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import type { BetaContentBlock } from '@anthropic-ai/sdk/resources/beta/messages/messages.mjs'
import type {
ContentBlock,
ContentBlockParam,
} from '@anthropic-ai/sdk/resources/index.mjs'
import { roughTokenCountEstimation as countTokens } from '../services/tokenEstimation.js'
import type {
AssistantMessage,
Message,
UserMessage,
} from '../types/message.js'
import { normalizeMessagesForAPI } from './messages.js'
import { jsonStringify } from './slowOperations.js'
type TokenStats = {
toolRequests: Map<string, number>
toolResults: Map<string, number>
humanMessages: number
assistantMessages: number
localCommandOutputs: number
other: number
attachments: Map<string, number>
duplicateFileReads: Map<string, { count: number; tokens: number }>
total: number
}
export function analyzeContext(messages: Message[]): TokenStats {
const stats: TokenStats = {
toolRequests: new Map(),
toolResults: new Map(),
humanMessages: 0,
assistantMessages: 0,
localCommandOutputs: 0,
other: 0,
attachments: new Map(),
duplicateFileReads: new Map(),
total: 0,
}
const toolIdsToToolNames = new Map<string, string>()
const readToolIdToFilePath = new Map<string, string>()
const fileReadStats = new Map<
string,
{ count: number; totalTokens: number }
>()
messages.forEach(msg => {
if (msg.type === 'attachment') {
const type = msg.attachment.type || 'unknown'
stats.attachments.set(type, (stats.attachments.get(type) || 0) + 1)
}
})
const normalizedMessages = normalizeMessagesForAPI(messages)
normalizedMessages.forEach(msg => {
const { content } = msg.message
// Not sure if this path is still used, but adding as a fallback
if (typeof content === 'string') {
const tokens = countTokens(content)
stats.total += tokens
// Check if this is a local command output
if (msg.type === 'user' && content.includes('local-command-stdout')) {
stats.localCommandOutputs += tokens
} else {
stats[msg.type === 'user' ? 'humanMessages' : 'assistantMessages'] +=
tokens
}
} else {
content.forEach(block =>
processBlock(
block,
msg,
stats,
toolIdsToToolNames,
readToolIdToFilePath,
fileReadStats,
),
)
}
})
// Calculate duplicate file reads
fileReadStats.forEach((data, path) => {
if (data.count > 1) {
const averageTokensPerRead = Math.floor(data.totalTokens / data.count)
const duplicateTokens = averageTokensPerRead * (data.count - 1)
stats.duplicateFileReads.set(path, {
count: data.count,
tokens: duplicateTokens,
})
}
})
return stats
}
function processBlock(
block: ContentBlockParam | ContentBlock | BetaContentBlock,
message: UserMessage | AssistantMessage,
stats: TokenStats,
toolIds: Map<string, string>,
readToolPaths: Map<string, string>,
fileReads: Map<string, { count: number; totalTokens: number }>,
): void {
const tokens = countTokens(jsonStringify(block))
stats.total += tokens
switch (block.type) {
case 'text':
// Check if this is a local command output
if (
message.type === 'user' &&
'text' in block &&
block.text.includes('local-command-stdout')
) {
stats.localCommandOutputs += tokens
} else {
stats[
message.type === 'user' ? 'humanMessages' : 'assistantMessages'
] += tokens
}
break
case 'tool_use': {
if ('name' in block && 'id' in block) {
const toolName = block.name || 'unknown'
increment(stats.toolRequests, toolName, tokens)
toolIds.set(block.id, toolName)
// Track Read tool file paths
if (
toolName === 'Read' &&
'input' in block &&
block.input &&
typeof block.input === 'object' &&
'file_path' in block.input
) {
const path = String(
(block.input as Record<string, unknown>).file_path,
)
readToolPaths.set(block.id, path)
}
}
break
}
case 'tool_result': {
if ('tool_use_id' in block) {
const toolName = toolIds.get(block.tool_use_id) || 'unknown'
increment(stats.toolResults, toolName, tokens)
// Track file read tokens
if (toolName === 'Read') {
const path = readToolPaths.get(block.tool_use_id)
if (path) {
const current = fileReads.get(path) || { count: 0, totalTokens: 0 }
fileReads.set(path, {
count: current.count + 1,
totalTokens: current.totalTokens + tokens,
})
}
}
}
break
}
case 'image':
case 'server_tool_use':
case 'web_search_tool_result':
case 'search_result':
case 'document':
case 'thinking':
case 'redacted_thinking':
case 'code_execution_tool_result':
case 'mcp_tool_use':
case 'mcp_tool_result':
case 'container_upload':
case 'web_fetch_tool_result':
case 'bash_code_execution_tool_result':
case 'text_editor_code_execution_tool_result':
case 'tool_search_tool_result':
case 'compaction':
// Don't care about these for now..
stats['other'] += tokens
break
}
}
function increment(map: Map<string, number>, key: string, value: number): void {
map.set(key, (map.get(key) || 0) + value)
}
export function tokenStatsToStatsigMetrics(
stats: TokenStats,
): Record<string, number> {
const metrics: Record<string, number> = {
total_tokens: stats.total,
human_message_tokens: stats.humanMessages,
assistant_message_tokens: stats.assistantMessages,
local_command_output_tokens: stats.localCommandOutputs,
other_tokens: stats.other,
}
stats.attachments.forEach((count, type) => {
metrics[`attachment_${type}_count`] = count
})
stats.toolRequests.forEach((tokens, tool) => {
metrics[`tool_request_${tool}_tokens`] = tokens
})
stats.toolResults.forEach((tokens, tool) => {
metrics[`tool_result_${tool}_tokens`] = tokens
})
const duplicateTotal = [...stats.duplicateFileReads.values()].reduce(
(sum, d) => sum + d.tokens,
0,
)
metrics.duplicate_read_tokens = duplicateTotal
metrics.duplicate_read_file_count = stats.duplicateFileReads.size
if (stats.total > 0) {
metrics.human_message_percent = Math.round(
(stats.humanMessages / stats.total) * 100,
)
metrics.assistant_message_percent = Math.round(
(stats.assistantMessages / stats.total) * 100,
)
metrics.local_command_output_percent = Math.round(
(stats.localCommandOutputs / stats.total) * 100,
)
metrics.duplicate_read_percent = Math.round(
(duplicateTotal / stats.total) * 100,
)
const toolRequestTotal = [...stats.toolRequests.values()].reduce(
(sum, v) => sum + v,
0,
)
const toolResultTotal = [...stats.toolResults.values()].reduce(
(sum, v) => sum + v,
0,
)
metrics.tool_request_percent = Math.round(
(toolRequestTotal / stats.total) * 100,
)
metrics.tool_result_percent = Math.round(
(toolResultTotal / stats.total) * 100,
)
// Add individual tool request percentages
stats.toolRequests.forEach((tokens, tool) => {
metrics[`tool_request_${tool}_percent`] = Math.round(
(tokens / stats.total) * 100,
)
})
// Add individual tool result percentages
stats.toolResults.forEach((tokens, tool) => {
metrics[`tool_result_${tool}_percent`] = Math.round(
(tokens / stats.total) * 100,
)
})
}
return metrics
}