Control Flow & The Orchestration Engine

UI RendererEditToolGrepToolReadToolTool BatcherLLM APIMain Loop (tt)UserUI RendererEditToolGrepToolReadToolTool BatcherLLM APIMain Loop (tt)Userpar[Parallel Execution]Sequential Execution"Search for TODO comments and update them"Stream request with contexttext_delta: "I'll search for TODOs..."Update displaytool_use: GrepTooltool_use: ReadTool (multiple files)message_stopExecute tool batchReadTool.call() [Read-only]GrepTool.call() [Read-only]Progress: "Reading file1.js"Progress: "Searching *.js"Result: File contentsResult: 5 matchesTool resultsContinue with resultstool_use: EditToolExecute write toolEditTool.call() [Write]Progress: "Editing file1.js"Result: SuccessEdit completeContinue with resulttext_delta: "Updated 5 TODOs..."Final response

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The heart of Claude Code is the tt async generator function—a sophisticated state machine that orchestrates the entire conversation flow. Let's examine its actual structure:

async function* tt( currentMessages: CliMessage[], baseSystemPromptString: string, currentGitContext: GitContext, currentClaudeMdContents: ClaudeMdContent[], permissionGranterFn: PermissionGranter, toolUseContext: ToolUseContext, activeStreamingToolUse?: ToolUseBlock, loopState: { turnId: string, turnCounter: number, compacted?: boolean, isResuming?: boolean } ): AsyncGenerator<CliMessage, void, void> { }

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The first critical decision in the control flow is whether the conversation needs compaction:

class ContextCompactionController { private static readonly COMPACTION_THRESHOLDS = { tokenCount: 100_000, messageCount: 200, costThreshold: 5.00 }; static async shouldCompact( messages: CliMessage[], model: string ): Promise<boolean> { if (messages.length < 50) return false; const tokenCount = await this.estimateTokens(messages, model); return tokenCount > this.COMPACTION_THRESHOLDS.tokenCount || messages.length > this.COMPACTION_THRESHOLDS.messageCount; } static async compact( messages: CliMessage[], context: ToolUseContext ): Promise<CompactionResult> { const preserve = this.identifyPreservedMessages(messages); const summary = await this.generateSummary( messages.filter(m => !preserve.has(m.uuid)), context ); return { messages: [ this.createSummaryMessage(summary), ...messages.filter(m => preserve.has(m.uuid)) ], tokensaved: this.calculateSavings(messages, summary) }; } }

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Performance Characteristics:

Token counting: O(n) where n is total message content length

Summary generation: One additional LLM call (~2-3s)

Memory impact: Temporarily doubles message storage during compaction

The system prompt assembly reveals a sophisticated caching and composition strategy:

class SystemPromptAssembler { private static cache = new Map<string, { content: string, hash: string, expiry: number }>(); static async assemble( basePrompt: string, claudeMd: ClaudeMdContent[], gitContext: GitContext, tools: ToolDefinition[], model: string ): Promise<string | ContentBlock[]> { const [ claudeMdSection, gitSection, directorySection, toolSection ] = await Promise.all([ this.formatClaudeMd(claudeMd), this.formatGitContext(gitContext), this.getDirectoryStructure(), this.formatToolDefinitions(tools) ]); const modelSection = this.getModelAdaptations(model); return this.compose({ base: basePrompt, model: modelSection, claudeMd: claudeMdSection, git: gitSection, directory: directorySection, tools: toolSection }); } private static getModelAdaptations(model: string): string { const adaptations = { 'claude-3-opus': { style: 'detailed', instructions: 'Think step by step. Show your reasoning.', tokenBudget: 0.3 }, 'claude-3-sonnet': { style: 'balanced', instructions: 'Be concise but thorough.', tokenBudget: 0.2 }, 'claude-3-haiku': { style: 'brief', instructions: 'Get to the point quickly.', tokenBudget: 0.1 } }; const config = adaptations[model] || adaptations['claude-3-sonnet']; return this.formatModelInstructions(config); } }

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The LLM streaming phase implements a complex event-driven state machine:

class StreamEventProcessor { private state: { phase: 'idle' | 'message_start' | 'content' | 'tool_input' | 'complete'; currentMessage: Partial<CliMessage>; contentBlocks: ContentBlock[]; activeToolInput?: { toolId: string; buffer: string; parser: StreamingToolInputParser; }; metrics: { firstTokenLatency?: number; tokensPerSecond: number[]; }; }; async *processStream( stream: AsyncIterable<StreamEvent> ): AsyncGenerator<UIEvent | CliMessage> { for await (const event of stream) { switch (event.type) { case 'message_start': this.state.phase = 'message_start'; this.state.metrics.firstTokenLatency = Date.now() - startTime; yield { type: 'ui_state', data: { status: 'assistant_responding' } }; break; case 'content_block_start': yield* this.handleContentBlockStart(event); break; case 'content_block_delta': yield* this.handleContentBlockDelta(event); break; case 'content_block_stop': yield* this.handleContentBlockStop(event); break; case 'message_stop': yield* this.finalizeMessage(event); break; case 'error': yield* this.handleError(event); break; } } } private async *handleContentBlockDelta( event: ContentBlockDeltaEvent ): AsyncGenerator<UIEvent> { const block = this.state.contentBlocks[event.index]; switch (event.delta.type) { case 'text_delta': block.text += event.delta.text; yield { type: 'ui_text_delta', data: { text: event.delta.text, blockIndex: event.index } }; break; case 'input_json_delta': if (this.state.activeToolInput) { this.state.activeToolInput.buffer += event.delta.partial_json; if (event.delta.partial_json.includes('}') || event.delta.partial_json.includes(']')) { const result = this.state.activeToolInput.parser.addChunk( event.delta.partial_json ); if (result.complete) { block.input = result.value; yield { type: 'ui_tool_preview', data: { toolId: this.state.activeToolInput.toolId, input: result.value } }; } } } break; } } }

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The tool execution system implements a sophisticated parallel/sequential execution strategy:

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class ToolExecutionOrchestrator { private static readonly CONCURRENCY_LIMIT = 10; static async *executeToolBatch( toolUses: ToolUseBlock[], context: ToolUseContext, permissionFn: PermissionGranter ): AsyncGenerator<CliMessage> { const { readOnly, writeTools } = this.categorizeTools(toolUses); if (readOnly.length > 0) { yield* this.executeParallel(readOnly, context, permissionFn); } for (const tool of writeTools) { yield* this.executeSequential(tool, context, permissionFn); } } private static async *executeParallel( tools: ToolUseBlock[], context: ToolUseContext, permissionFn: PermissionGranter ): AsyncGenerator<CliMessage> { const executions = tools.map(tool => this.createToolExecution(tool, context, permissionFn) ); yield* parallelMap(executions, this.CONCURRENCY_LIMIT); } } async function* parallelMap<T>( generators: AsyncGenerator<T>[], concurrency: number ): AsyncGenerator<T> { const executing = new Set<Promise<IteratorResult<T>>>(); const pending = [...generators]; while (executing.size < concurrency && pending.length > 0) { const gen = pending.shift()!; executing.add(gen.next()); } while (executing.size > 0) { const result = await Promise.race(executing); executing.delete(result as any); if (!result.done) { yield result.value; const nextPromise = result.generator.next(); executing.add(nextPromise); } if (executing.size < concurrency && pending.length > 0) { const gen = pending.shift()!; executing.add(gen.next()); } } }

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Execution Timing Analysis:

The permission system implements a multi-level decision tree:

class PermissionController { static async checkPermission( tool: ToolDefinition, input: any, context: ToolPermissionContext ): Promise<PermissionDecision> { const denyRule = this.findMatchingRule( tool, input, context.alwaysDenyRules ); if (denyRule) { return { behavior: 'deny', reason: denyRule }; } if (context.mode === 'bypassPermissions') { return { behavior: 'allow', reason: 'bypass_mode' }; } if (context.mode === 'acceptEdits' && this.isEditTool(tool) && this.isPathSafe(input.path)) { return { behavior: 'allow', reason: 'accept_edits_mode' }; } const allowRule = this.findMatchingRule( tool, input, context.alwaysAllowRules ); if (allowRule) { return { behavior: 'allow', reason: allowRule }; } return { behavior: 'ask', suggestions: this.generateRuleSuggestions(tool, input) }; } private static findMatchingRule( tool: ToolDefinition, input: any, rules: Record<PermissionRuleScope, string[]> ): string | null { const scopes: PermissionRuleScope[] = [ 'cliArg', 'localSettings', 'projectSettings', 'policySettings', 'userSettings' ]; for (const scope of scopes) { const scopeRules = rules[scope] || []; for (const rule of scopeRules) { if (this.matchesRule(tool, input, rule)) { return `${scope}:${rule}`; } } } return null; } }

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The control flow implements tail recursion for multi-turn interactions:

class TurnController { static async *manageTurn( messages: CliMessage[], toolResults: CliMessage[], context: FullContext, loopState: LoopState ): AsyncGenerator<CliMessage> { if (loopState.turnCounter >= 10) { yield this.createSystemMessage( "Maximum conversation depth reached. Please start a new query." ); return; } const nextState = { ...loopState, turnCounter: loopState.turnCounter + 1, compacted: false }; const nextMessages = [ ...messages, ...toolResults.sort(this.sortByToolRequestOrder) ]; yield* tt( nextMessages, context.basePrompt, context.gitContext, context.claudeMd, context.permissionFn, context.toolContext, undefined, nextState ); } }

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The input processing implements a sophisticated routing system:

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class InputRouter { static async routeInput( input: string, context: AppContext ): Promise<RouterAction> { const matchers: [RegExp, InputHandler][] = [ [/^\\/(\\w+)(.*)/, this.handleSlashCommand], [/^!(.+)/, this.handleBashMode], [/^#(.+)/, this.handleMemoryMode], [/^```[\\s\\S]+```$/, this.handleCodeBlock], ]; for (const [pattern, handler] of matchers) { const match = input.match(pattern); if (match) { return handler(match, context); } } return { type: 'prompt', message: this.createUserMessage(input) }; } private static handleBashMode( match: RegExpMatchArray, context: AppContext ): RouterAction { const command = match[1]; const syntheticMessages = [ { type: 'user', message: { role: 'user', content: `Run this command: ${command}` } }, { type: 'assistant', message: { role: 'assistant', content: [ { type: 'text', text: 'I\\'ll run that command for you.' }, { type: 'tool_use', id: `bash_${Date.now()}`, name: 'BashTool', input: { command, sandbox: false } } ] } } ]; return { type: 'synthetic_conversation', messages: syntheticMessages }; } }

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The streaming system implements sophisticated backpressure handling:

class StreamBackpressureController { private buffer: Array<StreamEvent> = []; private pressure = { current: 0, threshold: 1000, paused: false }; async *controlledStream( source: AsyncIterable<StreamEvent> ): AsyncGenerator<StreamEvent> { const iterator = source[Symbol.asyncIterator](); while (true) { if (this.pressure.current > this.pressure.threshold) { this.pressure.paused = true; await this.waitForDrain(); } const { done, value } = await iterator.next(); if (done) break; if (this.shouldBuffer(value)) { this.buffer.push(value); this.pressure.current++; } else { yield value; } if (this.buffer.length > 0 && !this.pressure.paused) { yield* this.drainBuffer(); } } yield* this.drainBuffer(); } private shouldBuffer(event: StreamEvent): boolean { return event.type === 'content_block_delta' && event.delta.type === 'text_delta'; } }

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The AgentTool implements a fascinating parent-child control structure:

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class AgentToolExecutor { static async *execute( input: AgentToolInput, context: ToolUseContext, parentMessage: CliMessage ): AsyncGenerator<ToolProgress | ToolResult> { const subtasks = this.analyzeTask(input.prompt); const subAgentPromises = subtasks.map(async (task, index) => { const subContext = { ...context, tools: context.tools.filter(t => t.name !== 'AgentTool'), abortController: this.createLinkedAbort(context.abortController), options: { ...context.options, maxThinkingTokens: this.calculateTokenBudget(input.prompt) } }; return this.runSubAgent(task, subContext, index); }); const results: SubAgentResult[] = []; for await (const update of this.trackProgress(subAgentPromises)) { if (update.type === 'progress') { yield { type: 'progress', toolUseID: parentMessage.id, data: update }; } else { results.push(update.result); } } const synthesized = await this.synthesizeResults(results, input); yield { type: 'result', data: synthesized }; } private static async synthesizeResults( results: SubAgentResult[], input: AgentToolInput ): Promise<string> { if (results.length === 1) { return results[0].content; } const synthesisPrompt = ` Synthesize these ${results.length} findings into a cohesive response: ${results.map((r, i) => `Finding ${i+1}:\\n${r.content}`).join('\\n\\n')} Original task: ${input.prompt} `; const synthesizer = new SubAgentExecutor({ prompt: synthesisPrompt, model: input.model || 'claude-3-haiku', isSynthesis: true }); return synthesizer.run(); } }

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The system implements sophisticated error recovery strategies:

class ErrorRecoveryController { private static recoveryStrategies = { 'rate_limit': this.handleRateLimit, 'context_overflow': this.handleContextOverflow, 'tool_error': this.handleToolError, 'network_error': this.handleNetworkError, 'permission_denied': this.handlePermissionDenied }; static async *handleError( error: any, context: ErrorContext ): AsyncGenerator<CliMessage> { const errorType = this.classifyError(error); const strategy = this.recoveryStrategies[errorType]; if (strategy) { yield* strategy(error, context); } else { yield this.createErrorMessage(error); } } private static async *handleContextOverflow( error: ContextOverflowError, context: ErrorContext ): AsyncGenerator<CliMessage> { if (error.details.requested_tokens > 4096) { yield this.createSystemMessage("Reducing response size..."); const retry = await this.retryWithReducedTokens( context.request, Math.floor(error.details.requested_tokens * 0.7) ); if (retry.success) { yield* retry.response; return; } } yield this.createSystemMessage("Compacting conversation history..."); const compacted = await this.forceCompaction(context.messages); yield* this.retryWithMessages(compacted, context); } private static async *handleRateLimit( error: RateLimitError, context: ErrorContext ): AsyncGenerator<CliMessage> { const providers = ['anthropic', 'bedrock', 'vertex']; const current = context.provider; const alternatives = providers.filter(p => p !== current); for (const provider of alternatives) { yield this.createSystemMessage( `Rate limited on ${current}, trying ${provider}...` ); try { const result = await this.retryWithProvider( context.request, provider ); yield* result; return; } catch (e) { continue; } } yield this.createErrorMessage( "All providers are rate limited. Please try again later." ); } }

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The control flow includes strategic profiling points:

class PerformanceProfiler { private static spans = new Map<string, PerformanceSpan>(); static instrument<T extends AsyncGenerator>( name: string, generator: T ): T { return (async function* () { const span = tracer.startSpan(name); const start = performance.now(); try { let itemCount = 0; for await (const item of generator) { itemCount++; if (itemCount > 1) { span.addEvent('yield', { 'yield.latency': performance.now() - lastYield }); } yield item; lastYield = performance.now(); } span.setAttributes({ 'generator.yield_count': itemCount, 'generator.total_time': performance.now() - start }); } finally { span.end(); } })() as T; } }

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