Merge pull request #115 from nankingjing/contrib/add-openai-compatible-backend

feat(model): add generic OpenAI-compatible LLM backend

docs/guide/new-backend.md

@@ -2,6 +2,59 @@
 
 SkillOpt supports multiple LLM backends. This guide shows how to add your own.
 
+## Built-in: the generic OpenAI-compatible backend
+
+Before writing a new backend, check whether your provider already speaks the
+OpenAI Chat Completions protocol. Most do — in which case you can use the
+built-in **`openai_compatible`** backend
+(`skillopt/model/openai_compatible_backend.py`) with no code changes.
+
+A single `base_url` + `api_key` pair lets you point SkillOpt at, for example:
+
+| Provider | `base_url` | Example model |
+|---|---|---|
+| DeepSeek | `https://api.deepseek.com/v1` | `deepseek-chat` |
+| Groq | `https://api.groq.com/openai/v1` | `llama-3.3-70b-versatile` |
+| Together AI | `https://api.together.xyz/v1` | `meta-llama/Llama-3.3-70B-Instruct-Turbo` |
+| Ollama (local) | `http://localhost:11434/v1` | `qwen2.5:7b` |
+| vLLM / SGLang / TGI | `http://localhost:8000/v1` | your served model |
+| LiteLLM proxy | `http://localhost:4000` | any proxied model |
+| OpenRouter / Fireworks / xAI / … | provider base URL | provider model id |
+
+Select it as the optimizer and/or target backend:
+
+```python
+import skillopt.model as model
+
+# Shorthand: use it for both optimizer and target.
+model.set_backend("openai_compatible")
+
+# Point it at a provider (shared, or per-role with optimizer_*/target_*).
+model.configure_openai_compatible(
+    base_url="https://api.deepseek.com/v1",
+    api_key="sk-...",
+    model="deepseek-chat",
+)
+```
+
+Or configure it entirely through environment variables (role-specific
+`OPTIMIZER_*` / `TARGET_*` variants override the shared ones):
+
+```bash
+export TARGET_BACKEND=openai_compatible
+export OPENAI_COMPATIBLE_BASE_URL="https://api.groq.com/openai/v1"
+export OPENAI_COMPATIBLE_API_KEY="gsk_..."
+export OPENAI_COMPATIBLE_MODEL="llama-3.3-70b-versatile"
+# Optional: OPENAI_COMPATIBLE_TEMPERATURE, _MAX_TOKENS, _TIMEOUT_SECONDS
+```
+
+The backend uses the official `openai` SDK, records token usage through the
+shared tracker, supports tool/function calling via
+`chat_target_messages(..., tools=...)`, and exposes
+`count_tokens()` (tiktoken with a character-based fallback for non-OpenAI
+models). Only write a brand-new backend if your provider is *not*
+OpenAI-compatible.
+
 ## Backend Architecture
 
 ```

skillopt/model/__init__.py

@@ -7,6 +7,7 @@ from typing import Any
 from skillopt.model import azure_openai as _openai
 from skillopt.model import claude_backend as _claude
 from skillopt.model import minimax_backend as _minimax
+from skillopt.model import openai_compatible_backend as _openai_compat
 from skillopt.model import qwen_backend as _qwen
 from skillopt.model.backend_config import (  # noqa: F401
     configure_claude_code_exec,
@@ -55,6 +56,10 @@ def set_backend(name: str | None) -> str:
         set_optimizer_backend("openai_chat")
         set_target_backend("minimax_chat")
         return "minimax_chat"
+    if normalized in {"openai_compatible", "openai_compatible_chat", "openai-compatible", "compat"}:
+        set_optimizer_backend("openai_compatible")
+        set_target_backend("openai_compatible")
+        return "openai_compatible"
     raise ValueError(f"Unsupported legacy backend: {name!r}")
 
 
@@ -74,6 +79,8 @@ def get_backend_name() -> str:
         return "qwen_chat"
     if optimizer == "openai_chat" and target == "minimax_chat":
         return "minimax_chat"
+    if optimizer == "openai_compatible" and target == "openai_compatible":
+        return "openai_compatible"
     return f"{optimizer}+{target}"
 
 
@@ -115,6 +122,16 @@ def chat_optimizer(
             reasoning_effort=reasoning_effort,
             timeout=timeout,
         )
+    if get_optimizer_backend() == "openai_compatible":
+        return _openai_compat.chat_optimizer(
+            system=system,
+            user=user,
+            max_completion_tokens=max_completion_tokens,
+            retries=retries,
+            stage=stage,
+            reasoning_effort=reasoning_effort,
+            timeout=timeout,
+        )
     return _openai.chat_optimizer(
         system=system,
         user=user,
@@ -163,10 +180,20 @@ def chat_target(
             stage=stage,
             reasoning_effort=reasoning_effort,
         )
+    if get_target_backend() == "openai_compatible":
+        return _openai_compat.chat_target(
+            system=system,
+            user=user,
+            max_completion_tokens=max_completion_tokens,
+            retries=retries,
+            stage=stage,
+            reasoning_effort=reasoning_effort,
+            timeout=timeout,
+        )
     if not is_target_chat_backend():
         raise NotImplementedError(
-            "chat_target is only supported with target_backend=openai_chat, claude_chat, qwen_chat, or minimax_chat. "
-            "Exec backends are handled in environment-specific rollout code."
+            "chat_target is only supported with target_backend=openai_chat, claude_chat, qwen_chat, minimax_chat, "
+            "or openai_compatible. Exec backends are handled in environment-specific rollout code."
         )
     return _openai.chat_target(
         system=system,
@@ -226,6 +253,18 @@ def chat_optimizer_messages(
             return_message=return_message,
             timeout=timeout,
         )
+    if get_optimizer_backend() == "openai_compatible":
+        return _openai_compat.chat_optimizer_messages(
+            messages=messages,
+            max_completion_tokens=max_completion_tokens,
+            retries=retries,
+            stage=stage,
+            reasoning_effort=reasoning_effort,
+            tools=tools,
+            tool_choice=tool_choice,
+            return_message=return_message,
+            timeout=timeout,
+        )
     return _openai.chat_optimizer_messages(
         messages=messages,
         max_completion_tokens=max_completion_tokens,
@@ -285,10 +324,22 @@ def chat_target_messages(
             tool_choice=tool_choice,
             return_message=return_message,
         )
+    if get_target_backend() == "openai_compatible":
+        return _openai_compat.chat_target_messages(
+            messages=messages,
+            max_completion_tokens=max_completion_tokens,
+            retries=retries,
+            stage=stage,
+            reasoning_effort=reasoning_effort,
+            tools=tools,
+            tool_choice=tool_choice,
+            return_message=return_message,
+            timeout=timeout,
+        )
     if not is_target_chat_backend():
         raise NotImplementedError(
-            "chat_target_messages is only supported with target_backend=openai_chat, claude_chat, qwen_chat, or minimax_chat. "
-            "Exec backends are handled in environment-specific rollout code."
+            "chat_target_messages is only supported with target_backend=openai_chat, claude_chat, qwen_chat, "
+            "minimax_chat, or openai_compatible. Exec backends are handled in environment-specific rollout code."
         )
     return _openai.chat_target_messages(
         messages=messages,
@@ -387,6 +438,17 @@ def get_token_summary() -> dict:
         summary[stage]["prompt_tokens"] += values["prompt_tokens"]
         summary[stage]["completion_tokens"] += values["completion_tokens"]
         summary[stage]["total_tokens"] += values["total_tokens"]
+    openai_compat_summary = _openai_compat.get_token_summary()
+    for stage, values in openai_compat_summary.items():
+        if stage == "_total":
+            continue
+        if stage not in summary:
+            summary[stage] = values
+            continue
+        summary[stage]["calls"] += values["calls"]
+        summary[stage]["prompt_tokens"] += values["prompt_tokens"]
+        summary[stage]["completion_tokens"] += values["completion_tokens"]
+        summary[stage]["total_tokens"] += values["total_tokens"]
     total = {
         "calls": 0,
         "prompt_tokens": 0,
@@ -409,6 +471,7 @@ def reset_token_tracker() -> None:
     _claude.reset_token_tracker()
     _qwen.reset_token_tracker()
     _minimax.reset_token_tracker()
+    _openai_compat.reset_token_tracker()
 
 
 def configure_azure_openai(
@@ -522,11 +585,43 @@ def configure_minimax_chat(
     )
 
 
+def configure_openai_compatible(
+    *,
+    base_url: str | None = None,
+    api_key: str | None = None,
+    model: str | None = None,
+    temperature: float | str | None = None,
+    timeout_seconds: float | str | None = None,
+    max_tokens: int | str | None = None,
+    optimizer_base_url: str | None = None,
+    optimizer_api_key: str | None = None,
+    optimizer_model: str | None = None,
+    target_base_url: str | None = None,
+    target_api_key: str | None = None,
+    target_model: str | None = None,
+) -> None:
+    _openai_compat.configure_openai_compatible(
+        base_url=base_url,
+        api_key=api_key,
+        model=model,
+        temperature=temperature,
+        timeout_seconds=timeout_seconds,
+        max_tokens=max_tokens,
+        optimizer_base_url=optimizer_base_url,
+        optimizer_api_key=optimizer_api_key,
+        optimizer_model=optimizer_model,
+        target_base_url=target_base_url,
+        target_api_key=target_api_key,
+        target_model=target_model,
+    )
+
+
 def set_reasoning_effort(effort: str | None) -> None:
     _openai.set_reasoning_effort(effort)
     _claude.set_reasoning_effort(effort)
     _qwen.set_reasoning_effort(effort)
     _minimax.set_reasoning_effort(effort)
+    _openai_compat.set_reasoning_effort(effort)
 
 
 def set_target_deployment(deployment: str) -> None:
@@ -534,9 +629,11 @@ def set_target_deployment(deployment: str) -> None:
     _claude.set_target_deployment(deployment)
     _qwen.set_target_deployment(deployment)
     _minimax.set_target_deployment(deployment)
+    _openai_compat.set_target_deployment(deployment)
 
 
 def set_optimizer_deployment(deployment: str) -> None:
     _openai.set_optimizer_deployment(deployment)
     _claude.set_optimizer_deployment(deployment)
     _qwen.set_optimizer_deployment(deployment)
+    _openai_compat.set_optimizer_deployment(deployment)

skillopt/model/backend_config.py

@@ -49,10 +49,10 @@ CLAUDE_CODE_EXEC_MAX_THINKING_TOKENS = max(
 def set_optimizer_backend(backend: str) -> None:
     global OPTIMIZER_BACKEND
     OPTIMIZER_BACKEND = normalize_backend_name(backend or "openai_chat")
-    if OPTIMIZER_BACKEND not in {"openai_chat", "claude_chat", "qwen_chat", "minimax_chat"}:
+    if OPTIMIZER_BACKEND not in {"openai_chat", "claude_chat", "qwen_chat", "minimax_chat", "openai_compatible"}:
         raise ValueError(
             f"Unsupported optimizer backend: {OPTIMIZER_BACKEND!r}. "
-            "Supported values are 'openai_chat', 'claude_chat', 'qwen_chat', and 'minimax_chat'."
+            "Supported values are 'openai_chat', 'claude_chat', 'qwen_chat', 'minimax_chat', and 'openai_compatible'."
         )
     os.environ["OPTIMIZER_BACKEND"] = OPTIMIZER_BACKEND
 
@@ -64,10 +64,10 @@ def get_optimizer_backend() -> str:
 def set_target_backend(backend: str) -> None:
     global TARGET_BACKEND
     TARGET_BACKEND = normalize_backend_name(backend or "openai_chat")
-    if TARGET_BACKEND not in {"openai_chat", "claude_chat", "qwen_chat", "minimax_chat", "codex_exec", "claude_code_exec"}:
+    if TARGET_BACKEND not in {"openai_chat", "claude_chat", "qwen_chat", "minimax_chat", "openai_compatible", "codex_exec", "claude_code_exec"}:
         raise ValueError(
             f"Unsupported target backend: {TARGET_BACKEND!r}. "
-            "Supported values are 'openai_chat', 'claude_chat', 'qwen_chat', 'minimax_chat', 'codex_exec', and 'claude_code_exec'."
+            "Supported values are 'openai_chat', 'claude_chat', 'qwen_chat', 'minimax_chat', 'openai_compatible', 'codex_exec', and 'claude_code_exec'."
         )
     os.environ["TARGET_BACKEND"] = TARGET_BACKEND
 
@@ -81,11 +81,11 @@ def is_target_exec_backend() -> bool:
 
 
 def is_optimizer_chat_backend() -> bool:
-    return OPTIMIZER_BACKEND in {"openai_chat", "claude_chat", "qwen_chat", "minimax_chat"}
+    return OPTIMIZER_BACKEND in {"openai_chat", "claude_chat", "qwen_chat", "minimax_chat", "openai_compatible"}
 
 
 def is_target_chat_backend() -> bool:
-    return TARGET_BACKEND in {"openai_chat", "claude_chat", "qwen_chat", "minimax_chat"}
+    return TARGET_BACKEND in {"openai_chat", "claude_chat", "qwen_chat", "minimax_chat", "openai_compatible"}
 
 
 def configure_codex_exec(

skillopt/model/common.py

@@ -26,6 +26,7 @@ _BACKEND_DEFAULT_MODELS = {
     "claude_code_exec": "claude-sonnet-4-6",
     "qwen_chat": "Qwen/Qwen3.5-4B",
     "minimax_chat": "MiniMax-M2.7",
+    "openai_compatible": "gpt-4o-mini",
 }
 
 _BACKEND_ALIASES = {
@@ -44,6 +45,10 @@ _BACKEND_ALIASES = {
     "qwen_chat": "qwen_chat",
     "minimax": "minimax_chat",
     "minimax_chat": "minimax_chat",
+    "openai_compatible": "openai_compatible",
+    "openai_compatible_chat": "openai_compatible",
+    "openai-compatible": "openai_compatible",
+    "compat": "openai_compatible",
 }
 
 

skillopt/model/openai_compatible_backend.py

@@ -0,0 +1,447 @@
+"""Generic OpenAI-compatible chat backend for optimizer and target paths.
+
+This backend talks to *any* service that exposes an OpenAI-compatible
+``/chat/completions`` endpoint through the official ``openai`` SDK. A single
+implementation therefore covers a large family of providers, for example:
+
+* DeepSeek           (``https://api.deepseek.com``)
+* Groq               (``https://api.groq.com/openai/v1``)
+* Together AI        (``https://api.together.xyz/v1``)
+* Mistral / Fireworks / OpenRouter / Perplexity / xAI Grok
+* Ollama             (``http://localhost:11434/v1``)
+* vLLM / SGLang / TGI self-hosted servers
+* LiteLLM proxy      (``http://localhost:4000``)
+* Azure OpenAI and OpenAI itself
+
+Unlike the Azure backend it never assumes Azure-specific auth or the Responses
+API — it only needs a ``base_url`` and an ``api_key`` (some local servers accept
+any key, so the key is optional and falls back to a harmless placeholder).
+
+The module mirrors the callable surface of the other chat backends
+(:mod:`skillopt.model.qwen_backend`, :mod:`skillopt.model.minimax_backend`) so
+it can be selected as the optimizer and/or target backend and routed through
+:mod:`skillopt.model`.
+"""
+from __future__ import annotations
+
+import os
+import threading
+import time
+from dataclasses import dataclass
+from typing import Any
+
+from openai import OpenAI
+
+from skillopt.model.common import (
+    TokenTracker,
+    compat_message_from_chat_message,
+    default_model_for_backend,
+    usage_from_openai_usage,
+)
+
+BACKEND_NAME = "openai_compatible"
+
+# A neutral, widely-available default. Real deployments should set the model
+# explicitly (e.g. "deepseek-chat", "llama-3.3-70b-versatile", "qwen2.5:7b").
+_DEFAULT_BASE_URL = "https://api.openai.com/v1"
+
+
+@dataclass
+class OpenAICompatibleConfig:
+    base_url: str
+    api_key: str
+    deployment: str
+    timeout_seconds: float
+    max_tokens: int
+    temperature: float | None
+
+
+def _parse_optional_float(value: Any) -> float | None:
+    if value is None:
+        return None
+    raw = str(value).strip()
+    return float(raw) if raw else None
+
+
+def _parse_int(value: Any, default: int) -> int:
+    if value is None:
+        return default
+    raw = str(value).strip()
+    return int(raw) if raw else default
+
+
+def _role_env(role: str, key: str, default: str) -> str:
+    """Resolve a config value, preferring role-specific over shared env vars."""
+    role_key = f"{role.upper()}_OPENAI_COMPATIBLE_{key}"
+    generic_key = f"OPENAI_COMPATIBLE_{key}"
+    return os.environ.get(role_key) or os.environ.get(generic_key) or default
+
+
+def _initial_config(role: str) -> OpenAICompatibleConfig:
+    role_upper = role.upper()
+    deployment_env = "OPTIMIZER_DEPLOYMENT" if role == "optimizer" else "TARGET_DEPLOYMENT"
+    return OpenAICompatibleConfig(
+        base_url=_role_env(role, "BASE_URL", _DEFAULT_BASE_URL),
+        api_key=_role_env(role, "API_KEY", ""),
+        deployment=(
+            os.environ.get(f"{role_upper}_OPENAI_COMPATIBLE_MODEL")
+            or os.environ.get("OPENAI_COMPATIBLE_MODEL")
+            or os.environ.get(deployment_env)
+            or default_model_for_backend(BACKEND_NAME)
+        ),
+        timeout_seconds=float(_role_env(role, "TIMEOUT_SECONDS", "300") or 300),
+        max_tokens=_parse_int(_role_env(role, "MAX_TOKENS", "8000"), 8000),
+        temperature=_parse_optional_float(_role_env(role, "TEMPERATURE", "")),
+    )
+
+
+OPTIMIZER_CONFIG = _initial_config("optimizer")
+TARGET_CONFIG = _initial_config("target")
+
+_config_lock = threading.Lock()
+_client_lock = threading.Lock()
+tracker = TokenTracker()
+
+_optimizer_client: OpenAI | None = None
+_target_client: OpenAI | None = None
+
+
+def _config_for(role: str) -> OpenAICompatibleConfig:
+    return OPTIMIZER_CONFIG if role == "optimizer" else TARGET_CONFIG
+
+
+def _build_client(config: OpenAICompatibleConfig) -> OpenAI:
+    return OpenAI(
+        base_url=config.base_url.rstrip("/") or _DEFAULT_BASE_URL,
+        # Some OpenAI-compatible servers (Ollama, vLLM, local proxies) do not
+        # require an API key. The SDK still expects a non-empty string, so fall
+        # back to a harmless placeholder when none is configured.
+        api_key=config.api_key or "dummy",
+        timeout=config.timeout_seconds,
+    )
+
+
+def _get_client(role: str) -> OpenAI:
+    global _optimizer_client, _target_client
+    with _client_lock:
+        if role == "optimizer":
+            if _optimizer_client is None:
+                _optimizer_client = _build_client(OPTIMIZER_CONFIG)
+            return _optimizer_client
+        if _target_client is None:
+            _target_client = _build_client(TARGET_CONFIG)
+        return _target_client
+
+
+def _reset_clients() -> None:
+    global _optimizer_client, _target_client
+    with _client_lock:
+        _optimizer_client = None
+        _target_client = None
+
+
+def count_tokens(text: str, model: str | None = None) -> int:
+    """Best-effort token count for a string.
+
+    Uses ``tiktoken`` when available (per-model encoding, falling back to the
+    ``cl100k_base`` encoding). If ``tiktoken`` is not installed or fails — which
+    is common for non-OpenAI models served through compatible APIs — it falls
+    back to a character-based estimate of roughly four characters per token.
+    """
+    if not text:
+        return 0
+    try:
+        import tiktoken
+
+        try:
+            encoding = tiktoken.encoding_for_model(model or "gpt-4o")
+        except Exception:  # noqa: BLE001 - unknown/non-OpenAI model name
+            encoding = tiktoken.get_encoding("cl100k_base")
+        return len(encoding.encode(text))
+    except Exception:  # noqa: BLE001 - tiktoken missing or encoding failure
+        # Rough heuristic: ~4 characters per token for English-like text.
+        return max(1, (len(text) + 3) // 4)
+
+
+def _chat_messages_impl(
+    messages: list[dict[str, Any]],
+    max_completion_tokens: int,
+    retries: int,
+    stage: str,
+    *,
+    role: str,
+    tools: list[dict[str, Any]] | None = None,
+    tool_choice: str | dict[str, Any] | None = None,
+    return_message: bool = False,
+    deployment: str | None = None,
+    timeout: float | None = None,
+) -> tuple[Any, dict[str, int]]:
+    config = _config_for(role)
+    client = _get_client(role)
+    kwargs: dict[str, Any] = {
+        "model": deployment or config.deployment,
+        "messages": messages,
+        # ``max_tokens`` (rather than ``max_completion_tokens``) is the field
+        # understood by the broadest set of OpenAI-compatible providers.
+        "max_tokens": min(max_completion_tokens, config.max_tokens),
+    }
+    if config.temperature is not None:
+        kwargs["temperature"] = config.temperature
+    if tools:
+        kwargs["tools"] = tools
+        if tool_choice is not None:
+            kwargs["tool_choice"] = tool_choice
+    if timeout is not None:
+        kwargs["timeout"] = timeout
+
+    last_err: Exception | None = None
+    for attempt in range(retries):
+        try:
+            resp = client.chat.completions.create(**kwargs)
+            choices = getattr(resp, "choices", None) or []
+            if not choices:
+                raise RuntimeError(
+                    f"OpenAI-compatible API returned no choices: {resp!r}"
+                )
+            message = choices[0].message
+            text = message.content or ""
+            usage_info = usage_from_openai_usage(getattr(resp, "usage", None))
+            tracker.record(
+                stage,
+                usage_info["prompt_tokens"],
+                usage_info["completion_tokens"],
+            )
+            if return_message:
+                return compat_message_from_chat_message(message), usage_info
+            return text, usage_info
+        except Exception as e:  # noqa: BLE001
+            last_err = e
+            time.sleep(min(2 ** attempt, 30))
+    raise RuntimeError(
+        f"OpenAI-compatible chat call failed after {retries} retries: {last_err}"
+    )
+
+
+# ── Public API (mirrors the other chat backends) ─────────────────────────────
+
+
+def chat_optimizer(
+    system: str,
+    user: str,
+    max_completion_tokens: int = 16384,
+    retries: int = 5,
+    stage: str = "optimizer",
+    reasoning_effort: str | None = None,
+    timeout: float | None = None,
+) -> tuple[str, dict[str, int]]:
+    del reasoning_effort  # not forwarded — kept for a uniform signature
+    messages = [
+        {"role": "system", "content": system},
+        {"role": "user", "content": user},
+    ]
+    return _chat_messages_impl(
+        messages,
+        max_completion_tokens,
+        retries,
+        stage,
+        role="optimizer",
+        timeout=timeout,
+    )
+
+
+def chat_target(
+    system: str,
+    user: str,
+    max_completion_tokens: int = 16384,
+    retries: int = 5,
+    stage: str = "target",
+    reasoning_effort: str | None = None,
+    timeout: float | None = None,
+) -> tuple[str, dict[str, int]]:
+    del reasoning_effort
+    messages = [
+        {"role": "system", "content": system},
+        {"role": "user", "content": user},
+    ]
+    return _chat_messages_impl(
+        messages,
+        max_completion_tokens,
+        retries,
+        stage,
+        role="target",
+        timeout=timeout,
+    )
+
+
+def chat_optimizer_messages(
+    messages: list[dict[str, Any]],
+    max_completion_tokens: int = 16384,
+    retries: int = 5,
+    stage: str = "optimizer",
+    reasoning_effort: str | None = None,
+    *,
+    tools: list[dict[str, Any]] | None = None,
+    tool_choice: str | dict[str, Any] | None = None,
+    return_message: bool = False,
+    timeout: float | None = None,
+) -> tuple[Any, dict[str, int]]:
+    del reasoning_effort
+    return _chat_messages_impl(
+        messages,
+        max_completion_tokens,
+        retries,
+        stage,
+        role="optimizer",
+        tools=tools,
+        tool_choice=tool_choice,
+        return_message=return_message,
+        timeout=timeout,
+    )
+
+
+def chat_target_messages(
+    messages: list[dict[str, Any]],
+    max_completion_tokens: int = 16384,
+    retries: int = 5,
+    stage: str = "target",
+    reasoning_effort: str | None = None,
+    *,
+    tools: list[dict[str, Any]] | None = None,
+    tool_choice: str | dict[str, Any] | None = None,
+    return_message: bool = False,
+    timeout: float | None = None,
+) -> tuple[Any, dict[str, int]]:
+    del reasoning_effort
+    return _chat_messages_impl(
+        messages,
+        max_completion_tokens,
+        retries,
+        stage,
+        role="target",
+        tools=tools,
+        tool_choice=tool_choice,
+        return_message=return_message,
+        timeout=timeout,
+    )
+
+
+# ── Configuration / lifecycle ────────────────────────────────────────────────
+
+
+def _update_config(
+    config: OpenAICompatibleConfig,
+    role: str,
+    *,
+    base_url: str | None = None,
+    api_key: str | None = None,
+    deployment: str | None = None,
+    temperature: float | str | None = None,
+    timeout_seconds: float | str | None = None,
+    max_tokens: int | str | None = None,
+) -> None:
+    env_prefix = role.upper()
+    if base_url is not None:
+        config.base_url = str(base_url).strip() or config.base_url
+        os.environ[f"{env_prefix}_OPENAI_COMPATIBLE_BASE_URL"] = config.base_url
+    if api_key is not None:
+        config.api_key = str(api_key).strip()
+        os.environ[f"{env_prefix}_OPENAI_COMPATIBLE_API_KEY"] = config.api_key
+    if deployment is not None:
+        config.deployment = str(deployment).strip() or config.deployment
+        os.environ[f"{env_prefix}_OPENAI_COMPATIBLE_MODEL"] = config.deployment
+    if temperature is not None:
+        raw = str(temperature).strip()
+        config.temperature = float(raw) if raw else None
+        os.environ[f"{env_prefix}_OPENAI_COMPATIBLE_TEMPERATURE"] = raw
+    if timeout_seconds is not None:
+        config.timeout_seconds = float(timeout_seconds)
+        os.environ[f"{env_prefix}_OPENAI_COMPATIBLE_TIMEOUT_SECONDS"] = str(timeout_seconds)
+    if max_tokens is not None:
+        config.max_tokens = int(max_tokens)
+        os.environ[f"{env_prefix}_OPENAI_COMPATIBLE_MAX_TOKENS"] = str(max_tokens)
+
+
+def configure_openai_compatible(
+    *,
+    base_url: str | None = None,
+    api_key: str | None = None,
+    model: str | None = None,
+    temperature: float | str | None = None,
+    timeout_seconds: float | str | None = None,
+    max_tokens: int | str | None = None,
+    optimizer_base_url: str | None = None,
+    optimizer_api_key: str | None = None,
+    optimizer_model: str | None = None,
+    target_base_url: str | None = None,
+    target_api_key: str | None = None,
+    target_model: str | None = None,
+) -> None:
+    """Configure the generic OpenAI-compatible backend at runtime.
+
+    Shared values apply to both the optimizer and target roles; the
+    ``optimizer_*`` / ``target_*`` variants override them per role.
+    """
+    with _config_lock:
+        if base_url is not None:
+            os.environ["OPENAI_COMPATIBLE_BASE_URL"] = str(base_url).strip()
+        if api_key is not None:
+            os.environ["OPENAI_COMPATIBLE_API_KEY"] = str(api_key).strip()
+        if model is not None:
+            os.environ["OPENAI_COMPATIBLE_MODEL"] = str(model).strip()
+        if temperature is not None:
+            os.environ["OPENAI_COMPATIBLE_TEMPERATURE"] = str(temperature).strip()
+        if timeout_seconds is not None:
+            os.environ["OPENAI_COMPATIBLE_TIMEOUT_SECONDS"] = str(timeout_seconds)
+        if max_tokens is not None:
+            os.environ["OPENAI_COMPATIBLE_MAX_TOKENS"] = str(max_tokens)
+        _update_config(
+            OPTIMIZER_CONFIG,
+            "optimizer",
+            base_url=optimizer_base_url if optimizer_base_url is not None else base_url,
+            api_key=optimizer_api_key if optimizer_api_key is not None else api_key,
+            deployment=optimizer_model if optimizer_model is not None else model,
+            temperature=temperature,
+            timeout_seconds=timeout_seconds,
+            max_tokens=max_tokens,
+        )
+        _update_config(
+            TARGET_CONFIG,
+            "target",
+            base_url=target_base_url if target_base_url is not None else base_url,
+            api_key=target_api_key if target_api_key is not None else api_key,
+            deployment=target_model if target_model is not None else model,
+            temperature=temperature,
+            timeout_seconds=timeout_seconds,
+            max_tokens=max_tokens,
+        )
+    _reset_clients()
+
+
+def get_max_tokens() -> int:
+    return TARGET_CONFIG.max_tokens
+
+
+def get_token_summary() -> dict[str, dict[str, int]]:
+    return tracker.summary()
+
+
+def reset_token_tracker() -> None:
+    tracker.reset()
+
+
+def set_reasoning_effort(effort: str | None) -> None:
+    # Reasoning effort is provider-specific and not universally supported by
+    # OpenAI-compatible endpoints, so it is intentionally a no-op here.
+    del effort
+
+
+def set_target_deployment(deployment: str) -> None:
+    TARGET_CONFIG.deployment = deployment or default_model_for_backend(BACKEND_NAME)
+    os.environ["TARGET_DEPLOYMENT"] = TARGET_CONFIG.deployment
+    _reset_clients()
+
+
+def set_optimizer_deployment(deployment: str) -> None:
+    OPTIMIZER_CONFIG.deployment = deployment or default_model_for_backend(BACKEND_NAME)
+    os.environ["OPTIMIZER_DEPLOYMENT"] = OPTIMIZER_CONFIG.deployment
+    _reset_clients()

tests/test_openai_compatible_backend.py

@@ -0,0 +1,146 @@
+"""Tests for the generic OpenAI-compatible model backend."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from typing import Any
+
+import pytest
+
+import skillopt.model as model
+from skillopt.model import backend_config
+from skillopt.model import openai_compatible_backend as backend
+
+
+class _CompletionRecorder:
+    def __init__(self) -> None:
+        self.calls: list[dict[str, Any]] = []
+
+    def create(self, **kwargs: Any) -> Any:
+        self.calls.append(kwargs)
+        message = SimpleNamespace(content="ok", tool_calls=[])
+        usage = SimpleNamespace(prompt_tokens=2, completion_tokens=3, total_tokens=5)
+        return SimpleNamespace(choices=[SimpleNamespace(message=message)], usage=usage)
+
+
+class _Client:
+    def __init__(self, recorder: _CompletionRecorder) -> None:
+        self.chat = SimpleNamespace(completions=recorder)
+
+
+@pytest.fixture(autouse=True)
+def isolate_backend_state(monkeypatch: pytest.MonkeyPatch):
+    optimizer_backend = backend_config.get_optimizer_backend()
+    target_backend = backend_config.get_target_backend()
+    optimizer_config = vars(backend.OPTIMIZER_CONFIG).copy()
+    target_config = vars(backend.TARGET_CONFIG).copy()
+    backend.reset_token_tracker()
+    yield
+    backend.reset_token_tracker()
+    vars(backend.OPTIMIZER_CONFIG).update(optimizer_config)
+    vars(backend.TARGET_CONFIG).update(target_config)
+    backend_config.set_optimizer_backend(optimizer_backend)
+    backend_config.set_target_backend(target_backend)
+    backend._reset_clients()
+
+
+def test_configure_preserves_role_specific_values() -> None:
+    model.configure_openai_compatible(
+        base_url="https://shared.example/v1",
+        api_key="shared-key",
+        model="shared-model",
+        optimizer_base_url="https://optimizer.example/v1",
+        optimizer_api_key="optimizer-key",
+        optimizer_model="optimizer-model",
+        target_base_url="https://target.example/v1",
+        target_api_key="target-key",
+        target_model="target-model",
+    )
+
+    assert backend.OPTIMIZER_CONFIG.base_url == "https://optimizer.example/v1"
+    assert backend.OPTIMIZER_CONFIG.api_key == "optimizer-key"
+    assert backend.OPTIMIZER_CONFIG.deployment == "optimizer-model"
+    assert backend.TARGET_CONFIG.base_url == "https://target.example/v1"
+    assert backend.TARGET_CONFIG.api_key == "target-key"
+    assert backend.TARGET_CONFIG.deployment == "target-model"
+
+
+def test_optimizer_and_target_route_to_their_own_clients(monkeypatch: pytest.MonkeyPatch) -> None:
+    optimizer_calls = _CompletionRecorder()
+    target_calls = _CompletionRecorder()
+    monkeypatch.setattr(
+        backend,
+        "_get_client",
+        lambda role: _Client(optimizer_calls if role == "optimizer" else target_calls),
+    )
+    model.set_optimizer_backend("openai_compatible")
+    model.set_target_backend("openai_compatible")
+    backend.OPTIMIZER_CONFIG.deployment = "optimizer-model"
+    backend.TARGET_CONFIG.deployment = "target-model"
+
+    model.chat_optimizer("system", "user", retries=1)
+    model.chat_target_messages([{"role": "user", "content": "question"}], retries=1)
+
+    assert optimizer_calls.calls[0]["model"] == "optimizer-model"
+    assert target_calls.calls[0]["model"] == "target-model"
+
+
+def test_client_creation_is_lazy(monkeypatch: pytest.MonkeyPatch) -> None:
+    builds: list[str] = []
+
+    def build(config: backend.OpenAICompatibleConfig) -> _Client:
+        builds.append(config.deployment)
+        return _Client(_CompletionRecorder())
+
+    backend._reset_clients()
+    monkeypatch.setattr(backend, "_build_client", build)
+    assert builds == []
+
+    backend._get_client("optimizer")
+    backend._get_client("optimizer")
+
+    assert builds == [backend.OPTIMIZER_CONFIG.deployment]
+
+
+def test_combined_token_summary_counts_each_backend_once(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    def make(stage: str) -> dict[str, dict[str, int]]:
+        return {
+            stage: {
+                "calls": 1,
+                "prompt_tokens": 2,
+                "completion_tokens": 3,
+                "total_tokens": 5,
+            },
+            "_total": {
+                "calls": 1,
+                "prompt_tokens": 2,
+                "completion_tokens": 3,
+                "total_tokens": 5,
+            },
+        }
+
+    monkeypatch.setattr(model._openai, "get_token_summary", lambda: make("azure"))
+    monkeypatch.setattr(model._claude, "get_token_summary", lambda: make("claude"))
+    monkeypatch.setattr(model._qwen, "get_token_summary", lambda: make("qwen"))
+    monkeypatch.setattr(model._minimax, "get_token_summary", lambda: make("minimax"))
+    monkeypatch.setattr(model._openai_compat, "get_token_summary", lambda: make("openai_compatible"))
+
+    combined = model.get_token_summary()
+
+    assert set(combined) - {"_total"} == {"azure", "claude", "qwen", "minimax", "openai_compatible"}
+    expected_stage_total = {
+        "calls": 1,
+        "prompt_tokens": 2,
+        "completion_tokens": 3,
+        "total_tokens": 5,
+    }
+    for stage in {"azure", "claude", "qwen", "minimax", "openai_compatible"}:
+        assert combined[stage] == expected_stage_total
+    assert combined["_total"] == {
+        "calls": 5,
+        "prompt_tokens": 10,
+        "completion_tokens": 15,
+        "total_tokens": 25,
+    }