tf_code/packages/tfcode/src/session/llm.ts

import { Installation } from "@/installation"
import { Provider } from "@/provider/provider"
import { Log } from "@/util/log"
import {
  streamText,
  wrapLanguageModel,
  type ModelMessage,
  type StreamTextResult,
  type Tool,
  type ToolSet,
  tool,
  jsonSchema,
} from "ai"
import { mergeDeep, pipe } from "remeda"
import { GitLabWorkflowLanguageModel } from "gitlab-ai-provider"
import { ProviderTransform } from "@/provider/transform"
import { Config } from "@/config/config"
import { Instance } from "@/project/instance"
import type { Agent } from "@/agent/agent"
import type { MessageV2 } from "./message-v2"
import { Plugin } from "@/plugin"
import { SystemPrompt } from "./system"
import { Flag } from "@/flag/flag"
import { Permission } from "@/permission"
import { Auth } from "@/auth"

export namespace LLM {
  const log = Log.create({ service: "llm" })
  export const OUTPUT_TOKEN_MAX = ProviderTransform.OUTPUT_TOKEN_MAX

  export type StreamInput = {
    user: MessageV2.User
    sessionID: string
    model: Provider.Model
    agent: Agent.Info
    permission?: Permission.Ruleset
    system: string[]
    abort: AbortSignal
    messages: ModelMessage[]
    small?: boolean
    tools: Record<string, Tool>
    retries?: number
    toolChoice?: "auto" | "required" | "none"
  }

  export type StreamOutput = StreamTextResult<ToolSet, unknown>

  export async function stream(input: StreamInput) {
    const l = log
      .clone()
      .tag("providerID", input.model.providerID)
      .tag("modelID", input.model.id)
      .tag("sessionID", input.sessionID)
      .tag("small", (input.small ?? false).toString())
      .tag("agent", input.agent.name)
      .tag("mode", input.agent.mode)
    l.info("stream", {
      modelID: input.model.id,
      providerID: input.model.providerID,
    })
    const [language, cfg, provider, auth] = await Promise.all([
      Provider.getLanguage(input.model),
      Config.get(),
      Provider.getProvider(input.model.providerID),
      Auth.get(input.model.providerID),
    ])
    // TODO: move this to a proper hook
    const isOpenaiOauth = provider.id === "openai" && auth?.type === "oauth"

    const system: string[] = []
    system.push(
      [
        // use agent prompt otherwise provider prompt
        ...(input.agent.prompt ? [input.agent.prompt] : SystemPrompt.provider(input.model)),
        // any custom prompt passed into this call
        ...input.system,
        // any custom prompt from last user message
        ...(input.user.system ? [input.user.system] : []),
      ]
        .filter((x) => x)
        .join("\n"),
    )

    const header = system[0]
    await Plugin.trigger(
      "experimental.chat.system.transform",
      { sessionID: input.sessionID, model: input.model },
      { system },
    )
    // rejoin to maintain 2-part structure for caching if header unchanged
    if (system.length > 2 && system[0] === header) {
      const rest = system.slice(1)
      system.length = 0
      system.push(header, rest.join("\n"))
    }

    const variant =
      !input.small && input.model.variants && input.user.variant ? input.model.variants[input.user.variant] : {}
    const base = input.small
      ? ProviderTransform.smallOptions(input.model)
      : ProviderTransform.options({
          model: input.model,
          sessionID: input.sessionID,
          providerOptions: provider.options,
        })
    const options: Record<string, any> = pipe(
      base,
      mergeDeep(input.model.options),
      mergeDeep(input.agent.options),
      mergeDeep(variant),
    )

    // Remove TF-specific options for non-ToothFairyAI providers
    if (input.model.providerID !== "toothfairyai") {
      delete options.tf_agent_id
      delete options.tf_auth_via
    }

    if (isOpenaiOauth) {
      options.instructions = system.join("\n")
    }

    const messages = isOpenaiOauth
      ? input.messages
      : [
          ...system.map(
            (x): ModelMessage => ({
              role: "system",
              content: x,
            }),
          ),
          ...input.messages,
        ]

    const params = await Plugin.trigger(
      "chat.params",
      {
        sessionID: input.sessionID,
        agent: input.agent,
        model: input.model,
        provider,
        message: input.user,
      },
      {
        temperature: input.model.capabilities.temperature
          ? (input.agent.temperature ?? ProviderTransform.temperature(input.model))
          : undefined,
        topP: input.agent.topP ?? ProviderTransform.topP(input.model),
        topK: ProviderTransform.topK(input.model),
        options,
      },
    )

    const { headers } = await Plugin.trigger(
      "chat.headers",
      {
        sessionID: input.sessionID,
        agent: input.agent,
        model: input.model,
        provider,
        message: input.user,
      },
      {
        headers: {},
      },
    )

    const maxOutputTokens =
      isOpenaiOauth || provider.id.includes("github-copilot")
        ? undefined
        : ProviderTransform.maxOutputTokens(input.model)

    const tools = await resolveTools(input)

    // LiteLLM and some Anthropic proxies require the tools parameter to be present
    // when message history contains tool calls, even if no tools are being used.
    // Add a dummy tool that is never called to satisfy this validation.
    // This is enabled for:
    // 1. Providers with "litellm" in their ID or API ID (auto-detected)
    // 2. Providers with explicit "litellmProxy: true" option (opt-in for custom gateways)
    const isLiteLLMProxy =
      provider.options?.["litellmProxy"] === true ||
      input.model.providerID.toLowerCase().includes("litellm") ||
      input.model.api.id.toLowerCase().includes("litellm")

    if (isLiteLLMProxy && Object.keys(tools).length === 0 && hasToolCalls(input.messages)) {
      tools["_noop"] = tool({
        description:
          "Placeholder for LiteLLM/Anthropic proxy compatibility - required when message history contains tool calls but no active tools are needed",
        inputSchema: jsonSchema({ type: "object", properties: {} }),
        execute: async () => ({ output: "", title: "", metadata: {} }),
      })
    }

    // Wire up toolExecutor for DWS workflow models so that tool calls
    // from the workflow service are executed via opencode's tool system
    // and results sent back over the WebSocket.
    if (language instanceof GitLabWorkflowLanguageModel) {
      const workflowModel = language
      workflowModel.toolExecutor = async (toolName, argsJson, _requestID) => {
        const t = tools[toolName]
        if (!t || !t.execute) {
          return { result: "", error: `Unknown tool: ${toolName}` }
        }
        try {
          const result = await t.execute!(JSON.parse(argsJson), {
            toolCallId: _requestID,
            messages: input.messages,
            abortSignal: input.abort,
          })
          const output = typeof result === "string" ? result : (result?.output ?? JSON.stringify(result))
          return {
            result: output,
            metadata: typeof result === "object" ? result?.metadata : undefined,
            title: typeof result === "object" ? result?.title : undefined,
          }
        } catch (e: any) {
          return { result: "", error: e.message ?? String(e) }
        }
      }
    }

    return streamText({
      onError(error) {
        l.error("stream error", {
          error,
        })
      },
      async experimental_repairToolCall(failed) {
        const lower = failed.toolCall.toolName.toLowerCase()
        if (lower !== failed.toolCall.toolName && tools[lower]) {
          l.info("repairing tool call", {
            tool: failed.toolCall.toolName,
            repaired: lower,
          })
          return {
            ...failed.toolCall,
            toolName: lower,
          }
        }
        return {
          ...failed.toolCall,
          input: JSON.stringify({
            tool: failed.toolCall.toolName,
            error: failed.error.message,
          }),
          toolName: "invalid",
        }
      },
      temperature: params.temperature,
      topP: params.topP,
      topK: params.topK,
      providerOptions: ProviderTransform.providerOptions(input.model, params.options),
      activeTools: Object.keys(tools).filter((x) => x !== "invalid"),
      tools,
      toolChoice: input.toolChoice,
      maxOutputTokens,
      abortSignal: input.abort,
      headers: {
        ...(input.model.providerID.startsWith("opencode")
          ? {
              "x-opencode-project": Instance.project.id,
              "x-opencode-session": input.sessionID,
              "x-opencode-request": input.user.id,
              "x-opencode-client": Flag.OPENCODE_CLIENT,
            }
          : {
              "User-Agent": `opencode/${Installation.VERSION}`,
            }),
        ...input.model.headers,
        ...headers,
      },
      maxRetries: input.retries ?? 0,
      messages,
      model: wrapLanguageModel({
        model: language,
        middleware: [
          {
            async transformParams(args) {
              if (args.type === "stream") {
                // @ts-expect-error
                args.params.prompt = ProviderTransform.message(args.params.prompt, input.model, options)
              }
              return args.params
            },
          },
        ],
      }),
      experimental_telemetry: {
        isEnabled: cfg.experimental?.openTelemetry,
        metadata: {
          userId: cfg.username ?? "unknown",
          sessionId: input.sessionID,
        },
      },
    })
  }

  async function resolveTools(input: Pick<StreamInput, "tools" | "agent" | "permission" | "user">) {
    const disabled = Permission.disabled(
      Object.keys(input.tools),
      Permission.merge(input.agent.permission, input.permission ?? []),
    )
    for (const tool of Object.keys(input.tools)) {
      if (input.user.tools?.[tool] === false || disabled.has(tool)) {
        delete input.tools[tool]
      }
    }
    return input.tools
  }

  // Check if messages contain any tool-call content
  // Used to determine if a dummy tool should be added for LiteLLM proxy compatibility
  export function hasToolCalls(messages: ModelMessage[]): boolean {
    for (const msg of messages) {
      if (!Array.isArray(msg.content)) continue
      for (const part of msg.content) {
        if (part.type === "tool-call" || part.type === "tool-result") return true
      }
    }
    return false
  }
}