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Orchestration-2nd-draft

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Comprehensive Orchestration Guide

Table of Contents

  1. Overview
  2. Core Components
  3. The Orchestration Flow
  4. Agent Configuration System
  5. Tool System
  6. State Management
  7. Streaming Architecture
  8. Multi-Agent System
  9. Data Policies
programming-language-concepts.md
zero-language-explanation.md
DB
01-introduction.md
02-relational-databases.md
03-database-design.md
04-indexing.md
05-transactions-acid.md
06-nosql-databases.md
07-query-optimization.md
08-replication-ha.md
09-sharding-partitioning.md
10-caching-strategies.md
11-cap-theorem.md
12-connection-pooling.md
13-backup-recovery.md
14-monitoring.md
15-database-selection.md
README.md
JS
Event loop
Merlin Backend
01-Orchestration.md
02-DeepResearch.md
03-Search.md
04-Scraping.md
05-Streaming.md
06-MultiProviderLLM.md
07-MemoryAndContext.md
08-ErrorHandling.md
09-RateLimiting.md
10-TaskQueue.md
11-SecurityAndAuth.md
Orchestration-2nd-draft
OpenAI Agents Python
00_OVERVIEW.md
01_AGENT_SYSTEM.md
02_RUNNER_SYSTEM.md
03_TOOL_SYSTEM.md
04_ITEMS_SYSTEM.md
05_GUARDRAILS.md
06_HANDOFFS.md
07_MEMORY_SESSIONS.md
08_MODEL_PROVIDERS.md
09_SANDBOX_SYSTEM.md
10_TRACING.md
11_RUN_STATE.md
12_CONTEXT.md
13_LIFECYCLE_HOOKS.md
14_CONFIGURATION.md
15_ERROR_HANDLING.md
16_STREAMING.md
17_EXTENSIONS.md
18_MCP_INTEGRATION.md
19_BEST_PRACTICES.md
20_ARCHITECTURE_PATTERNS.md
opencode-study
context-handling
core
Python
Alembic
Basics
sqlalchemy - fastapi
SQLAlchemy overview
tweets
system_design_for_agentic_apps.md
  • Usage Tracking
  • Error Handling
  • Advanced Features

    • Overview

    The orchestration layer is a custom-built, multi-agent tool orchestration system designed for AI assistant interactions. It manages tool execution, agent coordination, streaming responses, and context window optimization - all without relying on external frameworks like LangChain or AutoGPT.

    Key Design Principles
    • Multi-Agent Architecture: Different agents for different use cases (main thread, deep research, researcher)
    • Streaming-First: Real-time streaming of tool execution and LLM responses
    • Context Optimization: Intelligent token management with the token engine
    • Configurable Behavior: Agent configs allow complete customization of orchestration behavior
    • Policy-Based Data Control: Fine-grained control over what data gets stored
    • Graceful Degradation: Comprehensive error handling and fallback mechanisms
    High-Level Architecture
    Plain text
    User Request
    ↓
Unified API Endpoint
    ↓
ToolOrchestrator (with Agent Config)
    ↓
ToolRegistry (manages available tools)
    ↓
LLM Provider (Anthropic/OpenAI/Google)
    ↓
Tool Execution (with streaming)
    ↓
Token Engine (context optimization)
    ↓
Response Storage (with data policies)

    Core Components

    1. ToolOrchestrator Class

    Location: src/server/endpoints/unified/orchestrator/toolOrchestrator.ts

    The ToolOrchestrator is the heart of the orchestration system. It's a 1065-line class that manages the entire orchestration lifecycle.

    Constructor
    TypeScript
    constructor(
  chatCtx: TChatContext,
  agentName: TAgentName = AGENT_NAMES.MAIN_THREAD,
  toolRegistry: ToolRegistry,
  agentConfig: TAgentConfig,
  customToolCallLimit?: number,
)

    Parameters:

    • chatCtx: Chat context containing conversation history, model config, and chat state
    • agentName: Which agent to run (MainThreadAgent, DeepResearchSupervisor, ResearcherAgent)
    • toolRegistry: Registry of available tools for this session
    • agentConfig: Configuration object defining agent behavior
    • customToolCallLimit: Optional override for tool call limits
    Key Properties
    TypeScript
    private chatCtx: TChatContext;
private registry: ToolRegistry;
private agentName: TAgentName;
private customToolCallLimit?: number;
private agentConfig: TAgentConfig;
usageConfigArray: TUsageConfig[] = [];
private currentToolMetadata: TToolMetadata = {};
public currentExecutingTool?: {
  toolCallId: string;
  functionName: string;
  toolIndex: number;
};
    • chatCtx: Holds conversation state, model config, attachments, etc.
    • registry: Manages which tools are available
    • agentName: Determines which agent behavior to use
    • agentConfig: Defines how this agent behaves (hooks, limits, policies)
    • usageConfigArray: Tracks token usage across all iterations
    • currentToolMetadata: Metadata about tools being executed
    • currentExecutingTool: Currently running tool for error reporting
    Main Method: run()

    The run() method is the main entry point that executes the entire orchestration flow.

    TypeScript
    async run(config: TToolOrchestratorRunInput) {
  // 1. Initialize orchestration state
  const { state, agentContext, messages, ... } = await this.initializeOrchestrationState(config);
  
  // 2. Convert tools to OpenAI format
  const openAiTools = this.registry.getAll().map(t => ({
    type: "function",
    function: {
      name: t.name,
      description: t.description,
      parameters: zodToJsonSchema(t.parameters),
    },
  }));
  
  // 3. Main orchestration loop
  while (this.agentConfig.shouldContinueLoop(state)) {
    // 3a. Decide if tools should be sent
    const shouldSendTools = this.agentConfig.shouldSendTools(state, openAiTools, schema);
    
    // 3b. Call LLM with or without tools
    const chatRequest = await provider.chat(llmInput);
    const chatResponse = await chatRequest.stream();
    
    // 3c. Stream the response to client
    const streamOutput = await streamer.stream(...);
    
    // 3d. Parse tool calls from response
    const toolCalls = await getTokenizedToolCalls(toolCallParsed.data);
    
    // 3e. Execute tools (if any)
    for await (const chunk of this.executeRequestedTools(toolCalls, ...)) {
      // Handle streaming tool results
    }
    
    // 3f. Run token engine for context optimization
    const trimmedMessages = await engine({
      messages: currentMessages,
      inLoop: state.inLoopTrimmedMessages,
      contextLimit: contextLimit,
      response: { ... },
    });
    
    // 3g. Store results based on data policy
    if (dataPolicy.shouldStoreToolResults) {
      assistantMessageNode.contentV2.push({
        type: "TOOL_RESULT",
        toolResults: filterToolResultsByPolicy(toolResults, dataPolicy),
      });
    }
    
    // 3h. Update iteration counter
    state.currentIteration += iterativeToolCalls.length;
  }
  
  return {
    result: "OK",
    finalIteration: state.currentContentIndex,
    toolCallCount: state.currentIteration,
    usageConfigArray: this.usageConfigArray,
  };
}
    Tool Execution: executeRequestedTools()

    This is an async generator that executes tools and yields streaming results.

    TypeScript
    private async *executeRequestedTools(
  calls: { id: string; function: { name: TToolName; arguments: string } }[],
  currentContentIndex: number,
  agentContext: TAgentContext,
): AsyncIterable<TStreamChunk> {
  // 1. Create progress event manager
  const progress = EventManager.init(safeToolIndex, "V2");
  yield { type: "tool:progress", progress };
  
  // 2. Create tasks for all tools (parallel execution)
  const tasks = calls.map((call, index) =>
    this.runSingleTool(call.function.name, call.id, call.function.arguments, progress, agentContext, index),
  );
  
  // 3. Execute and stream results
  for await (const work of tasks) {
    for await (const chunk of work) yield chunk;
  }
}

    Stream Chunk Types:

    • tool:progress: Initial progress event with EventManager
    • tool:start: Tool execution begins
    • tool:stream: Streaming tool result (for tools that return streams)
    • tool:done: Tool execution completed with result
    • tool:error: Tool execution failed
    Single Tool Execution: runSingleTool()
    TypeScript
    private runSingleTool(
  name: TToolName,
  id: string,
  args: string,
  progress: EventManager,
  agentContext: TAgentContext,
  index: number,
) {
  return async function* (this: ToolOrchestrator): AsyncIterable<TStreamChunk> {
    yield { type: "tool:start", id, function: { name }, progress };
    
    // 1. Check if tool should be used
    if (tool.shouldUse && !(await tool.shouldUse(ctx))) {
      yield { type: "tool:error", id, function: { name }, error: "TOOL_NOT_ENABLED" };
      return;
    }
    
    // 2. Parse arguments (with agent-specific preprocessing)
    const parsedArgs = this.agentConfig.preprocessToolArguments
      ? await this.agentConfig.preprocessToolArguments(toolCall, ctx, this.toolMetadata, agentContext)
      : JSON.parse(args);
    
    // 3. Set current executing tool context
    this.currentExecutingTool = { toolCallId: id, functionName: name, toolIndex: index };
    
    // 4. Execute tool
    const resultOrStream = await tool.execute(parsedArgs, ctx, progress, toolContext);
    
    // 5. Handle streaming vs non-streaming results
    if (isAsyncIterable(resultOrStream)) {
      const iterator = resultOrStream[Symbol.asyncIterator]();
      while (true) {
        const { value, done } = await iterator.next();
        if (done) {
          yield { type: "tool:done", id, function: { name }, result: value };
          break;
        }
        if ("stream" in value) {
          yield { type: "tool:stream", id, function: { name }, content: value };
        }
      }
    } else {
      yield { type: "tool:done", id, function: { name }, result: resultOrStream };
    }
  }.bind(this)();
}
    2. ToolRegistry

    Location: src/server/endpoints/unified/tools/toolRegistry.ts

    The ToolRegistry manages the lifecycle of tools - registration, retrieval, and filtering.

    TypeScript
    export class ToolRegistry {
  private tools: Map<TToolName, Tool> = new Map();
  private basePrompt: string;
  
  constructor(tools: Tool[] = [], basePrompt: string = MERLIN_TOOL_PROMPT) {
    tools.forEach((tool) => this.register(tool));
    this.basePrompt = basePrompt;
  }
  
  static defaultRegistry(
    userAddedModes: TUserAddedChatModes[],
    settingsV3: TSettingsV3,
    requestClient: RequestClient,
  ) {
    const registry = new ToolRegistry(DEFAULT_TOOLS);
    
    // Add tools based on user added modes
    userAddedModes.forEach((mode) => {
      if (mode === "RAG" || mode === "CHATBOT") registry.register(ragTool);
      if (mode === "CHATBOT") registry.register(chatbotTool);
      if (mode === "DATA_ANALYSIS") registry.register(dataAnalysisTool);
    });
    
    // Clear all tools for deep research (has custom tools)
    if (userAddedModes.includes("DEEP_RESEARCH")) {
      registry.clear();
    }
    
    // Remove memory tools on mobile if disabled
    if (!settingsV3.memory.isEnabled) {
      registry.unregister(toolNames.Enum.store_memory_tool);
      registry.unregister(toolNames.Enum.retrieve_memory_tool);
    }
    
    return registry;
  }
  
  register(tool: Tool): void
  unregister(toolName: TToolName): void
  clear(): void
  get(name: TToolName): Tool | undefined
  getAll(): Tool[]
  getBasePrompt(): string
}

    Default Tools:

    • memoryRetrievalTool: Retrieve user memories
    • memoryStorageTool: Store information to user memories
    • craftTool: Generate code/crafts
    • webSearchTool: Search the web
    • imageGenTool: Generate images
    3. Agent Configuration System

    Location: src/server/endpoints/unified/orchestrator/configs/

    Agent configs define the behavior of different agent types. They use a hook-based pattern for maximum flexibility.

    TAgentConfig Interface
    TypeScript
    export type TAgentConfig = {
  // Agent properties
  enableGlobalContextUpdate: boolean;  // Whether to update global request context
  useParallelTools: boolean;            // Whether to use parallel tool calls
  
  // Lifecycle hooks
  initializeAgentState?: (state: TOrchestratorState) => void;
  shouldContinueLoop: (state: TOrchestratorState) => boolean;
  shouldSendTools: (state, openAiTools, schema) => boolean;
  getModelId?: (state, baseModelConfig) => TLLMModels;
  
  // Tool execution hooks
  onNoToolCalls?: (state, context, reason, toolCalls?) => Promise<{ shouldContinue: boolean }>;
  processToolCalls?: (toolCalls, state, context) => Promise<boolean>;
  onToolsSelected?: (toolCalls, state, context) => Promise<void>;
  onToolProgress?: (toolName, event, context) => void;
  
  // Tool argument/result hooks
  preprocessToolArguments?: (toolCall, context, toolMetadata, agentContext) => Promise<object>;
  handleSpecialTools?: (toolName, result, toolCall, context) => { shouldForceToolChoice?, modifiedResult?, modifiedMessages? };
  
  // Usage and data hooks
  calculateUsage?: (baseUsage, streamOutput, systemPromptTokens, openAiTools, shouldSendTools) => Promise<TUsageConfig>;
  getDataPolicy: () => TAgentDataPolicy;
};
    Main Thread Config

    Location: src/server/endpoints/unified/orchestrator/configs/mainThread.config.ts

    This is the default, user-facing agent that saves all data and has standard behavior.

    TypeScript
    export const MAIN_THREAD_CONFIG: TAgentConfig = {
  enableGlobalContextUpdate: true,
  useParallelTools: true,
  
  shouldContinueLoop: (state) => {
    return (
      state.hasToolCalls &&
      state.currentIteration <= state.effectiveToolCallsLimit &&
      !shouldReturnEarly()
    );
  },
  
  shouldSendTools: (state, openAiTools, schema) => {
    let shouldSendTools = !!schema?.toolsRequired && openAiTools.length > 0;
    const isMainThreadLimitReached = state.currentIteration >= state.effectiveToolCallsLimit - 1;
    
    if (isMainThreadLimitReached) {
      shouldSendTools = false;  // Don't send tools on last iteration
    }
    
    return shouldSendTools;
  },
  
  onNoToolCalls: async (state, context, reason, toolCalls) => {
    if (reason === "LIMIT_REACHED") {
      // Clear tools and inject final response prompt
      context.registry.clear();
      const updatedMessagesObject = await buildMessages(...);
      state.messages = updatedMessagesObject.messages;
      state.inLoopTrimmedMessages.push(
        SystemMessage({
          content: finalResponseGenrationPrompt(state.effectiveToolCallsLimit),
        }),
      );
      return { shouldContinue: true };
    }
    
    if (reason === "TOOLS_DISABLED" && toolCalls) {
      // Inform user about disabled tools
      const toolsNotEnabledPrompt = prompts.getEnableToolPrompt(state.toolsNotEnabledList[0]);
      streamMessage({ type: "text", content: toolsNotEnabledPrompt, ... });
      return { shouldContinue: false };
    }
    
    return { shouldContinue: false };
  },
  
  handleSpecialTools: (toolName, result, toolCall, context) => {
    // Special handling for RAG tool - inject context into user message
    if (toolName === toolNames.enum.rag_tool) {
      const ragResults = result as TGetRAGResultsResponse;
      if (!ragResults?.result?.pastedTextEmbeddings?.length) return;
      
      const pastedText = ragResults.result.pastedTextEmbeddings.join("");
      const modifiedMessages = JSON.parse(JSON.stringify(context.chatCtx?.messages || []));
      
      const userMessageIndex = modifiedMessages.findLastIndex(
        (msg) => msg.role === "user" && msg.content[0]?.type === "TEXT",
      );
      
      if (userMessageIndex !== -1) {
        const targetMessageContent = modifiedMessages[userMessageIndex].content[0];
        (targetMessageContent as { text: string }).text = `${pastedText}\nUser Query: ${(targetMessageContent as { text: string }).text}`;
      }
      
      return { modifiedMessages, modifiedResult: { ...ragResults, result: { ...ragResults.result, pastedTextEmbeddings: [""] } } };
    }
    
    // Special handling for chatbot tool - force RAG tool next
    if (toolName === toolNames.Enum.chatbot_tool) {
      const parsedArgs = JSON.parse(toolCall.function.arguments);
      if (parsedArgs?.shouldUseKnowledgeBase) {
        return {
          shouldForceToolChoice: {
            type: "function",
            function: { name: toolNames.Enum.rag_tool },
          },
        };
      }
    }
  },
  
  getDataPolicy: () => ({
    shouldStoreToolCalls: true,
    shouldStoreToolResults: true,
    shouldStoreText: true,
    filterToolCalls: null,
    filterToolResults: null,
  }),
};
    Deep Research Config

    Location: src/server/endpoints/unified/orchestrator/configs/deepResearch.config.ts

    This agent orchestrates deep research by spawning researcher sub-agents and managing report generation.

    TypeScript
    export const DEEP_RESEARCH_CONFIG: TAgentConfig = {
  enableGlobalContextUpdate: true,
  useParallelTools: true,
  
  initializeAgentState: (state) => {
    state.deepResearchRetryCount = 0;
  },
  
  shouldContinueLoop: (state) => {
    // Check if we should force report generation
    const isshouldForceReportGeneration = shouldForceReportGeneration(
      assistantMessageNode,
      state.currentIteration,
      state.effectiveToolCallsLimit,
      state.hasToolCalls,
      state.deepResearchRetryCount,
    );
    
    if (isshouldForceReportGeneration) {
      const reportConfig = setupForcedReportGeneration(...);
      state.currentIteration = reportConfig.updatedIteration;
      state.toolChoice = reportConfig.toolChoice;
      state.inLoopTrimmedMessages.push(reportConfig.systemMessage);
      state.hasToolCalls = true;
      return true;
    }
    
    return (
      state.hasToolCalls &&
      state.currentIteration < state.effectiveToolCallsLimit &&
      !shouldReturnEarly()
    );
  },
  
  getModelId: (state, baseModelConfig) => {
    const retryCount = state.deepResearchRetryCount || 0;
    // Use cheaper model after retries
    return retryCount > 1 ? getLLMModel("gpt-4.1-mini").id : baseModelConfig.id;
  },
  
  onNoToolCalls: async (state, context, reason) => {
    const retryCount = state.deepResearchRetryCount || 0;
    
    if (reason === "NO_CALLS_GENERATED") {
      // Retry logic for deep research
      if (shouldRetryDeepResearchSupervisor(...)) {
        state.deepResearchRetryCount = retryCount + 1;
        state.inLoopTrimmedMessages.push(
          SystemMessage({ content: NO_TOOL_CALLS_RETURNED_DEEPRESEARCH_PROMPT }),
        );
        state.hasToolCalls = true;
        return { shouldContinue: true };
      }
      
      // Force report generation if appropriate
      if (shouldForceReportGeneration(...)) {
        return { shouldContinue: true };
      }
    }
    
    return { shouldContinue: false };
  },
  
  preprocessToolArguments: async (toolCall, context, toolMetadata) => {
    // Special handling for report generation tool arguments
    if (toolCall.function.name === toolNames.Enum.report_generation_tool) {
      try {
        const parsedArgs = JSON.parse(toolCall.function.arguments);
        const isArgsValid = reportGenerationInputSchema.safeParse(parsedArgs);
        if (!isArgsValid.success) {
          throw new Error(`Invalid arguments: ${isArgsValid.error.message}`);
        }
        return parsedArgs;
      } catch (error) {
        // Fallback to extraction if direct parsing fails
        return await extractReportGenerationInputSchema(toolCall.function.arguments, toolCall.id, context, toolMetadata);
      }
    }
    
    // Default behavior
    try {
      return JSON.parse(toolCall.function.arguments);
    } catch (error) {
      if (!TOOLS_WITH_DEFAULT_ARGS.has(toolCall.function.name)) {
        throw error;
      }
      return {};  // Tools with default args
    }
  },
  
  getDataPolicy: () => {
    const toolFilter = createDeepResearchToolFilter();
    return {
      shouldStoreToolCalls: true,
      shouldStoreToolResults: true,
      shouldStoreText: true,
      filterToolCalls: toolFilter,  // Only store research-specific tools
      filterToolResults: toolFilter,
    };
  },
};

    Tool Filter for Deep Research:

    TypeScript
    export const createDeepResearchToolFilter = () => {
  return (toolName: TToolName) => {
    switch (toolName) {
      case toolNames.Enum.researcher_agent_tool:
      case toolNames.Enum.feedback_questions_tool:
      case toolNames.Enum.report_generation_tool:
        return true;  // Only store these
      default:
        return false;  // Filter out everything else
    }
  };
};
    Researcher Config

    Location: src/server/endpoints/unified/orchestrator/configs/researcher.config.ts

    This is a sub-agent used by the deep research supervisor for individual research tasks.

    TypeScript
    export const RESEARCHER_CONFIG: TAgentConfig = {
  enableGlobalContextUpdate: false,  // Don't update global context (sub-agent)
  useParallelTools: true,
  
  shouldContinueLoop: MAIN_THREAD_CONFIG.shouldContinueLoop,
  
  shouldSendTools: (_state, openAiTools) => {
    return openAiTools.length > 0;  // Always send tools if available
  },
  
  preprocessToolArguments: MAIN_THREAD_CONFIG.preprocessToolArguments,
  
  getDataPolicy: () => {
    const toolFilter = createResearcherAgentToolFilter();
    return {
      shouldStoreToolCalls: true,
      shouldStoreToolResults: true,
      shouldStoreText: true,
      filterToolCalls: toolFilter,
      filterToolResults: toolFilter,
    };
  },
};

    Tool Filter for Researcher:

    TypeScript
    export const createResearcherAgentToolFilter = () => {
  return (toolName: string) => toolName !== toolNames.Enum.get_search_history_tool;
};
    4. Helper Functions

    Location: src/server/endpoints/unified/orchestrator/helpers/baseUtils.ts

    This file contains utility functions used throughout the orchestration system.

    Message Building
    TypeScript
    export const buildMessages = async (
  chatCtx: TChatContext,
  config: TToolOrchestratorRunInput,
  registry: ToolRegistry,
  agentName: TAgentName,
  summary?: string,
) => {
  const { schema, request } = requestContext.get();
  const { userMessageNode, chatNode, user } = config;
  const modelConfig = getLLMModel(schema.schema.model);
  
  // Get context limit based on user plan and agent
  const { contextLimit } = getLimitsBasedOnUserPlan(user, modelConfig, agentName);
  
  // Handle edge case: user message too large
  if (userMessageNode.tokens > contextLimit) {
    logger.warn({ size: userMessageNode.tokens }, "USER_MESSAGE_TOO_LARGE");
    const trimmedMessageAsTokens = (await tokenizer.encode(userMessageNode.content)).splice(0, contextLimit);
    const decodedMessageContent = await tokenizer.decode(trimmedMessageAsTokens);
    userMessageContent = [{ text: decodedMessageContent, type: "TEXT", tokens: trimmedMessageAsTokens.length }];
  }
  
  // Build system prompt
  let systemPrompt = schema.schema.overiddenSystemPrompt && isMainThreadAgent(agentName)
    ? schema.schema.overiddenSystemPrompt
    : registry.getBasePrompt();
  
  // Add tool usage rules
  systemPrompt += buildToolUsagePrompt(registry.getAll(), systemPrompt);
  
  // Add personalization prompts
  if (settingsV3.personalization.isEnabled) {
    systemPrompt += await getPersonalizationPrompt(user);
  }
  
  // Add memory prompts
  if (settingsV3.memory.isEnabled) {
    systemPrompt += await getProfileMemoriesPrompt(user);
  }
  
  // Add project rules
  if (projectWithAttachments) {
    systemPrompt += getProjectRule(projectWithAttachments);
  }
  
  // Add attachment prompts
  const attachmentPrompt = await getAttachmentPrompt(chatState);
  if (attachmentPrompt) {
    systemPrompt += attachmentPrompt;
  }
  
  // Add few-shot examples if model supports it
  if (ALLOW_FEW_SHOT_MODELS.includes(modelConfig.id)) {
    messages.push(...FEW_SHOT_EXAMPLES);
  }
  
  // Add chat history
  const chatHistory = chatNode.thread.map((msg) => {
    // Convert each message to prompt format
    // ...
  });
  
  // Add summary if provided
  if (summary) {
    messages.push(
      SystemMessage({
        content: [
          {
            type: "TEXT",
            text: prompts.getUserChatHistorySummaryWithPrompt(summary),
            tokens: (await tokenizer.encode(summary)).length,
          },
        ],
      }),
    );
  }
  
  // Add current user message
  messages.push(
    UserMessage({
      content: userMessageContent,
    }),
  );
  
  return {
    messages,
    systemPromptTokens: (await tokenizer.encode(systemPrompt)).length,
  };
};
    Tool Choice Configuration
    TypeScript
    export const getToolChoiceConfig = (
  chatCtx: TChatContext,
  attachmentCount: number,
  registry: ToolRegistry,
  chatbotId?: string,
) => {
  let toolChoice: TToolChoice = "auto";
  
  // Force RAG tool if attachments present and RAG tool available
  if (attachmentCount > 0 && registry.get(toolNames.enum.rag_tool)) {
    toolChoice = {
      type: "function",
      function: { name: toolNames.Enum.rag_tool },
    };
  }
  
  // Force chatbot tool if chatbot ID present and chatbot tool available
  if (chatbotId && registry.get(toolNames.enum.chatbot_tool)) {
    toolChoice = {
      type: "function",
      function: { name: toolNames.Enum.chatbot_tool },
    };
  }
  
  return toolChoice;
};
    Tokenized Tool Calls
    TypeScript
    export async function getTokenizedToolCalls<T extends { id: string; function: { name: string; arguments: string } }>(
  toolCalls: T[] | undefined,
) {
  if (!toolCalls) return undefined;
  
  const toolTokens = await Promise.all(
    toolCalls.map(async (toolCall) => {
      const content = JSON.stringify({
        tool_call_id: toolCall.id,
        tool_name: toolCall.function.name,
        tool_args: toolCall.function.arguments,
      });
      return tokenizer.encode(content);
    }),
  );
  
  return toolCalls.map((toolCall, index) => ({
    ...toolCall,
    tokens: toolTokens[index].length,
  }));
}
    Tool Result Formatting
    TypeScript
    export const getFormattedAndTokenizedToolResult = async (
  result: TToolResultTypeWithMetadata,
  agentName: TAgentName,
) => {
  return {
    toolCallId: result.toolCallId,
    content:
      result.shouldIncludeInHistory ||
      isDeepResearchSupervisor(agentName) ||
      isResearcherAgent(agentName)
        ? result.content  // Full content for research agents
        : prompts.getUseAndThrowToolReducedVersion(result.summary ?? result.content),  // Reduced for main thread
    tokens: result.shouldIncludeInHistory
      ? result.tokens
      : (await tokenizer.encode(prompts.getUseAndThrowToolReducedVersion(result.summary ?? result.content))).length,
    function: { name: result.function.name },
  };
};
    Special Tool Result Formatting
    TypeScript
    export const toolResultWithSummaryTokens = async (result: TToolResultTypeWithMetadata) => {
  const tokens = (await tokenizer.encode(result.summary ?? prompts.getUseAndThrowToolReducedVersion(result.content))).length;
  
  switch (result.function.name) {
    case toolNames.Enum.craft_tool:
      // Wrap craft result to prevent re-generation
      const newToolResult = { ...result };
      newToolResult.content = `The craft has been generated and sent to the user. You are not supposed to send the same result again.
        
        <CRAFT_GENERATED_THAT_HAS_BEEN_SENT_TO_USER>
        ${result.content}
        </CRAFT_GENERATED_THAT_HAS_BEEN_SENT_TO_USER>`;
      newToolResult.summary = `A Craft was generated here, snippet: ${getTrimmedMessageWithWordCount(result.content, 100).trimmedText}`;
      newToolResult.summaryTokens = tokens;
      return newToolResult;
    
    case toolNames.Enum.feedback_questions_tool:
      // Similar wrapping for feedback questions
      // ...
    
    case toolNames.Enum.report_generation_tool:
      // Similar wrapping for report generation
      // ...
    
    default:
      return result;
  }
};
    Streaming Index Validation
    TypeScript
    export function validateAndSyncStreamingIndex(currentIndex: number) {
  const { currentContentIndex } = requestContext.get();
  
  if (currentIndex < currentContentIndex) {
    logger.warn(
      { previousIndex: currentIndex, correctedIndex: currentContentIndex },
      "WARN/TOOL_ORCHESTRATOR/INDEX_REGRESSION_PREVENTED",
    );
    return currentContentIndex;  // Prevent index regression
  }
  
  return currentIndex;
}
    Tool Context Creation
    TypeScript
    export function createToolContext(orchestrator: ToolOrchestrator) {
  return {
    addUsage: (usageConfig: TUsageConfig) => orchestrator.addUsage(usageConfig),
    currentExecutingTool: orchestrator.currentExecutingTool,
    toolMetadata: orchestrator.toolMetadata,
  };
}

    The Orchestration Flow

    Step-by-Step Execution
    1. Request Initialization

    When a user sends a request through the unified API:

    TypeScript
    // In the API endpoint
const toolRegistry = ToolRegistry.defaultRegistry(
  userAddedModes,
  settingsV3,
  requestClient,
);

const agentConfig = AGENT_CONFIGS[agentName];

const orchestrator = new ToolOrchestrator(
  chatCtx,
  agentName,
  toolRegistry,
  agentConfig,
  customToolCallLimit,
);
    2. State Initialization
    TypeScript
    private async initializeOrchestrationState(config: TToolOrchestratorRunInput) {
  const { unifiedApiVersion } = requestContext.get();
  const { input, assistantMessageNode, user } = config;
  const modelConfig: TModelConfig = this.chatCtx.model;
  
  // Build messages with system prompt, history, attachments, etc.
  const { messages, systemPromptTokens } = await buildMessages(
    this.chatCtx,
    config,
    this.registry,
    this.agentName,
  );
  
  // Get limits based on user plan and agent
  const { contextLimit, toolCallsLimit, shouldDoParallelToolCalls } = getLimitsBasedOnUserPlan(
    user,
    modelConfig,
    this.agentName,
  );
  
  // Initialize orchestrator state
  const state: TOrchestratorState = {
    currentIteration: 0,
    currentContentIndex: 0,
    hasToolCalls: true,
    toolChoice: getToolChoiceConfig(this.chatCtx, input.attachments.length, this.registry, input.metadata.chatbot?.id),
    effectiveToolCallsLimit: this.customToolCallLimit ?? toolCallsLimit,
    usageConfigArray: this.usageConfigArray,
    toolMetadata: this.currentToolMetadata,
    inLoopTrimmedMessages: [],
    messages,
    systemPromptTokens,
    lastCountedSubAgentTools: 0,
    lastIterationErroredOutTools: [],
    toolsNotEnabledList: [],
    isMainThreadFinalLoopPromptInjected: false,
  };
  
  // Initialize agent-specific state if needed
  if (this.agentConfig.initializeAgentState) {
    this.agentConfig.initializeAgentState(state);
  }
  
  // Create agent context
  const agentContext: TAgentContext = {
    agentName: this.agentName,
    registry: this.registry,
    modelConfig,
    unifiedApiVersion,
    assistantMessageNode,
    chatCtx: this.chatCtx,
    config,
  };
  
  return { state, agentContext, messages, systemPromptTokens, contextLimit, toolCallsLimit, shouldDoParallelToolCalls, modelConfig };
}
    3. Tool Schema Conversion
    TypeScript
    const openAiTools: ChatCompletionTool[] = this.registry.getAll().map((t) => ({
  type: "function",
  function: {
    name: t.name,
    description: t.description,
    parameters: zodToJsonSchema(t.parameters, { target: "openApi3" }),
  },
}));
    4. Streaming Index Initialization
    TypeScript
    const { currentContentIndex: globalContextIndex } = requestContext.get();

state.currentContentIndex = calculateInitialContentIndex(
  this.agentName,
  globalContextIndex ?? 0,
  eventManager.index,
  unifiedApiVersion,
);

// Persist to global request context
requestContext.set({ currentContentIndex: state.currentContentIndex });

// Sync EventManager index
eventManager.index = state.currentContentIndex;
    5. Main Orchestration Loop
    TypeScript
    while (this.agentConfig.shouldContinueLoop(state)) {
  // 5a. Track iteration info
  toolIterationInfo.push({
    iteration: state.currentIteration + 1,
    toolCalls: [],
    layout: {},
  });
  
  // 5b. Decide if tools should be sent
  const shouldSendTools = this.agentConfig.shouldSendTools(state, openAiTools, schema);
  
  // 5c. Get model ID (agent may override)
  const modelId = this.agentConfig.getModelId
    ? this.agentConfig.getModelId(state, modelConfig)
    : modelConfig.id;
  
  // 5d. Prepare LLM input
  const llmInput = {
    model: modelId,
    messages: [...currentMessages, ...state.inLoopTrimmedMessages],
    params: {
      ...(shouldSendTools ? {
        tools: openAiTools,
        tool_choice: state.toolChoice,
        parallel_tool_calls: shouldUseParallelToolCalls(this.agentName, shouldDoParallelTools || this.agentConfig.useParallelTools),
      } : {}),
      max_tokens: modelConfig.getMaxOutputTokens?.(false),
    },
  };
  
  // 5e. Call LLM
  const chatRequest = await provider.chat(llmInput);
  const chatResponse = await chatRequest.stream();
  
  // 5f. Stream response to client
  const streamOutput = unifiedApiVersion === "V2"
    ? await streamer.streamV2(config.request, config.response, chatResponse.data, { contentIndex: state.currentContentIndex })
    : await streamer.stream(config.request, config.response, chatResponse.data);
  
  // 5g. Parse tool calls
  const toolCallParsed = multipleToolCallSchema.safeParse(streamOutput?.toolCalls);
  if (!toolCallParsed.success) {
    // Handle invalid tool names
    state.inLoopTrimmedMessages.push(
      SystemMessage({
        content: [{ text: prompts.getInvalidToolNamePrompt(toolCallParsed.error.message), type: "TEXT", tokens: ... }],
      }),
    );
    state.hasToolCalls = true;
    continue;
  }
  
  const toolCalls = await getTokenizedToolCalls(toolCallParsed.data);
  
  // 5h. Extract content
  const llmResponseContent = streamOutput?.content.join("") || "";
  const reasoningContent = streamOutput?.reasoningContent.reduce((acc, current) => {
    if (!acc[current.index]) acc[current.index] = current;
    else {
      if (current.text) acc[current.index].text += current.text;
      if (current.id) acc[current.index].id = current.id;
      if (current.signature) acc[current.index].signature = current.signature;
    }
    return acc;
  }, []);
  
  // 5i. Process tool calls if agent has hook
  if (this.agentConfig.processToolCalls && toolCalls?.length > 0) {
    const shouldBreak = await this.agentConfig.processToolCalls(toolCalls, state, agentContext);
    if (shouldBreak) {
      assistantMessageNode.contentV2.push({
        type: "TEXT",
        text: llmResponseContent,
        tokens: (await tokenizer.encode(llmResponseContent)).length,
      });
      state.hasToolCalls = false;
      break;
    }
  }
  
  // 5j. Handle no tool calls
  if (!toolCalls || toolCalls.length === 0) {
    if (this.agentConfig.onNoToolCalls) {
      const noCallsGeneratedResult = await this.agentConfig.onNoToolCalls(state, agentContext, "NO_CALLS_GENERATED");
      if (noCallsGeneratedResult.shouldContinue) {
        if (state.messages) currentMessages = state.messages;
        continue;
      }
    }
    state.hasToolCalls = false;
  }
  
  // 5k. Execute tools
  const toolResults: TToolResultTypeWithMetadata[] = [];
  let toolExecutionProgress: EventManager | null = null;
  const successfulToolNames: TToolName[] = [];
  
  if (toolCalls?.length) {
    const toolExecutionEvent = eventManager.createEvent("Selecting tools", "LIGHTBULB");
    state.hasToolCalls = true;
    
    if (this.agentConfig.onToolsSelected) {
      await this.agentConfig.onToolsSelected(toolCalls, state, agentContext);
    }
    
    const toolExecutionResults: TToolExecutionResults = {};
    
    // Stream tool calls to client
    streamMessage({ type: "toolCalls", contentIndex: state.currentContentIndex, toolCalls });
    
    // Mark tools as used
    toolCalls.forEach((tool) => {
      this.chatCtx.chatState.addUsedMode(toolNameMap[tool.function.name]);
    });
    
    if (toolCalls.length) state.currentContentIndex++;
    
    state.lastIterationErroredOutTools = [];
    state.toolsNotEnabledList = [];
    let currentToolResultIndex = -1;
    
    // Execute tools with streaming
    for await (const chunk of this.executeRequestedTools(toolCalls, state.currentContentIndex, agentContext)) {
      if (eventManager.index > state.currentContentIndex) {
        state.currentContentIndex = eventManager.index;
      }
      
      if (this.agentConfig.enableGlobalContextUpdate) {
        requestContext.set({ currentContentIndex: state.currentContentIndex });
      }
      
      if (chunk.type !== "tool:progress" && this.agentConfig.onToolProgress) {
        this.agentConfig.onToolProgress(chunk.function.name, toolExecutionEvent, agentContext);
      }
      
      switch (chunk.type) {
        case "tool:progress":
          toolExecutionProgress = chunk.progress;
          state.currentContentIndex++;
          break;
        
        case "tool:error":
          state.lastIterationErroredOutTools.push(chunk.function.name);
          toolExecutionResults[chunk.id] = {
            type: chunk.type,
            result: chunk.error.slice(0, TOOL_ERROR_MESSAGE_MAX_LENGTH),
          };
          
          if (chunk.error === "TOOL_NOT_ENABLED") {
            state.toolsNotEnabledList.push(chunk.function.name);
          }
          
          this.currentToolMetadata[chunk.id] = {
            saveToDB: false,
            summary: toolExecutionResults[chunk.id].result,
          };
          break;
        
        case "tool:stream":
          // Handle streaming tool result
          lastLoopProgressEvent = toolExecutionProgress?.getOrCreateLatestEvent(getRandomGeneratingMessage(), "WAND");
          streamMessage({
            type: "toolCallResult",
            contentIndex: state.currentContentIndex,
            toolCallResult: {
              index: currentToolResultIndex,
              result: "",
              toolCallId: chunk.id,
              function: { name: chunk.function.name },
            },
          });
          
          const safeContentIndex = validateAndSyncStreamingIndex(state.currentContentIndex);
          
          const content = unifiedApiVersion === "V2"
            ? await streamer.streamV2(config.request, config.response, chunk.content.stream, {
                contentIndex: safeContentIndex,
                streamAsToolResult: { toolResultIndex: currentToolResultIndex },
              })
            : await streamer.stream(config.request, config.response, chunk.content.stream);
          
          // Calculate usage for tool stream
          try {
            const toolStreamUsage = await chunk.content.chatResponse.usage(content.content, {
              usage: content.usage,
              tools: [chunk.function.name],
            });
            this.addUsage(toolStreamUsage);
          } catch (error) {
            logger.warn({ error, toolId: chunk.id, toolName: chunk.function.name }, "WARN/TOOL/STREAM_USAGE_CALCULATION_FAILED");
          }
          
          if (toolExecutionResults[chunk.id]) {
            toolExecutionResults[chunk.id].result += content.content.join("");
          } else {
            toolExecutionResults[chunk.id] = { type: chunk.type, result: content.content.join("") };
          }
          
          this.currentToolMetadata[chunk.id] = { saveToDB: true };
          lastLoopProgressEvent?.end();
          break;
        
        case "tool:start":
          currentToolResultIndex++;
          break;
        
        case "tool:done":
          lastLoopProgressEvent = toolExecutionProgress?.getOrCreateLatestEvent(getRandomGeneratingMessage(), "WAND");
          
          if (!toolExecutionResults[chunk.id]) {
            toolExecutionResults[chunk.id] = {
              type: chunk.type,
              result: chunk.result.result,
            };
          }
          
          this.currentToolMetadata[chunk.id] = chunk.result;
          
          // Merge sub-agent tool metadata
          if (chunk.result.toolMetadata) {
            this.currentToolMetadata = { ...this.currentToolMetadata, ...chunk.result.toolMetadata };
          }
          
          // Add sub-agent tool call count
          if (chunk.result.toolCallCount) {
            state.currentIteration += chunk.result.toolCallCount;
          }
          
          // Merge sub-agent usage
          if (chunk.result.usageConfigArray?.length) {
            chunk.result.usageConfigArray.forEach((usageConfig) => {
              this.addUsage(usageConfig);
            });
          }
          
          lastLoopProgressEvent?.end();
          break;
      }
      
      if (this.agentConfig.enableGlobalContextUpdate) {
        requestContext.set({ currentContentIndex: state.currentContentIndex });
      }
    }
    
    toolExecutionEvent.end();
    
    // Process tool results
    for (const [index, call] of toolCalls.entries()) {
      const executedTool = toolExecutionResults[call.id];
      let result = executedTool?.result;
      const metadata = this.currentToolMetadata[call.id];
      
      if (executedTool.type !== "tool:error") {
        successfulToolNames.push(call.function.name);
      }
      
      // Handle special tools
      if (this.agentConfig.handleSpecialTools) {
        const specialToolResult = this.agentConfig.handleSpecialTools(call.function.name, result, call, agentContext);
        if (specialToolResult) {
          if (specialToolResult.modifiedResult) result = specialToolResult.modifiedResult;
          if (specialToolResult.shouldForceToolChoice) state.toolChoice = specialToolResult.shouldForceToolChoice;
          if (specialToolResult.modifiedMessages) currentMessages = specialToolResult.modifiedMessages;
        }
      }
      
      // Stream tool result to client
      if (toolExecutionResults[call.id].type !== "tool:stream" && toolExecutionResults[call.id].type !== "tool:error") {
        streamMessage({
          type: "toolCallResult",
          contentIndex: state.currentContentIndex,
          toolCallResult: {
            index,
            result: JSON.stringify(result),
            toolCallId: call.id,
            function: { name: call.function.name },
          },
        });
      }
      
      const content = JSON.stringify(result);
      
      // Add to tool results with metadata
      if (metadata.saveToDB) {
        toolResults.push({
          toolCallId: call.id,
          content: content,
          tokens: (await tokenizer.encode(content)).length,
          shouldIncludeInHistory: true,
          function: { name: call.function.name },
        });
      } else {
        toolResults.push({
          toolCallId: call.id,
          content: content,
          tokens: (await tokenizer.encode(content)).length,
          shouldIncludeInHistory: false,
          summary: JSON.stringify(metadata.summary),
          function: { name: call.function.name },
        });
      }
      
      // Track for tool iteration info
      toolIterationInfo[toolIterationInfo.length - 1].toolCalls.push({
        name: call.function.name,
        args: call.function.arguments,
        result: content,
      });
    }
  }
  
  // 5l. Calculate usage
  let streamUsage = await chatResponse.usage(streamOutput.content, {
    usage: streamOutput.usage,
    tools: successfulToolNames,
  });
  
  if (this.agentConfig.calculateUsage) {
    streamUsage = await this.agentConfig.calculateUsage(streamUsage, streamOutput, systemPromptTokens, openAiTools, shouldSendTools);
  }
  
  this.addUsage(streamUsage);
  
  // 5m. Run token engine for context optimization
  const trimmedMessages = await engine({
    messages: currentMessages,
    inLoop: state.inLoopTrimmedMessages,
    contextLimit: contextLimit,
    response: {
      tokens: streamUsage.tokens,
      toolCalls: toolCalls,
      toolResults: await formatToolResultsForLLM(toolResults),
      content: llmResponseContent,
      reasoning: reasoningContent,
    },
    agentName: this.agentName,
  }, toolIterationInfo[toolIterationInfo.length - 1]);
  
  state.inLoopTrimmedMessages = trimmedMessages.inLoop;
  
  // 5n. Add summary prompt if many tool results
  if (toolResults.length > 2) {
    state.inLoopTrimmedMessages.push(
      SystemMessage({
        content: [{ type: "TEXT", text: TOOL_RESULTS_CONTEXT_SUMMARY_PROMPT, tokens: TOOL_RESULTS_CONTEXT_SUMMARY_TOKENS }],
      }),
    );
  }
  
  // 5o. Handle summary from token engine
  if (trimmedMessages.summary) {
    const { messages: adjustedMessages } = await buildMessages(
      this.chatCtx,
      config,
      this.registry,
      this.agentName,
      trimmedMessages.summary,
    );
    
    currentMessages = adjustedMessages;
    
    if (trimmedMessages.shouldDumpSummaryInDB) {
      assistantMessageNode.isChatSummarizedSoFar = true;
      assistantMessageNode.summary = trimmedMessages.summary;
    }
  }
  
  // 5p. Update chat context messages
  this.chatCtx.messages = [...currentMessages, ...state.inLoopTrimmedMessages];
  
  // 5q. Store data based on policy
  const dataPolicy = this.agentConfig.getDataPolicy();
  
  if (dataPolicy.shouldStoreText) {
    const reasoningContentText = reasoningContent.reduce((acc, current) => acc + current.text, "");
    
    assistantMessageNode.contentV2.push({
      type: "TEXT",
      toolCalls: filterToolCallsByPolicy(dataPolicy, toolCalls),
      text: llmResponseContent,
      tokens: (await tokenizer.encode(llmResponseContent)).length,
      ...(reasoningContent ? {
        reasoning: {
          content: reasoningContentText,
          tokens: (await tokenizer.encode(reasoningContentText)).length,
        },
      } : {}),
    });
  }
  
  if (toolResults.length) {
    const progressEvents = toolExecutionProgress?.getEvents() || [];
    const steps = progressEvents.map((event) => event.serialize());
    
    assistantMessageNode.contentV2.push({
      type: "PROGRESS",
      name: toolExecutionProgress?.name,
      icon: toolExecutionProgress?.icon,
      steps: steps,
      metadata: toolExecutionProgress?.metadata,
    });
    
    if (dataPolicy.shouldStoreToolResults) {
      assistantMessageNode.contentV2.push({
        type: "TOOL_RESULT",
        toolResults: await settleAllOrThrow(
          filterToolResultsByPolicy(toolResults, dataPolicy).map((result) =>
            getFormattedAndTokenizedToolResult(result, this.agentName),
          ),
        ),
      });
    }
  }
  
  // 5r. Handle disabled tools
  if (state.toolsNotEnabledList.length > 0 && this.agentConfig.onNoToolCalls) {
    await this.agentConfig.onNoToolCalls(state, agentContext, "TOOLS_DISABLED", toolCalls);
  }
  
  // 5s. Update iteration counter
  const iterativeToolCalls = toolCalls?.filter(
    (call) => !NON_ITERATIVE_TOOLS.some((tool) => call.function.name === `${tool.toLowerCase()}_tool`),
  ) || [];
  
  if (toolCalls?.length) {
    state.currentIteration += iterativeToolCalls.length;
  } else {
    state.currentIteration++;
  }
  
  // 5t. Check for limit reached
  if (
    state.currentIteration >= state.effectiveToolCallsLimit - 1 &&
    state.hasToolCalls &&
    this.agentConfig.onNoToolCalls &&
    !state.isMainThreadFinalLoopPromptInjected
  ) {
    const limitReachedResult = await this.agentConfig.onNoToolCalls(state, agentContext, "LIMIT_REACHED");
    
    if (limitReachedResult.shouldContinue) {
      if (state.messages) {
        currentMessages = state.messages;
        state.isMainThreadFinalLoopPromptInjected = true;
        state.currentIteration = state.effectiveToolCallsLimit - 1;
      }
    }
  }
  
  state.currentContentIndex++;
}
    6. Final Cleanup
    TypeScript
    if (lastLoopProgressEvent) lastLoopProgressEvent.end();

if (eventManager) {
  eventManager.index = state.currentContentIndex;
}

if (this.agentConfig.enableGlobalContextUpdate) {
  requestContext.set({
    currentContentIndex: state.currentContentIndex,
    toolIterationInfo: toolIterationInfo,
  });
}

return {
  result: "OK",
  finalIteration: state.currentContentIndex,
  toolCallCount: state.currentIteration,
  usageConfigArray: this.usageConfigArray,
};

    Tool System

    Tool Interface
    TypeScript
    export class Tool {
  name: TToolName;
  description: string;
  parameters: z.ZodSchema;
  selectionRule: string;
  
  execute: (
    args: any,
    chatCtx: TChatContext,
    progress: EventManager,
    toolContext: any,
  ) => Promise<any | AsyncIterable<any>>;
  
  shouldUse?: (chatCtx: TChatContext) => Promise<boolean>;
}
    Tool Registration

    Tools are registered with the ToolRegistry:

    TypeScript
    const registry = new ToolRegistry(DEFAULT_TOOLS);

registry.register(customTool);
registry.unregister(toolName);
registry.clear();
    Tool Execution Flow
    1. Tool Selection: LLM selects tools based on user query
    2. Argument Parsing: Arguments are parsed (with agent-specific preprocessing)
    3. Enable Check: Tool's shouldUse() is called if present
    4. Execution: Tool's execute() is called
    5. Streaming: If tool returns async iterable, results are streamed
    6. Result Processing: Results are processed by agent's handleSpecialTools() hook
    7. Storage: Results are stored based on data policy
    Tool Metadata

    Tools can return metadata about their execution:

    TypeScript
    interface TToolMetadata {
  [toolCallId: string]: {
    saveToDB: boolean;
    summary?: string;
    // ... other metadata
  };
}

    This metadata controls:

    • Whether to save the result to the database
    • Summary for reduced context
    • Sub-agent tool call counts
    • Sub-agent usage arrays

    State Management

    Orchestrator State
    TypeScript
    export type TOrchestratorState = {
  // Core state
  currentIteration: number;              // Current loop iteration
  currentContentIndex: number;             // Streaming index
  hasToolCalls: boolean;                  // Whether tools were called
  toolChoice: TToolChoice;                // Tool choice for next LLM call
  
  // Tool execution state
  effectiveToolCallsLimit: number;        // Actual tool call limit
  usageConfigArray: TUsageConfig[];       // Token usage tracking
  toolMetadata: TToolMetadata;             // Tool execution metadata
  
  // Messaging state
  inLoopTrimmedMessages: TPromptMessage[];  // Messages from current loop
  messages: TPromptMessage[];              // Full message history
  systemPromptTokens?: number;             // System prompt token count
  
  // Error tracking
  lastIterationErroredOutTools: TToolName[];  // Tools that errored last iteration
  toolsNotEnabledList: TToolName[];           // Tools not enabled for user
  
  // Multi-agent state
  lastCountedSubAgentTools: number;        // Tool count from sub-agents
  
  // Main thread specific
  isMainThreadFinalLoopPromptInjected: boolean;  // Whether final prompt was injected
  
  // Agent-specific state
  deepResearchRetryCount?: number;         // Retry count for deep research
};
    State Lifecycle
    1. Initialization: State is created in initializeOrchestrationState()
    2. Agent Initialization: Agent's initializeAgentState() hook is called
    3. Loop Updates: State is updated each iteration
    4. Global Context Sync: State is synced to global request context if enabled
    5. Final Return: Final state is returned with results

    Streaming Architecture

    Streaming Index System

    The orchestration system uses a global streaming index to ensure consistent ordering of streamed content across agents and sub-agents.

    TypeScript
    // Initialize
state.currentContentIndex = calculateInitialContentIndex(
  this.agentName,
  globalContextIndex ?? 0,
  eventManager.index,
  unifiedApiVersion,
);

// Validate before use
const safeContentIndex = validateAndSyncStreamingIndex(currentContentIndex);

// Update after streaming
if (eventManager.index > state.currentContentIndex) {
  state.currentContentIndex = eventManager.index;
}

// Sync to global context
if (this.agentConfig.enableGlobalContextUpdate) {
  requestContext.set({ currentContentIndex: state.currentContentIndex });
}
    Stream Types
    TypeScript
    export type TStreamMessageOptions =
  | TTextStreamOptions          // Text content
  | TToolCallsStreamOptions     // Tool calls
  | TReasoningStreamOptions     // Reasoning content
  | TToolCallResultStreamOptions;  // Tool results
    Event Manager

    The EventManager manages progress events for tool execution:

    TypeScript
    const progress = EventManager.init(safeToolIndex, "V2");

// Create events
const event = progress.createEvent("Tool name", "ICON");
event.send([{ type: "TEXT", value: "Working with tool" }]);
event.end();

// Get serialized steps
const steps = progress.getEvents().map((event) => event.serialize());

    Multi-Agent System

    Agent Hierarchy
    Plain text
    MainThreadAgent (user-facing)
    ↓
DeepResearchSupervisor (orchestrates research)
    ↓
ResearcherAgent (sub-agent for individual tasks)
    Agent Communication

    Agents communicate through:

    1. Tool Results: Sub-agent results are passed back to parent
    2. Tool Metadata: Sub-agent metadata is merged with parent
    3. Usage Arrays: Sub-agent usage is aggregated
    4. Tool Call Counts: Sub-agent tool counts are added to parent iteration
    Sub-Agent Tool

    The researcher_agent_tool is used to spawn researcher sub-agents:

    TypeScript
    const researcherResult = await researcherAgentTool.execute(
  { query: "research topic", ... },
  chatCtx,
  progress,
  toolContext,
);

// Result includes:
// - result: Research findings
// - toolMetadata: Tool execution metadata
// - toolCallCount: Number of tools used by sub-agent
// - usageConfigArray: Token usage from sub-agent
    Global Context Update

    Main agents update global context, sub-agents do not:

    TypeScript
    // Main thread
enableGlobalContextUpdate: true,

// Researcher (sub-agent)
enableGlobalContextUpdate: false,

    This prevents sub-agents from interfering with the main thread's streaming index.

    Data Policies

    Policy Structure
    TypeScript
    export type TAgentDataPolicy = {
  shouldStoreToolCalls: boolean;      // Store tool calls to DB
  shouldStoreToolResults: boolean;    // Store tool results to DB
  shouldStoreText: boolean;           // Store LLM text to DB
  filterToolCalls?: (toolName: TToolName) => boolean | null;  // Filter which tools to store
  filterToolResults?: (toolName: TToolName) => boolean | null;  // Filter which results to store
};
    Policy Examples

    Main Thread Policy (store everything):

    TypeScript
    {
  shouldStoreToolCalls: true,
  shouldStoreToolResults: true,
  shouldStoreText: true,
  filterToolCalls: null,
  filterToolResults: null,
}

    Deep Research Policy (only research tools):

    TypeScript
    {
  shouldStoreToolCalls: true,
  shouldStoreToolResults: true,
  shouldStoreText: true,
  filterToolCalls: (toolName) => [
    toolNames.Enum.researcher_agent_tool,
    toolNames.Enum.feedback_questions_tool,
    toolNames.Enum.report_generation_tool,
  ].includes(toolName),
  filterToolResults: (toolName) => [
    toolNames.Enum.researcher_agent_tool,
    toolNames.Enum.feedback_questions_tool,
    toolNames.Enum.report_generation_tool,
  ].includes(toolName),
}
    Policy Application
    TypeScript
    // Filter tool calls before storage
const filteredToolCalls = filterToolCallsByPolicy(dataPolicy, toolCalls);

// Filter tool results before storage
const filteredToolResults = filterToolResultsByPolicy(toolResults, dataPolicy);

// Store based on policy
if (dataPolicy.shouldStoreToolCalls) {
  assistantMessageNode.contentV2.push({
    type: "TEXT",
    toolCalls: filteredToolCalls,
    ...
  });
}

if (dataPolicy.shouldStoreToolResults) {
  assistantMessageNode.contentV2.push({
    type: "TOOL_RESULT",
    toolResults: filteredToolResults,
    ...
  });
}

    Usage Tracking

    Usage Configuration
    TypeScript
    export type TUsageConfig = {
  model: string;
  tokens: {
    prompt: number;
    completion: number;
    cached: number;
    reasoning: number;
  };
  tools: string[];
};
    Usage Collection

    Usage is collected at multiple points:

    1. LLM Response: After each LLM call
    TypeScript
    let streamUsage = await chatResponse.usage(streamOutput.content, {
  usage: streamOutput.usage,
  tools: successfulToolNames,
});
    1. Tool Streaming: During tool result streaming
    TypeScript
    const toolStreamUsage = await chunk.content.chatResponse.usage(content.content, {
  usage: content.usage,
  tools: [chunk.function.name],
});
this.addUsage(toolStreamUsage);
    1. Sub-Agent Usage: Merged from sub-agents
    TypeScript
    if (chunk.result.usageConfigArray?.length) {
  chunk.result.usageConfigArray.forEach((usageConfig) => {
    this.addUsage(usageConfig);
  });
}
    1. Agent Modification: Agent can modify usage calculation
    TypeScript
    if (this.agentConfig.calculateUsage) {
  streamUsage = await this.agentConfig.calculateUsage(
    streamUsage,
    streamOutput,
    systemPromptTokens,
    openAiTools,
    shouldSendTools,
  );
}
    Usage Storage
    TypeScript
    addUsage(usageConfig: TUsageConfig) {
  this.usageConfigArray.push(usageConfig);
}

    Final usage array is returned and used for billing.

    Error Handling

    Tool Execution Errors
    TypeScript
    try {
  const parsedArgs = this.agentConfig.preprocessToolArguments
    ? await this.agentConfig.preprocessToolArguments(toolCall, ctx, this.toolMetadata, agentContext)
    : JSON.parse(args);
  
  const resultOrStream = await tool.execute(parsedArgs, ctx, progress, toolContext);
  // ... process result
} catch (e) {
  logger.warn({ error: e, args, name, id }, `WARN/TOOL/RUN-SINGLE-TOOL`);
  yield {
    type: "tool:error",
    id,
    function: { name },
    error: e instanceof Error ? e.message : String(e),
  };
}
    Tool Enablement Errors
    TypeScript
    if (tool.shouldUse && !(await tool.shouldUse(ctx))) {
  logger.info({ args, name, id }, `INFO/TOOL/TOOL_NOT_ENABLED`);
  yield {
    type: "tool:error",
    id,
    function: { name },
    error: "TOOL_NOT_ENABLED",
  };
  return;
}
    Invalid Tool Name Errors
    TypeScript
    const toolCallParsed = multipleToolCallSchema.safeParse(streamOutput?.toolCalls);
if (!toolCallParsed.success) {
  state.inLoopTrimmedMessages.push(
    SystemMessage({
      content: [{
        text: prompts.getInvalidToolNamePrompt(toolCallParsed.error.message),
        type: "TEXT",
        tokens: (await tokenizer.encode(toolCallParsed.error.message)).length,
      }],
    }),
  );
  state.hasToolCalls = true;
  continue;
}
    Error Tracking
    TypeScript
    state.lastIterationErroredOutTools: TToolName[];
state.toolsNotEnabledList: TToolName[];

// When tool errors
state.lastIterationErroredOutTools.push(chunk.function.name);

// When tool not enabled
if (chunk.error === "TOOL_NOT_ENABLED") {
  state.toolsNotEnabledList.push(chunk.function.name);
}

    Advanced Features

    Parallel Tool Execution

    Tools can be executed in parallel:

    TypeScript
    const tasks = calls.map((call, index) =>
  this.runSingleTool(call.function.name, call.id, call.function.arguments, progress, agentContext, index),
);

for await (const work of tasks) {
  for await (const chunk of work) yield chunk;
}

    Parallel execution is controlled by:

    • Agent config: useParallelTools: boolean
    • User plan: shouldDoParallelToolCalls
    • Model capability: ALWAYS_ALLOW_MULTI_TOOL_CALL_MODELS
    Tool Choice Forcing

    Agents can force specific tools:

    TypeScript
    // In handleSpecialTools hook
if (toolName === toolNames.Enum.chatbot_tool) {
  const parsedArgs = JSON.parse(toolCall.function.arguments);
  if (parsedArgs?.shouldUseKnowledgeBase) {
    return {
      shouldForceToolChoice: {
        type: "function",
        function: { name: toolNames.Enum.rag_tool },
      },
    };
  }
}

// Applied in next iteration
state.toolChoice = specialToolResult.shouldForceToolChoice;
    Message Modification

    Agents can modify messages based on tool results:

    TypeScript
    // In handleSpecialTools hook
if (toolName === toolNames.enum.rag_tool) {
  const modifiedMessages = JSON.parse(JSON.stringify(context.chatCtx?.messages || []));
  const userMessageIndex = modifiedMessages.findLastIndex(
    (msg) => msg.role === "user" && msg.content[0]?.type === "TEXT",
  );
  
  if (userMessageIndex !== -1) {
    const targetMessageContent = modifiedMessages[userMessageIndex].content[0];
    (targetMessageContent as { text: string }).text = `${pastedText}\nUser Query: ${(targetMessageContent as { text: string }).text}`;
  }
  
  return { modifiedMessages, modifiedResult };
}

// Applied
currentMessages = specialToolResult.modifiedMessages;
    Context Window Optimization

    The token engine optimizes context usage:

    TypeScript
    const trimmedMessages = await engine({
  messages: currentMessages,
  inLoop: state.inLoopTrimmedMessages,
  contextLimit: contextLimit,
  response: {
    tokens: streamUsage.tokens,
    toolCalls: toolCalls,
    toolResults: await formatToolResultsForLLM(toolResults),
    content: llmResponseContent,
    reasoning: reasoningContent,
  },
  agentName: this.agentName,
}, toolIterationInfo[toolIterationInfo.length - 1]);

    The engine:

    • Calculates token costs for different sections
    • Chooses optimal layout (FULL, SUMMARY, TOOL_PROVIDED_SUMMARY_IF_POSSIBLE)
    • Summarizes history if needed
    • Handles tool result summarization
    Few-Shot Examples

    Some models receive few-shot examples:

    TypeScript
    if (ALLOW_FEW_SHOT_MODELS.includes(modelConfig.id)) {
  messages.push(...FEW_SHOT_EXAMPLES);
}

    These examples show the LLM how to use tools correctly.

    Tool Result Wrapping

    Some tool results are wrapped to prevent re-generation:

    TypeScript
    case toolNames.Enum.craft_tool:
  const newToolResult = { ...result };
  newToolResult.content = `The craft has been generated and sent to the user. You are not supposed to send the same result again.
    
    <CRAFT_GENERATED_THAT_HAS_BEEN_SENT_TO_USER>
    ${result.content}
    </CRAFT_GENERATED_THAT_HAS_BEEN_SENT_TO_USER>`;
  return newToolResult;

    This prevents the LLM from re-generating content already sent to the user.

    Custom Tool Limits

    Agents can have custom tool call limits:

    TypeScript
    const orchestrator = new ToolOrchestrator(
  chatCtx,
  agentName,
  toolRegistry,
  agentConfig,
  customToolCallLimit,  // Optional override
);

    This is used in deep research to allow more iterations.

    Model Override

    Agents can override the model:

    TypeScript
    getModelId: (state, baseModelConfig) => {
  const retryCount = state.deepResearchRetryCount || 0;
  return retryCount > 1 ? getLLMModel("gpt-4.1-mini").id : baseModelConfig.id;
}

    This allows switching to cheaper models after retries.

    Summary

    The orchestration layer is a sophisticated, custom-built system that provides:

    1. Multi-Agent Architecture: Different agents for different use cases
    2. Streaming-First Design: Real-time streaming of all content
    3. Context Optimization: Intelligent token management
    4. Configurable Behavior: Hook-based agent configuration
    5. Policy-Based Control: Fine-grained data storage control
    6. Graceful Degradation: Comprehensive error handling
    7. Sub-Agent Support: Hierarchical agent system
    8. Usage Tracking: Detailed token usage collection
    9. Tool Management: Dynamic tool registration and filtering
    10. State Management: Comprehensive state tracking

    The system is production-ready and handles complex scenarios like deep research with sub-agents, streaming tool execution, and context window optimization - all without relying on external orchestration frameworks.