Project Monitor agent: LLM-powered project status reports with wiki+Gitea integration

agents/llm_client.py

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+#!/usr/bin/env python3
+"""
+LLM Client
+Unified interface for calling Anthropic, OpenAI, and LiteLLM/compatible APIs.
+Resolves the user's LLM config from the dashboard and routes accordingly.
+"""
+
+import json
+import sys
+from urllib import request, error as urlerror
+from shared import DASHBOARD_API, api_request
+
+
+# Default models per provider
+DEFAULT_MODELS = {
+    "anthropic": "claude-sonnet-4-5-20250514",
+    "openai": "gpt-4o-mini",
+    "litellm": "anthropic/claude-sonnet-4-5-20250514",
+    "ollama": "llama3",
+}
+
+# Default API URLs per provider
+DEFAULT_URLS = {
+    "anthropic": "https://api.anthropic.com",
+    "openai": "https://api.openai.com",
+    "litellm": "http://localhost:4000",
+    "ollama": "http://localhost:11434",
+}
+
+
+def get_llm_config(user_id):
+    """Get the resolved LLM config for a user from the dashboard API."""
+    config = api_request(f"{DASHBOARD_API}/api/users/{user_id}/llm", retries=1)
+    if config.get("source") == "none":
+        raise RuntimeError("No LLM configured. Set one up via the LLM button in the dashboard.")
+    return config
+
+
+def complete(user_id, prompt, system=None, max_tokens=4096):
+    """Send a completion request using the user's configured LLM.
+
+    Args:
+        user_id: Dashboard user ID (for config resolution)
+        prompt: The user message / prompt text
+        system: Optional system message
+        max_tokens: Max response tokens
+
+    Returns:
+        dict with keys: text (str), model (str), input_tokens (int), output_tokens (int)
+    """
+    config = get_llm_config(user_id)
+    provider = config.get("provider_type", "anthropic")
+    api_url = config.get("api_url") or DEFAULT_URLS.get(provider, "")
+    api_key = config.get("api_key", "")
+    model = config.get("default_model") or DEFAULT_MODELS.get(provider, "")
+
+    if provider == "anthropic":
+        return _call_anthropic(api_url, api_key, model, prompt, system, max_tokens)
+    elif provider in ("openai", "litellm"):
+        return _call_openai_compatible(api_url, api_key, model, prompt, system, max_tokens)
+    elif provider == "ollama":
+        return _call_openai_compatible(api_url, api_key, model, prompt, system, max_tokens, is_ollama=True)
+    else:
+        raise RuntimeError(f"Unknown LLM provider: {provider}")
+
+
+def _call_anthropic(api_url, api_key, model, prompt, system, max_tokens):
+    """Call the Anthropic Messages API."""
+    url = f"{api_url.rstrip('/')}/v1/messages"
+    body = {
+        "model": model,
+        "max_tokens": max_tokens,
+        "messages": [{"role": "user", "content": prompt}],
+    }
+    if system:
+        body["system"] = system
+
+    headers = {
+        "x-api-key": api_key,
+        "anthropic-version": "2023-06-01",
+        "content-type": "application/json",
+    }
+
+    data = json.dumps(body).encode()
+    req = request.Request(url, data=data, headers=headers, method="POST")
+
+    try:
+        with request.urlopen(req, timeout=120) as resp:
+            result = json.loads(resp.read().decode())
+    except urlerror.HTTPError as e:
+        err_body = e.read().decode() if e.fp else ""
+        raise RuntimeError(f"Anthropic API error {e.code}: {err_body[:500]}")
+
+    text = ""
+    for block in result.get("content", []):
+        if block.get("type") == "text":
+            text += block["text"]
+
+    usage = result.get("usage", {})
+    return {
+        "text": text,
+        "model": result.get("model", model),
+        "input_tokens": usage.get("input_tokens", 0),
+        "output_tokens": usage.get("output_tokens", 0),
+    }
+
+
+def _call_openai_compatible(api_url, api_key, model, prompt, system, max_tokens, is_ollama=False):
+    """Call an OpenAI-compatible API (works with OpenAI, LiteLLM, Ollama)."""
+    if is_ollama:
+        url = f"{api_url.rstrip('/')}/api/chat"
+    else:
+        url = f"{api_url.rstrip('/')}/v1/chat/completions"
+
+    messages = []
+    if system:
+        messages.append({"role": "system", "content": system})
+    messages.append({"role": "user", "content": prompt})
+
+    body = {
+        "model": model,
+        "messages": messages,
+        "max_tokens": max_tokens,
+    }
+
+    headers = {"content-type": "application/json"}
+    if api_key:
+        headers["authorization"] = f"Bearer {api_key}"
+
+    data = json.dumps(body).encode()
+    req = request.Request(url, data=data, headers=headers, method="POST")
+
+    try:
+        with request.urlopen(req, timeout=120) as resp:
+            result = json.loads(resp.read().decode())
+    except urlerror.HTTPError as e:
+        err_body = e.read().decode() if e.fp else ""
+        raise RuntimeError(f"API error {e.code}: {err_body[:500]}")
+
+    if is_ollama:
+        text = result.get("message", {}).get("content", "")
+        return {"text": text, "model": model, "input_tokens": 0, "output_tokens": 0}
+
+    choice = result.get("choices", [{}])[0]
+    text = choice.get("message", {}).get("content", "")
+    usage = result.get("usage", {})
+    return {
+        "text": text,
+        "model": result.get("model", model),
+        "input_tokens": usage.get("prompt_tokens", 0),
+        "output_tokens": usage.get("completion_tokens", 0),
+    }
+
+
+if __name__ == "__main__":
+    # Quick test
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--user-id", type=int, required=True)
+    parser.add_argument("--prompt", default="Say hello in one sentence.")
+    args = parser.parse_args()
+
+    result = complete(args.user_id, args.prompt)
+    print(f"Model: {result['model']}")
+    print(f"Tokens: {result['input_tokens']} in, {result['output_tokens']} out")
+    print(f"Response: {result['text']}")