{"version":3,"file":"prune.mjs","sources":["../../../src/messages/prune.ts"],"sourcesContent":["import {\n AIMessage,\n BaseMessage,\n ToolMessage,\n UsageMetadata,\n} from '@langchain/core/messages';\nimport type {\n ThinkingContentText,\n MessageContentComplex,\n ReasoningContentText,\n} from '@/types/stream';\nimport type { TokenCounter } from '@/types/run';\nimport { ContentTypes, Constants, Providers } from '@/common';\nimport type { ContextPruningConfig } from '@/types/graph';\nimport {\n calculateMaxToolResultChars,\n truncateToolResultContent,\n truncateToolInput,\n} from '@/utils/truncation';\nimport { applyContextPruning } from './contextPruning';\n\nexport type PruneMessagesFactoryParams = {\n provider?: Providers;\n maxTokens: number;\n startIndex: number;\n tokenCounter: TokenCounter;\n indexTokenCountMap: Record;\n thinkingEnabled?: boolean;\n /**\n * Optional position-based content degradation config. When `enabled: true`,\n * tool result messages outside the protected zone (system + last N assistant\n * turns + image content) are soft-trimmed (head + tail) once their\n * position-age exceeds `softTrimRatio`, then hard-cleared with a placeholder\n * once age exceeds `hardClearRatio`. Defaults disabled to preserve existing\n * behavior — opt in per deployment.\n */\n contextPruningConfig?: ContextPruningConfig;\n};\nexport type PruneMessagesParams = {\n messages: BaseMessage[];\n usageMetadata?: Partial;\n startType?: ReturnType;\n};\n\nfunction isIndexInContext(\n arrayA: unknown[],\n arrayB: unknown[],\n targetIndex: number\n): boolean {\n const startingIndexInA = arrayA.length - arrayB.length;\n return targetIndex >= startingIndexInA;\n}\n\nfunction addThinkingBlock(\n message: AIMessage,\n thinkingBlock: ThinkingContentText | ReasoningContentText\n): AIMessage {\n const content: MessageContentComplex[] = Array.isArray(message.content)\n ? (message.content as MessageContentComplex[])\n : [\n {\n type: ContentTypes.TEXT,\n text: message.content,\n },\n ];\n /** Edge case, the message already has the thinking block */\n if (content[0].type === thinkingBlock.type) {\n return message;\n }\n content.unshift(thinkingBlock);\n return new AIMessage({\n ...message,\n content,\n });\n}\n\n/**\n * Calculates the total tokens from a single usage object\n *\n * @param usage The usage metadata object containing token information\n * @returns An object containing the total input and output tokens\n */\nexport function calculateTotalTokens(\n usage: Partial\n): UsageMetadata {\n const baseInputTokens = Number(usage.input_tokens) || 0;\n const cacheCreation = Number(usage.input_token_details?.cache_creation) || 0;\n const cacheRead = Number(usage.input_token_details?.cache_read) || 0;\n\n const totalInputTokens = baseInputTokens + cacheCreation + cacheRead;\n const totalOutputTokens = Number(usage.output_tokens) || 0;\n\n return {\n input_tokens: totalInputTokens,\n output_tokens: totalOutputTokens,\n total_tokens: totalInputTokens + totalOutputTokens,\n };\n}\n\nexport type PruningResult = {\n context: BaseMessage[];\n remainingContextTokens: number;\n messagesToRefine: BaseMessage[];\n thinkingStartIndex?: number;\n};\n\n/**\n * Processes an array of messages and returns a context of messages that fit within a specified token limit.\n * It iterates over the messages from newest to oldest, adding them to the context until the token limit is reached.\n *\n * @param options Configuration options for processing messages\n * @returns Object containing the message context, remaining tokens, messages not included, and summary index\n */\n/**\n * Builds an emergency context from first and last message pairs when normal pruning\n * removes all AI messages. Preserves conversation continuity by keeping boundaries.\n */\nfunction buildEmergencyContext(\n messages: BaseMessage[],\n endIndex: number\n): BaseMessage[] {\n const result: BaseMessage[] = [];\n const instructions = endIndex > 0 ? messages[0] : undefined;\n if (instructions) {\n result.push(instructions);\n }\n\n /** Find first user+AI pair after system message */\n for (let i = endIndex; i < messages.length - 1; i++) {\n const current = messages[i];\n const next = messages[i + 1];\n if (current.getType() === 'human' && next.getType() === 'ai') {\n result.push(current, next);\n break;\n }\n }\n\n /** Find last user+AI pair */\n for (let i = messages.length - 1; i > endIndex; i--) {\n const current = messages[i];\n const prev = messages[i - 1];\n if (current.getType() === 'ai' && prev.getType() === 'human') {\n if (!result.includes(prev)) {\n result.push(prev, current);\n }\n break;\n }\n }\n\n return result;\n}\n\nexport function getMessagesWithinTokenLimit({\n messages: _messages,\n maxContextTokens,\n indexTokenCountMap,\n startType: _startType,\n thinkingEnabled,\n tokenCounter,\n thinkingStartIndex: _thinkingStartIndex = -1,\n reasoningType = ContentTypes.THINKING,\n instructionTokens: _instructionTokens = 0,\n}: {\n messages: BaseMessage[];\n maxContextTokens: number;\n indexTokenCountMap: Record;\n startType?: string | string[];\n thinkingEnabled?: boolean;\n tokenCounter: TokenCounter;\n thinkingStartIndex?: number;\n reasoningType?: ContentTypes.THINKING | ContentTypes.REASONING_CONTENT;\n /**\n * Token overhead for instructions (system message + tool schemas + summary)\n * that are NOT included in `messages`. When messages[0] is already a\n * SystemMessage the budget is deducted from its indexTokenCountMap entry\n * as before; otherwise this value is subtracted from the available budget.\n */\n instructionTokens?: number;\n}): PruningResult {\n // Every reply is primed with <|start|>assistant<|message|>, so we\n // start with 3 tokens for the label after all messages have been counted.\n let currentTokenCount = 3;\n const instructions =\n _messages[0]?.getType() === 'system' ? _messages[0] : undefined;\n const instructionsTokenCount =\n instructions != null ? (indexTokenCountMap[0] ?? 0) : _instructionTokens;\n const initialContextTokens = maxContextTokens - instructionsTokenCount;\n let remainingContextTokens = initialContextTokens;\n let startType = _startType;\n const originalLength = _messages.length;\n const messages = [..._messages];\n /**\n * IMPORTANT: this context array gets reversed at the end, since the latest messages get pushed first.\n *\n * This may be confusing to read, but it is done to ensure the context is in the correct order for the model.\n * */\n let context: Array = [];\n\n let thinkingStartIndex = _thinkingStartIndex;\n let thinkingEndIndex = -1;\n let thinkingBlock: ThinkingContentText | ReasoningContentText | undefined;\n const endIndex = instructions != null ? 1 : 0;\n const prunedMemory: BaseMessage[] = [];\n\n if (_thinkingStartIndex > -1) {\n const thinkingMessageContent = messages[_thinkingStartIndex]?.content;\n if (Array.isArray(thinkingMessageContent)) {\n thinkingBlock = thinkingMessageContent.find(\n (content) => content.type === reasoningType\n ) as ThinkingContentText | undefined;\n }\n }\n\n if (currentTokenCount < remainingContextTokens) {\n let currentIndex = messages.length;\n while (\n messages.length > 0 &&\n currentTokenCount < remainingContextTokens &&\n currentIndex > endIndex\n ) {\n currentIndex--;\n if (messages.length === 1 && instructions) {\n break;\n }\n const poppedMessage = messages.pop();\n if (!poppedMessage) continue;\n const messageType = poppedMessage.getType();\n if (\n thinkingEnabled === true &&\n thinkingEndIndex === -1 &&\n currentIndex === originalLength - 1 &&\n (messageType === 'ai' || messageType === 'tool')\n ) {\n thinkingEndIndex = currentIndex;\n }\n if (\n thinkingEndIndex > -1 &&\n !thinkingBlock &&\n thinkingStartIndex < 0 &&\n messageType === 'ai' &&\n Array.isArray(poppedMessage.content)\n ) {\n thinkingBlock = poppedMessage.content.find(\n (content) => content.type === reasoningType\n ) as ThinkingContentText | undefined;\n thinkingStartIndex = thinkingBlock != null ? currentIndex : -1;\n }\n /** False start, the latest message was not part of a multi-assistant/tool sequence of messages */\n if (\n thinkingEndIndex > -1 &&\n currentIndex === thinkingEndIndex - 1 &&\n messageType !== 'ai' &&\n messageType !== 'tool'\n ) {\n thinkingEndIndex = -1;\n }\n\n const tokenCount = indexTokenCountMap[currentIndex] ?? 0;\n\n if (\n prunedMemory.length === 0 &&\n currentTokenCount + tokenCount <= remainingContextTokens\n ) {\n context.push(poppedMessage);\n currentTokenCount += tokenCount;\n } else {\n prunedMemory.push(poppedMessage);\n if (thinkingEndIndex > -1 && thinkingStartIndex < 0) {\n continue;\n }\n break;\n }\n }\n\n if (context[context.length - 1]?.getType() === 'tool') {\n startType = ['ai', 'human'];\n }\n\n if (startType != null && startType.length > 0 && context.length > 0) {\n let requiredTypeIndex = -1;\n\n let totalTokens = 0;\n for (let i = context.length - 1; i >= 0; i--) {\n const currentType = context[i]?.getType() ?? '';\n if (\n Array.isArray(startType)\n ? startType.includes(currentType)\n : currentType === startType\n ) {\n requiredTypeIndex = i + 1;\n break;\n }\n const originalIndex = originalLength - 1 - i;\n totalTokens += indexTokenCountMap[originalIndex] ?? 0;\n }\n\n if (requiredTypeIndex > 0) {\n currentTokenCount -= totalTokens;\n context = context.slice(0, requiredTypeIndex);\n }\n }\n }\n\n if (instructions && originalLength > 0) {\n context.push(_messages[0] as BaseMessage);\n messages.shift();\n }\n\n remainingContextTokens -= currentTokenCount;\n const result: PruningResult = {\n remainingContextTokens,\n context: [] as BaseMessage[],\n messagesToRefine: prunedMemory,\n };\n\n if (thinkingStartIndex > -1) {\n result.thinkingStartIndex = thinkingStartIndex;\n }\n\n if (\n prunedMemory.length === 0 ||\n thinkingEndIndex < 0 ||\n (thinkingStartIndex > -1 &&\n isIndexInContext(_messages, context, thinkingStartIndex))\n ) {\n // we reverse at this step to ensure the context is in the correct order for the model, and we need to work backwards\n result.context = context.reverse() as BaseMessage[];\n return result;\n }\n\n if (thinkingEndIndex > -1 && thinkingStartIndex < 0) {\n throw new Error(\n 'The payload is malformed. There is a thinking sequence but no \"AI\" messages with thinking blocks.'\n );\n }\n\n if (!thinkingBlock) {\n throw new Error(\n 'The payload is malformed. There is a thinking sequence but no thinking block found.'\n );\n }\n\n // Since we have a thinking sequence, we need to find the last assistant message\n // in the latest AI/tool sequence to add the thinking block that falls outside of the current context\n // Latest messages are ordered first.\n let assistantIndex = -1;\n for (let i = 0; i < context.length; i++) {\n const currentMessage = context[i];\n const type = currentMessage?.getType();\n if (type === 'ai') {\n assistantIndex = i;\n }\n if (assistantIndex > -1 && (type === 'human' || type === 'system')) {\n break;\n }\n }\n\n if (assistantIndex === -1) {\n /** Emergency fallback: keep first and last message pairs instead of crashing */\n const emergencyContext = buildEmergencyContext(_messages, endIndex);\n return {\n remainingContextTokens: 0,\n context: emergencyContext,\n messagesToRefine: _messages.filter((m) => !emergencyContext.includes(m)),\n };\n }\n\n thinkingStartIndex = originalLength - 1 - assistantIndex;\n const thinkingTokenCount = tokenCounter(\n new AIMessage({ content: [thinkingBlock] })\n );\n const newRemainingCount = remainingContextTokens - thinkingTokenCount;\n const newMessage = addThinkingBlock(\n context[assistantIndex] as AIMessage,\n thinkingBlock\n );\n context[assistantIndex] = newMessage;\n if (newRemainingCount > 0) {\n result.context = context.reverse() as BaseMessage[];\n return result;\n }\n\n const thinkingMessage: AIMessage = context[assistantIndex] as AIMessage;\n // now we need to an additional round of pruning but making the thinking block fit\n const newThinkingMessageTokenCount =\n (indexTokenCountMap[thinkingStartIndex] ?? 0) + thinkingTokenCount;\n remainingContextTokens = initialContextTokens - newThinkingMessageTokenCount;\n currentTokenCount = 3;\n let newContext: BaseMessage[] = [];\n const secondRoundMessages = [..._messages];\n let currentIndex = secondRoundMessages.length;\n while (\n secondRoundMessages.length > 0 &&\n currentTokenCount < remainingContextTokens &&\n currentIndex > thinkingStartIndex\n ) {\n currentIndex--;\n const poppedMessage = secondRoundMessages.pop();\n if (!poppedMessage) continue;\n const tokenCount = indexTokenCountMap[currentIndex] ?? 0;\n if (currentTokenCount + tokenCount <= remainingContextTokens) {\n newContext.push(poppedMessage);\n currentTokenCount += tokenCount;\n } else {\n messages.push(poppedMessage);\n break;\n }\n }\n\n const firstMessage: AIMessage = newContext[newContext.length - 1];\n const firstMessageType = newContext[newContext.length - 1].getType();\n if (firstMessageType === 'tool') {\n startType = ['ai', 'human'];\n }\n\n if (startType != null && startType.length > 0 && newContext.length > 0) {\n let requiredTypeIndex = -1;\n\n let totalTokens = 0;\n for (let i = newContext.length - 1; i >= 0; i--) {\n const currentType = newContext[i]?.getType() ?? '';\n if (\n Array.isArray(startType)\n ? startType.includes(currentType)\n : currentType === startType\n ) {\n requiredTypeIndex = i + 1;\n break;\n }\n const originalIndex = originalLength - 1 - i;\n totalTokens += indexTokenCountMap[originalIndex] ?? 0;\n }\n\n if (requiredTypeIndex > 0) {\n currentTokenCount -= totalTokens;\n newContext = newContext.slice(0, requiredTypeIndex);\n }\n }\n\n if (firstMessageType === 'ai') {\n const newMessage = addThinkingBlock(firstMessage, thinkingBlock);\n newContext[newContext.length - 1] = newMessage;\n } else {\n newContext.push(thinkingMessage);\n }\n\n if (instructions && originalLength > 0) {\n newContext.push(_messages[0] as BaseMessage);\n secondRoundMessages.shift();\n }\n\n result.context = newContext.reverse();\n return result;\n}\n\nexport function checkValidNumber(value: unknown): value is number {\n return typeof value === 'number' && !isNaN(value) && value > 0;\n}\n\n/**\n * Returns the set of tool_call ids referenced by an AIMessage — both via the\n * canonical `tool_calls` array and via inline `tool_use` / `tool_call` content\n * blocks (Anthropic-style content arrays).\n */\nfunction getToolCallIds(message: BaseMessage): Set {\n if (message.getType() !== 'ai') {\n return new Set();\n }\n\n const ids = new Set();\n const aiMessage = message as AIMessage;\n for (const toolCall of aiMessage.tool_calls ?? []) {\n if (typeof toolCall.id === 'string' && toolCall.id.length > 0) {\n ids.add(toolCall.id);\n }\n }\n\n if (Array.isArray(aiMessage.content)) {\n for (const part of aiMessage.content) {\n if (typeof part !== 'object') {\n continue;\n }\n const record = part as { type?: unknown; id?: unknown };\n if (\n (record.type === 'tool_use' || record.type === 'tool_call') &&\n typeof record.id === 'string' &&\n record.id.length > 0\n ) {\n ids.add(record.id);\n }\n }\n }\n\n return ids;\n}\n\nfunction getToolResultId(message: BaseMessage): string | null {\n if (message.getType() !== 'tool') {\n return null;\n }\n const toolMessage = message as ToolMessage & {\n tool_call_id?: unknown;\n toolCallId?: unknown;\n };\n if (\n typeof toolMessage.tool_call_id === 'string' &&\n toolMessage.tool_call_id.length > 0\n ) {\n return toolMessage.tool_call_id;\n }\n if (\n typeof toolMessage.toolCallId === 'string' &&\n toolMessage.toolCallId.length > 0\n ) {\n return toolMessage.toolCallId;\n }\n return null;\n}\n\nfunction resolveTokenCountForMessage({\n message,\n messageIndexMap,\n tokenCounter,\n indexTokenCountMap,\n}: {\n message: BaseMessage;\n messageIndexMap: Map;\n tokenCounter: TokenCounter;\n indexTokenCountMap: Record;\n}): number {\n const originalIndex = messageIndexMap.get(message) ?? -1;\n if (originalIndex > -1 && indexTokenCountMap[originalIndex] != null) {\n return indexTokenCountMap[originalIndex] as number;\n }\n return tokenCounter(message);\n}\n\n/**\n * Repairs orphan tool messages in a pruned context. Drops `ToolMessage`s whose\n * matching `AIMessage` was pruned, and strips orphan `tool_use` content blocks\n * (and `tool_calls` entries) from `AIMessage`s whose corresponding tool results\n * are missing. Without this, Anthropic / Bedrock / OpenAI reject the request\n * with a structural validation 400.\n *\n * Intended to run on the post-prune `context` array (not the pre-prune\n * `allMessages`). The dropped messages are returned so the caller can append\n * them to `messagesToRefine` for summarization fidelity.\n */\nexport function repairOrphanedToolMessages({\n context,\n allMessages,\n tokenCounter,\n indexTokenCountMap,\n}: {\n context: BaseMessage[];\n allMessages: BaseMessage[];\n tokenCounter: TokenCounter;\n indexTokenCountMap: Record;\n}): {\n context: BaseMessage[];\n reclaimedTokens: number;\n droppedOrphanCount: number;\n /** Messages removed from context during orphan repair. These should be\n * appended to `messagesToRefine` so that summarization can still see them\n * (e.g. a ToolMessage whose parent AI was pruned). */\n droppedMessages: BaseMessage[];\n} {\n const messageIndexMap = new Map();\n for (let i = 0; i < allMessages.length; i++) {\n messageIndexMap.set(allMessages[i], i);\n }\n\n const validToolCallIds = new Set();\n const presentToolResultIds = new Set();\n for (const message of context) {\n for (const id of getToolCallIds(message)) {\n validToolCallIds.add(id);\n }\n const resultId = getToolResultId(message);\n if (resultId != null) {\n presentToolResultIds.add(resultId);\n }\n }\n\n let reclaimedTokens = 0;\n let droppedOrphanCount = 0;\n const repairedContext: BaseMessage[] = [];\n const droppedMessages: BaseMessage[] = [];\n\n for (const message of context) {\n if (message.getType() === 'tool') {\n const toolResultId = getToolResultId(message);\n if (toolResultId == null || !validToolCallIds.has(toolResultId)) {\n droppedOrphanCount += 1;\n reclaimedTokens += resolveTokenCountForMessage({\n message,\n tokenCounter,\n messageIndexMap,\n indexTokenCountMap,\n });\n droppedMessages.push(message);\n continue;\n }\n repairedContext.push(message);\n continue;\n }\n\n if (message.getType() === 'ai' && message instanceof AIMessage) {\n const toolCallIds = getToolCallIds(message);\n if (toolCallIds.size > 0) {\n let hasOrphanToolCalls = false;\n for (const id of toolCallIds) {\n if (!presentToolResultIds.has(id)) {\n hasOrphanToolCalls = true;\n break;\n }\n }\n if (hasOrphanToolCalls) {\n const originalTokens = resolveTokenCountForMessage({\n message,\n messageIndexMap,\n tokenCounter,\n indexTokenCountMap,\n });\n const stripped = stripOrphanToolUseBlocks(\n message,\n presentToolResultIds\n );\n if (stripped != null) {\n const strippedTokens = tokenCounter(stripped);\n reclaimedTokens += originalTokens - strippedTokens;\n repairedContext.push(stripped);\n } else {\n droppedOrphanCount += 1;\n reclaimedTokens += originalTokens;\n droppedMessages.push(message);\n }\n continue;\n }\n }\n }\n\n repairedContext.push(message);\n }\n\n return {\n context: repairedContext,\n reclaimedTokens,\n droppedOrphanCount,\n droppedMessages,\n };\n}\n\n/**\n * Strips tool_use content blocks and tool_calls entries from an AI message\n * when their corresponding ToolMessages are not in the context.\n * Returns null if the message has no content left after stripping.\n */\nfunction stripOrphanToolUseBlocks(\n message: AIMessage,\n presentToolResultIds: Set\n): AIMessage | null {\n const keptToolCalls = (message.tool_calls ?? []).filter(\n (tc) => typeof tc.id === 'string' && presentToolResultIds.has(tc.id)\n );\n\n let keptContent: MessageContentComplex[] | string;\n if (Array.isArray(message.content)) {\n const filtered = (message.content as MessageContentComplex[]).filter(\n (block) => {\n if (typeof block !== 'object') {\n return true;\n }\n const record = block as { type?: unknown; id?: unknown };\n if (\n (record.type === 'tool_use' || record.type === 'tool_call') &&\n typeof record.id === 'string'\n ) {\n return presentToolResultIds.has(record.id);\n }\n return true;\n }\n );\n\n if (filtered.length === 0) {\n return null;\n }\n keptContent = filtered;\n } else {\n keptContent = message.content;\n }\n\n return new AIMessage({\n ...message,\n content: keptContent,\n tool_calls: keptToolCalls.length > 0 ? keptToolCalls : undefined,\n });\n}\n\n/**\n * Lightweight structural cleanup: strips orphan tool_use blocks from AI messages\n * and drops orphan ToolMessages whose AI counterpart is missing.\n *\n * Unlike `repairOrphanedToolMessages`, this does NOT track tokens — it is\n * intended as a final safety net right before model invocation to prevent\n * Anthropic / Bedrock structural validation errors.\n *\n * Uses duck-typing instead of `getType()` because messages at this stage\n * may be plain objects (from LangGraph state serialization) rather than\n * proper BaseMessage class instances.\n *\n * Includes a fast-path: if every tool_call has a matching tool_result and\n * vice-versa, the original array is returned immediately with zero allocation.\n */\nexport function sanitizeOrphanToolBlocks(\n messages: BaseMessage[]\n): BaseMessage[] {\n const allToolCallIds = new Set();\n const allToolResultIds = new Set();\n\n for (const msg of messages) {\n const msgAny = msg as unknown as Record;\n const toolCalls = msgAny.tool_calls as Array<{ id?: string }> | undefined;\n if (Array.isArray(toolCalls)) {\n for (const tc of toolCalls) {\n if (\n typeof tc.id === 'string' &&\n tc.id.length > 0 &&\n !tc.id.startsWith(Constants.ANTHROPIC_SERVER_TOOL_PREFIX)\n ) {\n allToolCallIds.add(tc.id);\n }\n }\n }\n if (Array.isArray(msgAny.content)) {\n for (const block of msgAny.content as Array>) {\n if (\n typeof block === 'object' &&\n (block.type === 'tool_use' || block.type === 'tool_call') &&\n typeof block.id === 'string' &&\n !block.id.startsWith(Constants.ANTHROPIC_SERVER_TOOL_PREFIX)\n ) {\n allToolCallIds.add(block.id);\n }\n }\n }\n const toolCallId = msgAny.tool_call_id as string | undefined;\n if (typeof toolCallId === 'string' && toolCallId.length > 0) {\n allToolResultIds.add(toolCallId);\n }\n }\n\n let hasOrphans = false;\n for (const id of allToolCallIds) {\n if (!allToolResultIds.has(id)) {\n hasOrphans = true;\n break;\n }\n }\n if (!hasOrphans) {\n for (const id of allToolResultIds) {\n if (!allToolCallIds.has(id)) {\n hasOrphans = true;\n break;\n }\n }\n }\n if (!hasOrphans) {\n return messages;\n }\n\n const result: BaseMessage[] = [];\n const strippedAiIndices = new Set();\n\n for (const msg of messages) {\n const msgAny = msg as unknown as Record;\n const msgType =\n typeof (msg as { getType?: unknown }).getType === 'function'\n ? msg.getType()\n : ((msgAny.role as string | undefined) ??\n (msgAny._type as string | undefined));\n\n const toolCallId = msgAny.tool_call_id as string | undefined;\n if (\n (msgType === 'tool' || msg instanceof ToolMessage) &&\n typeof toolCallId === 'string' &&\n !allToolCallIds.has(toolCallId)\n ) {\n continue;\n }\n\n const toolCalls = msgAny.tool_calls as Array<{ id?: string }> | undefined;\n if (\n (msgType === 'ai' ||\n msgType === 'assistant' ||\n msg instanceof AIMessage) &&\n Array.isArray(toolCalls) &&\n toolCalls.length > 0\n ) {\n const hasOrphanCalls = toolCalls.some(\n (tc) => typeof tc.id === 'string' && !allToolResultIds.has(tc.id)\n );\n if (hasOrphanCalls) {\n if (msg instanceof AIMessage) {\n const stripped = stripOrphanToolUseBlocks(msg, allToolResultIds);\n if (stripped != null) {\n strippedAiIndices.add(result.length);\n result.push(stripped);\n }\n continue;\n }\n const keptToolCalls = toolCalls.filter(\n (tc) => typeof tc.id === 'string' && allToolResultIds.has(tc.id)\n );\n const keptContent = Array.isArray(msgAny.content)\n ? (msgAny.content as Array>).filter(\n (block) => {\n if (typeof block !== 'object') return true;\n if (\n (block.type === 'tool_use' || block.type === 'tool_call') &&\n typeof block.id === 'string'\n ) {\n return allToolResultIds.has(block.id);\n }\n return true;\n }\n )\n : msgAny.content;\n if (\n keptToolCalls.length === 0 &&\n Array.isArray(keptContent) &&\n keptContent.length === 0\n ) {\n continue;\n }\n strippedAiIndices.add(result.length);\n const patched = Object.create(\n Object.getPrototypeOf(msg),\n Object.getOwnPropertyDescriptors(msg)\n );\n patched.tool_calls = keptToolCalls.length > 0 ? keptToolCalls : [];\n patched.content = keptContent;\n result.push(patched as BaseMessage);\n continue;\n }\n }\n\n result.push(msg);\n }\n\n // Bedrock/Anthropic require the conversation to end with a user message;\n // a stripped AI message (tool_use removed) represents a dead-end exchange.\n while (result.length > 0 && strippedAiIndices.has(result.length - 1)) {\n result.pop();\n }\n\n return result;\n}\n\n/**\n * Pre-flight truncation: truncates oversized tool result string content before\n * the budget walker runs. Older results get smaller budgets (recency factor),\n * with a 200-char floor so even ancient entries keep diagnostic signal. Returns\n * the number of ToolMessages truncated.\n */\nexport function preFlightTruncateToolResults(params: {\n messages: BaseMessage[];\n maxContextTokens: number;\n indexTokenCountMap: Record;\n tokenCounter: TokenCounter;\n}): number {\n const { messages, maxContextTokens, indexTokenCountMap, tokenCounter } =\n params;\n const baseMaxChars = calculateMaxToolResultChars(maxContextTokens);\n let truncatedCount = 0;\n\n const toolIndices: number[] = [];\n for (let i = 0; i < messages.length; i++) {\n if (messages[i].getType() === 'tool') {\n toolIndices.push(i);\n }\n }\n\n for (let t = 0; t < toolIndices.length; t++) {\n const i = toolIndices[t];\n const message = messages[i];\n const content = message.content;\n if (typeof content !== 'string') {\n continue;\n }\n\n const position = toolIndices.length > 1 ? t / (toolIndices.length - 1) : 1;\n const recencyFactor = 0.2 + 0.8 * position;\n const maxChars = Math.max(200, Math.floor(baseMaxChars * recencyFactor));\n\n if (content.length <= maxChars) {\n continue;\n }\n\n const truncated = truncateToolResultContent(content, maxChars);\n const cloned = new ToolMessage({\n content: truncated,\n tool_call_id: (message as ToolMessage).tool_call_id,\n name: message.name,\n id: message.id,\n additional_kwargs: message.additional_kwargs,\n response_metadata: message.response_metadata,\n });\n messages[i] = cloned;\n indexTokenCountMap[i] = tokenCounter(cloned);\n truncatedCount++;\n }\n\n return truncatedCount;\n}\n\n/**\n * Pre-flight truncation: truncates oversized `tool_use` input fields in AI messages.\n *\n * Tool call inputs (arguments) can be very large — e.g., code evaluation payloads from\n * MCP tools like chrome-devtools. Since these tool calls have already been executed,\n * the model only needs a summary of what was called, not the full arguments. Truncating\n * them before pruning can prevent entire messages from being dropped.\n *\n * Uses 15% of the context window (in estimated characters, ~4 chars/token) as the\n * per-input cap, capped at 200K chars.\n *\n * @returns The number of AI messages that had tool_use inputs truncated.\n */\nexport function preFlightTruncateToolCallInputs(params: {\n messages: BaseMessage[];\n maxContextTokens: number;\n indexTokenCountMap: Record;\n tokenCounter: TokenCounter;\n}): number {\n const { messages, maxContextTokens, indexTokenCountMap, tokenCounter } =\n params;\n const maxInputChars = Math.min(\n Math.floor(maxContextTokens * 0.15) * 4,\n 200_000\n );\n let truncatedCount = 0;\n\n for (let i = 0; i < messages.length; i++) {\n const message = messages[i];\n if (message.getType() !== 'ai') {\n continue;\n }\n if (!Array.isArray(message.content)) {\n continue;\n }\n\n const originalContent = message.content as MessageContentComplex[];\n const state = { changed: false };\n const newContent = originalContent.map((block) => {\n if (typeof block !== 'object') {\n return block;\n }\n const record = block as Record;\n if (record.type !== 'tool_use' && record.type !== 'tool_call') {\n return block;\n }\n\n const input = record.input;\n if (input == null) {\n return block;\n }\n const serialized =\n typeof input === 'string' ? input : JSON.stringify(input);\n if (serialized.length <= maxInputChars) {\n return block;\n }\n\n state.changed = true;\n // Replaces original input with { _truncated, _originalChars } —\n // safe because the tool call already executed in a prior turn.\n return {\n ...record,\n input: truncateToolInput(serialized, maxInputChars),\n };\n });\n\n if (!state.changed) {\n continue;\n }\n\n const aiMsg = message as AIMessage;\n const newToolCalls = (aiMsg.tool_calls ?? []).map((tc) => {\n const serializedArgs = JSON.stringify(tc.args);\n if (serializedArgs.length <= maxInputChars) {\n return tc;\n }\n // Replaces original args with { _truncated, _originalChars } —\n // safe because the tool call already executed in a prior turn.\n return {\n ...tc,\n args: truncateToolInput(serializedArgs, maxInputChars),\n };\n });\n\n messages[i] = new AIMessage({\n ...aiMsg,\n content: newContent,\n tool_calls: newToolCalls.length > 0 ? newToolCalls : undefined,\n });\n indexTokenCountMap[i] = tokenCounter(messages[i]);\n truncatedCount++;\n }\n\n return truncatedCount;\n}\n\ntype ThinkingBlocks = {\n thinking_blocks?: Array<{\n type: 'thinking';\n thinking: string;\n signature: string;\n }>;\n};\n\nexport function createPruneMessages(factoryParams: PruneMessagesFactoryParams) {\n const indexTokenCountMap = { ...factoryParams.indexTokenCountMap };\n let lastTurnStartIndex = factoryParams.startIndex;\n let lastCutOffIndex = 0;\n let totalTokens = Object.values(indexTokenCountMap).reduce(\n (a = 0, b = 0) => a + b,\n 0\n ) as number;\n let runThinkingStartIndex = -1;\n /**\n * Original (pre-truncation) tool result content keyed by message index.\n * Lets the summarizer see full tool outputs even after pre-flight truncation\n * has shortened them in the live messages array. Cleared whenever the\n * factory is recreated (e.g. after a summarization checkpoint).\n */\n const originalToolContent = new Map();\n return function pruneMessages(params: PruneMessagesParams): {\n context: BaseMessage[];\n indexTokenCountMap: Record;\n messagesToRefine: BaseMessage[];\n originalToolContent?: Map;\n } {\n if (\n factoryParams.provider === Providers.OPENAI &&\n factoryParams.thinkingEnabled === true\n ) {\n for (let i = lastTurnStartIndex; i < params.messages.length; i++) {\n const m = params.messages[i];\n if (\n m.getType() === 'ai' &&\n typeof m.additional_kwargs.reasoning_content === 'string' &&\n Array.isArray(\n (\n m.additional_kwargs.provider_specific_fields as\n | ThinkingBlocks\n | undefined\n )?.thinking_blocks\n ) &&\n (m as AIMessage).tool_calls &&\n ((m as AIMessage).tool_calls?.length ?? 0) > 0\n ) {\n const message = m as AIMessage;\n const thinkingBlocks = (\n message.additional_kwargs.provider_specific_fields as ThinkingBlocks\n ).thinking_blocks;\n const signature =\n thinkingBlocks?.[thinkingBlocks.length - 1].signature;\n const thinkingBlock: ThinkingContentText = {\n signature,\n type: ContentTypes.THINKING,\n thinking: message.additional_kwargs.reasoning_content as string,\n };\n\n params.messages[i] = new AIMessage({\n ...message,\n content: [thinkingBlock],\n additional_kwargs: {\n ...message.additional_kwargs,\n reasoning_content: undefined,\n },\n });\n }\n }\n }\n\n let currentUsage: UsageMetadata | undefined;\n if (\n params.usageMetadata &&\n (checkValidNumber(params.usageMetadata.input_tokens) ||\n (checkValidNumber(params.usageMetadata.input_token_details) &&\n (checkValidNumber(\n params.usageMetadata.input_token_details.cache_creation\n ) ||\n checkValidNumber(\n params.usageMetadata.input_token_details.cache_read\n )))) &&\n checkValidNumber(params.usageMetadata.output_tokens)\n ) {\n currentUsage = calculateTotalTokens(params.usageMetadata);\n totalTokens = currentUsage.total_tokens;\n }\n\n const newOutputs = new Set();\n for (let i = lastTurnStartIndex; i < params.messages.length; i++) {\n const message = params.messages[i];\n if (\n i === lastTurnStartIndex &&\n indexTokenCountMap[i] === undefined &&\n currentUsage\n ) {\n indexTokenCountMap[i] = currentUsage.output_tokens;\n } else if (indexTokenCountMap[i] === undefined) {\n indexTokenCountMap[i] = factoryParams.tokenCounter(message);\n if (currentUsage) {\n newOutputs.add(i);\n }\n totalTokens += indexTokenCountMap[i] ?? 0;\n }\n }\n\n // If `currentUsage` is defined, we need to distribute the current total tokens to our `indexTokenCountMap`,\n // We must distribute it in a weighted manner, so that the total token count is equal to `currentUsage.total_tokens`,\n // relative the manually counted tokens in `indexTokenCountMap`.\n // EDGE CASE: when the resulting context gets pruned, we should not distribute the usage for messages that are not in the context.\n if (currentUsage) {\n let totalIndexTokens = 0;\n if (params.messages[0].getType() === 'system') {\n totalIndexTokens += indexTokenCountMap[0] ?? 0;\n }\n for (let i = lastCutOffIndex; i < params.messages.length; i++) {\n if (i === 0 && params.messages[0].getType() === 'system') {\n continue;\n }\n if (newOutputs.has(i)) {\n continue;\n }\n totalIndexTokens += indexTokenCountMap[i] ?? 0;\n }\n\n // Calculate ratio based only on messages that remain in the context\n const ratio = currentUsage.total_tokens / totalIndexTokens;\n const isRatioSafe = ratio >= 1 / 3 && ratio <= 2.5;\n\n // Apply the ratio adjustment only to messages at or after lastCutOffIndex, and only if the ratio is safe\n if (isRatioSafe) {\n if (\n params.messages[0].getType() === 'system' &&\n lastCutOffIndex !== 0\n ) {\n indexTokenCountMap[0] = Math.round(\n (indexTokenCountMap[0] ?? 0) * ratio\n );\n }\n\n for (let i = lastCutOffIndex; i < params.messages.length; i++) {\n if (newOutputs.has(i)) {\n continue;\n }\n indexTokenCountMap[i] = Math.round(\n (indexTokenCountMap[i] ?? 0) * ratio\n );\n }\n }\n }\n\n lastTurnStartIndex = params.messages.length;\n if (lastCutOffIndex === 0 && totalTokens <= factoryParams.maxTokens) {\n return {\n context: params.messages,\n indexTokenCountMap,\n messagesToRefine: [],\n };\n }\n\n /*\n * Pre-flight truncation: chop oversized tool result content and oversized\n * tool_use input args BEFORE the budget walker runs. Older results get\n * smaller per-message budgets (recencyFactor) so they degrade first while\n * recent turns stay intact. Saves the originals in `originalToolContent`\n * keyed by message index so the summarizer can later reconstruct full\n * fidelity even after the live array was shortened.\n */\n for (let i = 0; i < params.messages.length; i++) {\n const m = params.messages[i];\n if (\n m.getType() === 'tool' &&\n typeof m.content === 'string' &&\n !originalToolContent.has(i)\n ) {\n originalToolContent.set(i, m.content);\n }\n }\n preFlightTruncateToolResults({\n messages: params.messages,\n maxContextTokens: factoryParams.maxTokens,\n indexTokenCountMap,\n tokenCounter: factoryParams.tokenCounter,\n });\n preFlightTruncateToolCallInputs({\n messages: params.messages,\n maxContextTokens: factoryParams.maxTokens,\n indexTokenCountMap,\n tokenCounter: factoryParams.tokenCounter,\n });\n\n /*\n * Position-based content degradation: soft-trim then hard-clear older tool\n * results before the budget walker runs. Off by default — caller opts in\n * via `contextPruningConfig.enabled`.\n */\n if (factoryParams.contextPruningConfig?.enabled) {\n applyContextPruning({\n messages: params.messages,\n indexTokenCountMap,\n tokenCounter: factoryParams.tokenCounter,\n config: factoryParams.contextPruningConfig,\n });\n }\n\n const { context, thinkingStartIndex, messagesToRefine } =\n getMessagesWithinTokenLimit({\n maxContextTokens: factoryParams.maxTokens,\n messages: params.messages,\n indexTokenCountMap,\n startType: params.startType,\n thinkingEnabled: factoryParams.thinkingEnabled,\n tokenCounter: factoryParams.tokenCounter,\n reasoningType:\n factoryParams.provider === Providers.BEDROCK\n ? ContentTypes.REASONING_CONTENT\n : ContentTypes.THINKING,\n thinkingStartIndex:\n factoryParams.thinkingEnabled === true\n ? runThinkingStartIndex\n : undefined,\n });\n runThinkingStartIndex = thinkingStartIndex ?? -1;\n\n /*\n * Orphan repair pass — drops tool messages whose AIMessage was pruned and\n * strips orphan tool_use blocks from AIMessages whose tool results are\n * missing. Without this, the budget walker can split tool_use ↔ tool_result\n * pairs and the provider rejects the request with a structural 400.\n * Dropped messages flow into messagesToRefine for summarization.\n */\n const repaired = repairOrphanedToolMessages({\n context,\n allMessages: params.messages,\n tokenCounter: factoryParams.tokenCounter,\n indexTokenCountMap,\n });\n const finalContext = repaired.context;\n const finalMessagesToRefine =\n repaired.droppedMessages.length > 0\n ? 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