mcpctl/src/mcpd/src/services/llm/adapters/anthropic.ts

/**
 * Anthropic adapter — translates between OpenAI chat/completions format and
 * the Anthropic Messages API (`POST /v1/messages`).
 *
 * Key differences we translate:
 *   - OpenAI `role: 'system'` messages become a top-level `system` string.
 *   - Anthropic returns `content: [{ type: 'text', text }]` — we join into
 *     OpenAI's `content: "…"` string.
 *   - Streaming: Anthropic emits a sequence of
 *     `message_start / content_block_{start,delta,stop} / message_delta /
 *     message_stop` events. We translate those to OpenAI
 *     `chat.completion.chunk` deltas.
 *
 * This adapter implements the subset needed for plain-text chat — tool-use
 * translation is intentionally left out for this phase; agents that need tool
 * calling should target an OpenAI-compatible provider until the translator
 * covers it.
 */
import type {
  LlmAdapter,
  InferContext,
  NonStreamingResult,
  StreamingChunk,
  AdapterDeps,
  OpenAiMessage,
} from '../types.js';

const DEFAULT_ANTHROPIC_URL = 'https://api.anthropic.com';
const ANTHROPIC_VERSION = '2023-06-01';

interface AnthropicMessageResponse {
  id: string;
  model: string;
  role: 'assistant';
  content: Array<{ type: 'text'; text: string } | { type: string; [k: string]: unknown }>;
  stop_reason?: string;
  usage?: { input_tokens: number; output_tokens: number };
}

export class AnthropicAdapter implements LlmAdapter {
  readonly kind = 'anthropic';
  private readonly fetchImpl: typeof globalThis.fetch;

  constructor(deps: AdapterDeps = {}) {
    this.fetchImpl = deps.fetch ?? globalThis.fetch;
  }

  async infer(ctx: InferContext): Promise<NonStreamingResult> {
    const url = (ctx.url !== '' ? ctx.url : DEFAULT_ANTHROPIC_URL).replace(/\/+$/, '');
    const body = this.toAnthropicRequest(ctx, false);
    const res = await this.fetchImpl(`${url}/v1/messages`, {
      method: 'POST',
      headers: this.headers(ctx),
      body: JSON.stringify(body),
    });
    if (!res.ok) {
      const text = await res.text().catch(() => '');
      return {
        status: res.status,
        body: { error: { message: `anthropic: HTTP ${String(res.status)} ${text}` } },
      };
    }
    const anth = await res.json() as AnthropicMessageResponse;
    return { status: 200, body: this.toOpenAiResponse(anth) };
  }

  async *stream(ctx: InferContext): AsyncGenerator<StreamingChunk> {
    const url = (ctx.url !== '' ? ctx.url : DEFAULT_ANTHROPIC_URL).replace(/\/+$/, '');
    const body = this.toAnthropicRequest(ctx, true);
    const res = await this.fetchImpl(`${url}/v1/messages`, {
      method: 'POST',
      headers: this.headers(ctx),
      body: JSON.stringify(body),
    });
    if (!res.ok || res.body === null) {
      const text = await res.text().catch(() => '');
      throw new Error(`anthropic stream: HTTP ${String(res.status)} ${text}`);
    }

    const id = `chatcmpl-${cryptoNonce()}`;
    const model = body.model;
    const created = Math.floor(Date.now() / 1000);

    // Parse Anthropic SSE. Each event is `event: <name>\ndata: <json>\n\n`.
    const decoder = new TextDecoder();
    let buf = '';
    const reader = res.body.getReader();
    let emittedFirst = false;

    const baseChunk = (delta: Record<string, unknown>, finishReason?: string): string => {
      const chunk = {
        id,
        object: 'chat.completion.chunk',
        created,
        model,
        choices: [{
          index: 0,
          delta,
          finish_reason: finishReason ?? null,
        }],
      };
      return JSON.stringify(chunk);
    };

    try {
      // eslint-disable-next-line no-constant-condition
      while (true) {
        const { value, done } = await reader.read();
        if (done) break;
        buf += decoder.decode(value, { stream: true });

        let idx: number;
        while ((idx = buf.indexOf('\n\n')) !== -1) {
          const rawEvent = buf.slice(0, idx);
          buf = buf.slice(idx + 2);
          const parsed = parseSseEvent(rawEvent);
          if (parsed === null) continue;
          const { event, data } = parsed;

          if (event === 'content_block_delta') {
            const textDelta = (data as { delta?: { type?: string; text?: string } }).delta;
            if (textDelta?.type === 'text_delta' && typeof textDelta.text === 'string') {
              if (!emittedFirst) {
                yield { data: baseChunk({ role: 'assistant', content: '' }) };
                emittedFirst = true;
              }
              yield { data: baseChunk({ content: textDelta.text }) };
            }
          } else if (event === 'message_delta') {
            const stopReason = (data as { delta?: { stop_reason?: string } }).delta?.stop_reason;
            if (typeof stopReason === 'string') {
              yield { data: baseChunk({}, mapStopReason(stopReason)) };
            }
          } else if (event === 'message_stop') {
            yield { data: '[DONE]', done: true };
            return;
          } else if (event === 'error') {
            throw new Error(`anthropic stream error: ${JSON.stringify(data)}`);
          }
        }
      }
    } finally {
      reader.releaseLock();
    }
    // Anthropic closed without message_stop — give consumer a clean end.
    yield { data: '[DONE]', done: true };
  }

  private headers(ctx: InferContext): Record<string, string> {
    return {
      'Content-Type': 'application/json',
      'x-api-key': ctx.apiKey,
      'anthropic-version': ANTHROPIC_VERSION,
    };
  }

  /** Translate the OpenAI request to the Anthropic Messages shape. */
  private toAnthropicRequest(ctx: InferContext, stream: boolean): {
    model: string;
    max_tokens: number;
    messages: Array<{ role: 'user' | 'assistant'; content: string }>;
    system?: string;
    stream?: boolean;
    temperature?: number;
    top_p?: number;
    stop_sequences?: string[];
  } {
    const { body } = ctx;
    const systemParts: string[] = [];
    const messages: Array<{ role: 'user' | 'assistant'; content: string }> = [];

    for (const msg of body.messages) {
      const text = normaliseContent(msg);
      if (msg.role === 'system') {
        systemParts.push(text);
      } else if (msg.role === 'user' || msg.role === 'assistant') {
        messages.push({ role: msg.role, content: text });
      }
      // `tool` role messages are dropped — tool translation is out of scope
      // for this phase.
    }

    const out: ReturnType<typeof this.toAnthropicRequest> = {
      model: body.model !== '' ? body.model : ctx.modelOverride,
      max_tokens: typeof body.max_tokens === 'number' ? body.max_tokens : 1024,
      messages,
    };
    if (systemParts.length > 0) out.system = systemParts.join('\n\n');
    if (stream) out.stream = true;
    if (typeof body.temperature === 'number') out.temperature = body.temperature;
    if (typeof body.top_p === 'number') out.top_p = body.top_p;
    if (body.stop !== undefined) {
      out.stop_sequences = Array.isArray(body.stop) ? body.stop : [body.stop];
    }
    return out;
  }

  private toOpenAiResponse(anth: AnthropicMessageResponse): Record<string, unknown> {
    const text = anth.content
      .map((c) => (c.type === 'text' && typeof (c as { text?: unknown }).text === 'string'
        ? (c as { text: string }).text
        : ''))
      .join('');
    return {
      id: `chatcmpl-${anth.id}`,
      object: 'chat.completion',
      created: Math.floor(Date.now() / 1000),
      model: anth.model,
      choices: [{
        index: 0,
        message: { role: 'assistant', content: text },
        finish_reason: mapStopReason(anth.stop_reason ?? 'end_turn'),
      }],
      usage: anth.usage ? {
        prompt_tokens: anth.usage.input_tokens,
        completion_tokens: anth.usage.output_tokens,
        total_tokens: anth.usage.input_tokens + anth.usage.output_tokens,
      } : undefined,
    };
  }
}

function normaliseContent(msg: OpenAiMessage): string {
  if (typeof msg.content === 'string') return msg.content;
  return msg.content
    .map((part) => (typeof part.text === 'string' ? part.text : ''))
    .join('');
}

function mapStopReason(r: string): string {
  // Anthropic → OpenAI finish_reason
  if (r === 'end_turn' || r === 'stop_sequence') return 'stop';
  if (r === 'max_tokens') return 'length';
  if (r === 'tool_use') return 'tool_calls';
  return r;
}

function parseSseEvent(raw: string): { event: string; data: unknown } | null {
  let event = '';
  let dataLine = '';
  for (const line of raw.split('\n')) {
    if (line.startsWith('event:')) event = line.slice(6).trim();
    else if (line.startsWith('data:')) dataLine += line.slice(5).trim();
  }
  if (dataLine === '') return null;
  try {
    return { event, data: JSON.parse(dataLine) as unknown };
  } catch {
    return null;
  }
}

function cryptoNonce(): string {
  // Not security-sensitive — just a short randomish id.
  return Math.random().toString(36).slice(2, 10);
}