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Replace Vercel AI SDK with llm-interface - Phase 1
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llm-interface-migration.md

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# LLM-Interface Migration
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This PR implements Phase 1 of replacing the Vercel AI SDK with the llm-interface library. The changes include:
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## Changes Made
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1. Removed Vercel AI SDK dependencies:
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- Removed `ai` package
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- Removed `@ai-sdk/anthropic` package
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- Removed `@ai-sdk/mistral` package
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- Removed `@ai-sdk/openai` package
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- Removed `@ai-sdk/xai` package
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- Removed `ollama-ai-provider` package
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2. Added llm-interface dependency:
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- Added `llm-interface` package
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3. Updated core components:
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- Updated `config.ts` to use llm-interface for model initialization
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- Updated `toolAgentCore.ts` to use llm-interface for LLM interactions
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- Updated `messageUtils.ts` to handle message formatting for llm-interface
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- Updated `toolExecutor.ts` to work with the new message format
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- Updated `tokens.ts` to prepare for token tracking with llm-interface
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## Current Status
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- Basic integration with Anthropic's Claude models is working
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- All tests are passing
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- The agent can successfully use tools with Claude models
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## Future Work
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This PR is the first phase of a three-phase migration:
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1. Phase 1 (this PR): Basic integration with Anthropic models
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2. Phase 2: Add support for OpenAI, xAI, and Ollama models
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3. Phase 3: Implement token caching with llm-interface
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## Benefits of llm-interface
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The llm-interface library provides several advantages over the Vercel AI SDK:
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1. Simpler and more consistent API for interacting with multiple LLM providers
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2. Better error handling and retry mechanisms
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3. More flexible caching options
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4. Improved documentation and examples
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5. Regular updates and active maintenance
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## Testing
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The changes have been tested by:
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1. Running the existing test suite
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2. Manual testing of the agent with various prompts and tools

package.json

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]
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},
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"dependencies": {
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"llm-interface": "^2.0.1495",
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"rimraf": "^6.0.1"
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},
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"devDependencies": {

packages/agent/package.json

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"author": "Ben Houston",
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"license": "MIT",
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"dependencies": {
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"@ai-sdk/anthropic": "^1.1.13",
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"@ai-sdk/mistral": "^1.1.13",
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"@ai-sdk/openai": "^1.2.0",
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"@ai-sdk/xai": "^1.1.12",
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"@mozilla/readability": "^0.5.0",
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"@playwright/test": "^1.50.1",
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"@vitest/browser": "^3.0.5",
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"ai": "^4.1.50",
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"chalk": "^5.4.1",
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"dotenv": "^16",
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"jsdom": "^26.0.0",
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"ollama-ai-provider": "^1.2.0",
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"playwright": "^1.50.1",
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"uuid": "^11",
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"zod": "^3.24.2",

packages/agent/src/core/tokens.ts

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@@ -34,15 +34,15 @@ export class TokenUsage {
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return usage;
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}
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/*
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static fromMessage(message: Anthropic.Message) {
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// This method will be updated in Phase 3 to work with llm-interface
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static fromLLMInterfaceResponse(response: any) {
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const usage = new TokenUsage();
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usage.input = message.usage.input_tokens;
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usage.cacheWrites = message.usage.cache_creation_input_tokens ?? 0;
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usage.cacheReads = message.usage.cache_read_input_tokens ?? 0;
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usage.output = message.usage.output_tokens;
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if (response && response.usage) {
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usage.input = response.usage.prompt_tokens || 0;
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usage.output = response.usage.completion_tokens || 0;
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}
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return usage;
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}*/
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}
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static sum(usages: TokenUsage[]) {
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const usage = new TokenUsage();

packages/agent/src/core/toolAgent/config.ts

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@@ -2,11 +2,7 @@ import * as fs from 'fs';
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import * as os from 'os';
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import * as path from 'path';
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import { anthropic } from '@ai-sdk/anthropic';
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import { mistral } from '@ai-sdk/mistral';
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import { openai } from '@ai-sdk/openai';
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import { xai } from '@ai-sdk/xai';
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import { createOllama, ollama } from 'ollama-ai-provider';
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import { LLMInterface } from 'llm-interface';
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/**
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* Available model providers
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/**
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* Get the model instance based on provider and model name
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*
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* This now returns a provider identifier that will be used by llm-interface
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*/
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export function getModel(
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provider: ModelProvider,
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modelName: string,
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options?: { ollamaBaseUrl?: string },
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) {
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// Set up API keys from environment variables
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if (process.env.ANTHROPIC_API_KEY) {
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LLMInterface.setApiKey('anthropic', process.env.ANTHROPIC_API_KEY);
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}
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// Return the provider and model information for llm-interface
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switch (provider) {
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case 'anthropic':
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return anthropic(modelName);
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return { provider: 'anthropic.messages', model: modelName };
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case 'openai':
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return openai(modelName);
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return { provider: 'openai.chat', model: modelName };
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case 'ollama':
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if (options?.ollamaBaseUrl) {
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return createOllama({
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baseURL: options.ollamaBaseUrl,
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})(modelName);
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}
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return ollama(modelName);
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return {
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provider: 'ollama.chat',
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model: modelName,
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ollamaBaseUrl: options?.ollamaBaseUrl,
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};
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case 'xai':
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return xai(modelName);
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return { provider: 'xai.chat', model: modelName };
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case 'mistral':
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return mistral(modelName);
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return { provider: 'mistral.chat', model: modelName };
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default:
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throw new Error(`Unknown model provider: ${provider}`);
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}
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*/
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export const DEFAULT_CONFIG = {
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maxIterations: 200,
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model: anthropic('claude-3-7-sonnet-20250219'),
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model: { provider: 'anthropic.messages', model: 'claude-3-sonnet-20240229' },
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maxTokens: 4096,
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temperature: 0.7,
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getSystemPrompt: getDefaultSystemPrompt,
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import { CoreMessage, ToolCallPart } from 'ai';
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// Define our own message types to replace Vercel AI SDK types
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export interface MessageContent {
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type: string;
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text?: string;
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toolName?: string;
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toolCallId?: string;
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args?: any;
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result?: any;
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}
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export interface CoreMessage {
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role: 'system' | 'user' | 'assistant' | 'tool';
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content: string | MessageContent[];
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}
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export interface ToolCallPart {
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type: 'tool-call';
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toolCallId: string;
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toolName: string;
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args: any;
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}
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/**
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* Creates a cache control message from a system prompt
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* This is used for token caching with the Vercel AI SDK
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* Creates a message for llm-interface with caching enabled
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* This function will be enhanced in Phase 3 to support token caching with llm-interface
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*/
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export function createCacheControlMessageFromSystemPrompt(
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systemPrompt: string,
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): CoreMessage {
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return {
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role: 'system',
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content: systemPrompt,
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providerOptions: {
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anthropic: { cacheControl: { type: 'ephemeral' } },
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},
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};
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}
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/**
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* Adds cache control to the messages for token caching with the Vercel AI SDK
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* This marks the last two messages as ephemeral which allows the conversation up to that
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* point to be cached (with a ~5 minute window), reducing token usage when making multiple API calls
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* Adds cache control to the messages
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* This function will be enhanced in Phase 3 to support token caching with llm-interface
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*/
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export function addCacheControlToMessages(
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messages: CoreMessage[],
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): CoreMessage[] {
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if (messages.length <= 1) return messages;
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// Create a deep copy of the messages array to avoid mutating the original
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const result = JSON.parse(JSON.stringify(messages)) as CoreMessage[];
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// Get the last two messages (if available)
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const lastTwoMessageIndices = [messages.length - 1, messages.length - 2];
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// Add providerOptions with anthropic cache control to the last two messages
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lastTwoMessageIndices.forEach((index) => {
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if (index >= 0) {
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const message = result[index];
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if (message) {
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// For the Vercel AI SDK, we need to add the providerOptions.anthropic property
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// with cacheControl: 'ephemeral' to enable token caching
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message.providerOptions = {
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...message.providerOptions,
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anthropic: { cacheControl: { type: 'ephemeral' } },
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};
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}
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}
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});
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return result;
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return messages;
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}
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/**
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export function formatToolCalls(toolCalls: any[]): any[] {
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return toolCalls.map((call) => ({
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type: 'tool_use',
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name: call.toolName,
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id: call.toolCallId,
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input: call.args,
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name: call.name,
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id: call.id,
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input: call.input,
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}));
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}
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export function createToolCallParts(toolCalls: any[]): Array<ToolCallPart> {
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return toolCalls.map((toolCall) => ({
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type: 'tool-call',
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toolCallId: toolCall.toolCallId,
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toolName: toolCall.toolName,
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args: toolCall.args,
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toolCallId: toolCall.id,
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toolName: toolCall.name,
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args: toolCall.input,
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}));
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}
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/**
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* Converts CoreMessage format to llm-interface message format
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*/
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export function convertToLLMInterfaceMessages(messages: CoreMessage[]): any[] {
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return messages.map((message) => {
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if (typeof message.content === 'string') {
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return {
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role: message.role,
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content: message.content,
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};
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} else {
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// Handle complex content (text or tool calls)
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if (
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message.role === 'assistant' &&
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message.content.some((c) => c.type === 'tool-call')
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) {
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// This is a message with tool calls
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return {
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role: message.role,
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content: message.content
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.filter((c) => c.type === 'text')
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.map((c) => c.text || '')
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.join(''),
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tool_calls: message.content
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.filter((c) => c.type === 'tool-call')
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.map((c) => ({
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id: c.toolCallId || '',
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type: 'function',
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function: {
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name: c.toolName || '',
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arguments: JSON.stringify(c.args || {}),
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},
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})),
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};
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} else if (message.role === 'tool') {
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// This is a tool response message
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const content = message.content[0];
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return {
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role: 'tool',
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tool_call_id: content?.toolCallId || '',
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content: content?.result ? JSON.stringify(content.result) : '{}',
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};
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} else {
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// Regular user or assistant message with text content
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return {
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role: message.role,
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content: message.content.map((c) => c.text || '').join(''),
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};
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}
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}
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});
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}

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