Merge PR #463: multi-provider LLM support via Prompture

# Conflicts:
#	.env.example
#	backend/app/utils/llm_client.py

.env.example

@@ -1,19 +1,44 @@
-# LLM API配置（支持 OpenAI SDK 格式的任意 LLM API）
+# ===== LLM API Configuration =====
-# 推荐使用阿里百炼平台qwen-plus模型：https://bailian.console.aliyun.com/
+# Default: any OpenAI-compatible API
-# 注意消耗较大，可先进行小于40轮的模拟尝试
+# With Prompture installed (pip install prompture): 12+ providers supported
+#
+# ── OpenAI-compatible (default, no Prompture needed) ──
 LLM_API_KEY=your_api_key_here
 LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
 LLM_MODEL_NAME=qwen-plus
+#
+# ── With Prompture: use "provider/model" format ──
+# LM Studio (free, local):
+#   LLM_MODEL_NAME=lmstudio/local-model
+#   LLM_BASE_URL=http://localhost:1234/v1
+#   LLM_API_KEY=lm-studio
+#
+# Ollama (free, local):
+#   LLM_MODEL_NAME=ollama/llama3.1:8b
+#
+# Kimi / Moonshot:
+#   LLM_MODEL_NAME=moonshot/moonshot-v1-8k
+#   LLM_API_KEY=your_moonshot_key
+#
+# Claude:
+#   LLM_MODEL_NAME=claude/claude-sonnet-4-20250514
+#   LLM_API_KEY=sk-ant-...
+#
+# Groq (fast, free tier):
+#   LLM_MODEL_NAME=groq/llama-3.1-70b-versatile
+#   LLM_API_KEY=gsk_...
+#
+# See all providers: https://github.com/jhd3197/prompture#providers
 
-# ===== ZEP记忆图谱配置 =====
+# ===== ZEP Memory Graph =====
-# 每月免费额度即可支撑简单使用：https://app.getzep.com/
+# Free monthly quota: https://app.getzep.com/
 ZEP_API_KEY=your_zep_api_key_here
 
-# ===== 加速 LLM 配置（可选）=====
+# ===== Boost LLM (optional) =====
-# 注意如果不使用加速配置，env文件中就不要出现下面的配置项
+# LLM_BOOST_API_KEY=your_api_key_here
-LLM_BOOST_API_KEY=your_api_key_here
+# LLM_BOOST_BASE_URL=your_base_url_here
-LLM_BOOST_BASE_URL=your_base_url_here
+# LLM_BOOST_MODEL_NAME=your_model_name_here
-LLM_BOOST_MODEL_NAME=your_model_name_here
+
-# ===== 前端API超时配置（可选）=====
+# ===== Frontend API timeout (optional) =====
-# 本地大模型响应较慢时可以增加此值（毫秒）
+# Increase this value for slow local LLMs (milliseconds)
-# VITE_API_TIMEOUT=600000  # 10分钟
+# VITE_API_TIMEOUT=600000  # 10 minutes

README.md

@@ -127,6 +127,29 @@ LLM_MODEL_NAME=qwen-plus
 ZEP_API_KEY=your_zep_api_key
 ```
 
+#### Multi-Provider Support (Optional)
+
+Install [Prompture](https://github.com/jhd3197/prompture) to unlock 12+ LLM providers beyond OpenAI-compatible APIs:
+
+```bash
+pip install prompture
+```
+
+Then use `"provider/model"` format in your `.env`:
+
+| Provider | `LLM_MODEL_NAME` | Cost |
+|---|---|---|
+| LM Studio | `lmstudio/local-model` | Free (local) |
+| Ollama | `ollama/llama3.1:8b` | Free (local) |
+| OpenAI | `openai/gpt-4o` | Paid |
+| Claude | `claude/claude-sonnet-4-20250514` | Paid |
+| Kimi / Moonshot | `moonshot/moonshot-v1-8k` | Paid |
+| Groq | `groq/llama-3.1-70b-versatile` | Free tier |
+| Google | `google/gemini-1.5-pro` | Paid |
+| OpenRouter | `openrouter/anthropic/claude-2` | Paid |
+
+> Without Prompture, the original OpenAI SDK backend works as before — no changes needed.
+
 #### 2. Install Dependencies
 
 ```bash

backend/app/utils/llm_client.py

@@ -1,43 +1,112 @@
 """
 LLM客户端封装
-统一使用OpenAI格式调用
+Supports two backends:
+  1. Prompture (optional) — 12+ providers: LM Studio, Ollama, Claude, Groq, Kimi, etc.
+  2. OpenAI SDK (default fallback) — any OpenAI-compatible API
+Install Prompture for multi-provider support: pip install prompture
 """
 
 import json
 import re
 from typing import Optional, Dict, Any, List
-from openai import OpenAI
 
 from ..config import Config
 
+# Try to import Prompture; fall back to OpenAI SDK if not installed
+try:
+    from prompture.agents import Conversation
+    from prompture.infra.provider_env import ProviderEnvironment
+    from prompture.extraction.tools import strip_think_tags, clean_json_text
+    _HAS_PROMPTURE = True
+except ImportError:
+    _HAS_PROMPTURE = False
+
+if not _HAS_PROMPTURE:
+    from openai import OpenAI
+
+
+# Provider name → ProviderEnvironment field name
+_KEY_MAP = {
+    "openai": "openai_api_key",
+    "claude": "claude_api_key",
+    "google": "google_api_key",
+    "groq": "groq_api_key",
+    "grok": "grok_api_key",
+    "openrouter": "openrouter_api_key",
+    "moonshot": "moonshot_api_key",
+}
+
 
 class LLMClient:
-    """LLM客户端"""
+    """LLM客户端
+
+    When Prompture is installed, ``model`` accepts the ``"provider/model"``
+    format for multi-provider support::
+
+        "lmstudio/local-model"        → LM Studio (free, local)
+        "ollama/llama3.1:8b"          → Ollama (free, local)
+        "openai/gpt-4o"               → OpenAI
+        "claude/claude-sonnet-4-20250514"     → Anthropic
+        "moonshot/moonshot-v1-8k"     → Kimi / Moonshot
+        "groq/llama-3.1-70b"          → Groq
+
+    Without Prompture, the original OpenAI SDK backend is used (any
+    OpenAI-compatible API via LLM_BASE_URL).
+    """
 
     def __init__(
         self,
         api_key: Optional[str] = None,
         base_url: Optional[str] = None,
-        model: Optional[str] = None
+        model: Optional[str] = None,
     ):
         self.api_key = api_key or Config.LLM_API_KEY
         self.base_url = base_url or Config.LLM_BASE_URL
         self.model = model or Config.LLM_MODEL_NAME
 
+        if _HAS_PROMPTURE:
+            self._init_prompture()
+        else:
+            self._init_openai()
+
+    # ── Prompture backend ──────────────────────────────────────────
+
+    def _init_prompture(self):
+        env_kwargs: Dict[str, Any] = {}
+        if self.api_key:
+            provider = self.model.split("/")[0] if "/" in self.model else "openai"
+            env_field = _KEY_MAP.get(provider)
+            if env_field:
+                env_kwargs[env_field] = self.api_key
+
+        self._env = ProviderEnvironment(**env_kwargs) if env_kwargs else None
+        self._driver_options: Dict[str, Any] = {}
+        if self.base_url:
+            self._driver_options["base_url"] = self.base_url
+
+    def _make_conversation(self, temperature: float, max_tokens: int) -> "Conversation":
+        opts: Dict[str, Any] = {
+            "temperature": temperature,
+            "max_tokens": max_tokens,
+            **self._driver_options,
+        }
+        return Conversation(self.model, options=opts, env=self._env)
+
+    # ── OpenAI fallback backend ────────────────────────────────────
+
+    def _init_openai(self):
         if not self.api_key:
             raise ValueError("LLM_API_KEY 未配置")
+        self.client = OpenAI(api_key=self.api_key, base_url=self.base_url)
 
-        self.client = OpenAI(
+    # ── Public API ─────────────────────────────────────────────────
-            api_key=self.api_key,
-            base_url=self.base_url
-        )
 
     def chat(
         self,
         messages: List[Dict[str, str]],
         temperature: float = 0.7,
         max_tokens: int = 4096,
-        response_format: Optional[Dict] = None
+        response_format: Optional[Dict] = None,
     ) -> str:
         """
         发送聊天请求
@@ -51,27 +120,19 @@ class LLMClient:
         Returns:
             模型响应文本
         """
-        kwargs = {
+        if _HAS_PROMPTURE:
-            "model": self.model,
+            content = self._chat_prompture(messages, temperature, max_tokens)
-            "messages": messages,
+            return strip_think_tags(content)
-            "temperature": temperature,
+        else:
-            "max_tokens": max_tokens,
+            content = self._chat_openai(messages, temperature, max_tokens, response_format)
-        }
+            # Fallback: strip think tags with regex when Prompture is not available
-        
+            return re.sub(r'<think>[\s\S]*?</think>', '', content).strip()
-        if response_format:
-            kwargs["response_format"] = response_format
-        
-        response = self.client.chat.completions.create(**kwargs)
-        content = response.choices[0].message.content
-        # 部分模型（如MiniMax M2.5）会在content中包含<think>思考内容，需要移除
-        content = re.sub(r'<think>[\s\S]*?</think>', '', content).strip()
-        return content
 
     def chat_json(
         self,
         messages: List[Dict[str, str]],
         temperature: float = 0.3,
-        max_tokens: int = 4096
+        max_tokens: int = 4096,
     ) -> Dict[str, Any]:
         """
         发送聊天请求并返回JSON
@@ -84,21 +145,65 @@ class LLMClient:
         Returns:
             解析后的JSON对象
         """
-        response = self.chat(
+        if _HAS_PROMPTURE:
-            messages=messages,
+            response = self._chat_prompture(messages, temperature, max_tokens)
-            temperature=temperature,
+            # Prompture's clean_json_text strips think tags + markdown fences
-            max_tokens=max_tokens,
+            cleaned = clean_json_text(response)
-            # 不設 response_format 以相容 LM Studio / Ollama 等本地模型
+        else:
-            # 依賴 prompt 中的 JSON 指示 + 下方的 markdown 清理邏輯
+            response = self._chat_openai(
-        )
+                messages, temperature, max_tokens
-        # 清理markdown代码块标记
+            )
-        cleaned_response = response.strip()
+            # Fallback cleaning when Prompture is not available
-        cleaned_response = re.sub(r'^```(?:json)?\s*\n?', '', cleaned_response, flags=re.IGNORECASE)
+            cleaned = re.sub(r'<think>[\s\S]*?</think>', '', response).strip()
-        cleaned_response = re.sub(r'\n?```\s*$', '', cleaned_response)
+            cleaned = re.sub(r'^```(?:json)?\s*\n?', '', cleaned, flags=re.IGNORECASE)
-        cleaned_response = cleaned_response.strip()
+            cleaned = re.sub(r'\n?```\s*$', '', cleaned)
+            cleaned = cleaned.strip()
 
         try:
-            return json.loads(cleaned_response)
+            return json.loads(cleaned)
         except json.JSONDecodeError:
-            raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response}")
+            raise ValueError(f"LLM返回的JSON格式无效: {cleaned}")
 
+    # ── Private: Prompture path ────────────────────────────────────
+
+    def _chat_prompture(
+        self,
+        messages: List[Dict[str, str]],
+        temperature: float,
+        max_tokens: int,
+    ) -> str:
+        conv = self._make_conversation(temperature, max_tokens)
+
+        # Inject system prompt
+        system_parts = [m["content"] for m in messages if m["role"] == "system"]
+        if system_parts:
+            conv._messages.append({"role": "system", "content": "\n".join(system_parts)})
+
+        # Replay prior turns
+        non_system = [m for m in messages if m["role"] != "system"]
+        for msg in non_system[:-1]:
+            conv._messages.append({"role": msg["role"], "content": msg["content"]})
+
+        prompt = non_system[-1]["content"] if non_system else ""
+        return conv.ask(prompt)
+
+    # ── Private: OpenAI fallback path ──────────────────────────────
+
+    def _chat_openai(
+        self,
+        messages: List[Dict[str, str]],
+        temperature: float,
+        max_tokens: int,
+        response_format: Optional[Dict] = None,
+    ) -> str:
+        kwargs = {
+            "model": self.model,
+            "messages": messages,
+            "temperature": temperature,
+            "max_tokens": max_tokens,
+        }
+        if response_format:
+            kwargs["response_format"] = response_format
+
+        response = self.client.chat.completions.create(**kwargs)
+        return response.choices[0].message.content

backend/requirements.txt

@@ -10,9 +10,14 @@ flask>=3.0.0
 flask-cors>=6.0.0
 
 # ============= LLM 相关 =============
-# OpenAI SDK（统一使用 OpenAI 格式调用 LLM）
+# OpenAI SDK（默认 LLM 后端）
 openai>=1.0.0
 
+# Prompture（可选）— 多供应商 LLM 支持：LM Studio, Ollama, Claude, Groq, Kimi 等
+# Install for multi-provider support: pip install prompture
+# https://github.com/jhd3197/prompture
+# prompture>=0.1.0
+
 # ============= Zep Cloud =============
 zep-cloud==3.13.0
 

backend/scripts/test_lmstudio.py

@@ -0,0 +1,68 @@
+"""
+Quick test: MiroFish LLMClient → LM Studio via Prompture
+"""
+import sys, os
+
+# Add backend to path so we can import app modules
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+# Override env vars for LM Studio before Config loads
+os.environ["LLM_MODEL_NAME"] = "lmstudio/deepseek/deepseek-r1-0528-qwen3-8b"
+os.environ["LLM_BASE_URL"] = "http://localhost:1234/v1"
+os.environ["LLM_API_KEY"] = "lm-studio"
+# Provide a dummy ZEP key so Config.validate() won't complain
+os.environ.setdefault("ZEP_API_KEY", "dummy")
+
+from app.utils.llm_client import LLMClient
+
+def test_basic_chat():
+    print("=== Test 1: Basic chat ===")
+    client = LLMClient()
+    from app.utils.llm_client import _HAS_PROMPTURE
+    print(f"  Backend: Prompture={_HAS_PROMPTURE}")
+    print(f"  Model:   {client.model}")
+    response = client.chat([
+        {"role": "system", "content": "You are a helpful assistant. Reply in one sentence."},
+        {"role": "user", "content": "What is social media simulation?"},
+    ], temperature=0.5, max_tokens=256)
+    print(f"  Response: {response[:300]}")
+    print()
+
+def test_json_chat():
+    print("=== Test 2: JSON response ===")
+    client = LLMClient()
+    result = client.chat_json([
+        {"role": "system", "content": "You are a JSON-only assistant. Always respond with valid JSON."},
+        {"role": "user", "content": 'Return a JSON object with keys "platform" and "agents" (an integer). Example: {"platform":"twitter","agents":5}'},
+    ], temperature=0.2, max_tokens=256)
+    print(f"  Parsed JSON: {result}")
+    print(f"  Type: {type(result)}")
+    print()
+
+def test_multi_turn():
+    print("=== Test 3: Multi-turn conversation ===")
+    client = LLMClient()
+    r1 = client.chat([
+        {"role": "user", "content": "My name is MiroFish. Remember it."},
+    ], max_tokens=128)
+    print(f"  Turn 1: {r1[:200]}")
+
+    r2 = client.chat([
+        {"role": "user", "content": "My name is MiroFish. Remember it."},
+        {"role": "assistant", "content": r1},
+        {"role": "user", "content": "What is my name?"},
+    ], max_tokens=128)
+    print(f"  Turn 2: {r2[:200]}")
+    print()
+
+if __name__ == "__main__":
+    print(f"Prompture installed: True")
+    print(f"LM Studio endpoint: http://localhost:1234/v1\n")
+    try:
+        test_basic_chat()
+        test_json_chat()
+        test_multi_turn()
+        print("All tests passed!")
+    except Exception as e:
+        print(f"ERROR: {e}")
+        import traceback; traceback.print_exc()