— // StateKeyRegistrationCoverageTest 专门兜这条。 .addStrategy(MateClawStateKeys.SOURCE_EVIDENCE_LEDGER, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.ACTION_EXECUTION_LEDGER, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.ACTION_COMPLETION_REQUIRED, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.ACTION_COMPLETION_RETRY_COUNT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.CONTINUE_REASONING, KeyStrategy.REPLACE) // Multimodal sidecar routing decision for the current turn. .addStrategy(MateClawStateKeys.ROUTING_DECISION, KeyStrategy.REPLACE) // RFC 48 — persistent goal state keys must be registered in // BOTH graph KeyStrategyFactory blocks. See // GoalStateKeyDoubleRegistrationTest for the strict // double-registration check. .addStrategy(MateClawStateKeys.ACTIVE_GOAL, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_EVALUATION_RESULT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_FOLLOWUP_INJECTED, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_FOLLOWUP_PROMPT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_EVALUATED_THIS_RUN, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_FOLLOWUP_COUNT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_ACCOUNTED_LLM_CALL_COUNT, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.GOAL_HARD_CONTINUATION_COUNT, KeyStrategy.REPLACE) // Skill progressive disclosure — pinned skills loaded this // run. Registered in BOTH graphs so the read-merge-write in // ActionNode is not dropped on multi-node merges. .addStrategy(MateClawStateKeys.LOADED_SKILLS, KeyStrategy.REPLACE) // Tool progressive disclosure — extensions enabled this run. // Registered in BOTH graphs for the same merge-safety reason. .addStrategy(MateClawStateKeys.ENABLED_EXTENSION_TOOLS, KeyStrategy.REPLACE) // Tool-call loop guard counters + one-shot post-mutation // verification reminder flag. Read-merge-write by // ObservationNode; registered in BOTH graphs so the // counters survive multi-node merges. .addStrategy(MateClawStateKeys.TOOL_LOOP_STATS, KeyStrategy.REPLACE) .addStrategy(MateClawStateKeys.MUTATION_REMINDER_INJECTED, KeyStrategy.REPLACE) .build(); GoalEvaluationNode goalEvalNode = new GoalEvaluationNode( goalEvaluationService, goalFollowupService, goalService, goalProperties, conversationWindowManager, conversationService, vip.mate.goal.service.GraphFlavor.REACT); StateGraph graph = new StateGraph("react-agent-v2", keyStrategyFactory) .addNode(MateClawStateKeys.REASONING_NODE, AsyncNodeAction.node_async(reasoningNode)) .addNode(MateClawStateKeys.ACTION_NODE, AsyncNodeAction.node_async(actionNode)) .addNode(MateClawStateKeys.OBSERVATION_NODE, AsyncNodeAction.node_async(observationNode)) .addNode(MateClawStateKeys.SUMMARIZING_NODE, AsyncNodeAction.node_async(summarizingNode)) .addNode(MateClawStateKeys.LIMIT_EXCEEDED_NODE, AsyncNodeAction.node_async(limitExceededNode)) .addNode(MateClawStateKeys.FINAL_ANSWER_NODE, AsyncNodeAction.node_async(finalAnswerNode)) .addNode(MateClawStateKeys.GOAL_EVALUATION_NODE, AsyncNodeAction.node_async(goalEvalNode)) .addEdge(StateGraph.START, MateClawStateKeys.REASONING_NODE) .addConditionalEdges(MateClawStateKeys.REASONING_NODE, AsyncEdgeAction.edge_async(new ReasoningDispatcher()), Map.of(MateClawStateKeys.REASONING_NODE, MateClawStateKeys.REASONING_NODE, MateClawStateKeys.ACTION_NODE, MateClawStateKeys.ACTION_NODE, MateClawStateKeys.SUMMARIZING_NODE, MateClawStateKeys.SUMMARIZING_NODE, MateClawStateKeys.FINAL_ANSWER_NODE, MateClawStateKeys.FINAL_ANSWER_NODE, MateClawStateKeys.LIMIT_EXCEEDED_NODE, MateClawStateKeys.LIMIT_EXCEEDED_NODE)) .addEdge(MateClawStateKeys.ACTION_NODE, MateClawStateKeys.OBSERVATION_NODE) .addConditionalEdges(MateClawStateKeys.OBSERVATION_NODE, AsyncEdgeAction.edge_async(new ObservationDispatcher()), Map.of(MateClawStateKeys.REASONING_NODE, MateClawStateKeys.REASONING_NODE, MateClawStateKeys.SUMMARIZING_NODE, MateClawStateKeys.SUMMARIZING_NODE, MateClawStateKeys.LIMIT_EXCEEDED_NODE, MateClawStateKeys.LIMIT_EXCEEDED_NODE, MateClawStateKeys.FINAL_ANSWER_NODE, MateClawStateKeys.FINAL_ANSWER_NODE)) .addEdge(MateClawStateKeys.SUMMARIZING_NODE, MateClawStateKeys.REASONING_NODE) .addEdge(MateClawStateKeys.LIMIT_EXCEEDED_NODE, MateClawStateKeys.FINAL_ANSWER_NODE) // FinalAnswer -> (active goal && not yet evaluated this run) ? GoalEvaluation : END .addConditionalEdges(MateClawStateKeys.FINAL_ANSWER_NODE, AsyncEdgeAction.edge_async(state -> { MateClawStateAccessor a = new MateClawStateAccessor(state); // Same-turn activation: fall back to a DB lookup (gated on the // feature flag) so a goal the agent set THIS turn is evaluated now, // not only from the next message. See GoalEvaluationNode.resolveActiveGoal. boolean hasGoal = goalProperties.isEnabled() && GoalEvaluationNode.resolveActiveGoal(state, goalService).isPresent(); boolean already = a.goalEvaluatedThisRun(); return (hasGoal && !already) ? MateClawStateKeys.GOAL_EVALUATION_NODE : StateGraph.END; }), Map.of( MateClawStateKeys.GOAL_EVALUATION_NODE, MateClawStateKeys.GOAL_EVALUATION_NODE, StateGraph.END, StateGraph.END)) // GoalEvaluation -> (followup injected) ? Reasoning : END .addConditionalEdges(MateClawStateKeys.GOAL_EVALUATION_NODE, AsyncEdgeAction.edge_async(new vip.mate.agent.graph.edge.GoalEvaluationDispatcher( MateClawStateKeys.REASONING_NODE, StateGraph.END)), Map.of( MateClawStateKeys.REASONING_NODE, MateClawStateKeys.REASONING_NODE, StateGraph.END, StateGraph.END)); return graph.compile(CompileConfig.builder() .recursionLimit(frameworkRecursionLimit()) .withLifecycleListener(new ReActLifecycleListener()) .build()); } catch (Exception e) { throw new MateClawException("err.agent.graph_compile_failed", "StateGraph v2 编译失败: " + e.getMessage()); } } // ==================== 协议能力判断 ==================== private boolean supportsStateGraph(ModelProtocol protocol) { return protocol == ModelProtocol.DASHSCOPE_NATIVE || protocol == ModelProtocol.OPENAI_COMPATIBLE || protocol == ModelProtocol.ANTHROPIC_MESSAGES // RFC-062: Claude Code OAuth tunnels through the same Messages API // wrapped in AnthropicChatModel — same StateGraph capability surface. || protocol == ModelProtocol.ANTHROPIC_CLAUDE_CODE || protocol == ModelProtocol.OPENAI_CHATGPT // Gemini native generateContent — GeminiChatModel exposes the same // streaming + tool-calling surface the StateGraph nodes rely on. || protocol == ModelProtocol.GEMINI_NATIVE; } // ==================== 模型构建 ==================== /** * 构建运行时 ChatModel（不包装为 ChatClient） * 用于 StateGraph 节点直接调用。使用注入的共享 {@link #retryTemplate} 作为 Spring AI * 内层重试策略。 */ public ChatModel buildRuntimeChatModel(ModelConfigEntity runtimeModel) { return buildRuntimeChatModel(runtimeModel, this.retryTemplate); } /** * Resolve the user-facing locale used for sidecar caption prompts. * Reads {@code language} from system settings; falls back to * {@code zh-CN} so CN deployments stay consistent with the chat UI. */ private java.util.Locale resolveLocale() { try { String lang = systemSettingService.getLanguage(); if (lang == null || lang.isBlank()) return java.util.Locale.SIMPLIFIED_CHINESE; return java.util.Locale.forLanguageTag(lang); } catch (Exception e) { return java.util.Locale.SIMPLIFIED_CHINESE; } } /** * 构建运行时 ChatModel，并指定自定义的 Spring AI {@link RetryTemplate}。 *

* 用于调用方（如 Wiki 消化管线）已经有自己的外层重试策略， * 希望绕过 Spring AI 内层重试、独占重试控制权的场景：传入 * {@code RetryTemplate.builder().maxAttempts(1).build()} 即可把内层降级为"只跑一次"。 *

* DashScope 和 OpenAI-ChatGPT 分支不走 Spring AI 的 RetryTemplate 接口， * 本参数对它们无效（它们各自有内部重试或直通）。 */ public ChatModel buildRuntimeChatModel(ModelConfigEntity runtimeModel, RetryTemplate retryOverride) { // PR-0 (RFC-009 Phase 4 prelude): protocol switch extracted to // ProviderChatModelFactory + per-protocol ChatModelBuilder strategies. // Per-protocol builders (DashScope / OpenAI-compatible / Anthropic / // ChatGPT-Responses) live in vip.mate.agent.chatmodel + vip.mate.llm.chatmodel. // See RFC-009 Phase 4 plan for the rationale (circular-dep break for // ProviderInitProbe + AgentGraphBuilder slimming). return chatModelFactory.buildFor(runtimeModel, retryOverride); } /** * RFC-009: build the full multi-provider failover chain for a primary * model. Providers are read from {@code mate_model_provider} ordered by * {@code fallback_priority ASC} (positive values only), each resolved to * its default {@link ModelConfigEntity} and turned into a {@link ChatModel} * via {@link #buildRuntimeChatModel(ModelConfigEntity, RetryTemplate)}. * *

Providers whose API key / base URL is missing (build throws) are * silently skipped with a warning — fallback should never break * the primary call path. The returned list preserves chain order; the * streaming helper tries entries in order until one succeeds.

* *

The primary model is excluded from the chain when its provider + * model name matches a chain entry. Previously only reference equality * was checked, which meant a DashScope-primary deployment ended up with * {@code null} fallback — exactly the case RFC-009 targets.

* * @param primaryModelConfig the {@code ModelConfigEntity} used to build * the primary model; used to identity-filter the chain * @return ordered, possibly-empty list of fallback {@link ChatModel}s */ List buildFallbackChain(ModelConfigEntity primaryModelConfig) { return buildFallbackChain(primaryModelConfig, null); } /** * RFC-009 PR-3 overload: when {@code agentId} is non-null, the agent's * {@code mate_agent_provider_preference} rows bias the chain order — listed * providers come first in their declared {@code sort_order}, then the * remaining providers fall in by global {@code fallback_priority} ascending, * tie-broken by provider id alphabetically. {@code null} agentId keeps the * pre-PR-3 ordering (pure global priority) — that's the path for legacy * callers and tests. * *

Source = the available pool (RFC-009 follow-up). Earlier this * method only considered providers with {@code fallback_priority > 0}, which * meant any provider the user hadn't explicitly opted into the chain was * silently excluded — even if it was healthy and in the pool. The pool is * the source of truth for "what's usable right now"; {@code fallback_priority} * is just an ordering hint within the pool.

* *

Per-provider model selection falls back gracefully: the * provider's {@code is_default=true} chat model wins, otherwise we pick * the first enabled chat model on that provider. Forcing users to mark a * default per provider was administrative friction with no real benefit.

*/ List buildFallbackChain(ModelConfigEntity primaryModelConfig, Long agentId) { List providers; try { // Pull every configured provider, not just the ones with // fallback_priority > 0 — pool membership is what gates usability, // not this admin-set hint. providers = modelProviderService.listProviders().stream() .filter(dto -> Boolean.TRUE.equals(dto.getConfigured())) .map(dto -> { try { return modelProviderService.getProviderConfig(dto.getId()); } catch (Exception e) { return null; } }) .filter(java.util.Objects::nonNull) .collect(java.util.stream.Collectors.toCollection(ArrayList::new)); } catch (Exception e) { log.warn("[LlmFailover] failed to load configured providers: {}; running without fallback", e.getMessage()); return List.of(); } if (providers.isEmpty()) { return List.of(); } // Order: explicit fallback_priority > 0 wins (asc), priority == 0 trails alphabetically. providers.sort((a, b) -> { int pa = a.getFallbackPriority() == null ? 0 : a.getFallbackPriority(); int pb = b.getFallbackPriority() == null ? 0 : b.getFallbackPriority(); if (pa > 0 && pb > 0) return Integer.compare(pa, pb); if (pa > 0) return -1; // a has explicit priority, comes first if (pb > 0) return 1; // b has explicit priority, comes first return a.getProviderId().compareTo(b.getProviderId()); // both 0: alphabetical }); String primaryProviderId = primaryModelConfig != null ? primaryModelConfig.getProvider() : null; String primaryModelName = primaryModelConfig != null ? primaryModelConfig.getModelName() : null; // RFC-090 §9.2 调整 C — lift providers that satisfy the bound-skill // capability set (vision / video / audio) ahead of those that don't. // Run before planning so the non-preferred tail inherits this order; // the explicit preferred-model head keeps the user's declared order. try { providers = new ArrayList<>(providerRouter.reorderForCapabilities(agentId, providers)); } catch (Exception e) { log.debug("[ProviderRouter] chain reorder failed: {}", e.getMessage()); } // Preferred-model chain: explicit (provider, model) entries lead in the // user's order — the same provider may repeat with different models — // then every non-preferred provider follows with its default model. List preferred = agentId == null ? java.util.Collections.emptyList() : agentBindingService.getPreferredProviderModels(agentId); List globalProviderIds = providers.stream() .map(ModelProviderEntity::getProviderId) .toList(); List plan = planFallbackOrder(preferred, globalProviderIds); if (!preferred.isEmpty()) { log.debug("[LlmFailover] agent={} preferred-model chain={} -> plan={}", agentId, preferred, plan); } // Dedup by exact (provider, model) — seeded with the primary so we never // rebuild the primary call, but OTHER models of the primary provider are // still legitimate fallback entries. List chain = new ArrayList<>(); Set seen = new java.util.HashSet<>(); if (primaryProviderId != null && primaryModelName != null) { seen.add(primaryProviderId + "::" + primaryModelName); } for (ProviderModelRef ref : plan) { String pid = ref.providerId(); // RFC-009 Phase 4: skip providers known-bad at build time. The runtime // walker re-checks pool membership per request, so a provider that // re-enters the pool later still gets used (graph rebuilt on // ModelConfigChangedEvent). if (providerPool != null && !providerPool.contains(pid)) { log.debug("[LlmFailover] skipping provider {} — not in available pool", pid); continue; } ModelConfigEntity fallbackConfig = resolveChainModel(ref); if (fallbackConfig == null) { log.debug("[LlmFailover] skipping {} — no usable chat model", pid); continue; } String key = pid + "::" + fallbackConfig.getModelName(); if (!seen.add(key)) { // Exact (provider, model) already queued or equal to the primary. continue; } try { ChatModel m = buildRuntimeChatModel(fallbackConfig, RetryTemplate.builder().maxAttempts(1).build()); chain.add(new vip.mate.llm.failover.FallbackEntry(pid, m)); log.info("[LlmFailover] chain[{}] = {}/{}", chain.size(), pid, fallbackConfig.getModelName()); } catch (Exception e) { log.warn("[LlmFailover] skipping provider {} — chat model build failed: {}", pid, e.getMessage()); } } return chain; } /** * Resolve a planned chain entry to a concrete chat model. A pinned model * ({@code modelId != null}) is used when it still exists and is enabled; * otherwise we fall back to the provider's default chat model so a deleted * or disabled pin keeps the provider in the chain. */ private ModelConfigEntity resolveChainModel(ProviderModelRef ref) { if (ref.modelId() != null) { try { ModelConfigEntity m = modelConfigService.getModel(ref.modelId()); // Honour the pin only when it is a usable chat model that actually // belongs to this entry's provider. The FallbackEntry is keyed by // ref.providerId() for cooldown/pool, so a model from a different // provider would mis-key the chain; an embedding model would never // serve as a chat fallback. Either case falls back to the // provider's default chat model. if (m != null && Boolean.TRUE.equals(m.getEnabled()) && ref.providerId().equals(m.getProvider()) && (m.getModelType() == null || "chat".equals(m.getModelType()))) { return m; } log.info("[LlmFailover] pinned model {} for provider {} not usable " + "(disabled / wrong provider / non-chat), using provider default", ref.modelId(), ref.providerId()); } catch (Exception e) { log.info("[LlmFailover] pinned model {} for provider {} unresolved ({}), using provider default", ref.modelId(), ref.providerId(), e.getMessage()); } } return pickFallbackModel(ref.providerId()); } /** * Pick a chat model to use as a fallback for the given provider: *
    *
  1. Provider's explicit default ({@code is_default=true}) — most user-aligned.
    2. *
  2. First enabled chat model on the provider — pragmatic fallback so the user * isn't required to mark a default per provider just to participate in failover.
    3. *
* Returns {@code null} when the provider has no usable chat model. */ private ModelConfigEntity pickFallbackModel(String providerId) { try { ModelConfigEntity defaultModel = modelConfigService.getDefaultModelByProvider(providerId); if (defaultModel != null) return defaultModel; } catch (Exception ignored) { // No default — fall through to first-enabled lookup. } try { return modelConfigService.listModelsByProvider(providerId).stream() .filter(m -> Boolean.TRUE.equals(m.getEnabled())) .filter(m -> m.getModelType() == null || "chat".equals(m.getModelType())) .findFirst() .orElse(null); } catch (Exception e) { log.warn("[LlmFailover] cannot list models for provider {}: {}", providerId, e.getMessage()); return null; } } /** * Plan the fallback order as a list of (provider, model) refs. * *

Head: the agent's explicit preference entries in declared order, * model-granular — the same provider may appear more than once with * different models. Exact (provider, model) duplicates are dropped. * *

Tail: every provider not named in the preferences, in the supplied * global order, each using its default model ({@code modelId == null}). * *

Preference entries with a blank provider id are ignored. Package-private * for unit testing — see {@code AgentGraphBuilderPreferenceTest}. */ static List planFallbackOrder(List preferred, List globalProviderIds) { List plan = new ArrayList<>(); Set headEntryKeys = new java.util.HashSet<>(); Set headProviderIds = new java.util.HashSet<>(); if (preferred != null) { for (ProviderModelRef ref : preferred) { if (ref == null || ref.providerId() == null || ref.providerId().isBlank()) continue; String key = ref.providerId() + "::" + (ref.modelId() == null ? "" : ref.modelId()); if (!headEntryKeys.add(key)) continue; // exact (provider, model) dup plan.add(ref); headProviderIds.add(ref.providerId()); } } if (globalProviderIds != null) { for (String pid : globalProviderIds) { if (pid == null || pid.isBlank()) continue; if (headProviderIds.contains(pid)) continue; // already led by an explicit entry plan.add(new ProviderModelRef(pid, null)); } } return plan; } /** * Resolve the effective working directory for an agent. *

Precedence: *

    *
  1. When the agent-level override is set, it wins.
    2. *
  2. An absolute override is used verbatim, but only when it sits * inside the workspace basePath (or when the workspace has no * basePath of its own). An absolute path that points outside a * configured workspace root is rejected — otherwise a less-trusted * user with agent-edit access could set * {@code workspaceBasePath="/"} and bypass workspace scoping.
    3. *
  3. A relative override is resolved under the workspace basePath * when the workspace has one, matching the UI hint that agent paths * are relative to the workspace root.
    4. *
  4. A relative override with no workspace basePath is used as-is * (resolves against the JVM working directory at file-tool time).
    5. *
  5. With no override, the workspace basePath is inherited verbatim; * returns {@code null} when neither side has a value.
    6. *
* * @throws IllegalArgumentException when an absolute override escapes the * workspace root */ public static String resolveAgentBasePath(String agentOverride, String workspaceBase) { boolean hasOverride = agentOverride != null && !agentOverride.isBlank(); boolean hasWorkspace = workspaceBase != null && !workspaceBase.isBlank(); if (!hasOverride) { return hasWorkspace ? workspaceBase : null; } Path overridePath = Paths.get(agentOverride); if (overridePath.isAbsolute()) { if (hasWorkspace) { Path wsRoot = Paths.get(workspaceBase).toAbsolutePath().normalize(); Path absOverride = overridePath.toAbsolutePath().normalize(); if (!absOverride.startsWith(wsRoot)) { throw new IllegalArgumentException( "Agent workspaceBasePath override must be inside the workspace root: " + absOverride + " is not under " + wsRoot); } } return agentOverride; } if (hasWorkspace) { // Relative override resolves under the workspace root; reject any value // that escapes it via "../" so attachment/media/tool I/O stays contained. Path wsRoot = Paths.get(workspaceBase).toAbsolutePath().normalize(); Path resolved = wsRoot.resolve(agentOverride).normalize(); if (!resolved.startsWith(wsRoot)) { throw new IllegalArgumentException( "Agent workspaceBasePath override must stay inside the workspace root: " + resolved + " escapes " + wsRoot); } return Paths.get(workspaceBase).resolve(agentOverride).toString(); } return agentOverride; } /** * Finds the first enabled chat model whose provider is fully configured. * Used as a fallback when the default model's provider is not available. */ private ModelConfigEntity findFirstAvailableChatModel() { return modelConfigService.listByType("chat").stream() .filter(m -> Boolean.TRUE.equals(m.getEnabled())) .filter(m -> { try { return modelProviderService.isProviderConfigured(m.getProvider()); } catch (Exception e) { return false; } }) .findFirst() .orElse(null); } // PR-0b: legacy single-fallback buildFallbackModel deleted (already @Deprecated, no callers). // PR-0b: isDashScopeSearchEnabled moved to DashScopeChatModelBuilder. // ==================== Prompt 构建 ==================== /** * Cache-stable platform identity, appended to every agent's system * prompt. Answers "who are you / what are you based on". The volatile * "which model right now" fact is injected per-turn by * {@link vip.mate.agent.context.RuntimeContextInjector} instead, to * keep this prefix's prompt-cache hash stable. */ static final String ABOUT_YOU_BLOCK = """ ## About You You are powered by MateClaw — a multi-user AI Agent platform built on Spring Boot 3.5 and Spring AI Alibaba Graph. You are reachable through WebChat and 8+ IM channels (DingTalk, Feishu, WeCom, WeChat, Telegram, Discord, QQ, Slack). If asked who you are or what you are based on, answer with MateClaw and the technology stack above. """; private String buildEnhancedPrompt(AgentEntity entity, boolean builtinSearchEnabled) { return buildEnhancedPrompt(entity, builtinSearchEnabled, Integer.MAX_VALUE); } private String buildEnhancedPrompt(AgentEntity entity, boolean builtinSearchEnabled, int memoryBudgetTokens) { // The agent's own systemPrompt encodes its identity (role / goal / // backstory). The memory block from workspace files (AGENTS.md, SOUL.md, // PROFILE.md, MEMORY.md, ...) augments that identity with durable // context. Both are independently optional, but when both exist they // must be joined — earlier this branch picked memory and silently // dropped the identity prompt, so editor-side identity changes never // reached runtime if the agent had any workspace files. String identityPrompt = entity.getSystemPrompt() != null ? entity.getSystemPrompt().trim() : ""; String memoryPrompt = memoryManager.buildSystemPromptBlock(entity.getId(), memoryBudgetTokens); StringBuilder basePromptBuilder = new StringBuilder(); if (!identityPrompt.isEmpty()) { basePromptBuilder.append(identityPrompt); } if (memoryPrompt != null && !memoryPrompt.isBlank()) { if (basePromptBuilder.length() > 0) { basePromptBuilder.append("\n\n"); } basePromptBuilder.append(memoryPrompt); } String basePrompt = basePromptBuilder.toString(); // The skill catalog (## Skills) is NOT baked here. It is rendered at // runtime by the reasoning / step-execution nodes via // SkillCatalogRenderer so its ordering can react to skills loaded this // run (load_skill pins). Keeping it out of the baked system prompt also // keeps the prompt-cache prefix stable across turns. // 工具调用指导 String toolGuidance = """ ## Runtime Context - Current Agent ID: %s ## Workspace Memory Guidelines Your durable memory is stored in database-backed workspace markdown files for this agent: - `PROFILE.md`: stable user profile, preferences, collaboration style - `MEMORY.md`: distilled long-term memory, durable facts, lessons, recurring patterns - `memory/YYYY-MM-DD.md`: daily notes, raw events, temporary observations, open loops Use workspace memory tools instead of local filesystem tools for those files: - `list_workspace_memory_files(agentId=..., filenamePrefix=...)` - `read_workspace_memory_file(agentId=..., filename=...)` - `write_workspace_memory_file(agentId=..., filename=..., content=...)` - `edit_workspace_memory_file(agentId=..., filename=..., oldText=..., newText=...)` Memory writing policy: - Stable user preference, identity, collaboration habit -> `PROFILE.md` - Stable project fact, workflow, tool setup, lesson learned, recurring decision -> `MEMORY.md` - One-off event, meeting note, temporary context, today's decision trace -> `memory/YYYY-MM-DD.md` - Read before write unless you are creating a brand new daily note - Do not store secrets or highly sensitive data unless the user explicitly asks - Updating workspace memory files is internal state maintenance for this agent and can be done proactively when useful Memory emergence policy: - If the same preference, constraint, workflow, or lesson appears repeatedly, consolidate it from daily notes into `MEMORY.md` - Prefer updating an existing section over appending duplicate bullets - Treat `MEMORY.md` as a compact mental model, not a raw transcript dump - When answering tasks involving prior decisions, preferences, habits, or ongoing work, proactively consult relevant workspace memory first ## Structured Memory Tools For discrete, typed facts use structured memory tools (separate from workspace files): - `remember_structured(agentId, type, key, content)` — store a typed entry - `recall_structured(agentId, type, keyword)` — search entries by type and/or keyword - `forget_structured(agentId, type, key)` — remove an entry Types: - `user`: preferences, expertise, communication style, role - `feedback`: behavioral corrections or confirmed approaches (include WHY) - `project`: decisions, deadlines, constraints not derivable from code/git - `reference`: pointers to external systems (Linear boards, Grafana dashboards, Slack channels) Use workspace memory tools (MEMORY.md, daily notes) for long-form narrative notes. Use structured memory tools for key-value facts the system can query efficiently. ## Memory vs Knowledge Base Precedence When a question is about the user themselves — who they are, their current project, its name/codename, tech stack, goals, metrics, budget, team, or what they are working on — your recalled memory (the block plus structured/workspace memory) is the authoritative source. Knowledge-base / wiki pages are reference material that may describe unrelated, example, or upstream projects; do NOT treat a KB page's subject as the user's own project. Only read the knowledge base for explicit reference lookups, never to decide what the user's project is. If memory and a KB page disagree about the user's project, trust memory. If memory has no answer, say you do not have it rather than adopting a KB article as the user's project. ## Session Search - `session_search(agentId, mode, query, limit)` — search conversation history - mode="recent": list recent conversations (titles, times, message counts) - mode="search": keyword full-text search across past messages - Use this to recall previous discussions, look up past decisions, or find context from earlier conversations - Only completed sessions (not currently running) are included in results ## Tool Usage Guidelines When you have available tools, use them to access local system information, files, or execute commands. Do not assume you cannot access local resources - try calling the appropriate tool first. If a tool requires approval due to security policies, the system will prompt the user for confirmation. Only state you cannot access something if no relevant tool is available. Do not claim a tool-generated file, URL, UUID, path, task id, or success result before the corresponding tool call has completed. If a tool is needed, call the tool first, then report only the actual returned result. ## MCP Tool Naming Tools from MCP servers have names shaped like `mcp___`: - `` is a numeric ID identifying which MCP server the tool belongs to. - Tools from DIFFERENT servers have DIFFERENT serverId prefixes, even if they have the same raw name (e.g. `search` on server A vs server B) — they are DIFFERENT tools and are NOT interchangeable. - Each MCP tool's description starts with `[MCP server: ]` so you can identify the source server by its human-readable name. - MCP tools are listed in the Extension Tools catalog by default. Call `tool_call(toolName="", arguments={...})` to execute one in the same action round; use `tool_search` first only when the exact name is unknown. - Always call tools by the EXACT name shown in the tool list. Do NOT reconstruct a tool name by swapping the slug into a serverId you remember from a previous successful call — that produces a non-existent tool name and the call will fail. - If a tool call returns "Tool not found" with candidate suggestions, pick the correct one from the candidates verbatim. ## Multi-Part Question Guidelines When the user asks multiple questions or requests multiple tasks in a single message: 1. Structure your final answer with numbered sections, one per sub-task 2. Each section must contain the complete, detailed result for that sub-task 3. Never compress earlier sub-tasks into summary sentences while expanding the last one 4. If observations were summarized during processing, reconstruct each section from the summary 5. Treat each sub-task's result as equally important regardless of processing order ## File Reading Guidelines **Text Files** (use read_file): For .txt, .md, .json, .yaml, .csv, .log, .py, .java, .js, .html, .xml, .sql, .conf, .ini, .toml files. **Office/PDF Documents** (DO NOT use read_file): For .pdf, .docx, .doc, .xlsx, .xls, .pptx, .ppt files, NEVER use read_file. Instead use: - detect_file_type(filePath="...") - to check file type first - extract_document_text(filePath="...") - general document extraction - extract_pdf_text(filePath="...") - for PDF files - extract_docx_text(filePath="...") - for Word documents Example workflow for document: 1. detect_file_type(filePath="/path/to/document.pdf") 2. Based on result, use extract_pdf_text() or extract_document_text() 3. Process the extracted text content If you try to read a PDF/Office file with read_file, you will get binary garbage or an error. ## ProgressLedger Discipline (mandatory) The `## 当前任务进度` block injected near the top of every turn is the **authoritative record** of what you have done and what remains. Treat it as ground truth, not as a scratchpad you may ignore. - **On starting any multi-step task** (≥3 tool calls expected), call `progress_update` in parallel batches of at most 16 calls to register every pending step BEFORE doing the work. Split larger ledgers across turns so the executor cap never drops entries. - **After each completed sub-step**, immediately call `progress_update` to flip its status to `done`. "Immediately" means in the same tool_calls batch that returns the result, not after the next reasoning turn. - **Never re-execute a step the ledger shows as `done`** unless you can articulate why the prior result is stale. - **🔒 固定约束 entries** (pinned from skill manifests) are non-negotiable. They survive context compression for a reason — re-read them every turn and make sure your planned action still satisfies them. - **🔧 自动记录 entries** are auto-filled by Java after each tool call. They are a safety net, not a substitute for your own `progress_update` — if you only rely on them, you will lose the pending/blocked view that drives planning. - If you see a `⚠️ 进度账本已 N 秒未更新` reminder, **stop whatever you are doing and update the ledger first**. Continuing to call tools without updating the ledger is the #1 cause of duplicate work and missed steps. - The ledger is per-conversation and persists across context trims; treating it as ephemeral will cause you to repeat work after every compaction. ## Environment Change Notifications Occasionally you will see a `## 📢 环境变更通知` block injected near the top of a turn. It is generated by Java when an external event affects your runtime — an MCP server disconnecting, a skill being updated/removed, or a tool binding change. - These notifications are **authoritative** — Java detected the change; do not second-guess them by re-probing the tool. - If a notification says an MCP server is unavailable, **immediately stop calling tools prefixed with that server's id** and either switch to an alternative or report the gap to the user. - If a notification says a skill was updated, **re-load it via `load_skill`** to refresh its constraints in your pinned ledger; the old constraints you remember may no longer apply. - If a notification says a tool was removed, **do not attempt to call it**; pick a different approach or ask the user. - These notifications appear at most once per event; if you miss one, it will not be repeated, so act on it in the turn you see it. """.formatted(entity.getId()); // Web-search vs browser_use priority guidance — emitted unconditionally so the rule // also reaches OpenAI-compatible / Anthropic / Gemini / DeepSeek / Ollama agents that // do not have builtin search. Issue #40: without this rule the model treats // browser_use as a search tool and gets stuck in a Playwright launch loop on Windows. String searchGuidance = """ ## Web Search Capability ### Tool Priority - For plain web search or fetching public page content, call the `search` tool. It supports advanced parameters: `freshness` (day/week/month/year), `language` (zh-CN/en), `count` (1-10). - Call `browser_use` ONLY when you need to interact with a page (click, fill forms, screenshot, run JS, follow a logged-in flow). Do NOT use `browser_use` as a search alternative. - **NEVER** call both `browser_use` and `search` for the same query. - When searching for news, use the standard format: `📰 [Category] Title — Source | Time + Summary`, up to 5 results per category. """; if (builtinSearchEnabled) { searchGuidance += """ ### Built-in Search (preferred when available) Your responses automatically incorporate live web search results from the model provider. For most queries, answer directly — your reply already includes real-time search data. Do NOT say you cannot search. Use the `search` tool ONLY when you need precise time filtering (e.g., "yesterday's news" → freshness=day), a specific language, or when built-in results feel insufficient. """; } // Wiki 知识库上下文注入。Share the same prefix budget as the memory // block so a large KB's page listing can't consume a fixed // maxContextChars-sized slice of a small model's window every turn // (issue #521). Integer.MAX_VALUE (the unbudgeted default path) keeps // the legacy chars-only cap for large cloud models. Integer wikiBudgetTokens = memoryBudgetTokens == Integer.MAX_VALUE ? null : memoryBudgetTokens; String wikiContext = wikiContextService.buildWikiContext(entity.getId(), wikiBudgetTokens); // Team context (role-specific board playbook, or a negative notice for // agents outside any team). Baked here so it shares the prompt-cache // prefix; TeamChangedEvent evicts the cached agent on composition changes. String teamContext = teamContextBuilder.buildTeamContext(entity.getId()); return basePrompt + ABOUT_YOU_BLOCK + toolGuidance + searchGuidance + wikiContext + teamContext; } /** * Build the per-agent {@link SkillCatalogRenderer}. Captures the agent's * bound skills, effective tool allowlist, model window and workspace once; * the returned renderer is invoked each turn with the skills loaded so far * this run so {@code load_skill} pins float to the top of the catalog. * *

agent-4: when any loaded skill declares structured {@code constraints} * in its manifest, the rendered catalog gets a trailing anchor note * {@code "🔒 = 含固定约束的 skill（详见 ProgressLedger）"} so the LLM has * a visible cue that some skills carry non-negotiable rules pinned into * the ledger. The cue is appended (not interleaved) to keep the * catalog's prompt-cache hash stable for the unchanged prefix. */ private SkillCatalogRenderer buildSkillCatalogRenderer(AgentEntity entity, Set boundTools, Integer maxInputTokens) { Set boundSkillIds = agentBindingService.getBoundSkillIds(entity.getId()); Long agentId = entity.getId(); Long workspaceId = entity.getWorkspaceId(); return loaded -> { String catalog = skillRuntimeService.buildSkillPromptEnhancement( boundSkillIds, boundTools, maxInputTokens, agentId, workspaceId, loaded); if (catalog == null || catalog.isBlank() || loaded == null || loaded.isEmpty()) { return catalog; } // Scan loaded skills for any with structured constraints. We only // surface the anchor when at least one matches, to avoid noisy // output on constraint-free skills. boolean anyHasConstraints = false; for (String skillName : loaded) { try { vip.mate.skill.runtime.model.ResolvedSkill skill = skillRuntimeService.findActiveSkill(skillName, workspaceId); if (skill != null && skill.getManifest() != null) { List constraints = skill.getManifest().getConstraints(); if (constraints != null && !constraints.isEmpty()) { anyHasConstraints = true; break; } } } catch (Exception ignored) { // best-effort lookup; do not fail the catalog render } } if (!anyHasConstraints) { return catalog; } return catalog + "\n\n🔒 = 含固定约束的 skill（已写入 ProgressLedger，详见 🔒 固定约束 段落，全程不可忽略）"; }; } /** * Resolve the {@code reasoning_effort} to pass to the reasoning / * step-execution nodes for the given model. */ private String resolveReasoningEffortForModel(ModelConfigEntity runtimeModel) { ModelProviderEntity provider = modelProviderService.getProviderConfig(runtimeModel.getProvider()); Map kwargs = modelProviderService.readProviderGenerateKwargs(provider); ModelFamily family = ModelFamily.detect(runtimeModel.getModelName()); return ReasoningEffortResolver.resolveReasoningEffort(runtimeModel.getModelName(), kwargs, family); } }