chore: merge dev into v2 (#36144)
Co-authored-by: opencode-agent[bot] <opencode-agent[bot]@users.noreply.github.com> Co-authored-by: usrnk1 <7547651+usrnk1@users.noreply.github.com> Co-authored-by: James Long <longster@gmail.com> Co-authored-by: opencode-agent[bot] <219766164+opencode-agent[bot]@users.noreply.github.com> Co-authored-by: Aiden Cline <63023139+rekram1-node@users.noreply.github.com> Co-authored-by: Brendan Allan <14191578+Brendonovich@users.noreply.github.com> Co-authored-by: Jack <jack@anoma.ly> Co-authored-by: opencode <opencode@sst.dev> Co-authored-by: Frank <frank@anoma.ly> Co-authored-by: Jay <53023+jayair@users.noreply.github.com> Co-authored-by: Luke Parker <10430890+Hona@users.noreply.github.com> Co-authored-by: Aarav Sareen <96787824+arvsrn@users.noreply.github.com> Co-authored-by: Julian Coy <julian@ex-machina.co> Co-authored-by: Brendan Allan <git@brendonovich.dev> Co-authored-by: Vladimir Glafirov <vglafirov@gitlab.com> Co-authored-by: Adam <2363879+adamdotdevin@users.noreply.github.com> Co-authored-by: Dustin Deus <deusdustin@gmail.com> Co-authored-by: Kit Langton <kit.langton@gmail.com> Co-authored-by: Simon Klee <hello@simonklee.dk> Co-authored-by: Jay <air@live.ca> Co-authored-by: David Hill <1879069+iamdavidhill@users.noreply.github.com> Co-authored-by: Aiden Cline <aidenpcline@gmail.com> Co-authored-by: James Long <jlongster@users.noreply.github.com> Co-authored-by: 冯基魁 <56265583+fengjikui@users.noreply.github.com> Co-authored-by: Aiden Cline <rekram1-node@users.noreply.github.com>
This commit is contained in:
opencode-agent[bot]
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commit
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129 changed files with 3726 additions and 937 deletions
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@ -1,6 +1,6 @@
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{
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"$schema": "https://json.schemastore.org/package.json",
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"version": "1.17.15",
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"version": "1.17.18",
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"name": "opencode",
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"type": "module",
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"license": "MIT",
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@ -73,7 +73,7 @@
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"@ai-sdk/provider": "3.0.8",
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"@ai-sdk/togetherai": "2.0.41",
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"@ai-sdk/vercel": "2.0.39",
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"@ai-sdk/xai": "3.0.82",
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"@ai-sdk/xai": "3.0.102",
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"@aws-sdk/credential-providers": "3.1057.0",
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"@clack/prompts": "1.0.0-alpha.1",
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"@effect/opentelemetry": "catalog:",
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@ -99,7 +99,7 @@ function build(key: string, remote: SelectableItem, url: string, prev?: Model):
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: undefined
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const prices = remote.billing?.token_prices
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// Copilot prices are AIC per billing batch; OpenCode stores USD per million tokens.
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const usdPerMillion = prices ? 10_000 / prices.batch_size : 0
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const usdPerMillion = prices && prices.batch_size > 0 ? 10_000 / prices.batch_size : 0
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const model: CopilotModel = {
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id: key,
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@ -207,6 +207,13 @@ function custom(dep: CustomDep): Record<string, CustomLoader> {
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},
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options: { headerTimeout: OPENAI_HEADER_TIMEOUT_DEFAULT },
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}),
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meta: () =>
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Effect.succeed({
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autoload: false,
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async getModel(sdk: any, modelID: string, _options?: Record<string, any>) {
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return sdk.responses(modelID)
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},
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}),
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xai: () =>
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Effect.succeed({
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autoload: false,
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@ -1064,11 +1071,17 @@ export type ConfigProvidersResult = Types.DeepMutable<Schema.Schema.Type<typeof
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export function toPublicInfo(provider: Info): Info {
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return JSON.parse(
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JSON.stringify(provider, (_, value) => {
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if (typeof value === "function" || typeof value === "symbol" || value === undefined) return undefined
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if (typeof value === "bigint") return value.toString()
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return value
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}),
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JSON.stringify(
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{
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...provider,
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models: Object.fromEntries(Object.entries(provider.models).filter(([, model]) => Schema.is(Model)(model))),
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},
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(_, value) => {
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if (typeof value === "function" || typeof value === "symbol" || value === undefined) return undefined
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if (typeof value === "bigint") return value.toString()
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return value
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},
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),
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)
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}
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@ -736,7 +736,6 @@ export function variants(model: Provider.Model): Record<string, Record<string, a
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high: { reasoningEffort: "high" },
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}
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}
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if (id.includes("grok")) return {}
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switch (model.api.npm) {
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case "@openrouter/ai-sdk-provider":
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@ -887,6 +886,18 @@ export function variants(model: Provider.Model): Record<string, Record<string, a
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)
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case "@ai-sdk/amazon-bedrock/mantle":
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case "@ai-sdk/openai": {
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if (model.providerID === "meta") {
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return Object.fromEntries(
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OPENAI_EFFORTS.map((effort) => [
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effort,
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{
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reasoningEffort: effort,
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reasoningSummary: "auto",
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include: INCLUDE_ENCRYPTED_REASONING,
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},
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]),
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)
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}
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// https://v5.ai-sdk.dev/providers/ai-sdk-providers/openai
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const efforts = openaiReasoningEfforts(model.api.id, model.release_date)
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return Object.fromEntries(
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@ -1129,10 +1140,22 @@ export function options(input: {
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}
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}
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if (input.model.providerID === "openai" || input.providerOptions?.setCacheKey) {
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if (
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input.providerOptions?.setCacheKey !== false &&
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(input.model.providerID === "openai" ||
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input.model.api.npm === "@ai-sdk/openai" ||
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input.model.api.npm === "@ai-sdk/xai" ||
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input.providerOptions?.setCacheKey)
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) {
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result["promptCacheKey"] = input.sessionID
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}
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if (input.model.providerID === "meta" && input.model.api.npm === "@ai-sdk/openai") {
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result["reasoningEffort"] = "high"
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result["reasoningSummary"] = "auto"
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result["include"] = INCLUDE_ENCRYPTED_REASONING
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}
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if (input.model.api.npm === "@ai-sdk/google" || input.model.api.npm === "@ai-sdk/google-vertex") {
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if (input.model.capabilities.reasoning) {
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result["thinkingConfig"] = {
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76
packages/opencode/src/session/prompt/meta.txt
Normal file
76
packages/opencode/src/session/prompt/meta.txt
Normal file
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@ -0,0 +1,76 @@
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You are OpenCode, the best coding agent on the planet.
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You are based on a large language model trained by Meta MSL named Muse Spark.
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When asked who you are, identify yourself as OpenCode powered by Meta Muse Spark by name.
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You are an interactive CLI tool that helps users with software engineering tasks. Use the instructions below and the tools available to you to assist the user.
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IMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local files.
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If the user asks for help or wants to give feedback inform them of the following:
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- ctrl+p to list available actions
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- To give feedback, users should report the issue at
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https://github.com/anomalyco/opencode
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When the user directly asks about OpenCode (eg. "can OpenCode do...", "does OpenCode have..."), or asks in second person (eg. "are you able...", "can you do..."), or asks how to use a specific OpenCode feature (eg. implement a hook, write a slash command, or install an MCP server), use the WebFetch tool to gather information to answer the question from OpenCode docs. The list of available docs is available at https://opencode.ai/docs
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# Tone and style
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- Only use emojis if the user explicitly requests it. Avoid using emojis in all communication unless asked.
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||||
- Your output will be displayed on a command line interface. Your responses should be short and concise. You can use GitHub-flavored markdown for formatting, and will be rendered in a monospace font using the CommonMark specification.
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- Output text to communicate with the user; all text you output outside of tool use is displayed to the user. Only use tools to complete tasks. Never use tools like Bash or code comments as means to communicate with the user during the session.
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||||
- NEVER create files unless they're absolutely necessary for achieving your goal. ALWAYS prefer editing an existing file to creating a new one. This includes markdown files.
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||||
# Professional objectivity
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Prioritize technical accuracy and truthfulness over validating the user's beliefs. Focus on facts and problem-solving, providing direct, objective technical info without any unnecessary superlatives, praise, or emotional validation. It is best for the user if OpenCode honestly applies the same rigorous standards to all ideas and disagrees when necessary, even if it may not be what the user wants to hear. Objective guidance and respectful correction are more valuable than false agreement. Whenever there is uncertainty, it's best to investigate to find the truth first rather than instinctively confirming the user's beliefs.
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# Task Management
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You have access to the TodoWrite tools to help you manage and plan tasks. Use these tools VERY frequently to ensure that you are tracking your tasks and giving the user visibility into your progress.
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These tools are also EXTREMELY helpful for planning tasks, and for breaking down larger complex tasks into smaller steps. If you do not use this tool when planning, you may forget to do important tasks - and that is unacceptable.
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It is critical that you mark todos as completed as soon as you are done with a task. Do not batch up multiple tasks before marking them as completed.
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||||
Examples:
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<example>
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user: Run the build and fix any type errors
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assistant: I'm going to use the TodoWrite tool to write the following items to the todo list:
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- Run the build
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- Fix any type errors
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I'm now going to run the build using Bash.
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Looks like I found 10 type errors. I'm going to use the TodoWrite tool to write 10 items to the todo list.
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marking the first todo as in_progress
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Let me start working on the first item...
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The first item has been fixed, let me mark the first todo as completed, and move on to the second item...
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..
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..
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</example>
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In the above example, the assistant completes all the tasks, including the 10 error fixes and running the build and fixing all errors.
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<example>
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user: Help me write a new feature that allows users to track their usage metrics and export them to various formats
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assistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the TodoWrite tool to plan this task.
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Adding the following todos to the todo list:
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1. Research existing metrics tracking in the codebase
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2. Design the metrics collection system
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3. Implement core metrics tracking functionality
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4. Create export functionality for different formats
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Let me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.
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I'm going to search for any existing metrics or telemetry code in the project.
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I've found some existing telemetry code. Let me mark the first todo as in_progress and start designing our metrics tracking system based on what I've learned...
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[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]
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||||
</example>
|
||||
# Doing tasks
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||||
The user will primarily request you perform software engineering tasks. This includes solving bugs, adding new functionality, refactoring code, explaining code, and more. For these tasks the following steps are recommended:
|
||||
- Use the TodoWrite tool to plan the task if required
|
||||
- Tool results and user messages may include <system-reminder> tags. <system-reminder> tags contain useful information and reminders. They are automatically added by the system, and bear no direct relation to the specific tool results or user messages in which they appear.
|
||||
# Tool usage policy
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||||
- When doing file search, prefer to use the Task tool in order to reduce context usage.
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||||
- You should proactively use the Task tool with specialized agents when the task at hand matches the agent's description.
|
||||
- When WebFetch returns a message about a redirect to a different host, you should immediately make a new WebFetch request with the redirect URL provided in the response.
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||||
- You can call multiple tools in a single response. If you intend to call multiple tools and there are no dependencies between them, make all independent tool calls in parallel. Maximize use of parallel tool calls where possible to increase efficiency. However, if some tool calls depend on previous calls to inform dependent values, do NOT call these tools in parallel and instead call them sequentially. For instance, if one operation must complete before another starts, run these operations sequentially instead. Never use placeholders or guess missing parameters in tool calls.
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||||
- If the user specifies that they want you to run tools "in parallel", you MUST send a single message with multiple tool use content blocks. For example, if you need to launch multiple agents in parallel, send a single message with multiple Task tool calls.
|
||||
- Use specialized tools instead of bash commands when possible, as this provides a better user experience. For file operations, use dedicated tools: Read for reading files instead of cat/head/tail, Edit for editing instead of sed/awk, and Write for creating files instead of cat with heredoc or echo redirection. Reserve bash tools exclusively for actual system commands and terminal operations that require shell execution. NEVER use bash echo or other command-line tools to communicate thoughts, explanations, or instructions to the user. Output all communication directly in your response text instead.
|
||||
- VERY IMPORTANT: When exploring the codebase to gather context or to answer a question that is not a needle query for a specific file/class/function, it is CRITICAL that you use the Task tool instead of running search commands directly.
|
||||
<example>
|
||||
user: Where are errors from the client handled?
|
||||
assistant: [Uses the Task tool to find the files that handle client errors instead of using Glob or Grep directly]
|
||||
</example>
|
||||
<example>
|
||||
user: What is the codebase structure?
|
||||
assistant: [Uses the Task tool]
|
||||
</example>
|
||||
IMPORTANT: Always use the TodoWrite tool to plan and track tasks throughout the conversation.
|
||||
# Code References
|
||||
When referencing specific functions or pieces of code include the pattern `file_path:line_number` to allow the user to easily navigate to the source code location.
|
||||
<example>
|
||||
user: Where are errors from the client handled?
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||||
assistant: Clients are marked as failed in the `connectToServer` function in src/services/process.ts:712.
|
||||
</example>
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||||
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@ -9,6 +9,7 @@ import PROMPT_BEAST from "./prompt/beast.txt"
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import PROMPT_GEMINI from "./prompt/gemini.txt"
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import PROMPT_GPT from "./prompt/gpt.txt"
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||||
import PROMPT_KIMI from "./prompt/kimi.txt"
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||||
import PROMPT_META from "./prompt/meta.txt"
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||||
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||||
import PROMPT_CODEX from "./prompt/codex.txt"
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||||
import PROMPT_TRINITY from "./prompt/trinity.txt"
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@ -24,6 +25,7 @@ import { MCP } from "@/mcp"
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import { PermissionV1 } from "@opencode-ai/core/v1/permission"
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||||
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||||
export function provider(model: Provider.Model) {
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||||
if (model.api.id.includes("muse-spark")) return [PROMPT_META]
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||||
if (model.api.id.includes("gpt-4") || model.api.id.includes("o1") || model.api.id.includes("o3"))
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||||
return [PROMPT_BEAST]
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||||
if (model.api.id.includes("gpt")) {
|
||||
|
|
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|||
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@ -187,6 +187,60 @@ test("converts Copilot AIC token prices to USD per million tokens", async () =>
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|||
expect(models["ignored-non-chat-record"]).toBeUndefined()
|
||||
})
|
||||
|
||||
test("uses zero cost when Copilot reports a zero billing batch size", async () => {
|
||||
globalThis.fetch = mock(() =>
|
||||
Promise.resolve(
|
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new Response(
|
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JSON.stringify({
|
||||
data: [
|
||||
{
|
||||
model_picker_enabled: true,
|
||||
id: "mercury-alpha",
|
||||
name: "Mercury Alpha",
|
||||
version: "mercury-alpha-2026-07-09",
|
||||
billing: {
|
||||
token_prices: {
|
||||
batch_size: 0,
|
||||
default: {
|
||||
input_price: 0,
|
||||
output_price: 0,
|
||||
cache_price: 0,
|
||||
},
|
||||
},
|
||||
},
|
||||
capabilities: {
|
||||
family: "mercury",
|
||||
limits: {
|
||||
max_context_window_tokens: 128000,
|
||||
max_output_tokens: 16384,
|
||||
max_prompt_tokens: 128000,
|
||||
},
|
||||
supports: {
|
||||
streaming: true,
|
||||
tool_calls: true,
|
||||
},
|
||||
},
|
||||
},
|
||||
],
|
||||
}),
|
||||
{ status: 200 },
|
||||
),
|
||||
),
|
||||
) as unknown as typeof fetch
|
||||
|
||||
const model = (await CopilotModels.get("https://api.githubcopilot.com")).models["mercury-alpha"]
|
||||
|
||||
expect(model.cost).toEqual({
|
||||
input: 0,
|
||||
output: 0,
|
||||
cache: {
|
||||
read: 0,
|
||||
write: 0,
|
||||
},
|
||||
})
|
||||
expect(JSON.stringify(model)).not.toContain("null")
|
||||
})
|
||||
|
||||
test("records Copilot advertised responses endpoint for non-GPT model IDs", async () => {
|
||||
globalThis.fetch = mock(() =>
|
||||
Promise.resolve(
|
||||
|
|
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|||
|
|
@ -24,6 +24,8 @@ import { testEffect } from "../lib/effect"
|
|||
import { ProviderV2 } from "@opencode-ai/core/provider"
|
||||
import { ModelV2 } from "@opencode-ai/core/model"
|
||||
|
||||
type ModelsDevProvider = Parameters<typeof Provider.fromModelsDevProvider>[0]
|
||||
|
||||
const originalEnv = new Map<string, string | undefined>()
|
||||
|
||||
const rememberEnv = (k: string) => {
|
||||
|
|
@ -1386,8 +1388,7 @@ test("mode cost preserves over-200k pricing from base model", () => {
|
|||
},
|
||||
},
|
||||
},
|
||||
// @ts-expect-error dead V1 fixture uses the removed pre-normalized ModelsDev provider type.
|
||||
} as unknown as ModelsDev.Provider
|
||||
} as unknown as ModelsDevProvider
|
||||
|
||||
const model = Provider.fromModelsDevProvider(provider).models["gpt-5.4-fast"]
|
||||
expect(model.cost.input).toEqual(5)
|
||||
|
|
@ -1416,8 +1417,7 @@ test("models.dev normalization fills required response fields", () => {
|
|||
limit: { context: 1_050_000, input: 922_000, output: 128_000 },
|
||||
},
|
||||
},
|
||||
// @ts-expect-error dead V1 fixture uses the removed pre-normalized ModelsDev provider type.
|
||||
} as unknown as ModelsDev.Provider
|
||||
} as unknown as ModelsDevProvider
|
||||
|
||||
const model = Provider.fromModelsDevProvider(provider).models["gpt-5.4"]
|
||||
expect(model.api.url).toBe("")
|
||||
|
|
@ -1428,6 +1428,32 @@ test("models.dev normalization fills required response fields", () => {
|
|||
expect(model.release_date).toBe("")
|
||||
})
|
||||
|
||||
test("public provider info omits invalid models", () => {
|
||||
const provider = Provider.fromModelsDevProvider({
|
||||
id: "test",
|
||||
name: "Test",
|
||||
env: [],
|
||||
models: {
|
||||
valid: {
|
||||
id: "valid",
|
||||
name: "Valid",
|
||||
cost: { input: 1, output: 1 },
|
||||
limit: { context: 128_000, output: 16_000 },
|
||||
},
|
||||
},
|
||||
} as unknown as ModelsDevProvider)
|
||||
provider.models.invalid = {
|
||||
...provider.models.valid,
|
||||
id: ModelV2.ID.make("invalid"),
|
||||
cost: { ...provider.models.valid.cost, input: Number.NaN },
|
||||
}
|
||||
|
||||
const result = Provider.toPublicInfo(provider)
|
||||
|
||||
expect(result.models.valid).toBeDefined()
|
||||
expect(result.models.invalid).toBeUndefined()
|
||||
})
|
||||
|
||||
it.instance("model variants are generated for reasoning models", () =>
|
||||
Effect.gen(function* () {
|
||||
yield* set("ANTHROPIC_API_KEY", "test-api-key")
|
||||
|
|
|
|||
|
|
@ -73,7 +73,7 @@ describe("ProviderTransform.options - setCacheKey", () => {
|
|||
expect(result.promptCacheKey).toBeUndefined()
|
||||
})
|
||||
|
||||
test("should set promptCacheKey for openai provider regardless of setCacheKey", () => {
|
||||
test("should set promptCacheKey for openai provider by default", () => {
|
||||
const openaiModel = {
|
||||
...mockModel,
|
||||
providerID: "openai",
|
||||
|
|
@ -87,6 +87,56 @@ describe("ProviderTransform.options - setCacheKey", () => {
|
|||
expect(result.promptCacheKey).toBe(sessionID)
|
||||
})
|
||||
|
||||
test("should not set promptCacheKey for openai when explicitly disabled", () => {
|
||||
const openaiModel = {
|
||||
...mockModel,
|
||||
providerID: "openai",
|
||||
api: {
|
||||
id: "gpt-4",
|
||||
url: "https://api.openai.com",
|
||||
npm: "@ai-sdk/openai",
|
||||
},
|
||||
}
|
||||
const result = ProviderTransform.options({
|
||||
model: openaiModel,
|
||||
sessionID,
|
||||
providerOptions: { setCacheKey: false },
|
||||
})
|
||||
expect(result.promptCacheKey).toBeUndefined()
|
||||
})
|
||||
|
||||
test("should set promptCacheKey for the xAI SDK by default regardless of provider ID", () => {
|
||||
const xaiModel = {
|
||||
...mockModel,
|
||||
providerID: "custom-xai",
|
||||
api: {
|
||||
id: "grok-4",
|
||||
url: "https://api.x.ai",
|
||||
npm: "@ai-sdk/xai",
|
||||
},
|
||||
}
|
||||
const result = ProviderTransform.options({ model: xaiModel, sessionID, providerOptions: {} })
|
||||
expect(result.promptCacheKey).toBe(sessionID)
|
||||
})
|
||||
|
||||
test("should not set promptCacheKey for the xAI SDK when explicitly disabled", () => {
|
||||
const xaiModel = {
|
||||
...mockModel,
|
||||
providerID: "xai",
|
||||
api: {
|
||||
id: "grok-4",
|
||||
url: "https://api.x.ai",
|
||||
npm: "@ai-sdk/xai",
|
||||
},
|
||||
}
|
||||
const result = ProviderTransform.options({
|
||||
model: xaiModel,
|
||||
sessionID,
|
||||
providerOptions: { setCacheKey: false },
|
||||
})
|
||||
expect(result.promptCacheKey).toBeUndefined()
|
||||
})
|
||||
|
||||
test("should set store=false for openai provider", () => {
|
||||
const openaiModel = {
|
||||
...mockModel,
|
||||
|
|
@ -3289,7 +3339,7 @@ describe("ProviderTransform.variants", () => {
|
|||
expect(Object.keys(result)).toEqual(["low", "medium", "high"])
|
||||
})
|
||||
|
||||
test("grok-4 returns empty object", () => {
|
||||
test("grok-4 uses the provider's standard efforts", () => {
|
||||
const model = createMockModel({
|
||||
id: "openrouter/grok-4",
|
||||
providerID: "openrouter",
|
||||
|
|
@ -3300,7 +3350,8 @@ describe("ProviderTransform.variants", () => {
|
|||
},
|
||||
})
|
||||
const result = ProviderTransform.variants(model)
|
||||
expect(result).toEqual({})
|
||||
expect(Object.keys(result)).toEqual(["low", "medium", "high"])
|
||||
expect(result.medium).toEqual({ reasoning: { effort: "medium" } })
|
||||
})
|
||||
|
||||
test("grok-3-mini returns low and high with reasoning", () => {
|
||||
|
|
@ -3716,18 +3767,19 @@ describe("ProviderTransform.variants", () => {
|
|||
})
|
||||
|
||||
describe("@ai-sdk/xai", () => {
|
||||
test("grok-3 returns empty object", () => {
|
||||
test("grok-4.5 uses standard reasoning efforts", () => {
|
||||
const model = createMockModel({
|
||||
id: "xai/grok-3",
|
||||
id: "xai/grok-4.5",
|
||||
providerID: "xai",
|
||||
api: {
|
||||
id: "grok-3",
|
||||
id: "grok-4.5",
|
||||
url: "https://api.x.ai",
|
||||
npm: "@ai-sdk/xai",
|
||||
},
|
||||
})
|
||||
const result = ProviderTransform.variants(model)
|
||||
expect(result).toEqual({})
|
||||
expect(Object.keys(result)).toEqual(["low", "medium", "high"])
|
||||
expect(result.medium).toEqual({ reasoningEffort: "medium" })
|
||||
})
|
||||
|
||||
test("grok-3-mini returns low and high with reasoningEffort", () => {
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
import { describe, expect } from "bun:test"
|
||||
import { describe, expect, test } from "bun:test"
|
||||
import { LayerNode } from "@opencode-ai/core/effect/layer-node"
|
||||
import { Effect, Layer } from "effect"
|
||||
import type { Agent } from "../../src/agent/agent"
|
||||
import { NamedError } from "@opencode-ai/core/util/error"
|
||||
import { Skill } from "../../src/skill"
|
||||
import { Permission } from "../../src/permission"
|
||||
import type { Provider } from "../../src/provider/provider"
|
||||
import { SystemPrompt } from "../../src/session/system"
|
||||
import { MCP } from "../../src/mcp"
|
||||
import { testEffect } from "../lib/effect"
|
||||
|
|
@ -83,6 +84,12 @@ const it = testEffect(
|
|||
)
|
||||
|
||||
describe("session.system", () => {
|
||||
test("selects the Meta prompt for Muse Spark model IDs", () => {
|
||||
expect(SystemPrompt.provider({ api: { id: "meta/muse-spark-preview" } } as Provider.Model)[0]).toContain(
|
||||
"Meta Muse Spark",
|
||||
)
|
||||
})
|
||||
|
||||
it.effect("skills output is sorted by name and stable across calls", () =>
|
||||
Effect.gen(function* () {
|
||||
const prompt = yield* SystemPrompt.Service
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue