feat: add Claude Code OAuth and Codex CLI as LLM providers (#1166)

* feat: add Claude Code OAuth and Codex CLI providers

Port of bytedance/deer-flow#1136 from @solanian's feat/cli-oauth-providers branch.\n\nCarries the feature forward on top of current main without the original CLA-blocked commit metadata, while preserving attribution in the commit message for review.

* fix: harden CLI credential loading

Align Codex auth loading with the current ~/.codex/auth.json shape, make Docker credential mounts directory-based to avoid broken file binds on hosts without exported credential files, and add focused loader tests.

* refactor: tighten codex auth typing

Replace the temporary Any return type in CodexChatModel._load_codex_auth with the concrete CodexCliCredential type after the credential loader was stabilized.

* fix: load Claude Code OAuth from Keychain

Match Claude Code's macOS storage strategy more closely by checking the Keychain-backed credentials store before falling back to ~/.claude/.credentials.json. Keep explicit file overrides and add focused tests for the Keychain path.

* fix: require explicit Claude OAuth handoff

* style: format thread hooks reasoning request

* docs: document CLI-backed auth providers

* fix: address provider review feedback

* fix: harden provider edge cases

* Fix deferred tools, Codex message normalization, and local sandbox paths

* chore: narrow PR scope to OAuth providers

* chore: remove unrelated frontend changes

* chore: reapply OAuth branch frontend scope cleanup

* fix: preserve upload guards with reasoning effort wiring

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>

backend/docs/CONFIGURATION.md

@@ -36,8 +36,35 @@ models:
 - OpenAI (`langchain_openai:ChatOpenAI`)
 - Anthropic (`langchain_anthropic:ChatAnthropic`)
 - DeepSeek (`langchain_deepseek:ChatDeepSeek`)
+- Claude Code OAuth (`deerflow.models.claude_provider:ClaudeChatModel`)
+- Codex CLI (`deerflow.models.openai_codex_provider:CodexChatModel`)
 - Any LangChain-compatible provider
 
+CLI-backed provider examples:
+
+```yaml
+models:
+  - name: gpt-5.4
+    display_name: GPT-5.4 (Codex CLI)
+    use: deerflow.models.openai_codex_provider:CodexChatModel
+    model: gpt-5.4
+    supports_thinking: true
+    supports_reasoning_effort: true
+
+  - name: claude-sonnet-4.6
+    display_name: Claude Sonnet 4.6 (Claude Code OAuth)
+    use: deerflow.models.claude_provider:ClaudeChatModel
+    model: claude-sonnet-4-6
+    max_tokens: 4096
+    supports_thinking: true
+```
+
+**Auth behavior for CLI-backed providers**:
+- `CodexChatModel` loads Codex CLI auth from `~/.codex/auth.json`
+- The Codex Responses endpoint currently rejects `max_tokens` and `max_output_tokens`, so `CodexChatModel` does not expose a request-level token cap
+- `ClaudeChatModel` accepts `CLAUDE_CODE_OAUTH_TOKEN`, `ANTHROPIC_AUTH_TOKEN`, `CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR`, `CLAUDE_CODE_CREDENTIALS_PATH`, or plaintext `~/.claude/.credentials.json`
+- On macOS, DeerFlow does not probe Keychain automatically. Use `scripts/export_claude_code_oauth.py` to export Claude Code auth explicitly when needed
+
 To use OpenAI's `/v1/responses` endpoint with LangChain, keep using `langchain_openai:ChatOpenAI` and set:
 
 ```yaml

backend/packages/harness/deerflow/models/claude_provider.py

@@ -0,0 +1,262 @@
+"""Custom Claude provider with OAuth Bearer auth, prompt caching, and smart thinking.
+
+Supports two authentication modes:
+  1. Standard API key (x-api-key header) — default ChatAnthropic behavior
+  2. Claude Code OAuth token (Authorization: Bearer header)
+     - Detected by sk-ant-oat prefix
+     - Requires anthropic-beta: oauth-2025-04-20,claude-code-20250219
+
+Auto-loads credentials from explicit runtime handoff:
+  - $ANTHROPIC_API_KEY environment variable
+  - $CLAUDE_CODE_OAUTH_TOKEN or $ANTHROPIC_AUTH_TOKEN
+  - $CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR
+  - $CLAUDE_CODE_CREDENTIALS_PATH
+  - ~/.claude/.credentials.json
+"""
+
+import logging
+import time
+from typing import Any
+
+import anthropic
+from langchain_anthropic import ChatAnthropic
+from langchain_core.messages import BaseMessage
+
+logger = logging.getLogger(__name__)
+
+MAX_RETRIES = 3
+THINKING_BUDGET_RATIO = 0.8
+
+
+class ClaudeChatModel(ChatAnthropic):
+    """ChatAnthropic with OAuth Bearer auth, prompt caching, and smart thinking.
+
+    Config example:
+        - name: claude-sonnet-4.6
+          use: deerflow.models.claude_provider:ClaudeChatModel
+          model: claude-sonnet-4-6
+          max_tokens: 16384
+          enable_prompt_caching: true
+    """
+
+    # Custom fields
+    enable_prompt_caching: bool = True
+    prompt_cache_size: int = 3
+    auto_thinking_budget: bool = True
+    retry_max_attempts: int = MAX_RETRIES
+    _is_oauth: bool = False
+    _oauth_access_token: str = ""
+
+    model_config = {"arbitrary_types_allowed": True}
+
+    def _validate_retry_config(self) -> None:
+        if self.retry_max_attempts < 1:
+            raise ValueError("retry_max_attempts must be >= 1")
+
+    def model_post_init(self, __context: Any) -> None:
+        """Auto-load credentials and configure OAuth if needed."""
+        from pydantic import SecretStr
+
+        from deerflow.models.credential_loader import (
+            OAUTH_ANTHROPIC_BETAS,
+            is_oauth_token,
+            load_claude_code_credential,
+        )
+
+        self._validate_retry_config()
+
+        # Extract actual key value (SecretStr.str() returns '**********')
+        current_key = ""
+        if self.anthropic_api_key:
+            if hasattr(self.anthropic_api_key, "get_secret_value"):
+                current_key = self.anthropic_api_key.get_secret_value()
+            else:
+                current_key = str(self.anthropic_api_key)
+
+        # Try the explicit Claude Code OAuth handoff sources if no valid key.
+        if not current_key or current_key in ("your-anthropic-api-key",):
+            cred = load_claude_code_credential()
+            if cred:
+                current_key = cred.access_token
+                logger.info(f"Using Claude Code CLI credential (source: {cred.source})")
+            else:
+                logger.warning("No Anthropic API key or explicit Claude Code OAuth credential found.")
+
+        # Detect OAuth token and configure Bearer auth
+        if is_oauth_token(current_key):
+            self._is_oauth = True
+            self._oauth_access_token = current_key
+            # Set the token as api_key temporarily (will be swapped to auth_token on client)
+            self.anthropic_api_key = SecretStr(current_key)
+            # Add required beta headers for OAuth
+            self.default_headers = {
+                **(self.default_headers or {}),
+                "anthropic-beta": OAUTH_ANTHROPIC_BETAS,
+            }
+            # OAuth tokens have a limit of 4 cache_control blocks — disable prompt caching
+            self.enable_prompt_caching = False
+            logger.info("OAuth token detected — will use Authorization: Bearer header")
+        else:
+            if current_key:
+                self.anthropic_api_key = SecretStr(current_key)
+
+        # Ensure api_key is SecretStr
+        if isinstance(self.anthropic_api_key, str):
+            self.anthropic_api_key = SecretStr(self.anthropic_api_key)
+
+        super().model_post_init(__context)
+
+        # Patch clients immediately after creation for OAuth Bearer auth.
+        # This must happen after super() because clients are lazily created.
+        if self._is_oauth:
+            self._patch_client_oauth(self._client)
+            self._patch_client_oauth(self._async_client)
+
+    def _patch_client_oauth(self, client: Any) -> None:
+        """Swap api_key → auth_token on an Anthropic SDK client for OAuth Bearer auth."""
+        if hasattr(client, "api_key") and hasattr(client, "auth_token"):
+            client.api_key = None
+            client.auth_token = self._oauth_access_token
+
+    def _get_request_payload(
+        self,
+        input_: Any,
+        *,
+        stop: list[str] | None = None,
+        **kwargs: Any,
+    ) -> dict:
+        """Override to inject prompt caching and thinking budget."""
+        payload = super()._get_request_payload(input_, stop=stop, **kwargs)
+
+        if self.enable_prompt_caching:
+            self._apply_prompt_caching(payload)
+
+        if self.auto_thinking_budget:
+            self._apply_thinking_budget(payload)
+
+        return payload
+
+    def _apply_prompt_caching(self, payload: dict) -> None:
+        """Apply ephemeral cache_control to system and recent messages."""
+        # Cache system messages
+        system = payload.get("system")
+        if system and isinstance(system, list):
+            for block in system:
+                if isinstance(block, dict) and block.get("type") == "text":
+                    block["cache_control"] = {"type": "ephemeral"}
+        elif system and isinstance(system, str):
+            payload["system"] = [
+                {
+                    "type": "text",
+                    "text": system,
+                    "cache_control": {"type": "ephemeral"},
+                }
+            ]
+
+        # Cache recent messages
+        messages = payload.get("messages", [])
+        cache_start = max(0, len(messages) - self.prompt_cache_size)
+        for i in range(cache_start, len(messages)):
+            msg = messages[i]
+            if not isinstance(msg, dict):
+                continue
+            content = msg.get("content")
+            if isinstance(content, list):
+                for block in content:
+                    if isinstance(block, dict):
+                        block["cache_control"] = {"type": "ephemeral"}
+            elif isinstance(content, str) and content:
+                msg["content"] = [
+                    {
+                        "type": "text",
+                        "text": content,
+                        "cache_control": {"type": "ephemeral"},
+                    }
+                ]
+
+        # Cache the last tool definition
+        tools = payload.get("tools", [])
+        if tools and isinstance(tools[-1], dict):
+            tools[-1]["cache_control"] = {"type": "ephemeral"}
+
+    def _apply_thinking_budget(self, payload: dict) -> None:
+        """Auto-allocate thinking budget (80% of max_tokens)."""
+        thinking = payload.get("thinking")
+        if not thinking or not isinstance(thinking, dict):
+            return
+        if thinking.get("type") != "enabled":
+            return
+        if thinking.get("budget_tokens"):
+            return
+
+        max_tokens = payload.get("max_tokens", 8192)
+        thinking["budget_tokens"] = int(max_tokens * THINKING_BUDGET_RATIO)
+
+    def _generate(self, messages: list[BaseMessage], stop: list[str] | None = None, **kwargs: Any) -> Any:
+        """Override with OAuth patching and retry logic."""
+        if self._is_oauth:
+            self._patch_client_oauth(self._client)
+
+        last_error = None
+        for attempt in range(1, self.retry_max_attempts + 1):
+            try:
+                return super()._generate(messages, stop=stop, **kwargs)
+            except anthropic.RateLimitError as e:
+                last_error = e
+                if attempt >= self.retry_max_attempts:
+                    raise
+                wait_ms = self._calc_backoff_ms(attempt, e)
+                logger.warning(f"Rate limited, retrying attempt {attempt}/{self.retry_max_attempts} after {wait_ms}ms")
+                time.sleep(wait_ms / 1000)
+            except anthropic.InternalServerError as e:
+                last_error = e
+                if attempt >= self.retry_max_attempts:
+                    raise
+                wait_ms = self._calc_backoff_ms(attempt, e)
+                logger.warning(f"Server error, retrying attempt {attempt}/{self.retry_max_attempts} after {wait_ms}ms")
+                time.sleep(wait_ms / 1000)
+        raise last_error
+
+    async def _agenerate(self, messages: list[BaseMessage], stop: list[str] | None = None, **kwargs: Any) -> Any:
+        """Async override with OAuth patching and retry logic."""
+        import asyncio
+
+        if self._is_oauth:
+            self._patch_client_oauth(self._async_client)
+
+        last_error = None
+        for attempt in range(1, self.retry_max_attempts + 1):
+            try:
+                return await super()._agenerate(messages, stop=stop, **kwargs)
+            except anthropic.RateLimitError as e:
+                last_error = e
+                if attempt >= self.retry_max_attempts:
+                    raise
+                wait_ms = self._calc_backoff_ms(attempt, e)
+                logger.warning(f"Rate limited, retrying attempt {attempt}/{self.retry_max_attempts} after {wait_ms}ms")
+                await asyncio.sleep(wait_ms / 1000)
+            except anthropic.InternalServerError as e:
+                last_error = e
+                if attempt >= self.retry_max_attempts:
+                    raise
+                wait_ms = self._calc_backoff_ms(attempt, e)
+                logger.warning(f"Server error, retrying attempt {attempt}/{self.retry_max_attempts} after {wait_ms}ms")
+                await asyncio.sleep(wait_ms / 1000)
+        raise last_error
+
+    @staticmethod
+    def _calc_backoff_ms(attempt: int, error: Exception) -> int:
+        """Exponential backoff with a fixed 20% buffer."""
+        backoff_ms = 2000 * (1 << (attempt - 1))
+        jitter_ms = int(backoff_ms * 0.2)
+        total_ms = backoff_ms + jitter_ms
+
+        if hasattr(error, "response") and error.response is not None:
+            retry_after = error.response.headers.get("Retry-After")
+            if retry_after:
+                try:
+                    total_ms = int(retry_after) * 1000
+                except (ValueError, TypeError):
+                    pass
+
+        return total_ms

backend/packages/harness/deerflow/models/credential_loader.py

@@ -0,0 +1,212 @@
+"""Auto-load credentials from Claude Code CLI and Codex CLI.
+
+Implements two credential strategies:
+  1. Claude Code OAuth token from explicit env vars or an exported credentials file
+     - Uses Authorization: Bearer header (NOT x-api-key)
+     - Requires anthropic-beta: oauth-2025-04-20,claude-code-20250219
+     - Supports $CLAUDE_CODE_OAUTH_TOKEN, $CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR, and $ANTHROPIC_AUTH_TOKEN
+     - Override path with $CLAUDE_CODE_CREDENTIALS_PATH
+  2. Codex CLI token from ~/.codex/auth.json
+     - Uses chatgpt.com/backend-api/codex/responses endpoint
+     - Supports both legacy top-level tokens and current nested tokens shape
+     - Override path with $CODEX_AUTH_PATH
+"""
+
+import json
+import logging
+import os
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+# Required beta headers for Claude Code OAuth tokens
+OAUTH_ANTHROPIC_BETAS = "oauth-2025-04-20,claude-code-20250219,interleaved-thinking-2025-05-14"
+
+
+def is_oauth_token(token: str) -> bool:
+    """Check if a token is a Claude Code OAuth token (not a standard API key)."""
+    return isinstance(token, str) and "sk-ant-oat" in token
+
+
+@dataclass
+class ClaudeCodeCredential:
+    """Claude Code CLI OAuth credential."""
+
+    access_token: str
+    refresh_token: str = ""
+    expires_at: int = 0
+    source: str = ""
+
+    @property
+    def is_expired(self) -> bool:
+        if self.expires_at <= 0:
+            return False
+        return time.time() * 1000 > self.expires_at - 60_000  # 1 min buffer
+
+
+@dataclass
+class CodexCliCredential:
+    """Codex CLI credential."""
+
+    access_token: str
+    account_id: str = ""
+    source: str = ""
+
+
+def _resolve_credential_path(env_var: str, default_relative_path: str) -> Path:
+    configured_path = os.getenv(env_var)
+    if configured_path:
+        return Path(configured_path).expanduser()
+    return Path.home() / default_relative_path
+
+
+def _load_json_file(path: Path, label: str) -> dict[str, Any] | None:
+    if not path.exists():
+        logger.debug(f"{label} not found: {path}")
+        return None
+    if path.is_dir():
+        logger.warning(f"{label} path is a directory, expected a file: {path}")
+        return None
+
+    try:
+        return json.loads(path.read_text())
+    except (json.JSONDecodeError, OSError) as e:
+        logger.warning(f"Failed to read {label}: {e}")
+        return None
+
+
+def _read_secret_from_file_descriptor(env_var: str) -> str | None:
+    fd_value = os.getenv(env_var)
+    if not fd_value:
+        return None
+
+    try:
+        fd = int(fd_value)
+    except ValueError:
+        logger.warning(f"{env_var} must be an integer file descriptor, got: {fd_value}")
+        return None
+
+    try:
+        secret = Path(f"/dev/fd/{fd}").read_text().strip()
+    except OSError as e:
+        logger.warning(f"Failed to read {env_var}: {e}")
+        return None
+
+    return secret or None
+
+
+def _credential_from_direct_token(access_token: str, source: str) -> ClaudeCodeCredential | None:
+    token = access_token.strip()
+    if not token:
+        return None
+    return ClaudeCodeCredential(access_token=token, source=source)
+
+
+def _iter_claude_code_credential_paths() -> list[Path]:
+    paths: list[Path] = []
+    override_path = os.getenv("CLAUDE_CODE_CREDENTIALS_PATH")
+    if override_path:
+        paths.append(Path(override_path).expanduser())
+
+    default_path = Path.home() / ".claude/.credentials.json"
+    if not paths or paths[-1] != default_path:
+        paths.append(default_path)
+
+    return paths
+
+
+def _extract_claude_code_credential(data: dict[str, Any], source: str) -> ClaudeCodeCredential | None:
+    oauth = data.get("claudeAiOauth", {})
+    access_token = oauth.get("accessToken", "")
+    if not access_token:
+        logger.debug("Claude Code credentials container exists but no accessToken found")
+        return None
+
+    cred = ClaudeCodeCredential(
+        access_token=access_token,
+        refresh_token=oauth.get("refreshToken", ""),
+        expires_at=oauth.get("expiresAt", 0),
+        source=source,
+    )
+
+    if cred.is_expired:
+        logger.warning("Claude Code OAuth token is expired. Run 'claude' to refresh.")
+        return None
+
+    return cred
+
+
+def load_claude_code_credential() -> ClaudeCodeCredential | None:
+    """Load OAuth credential from explicit Claude Code handoff sources.
+
+    Lookup order:
+      1. $CLAUDE_CODE_OAUTH_TOKEN or $ANTHROPIC_AUTH_TOKEN
+      2. $CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR
+      3. $CLAUDE_CODE_CREDENTIALS_PATH
+      4. ~/.claude/.credentials.json
+
+    Exported credentials files contain:
+    {
+      "claudeAiOauth": {
+        "accessToken": "sk-ant-oat01-...",
+        "refreshToken": "sk-ant-ort01-...",
+        "expiresAt": 1773430695128,
+        "scopes": ["user:inference", ...],
+        ...
+      }
+    }
+    """
+    direct_token = os.getenv("CLAUDE_CODE_OAUTH_TOKEN") or os.getenv("ANTHROPIC_AUTH_TOKEN")
+    if direct_token:
+        cred = _credential_from_direct_token(direct_token, "claude-cli-env")
+        if cred:
+            logger.info("Loaded Claude Code OAuth credential from environment")
+        return cred
+
+    fd_token = _read_secret_from_file_descriptor("CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR")
+    if fd_token:
+        cred = _credential_from_direct_token(fd_token, "claude-cli-fd")
+        if cred:
+            logger.info("Loaded Claude Code OAuth credential from file descriptor")
+        return cred
+
+    override_path = os.getenv("CLAUDE_CODE_CREDENTIALS_PATH")
+    override_path_obj = Path(override_path).expanduser() if override_path else None
+    for cred_path in _iter_claude_code_credential_paths():
+        data = _load_json_file(cred_path, "Claude Code credentials")
+        if data is None:
+            continue
+        cred = _extract_claude_code_credential(data, "claude-cli-file")
+        if cred:
+            source_label = "override path" if override_path_obj is not None and cred_path == override_path_obj else "plaintext file"
+            logger.info(f"Loaded Claude Code OAuth credential from {source_label} (expires_at={cred.expires_at})")
+            return cred
+
+    return None
+
+
+def load_codex_cli_credential() -> CodexCliCredential | None:
+    """Load credential from Codex CLI (~/.codex/auth.json)."""
+    cred_path = _resolve_credential_path("CODEX_AUTH_PATH", ".codex/auth.json")
+    data = _load_json_file(cred_path, "Codex CLI credentials")
+    if data is None:
+        return None
+    tokens = data.get("tokens", {})
+    if not isinstance(tokens, dict):
+        tokens = {}
+
+    access_token = data.get("access_token") or data.get("token") or tokens.get("access_token", "")
+    account_id = data.get("account_id") or tokens.get("account_id", "")
+    if not access_token:
+        logger.debug("Codex CLI credentials file exists but no token found")
+        return None
+
+    logger.info("Loaded Codex CLI credential")
+    return CodexCliCredential(
+        access_token=access_token,
+        account_id=account_id,
+        source="codex-cli",
+    )

backend/packages/harness/deerflow/models/factory.py

@@ -61,6 +61,22 @@ def create_chat_model(name: str | None = None, thinking_enabled: bool = False, *
     if not model_config.supports_reasoning_effort and "reasoning_effort" in kwargs:
         del kwargs["reasoning_effort"]
 
+    # For Codex Responses API models: map thinking mode to reasoning_effort
+    from deerflow.models.openai_codex_provider import CodexChatModel
+
+    if issubclass(model_class, CodexChatModel):
+        # The ChatGPT Codex endpoint currently rejects max_tokens/max_output_tokens.
+        model_settings_from_config.pop("max_tokens", None)
+
+        # Use explicit reasoning_effort from frontend if provided (low/medium/high)
+        explicit_effort = kwargs.pop("reasoning_effort", None)
+        if not thinking_enabled:
+            model_settings_from_config["reasoning_effort"] = "none"
+        elif explicit_effort and explicit_effort in ("low", "medium", "high", "xhigh"):
+            model_settings_from_config["reasoning_effort"] = explicit_effort
+        elif "reasoning_effort" not in model_settings_from_config:
+            model_settings_from_config["reasoning_effort"] = "medium"
+
     model_instance = model_class(**kwargs, **model_settings_from_config)
 
     if is_tracing_enabled():

backend/packages/harness/deerflow/models/openai_codex_provider.py

@@ -0,0 +1,396 @@
+"""Custom OpenAI Codex provider using ChatGPT Codex Responses API.
+
+Uses Codex CLI OAuth tokens with chatgpt.com/backend-api/codex/responses endpoint.
+This is the same endpoint that the Codex CLI uses internally.
+
+Supports:
+- Auto-load credentials from ~/.codex/auth.json
+- Responses API format (not Chat Completions)
+- Tool calling
+- Streaming (required by the endpoint)
+- Retry with exponential backoff
+"""
+
+import json
+import logging
+import time
+from typing import Any
+
+import httpx
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage, ToolMessage
+from langchain_core.outputs import ChatGeneration, ChatResult
+
+from deerflow.models.credential_loader import CodexCliCredential, load_codex_cli_credential
+
+logger = logging.getLogger(__name__)
+
+CODEX_BASE_URL = "https://chatgpt.com/backend-api/codex"
+MAX_RETRIES = 3
+
+
+class CodexChatModel(BaseChatModel):
+    """LangChain chat model using ChatGPT Codex Responses API.
+
+    Config example:
+        - name: gpt-5.4
+          use: deerflow.models.openai_codex_provider:CodexChatModel
+          model: gpt-5.4
+          reasoning_effort: medium
+    """
+
+    model: str = "gpt-5.4"
+    reasoning_effort: str = "medium"
+    retry_max_attempts: int = MAX_RETRIES
+    _access_token: str = ""
+    _account_id: str = ""
+
+    model_config = {"arbitrary_types_allowed": True}
+
+    @property
+    def _llm_type(self) -> str:
+        return "codex-responses"
+
+    def _validate_retry_config(self) -> None:
+        if self.retry_max_attempts < 1:
+            raise ValueError("retry_max_attempts must be >= 1")
+
+    def model_post_init(self, __context: Any) -> None:
+        """Auto-load Codex CLI credentials."""
+        self._validate_retry_config()
+
+        cred = self._load_codex_auth()
+        if cred:
+            self._access_token = cred.access_token
+            self._account_id = cred.account_id
+            logger.info(f"Using Codex CLI credential (account: {self._account_id[:8]}...)")
+        else:
+            raise ValueError("Codex CLI credential not found. Expected ~/.codex/auth.json or CODEX_AUTH_PATH.")
+
+        super().model_post_init(__context)
+
+    def _load_codex_auth(self) -> CodexCliCredential | None:
+        """Load access_token and account_id from Codex CLI auth."""
+        return load_codex_cli_credential()
+
+    @classmethod
+    def _normalize_content(cls, content: Any) -> str:
+        """Flatten LangChain content blocks into plain text for Codex."""
+        if isinstance(content, str):
+            return content
+
+        if isinstance(content, list):
+            parts = [cls._normalize_content(item) for item in content]
+            return "\n".join(part for part in parts if part)
+
+        if isinstance(content, dict):
+            for key in ("text", "output"):
+                value = content.get(key)
+                if isinstance(value, str):
+                    return value
+            nested_content = content.get("content")
+            if nested_content is not None:
+                return cls._normalize_content(nested_content)
+            try:
+                return json.dumps(content, ensure_ascii=False)
+            except TypeError:
+                return str(content)
+
+        try:
+            return json.dumps(content, ensure_ascii=False)
+        except TypeError:
+            return str(content)
+
+    def _convert_messages(self, messages: list[BaseMessage]) -> tuple[str, list[dict]]:
+        """Convert LangChain messages to Responses API format.
+
+        Returns (instructions, input_items).
+        """
+        instructions_parts: list[str] = []
+        input_items = []
+
+        for msg in messages:
+            if isinstance(msg, SystemMessage):
+                content = self._normalize_content(msg.content)
+                if content:
+                    instructions_parts.append(content)
+            elif isinstance(msg, HumanMessage):
+                content = self._normalize_content(msg.content)
+                input_items.append({"role": "user", "content": content})
+            elif isinstance(msg, AIMessage):
+                if msg.content:
+                    content = self._normalize_content(msg.content)
+                    input_items.append({"role": "assistant", "content": content})
+                if msg.tool_calls:
+                    for tc in msg.tool_calls:
+                        input_items.append(
+                            {
+                                "type": "function_call",
+                                "name": tc["name"],
+                                "arguments": json.dumps(tc["args"]) if isinstance(tc["args"], dict) else tc["args"],
+                                "call_id": tc["id"],
+                            }
+                        )
+            elif isinstance(msg, ToolMessage):
+                input_items.append(
+                    {
+                        "type": "function_call_output",
+                        "call_id": msg.tool_call_id,
+                        "output": self._normalize_content(msg.content),
+                    }
+                )
+
+        instructions = "\n\n".join(instructions_parts) or "You are a helpful assistant."
+
+        return instructions, input_items
+
+    def _convert_tools(self, tools: list[dict]) -> list[dict]:
+        """Convert LangChain tool format to Responses API format."""
+        responses_tools = []
+        for tool in tools:
+            if tool.get("type") == "function" and "function" in tool:
+                fn = tool["function"]
+                responses_tools.append(
+                    {
+                        "type": "function",
+                        "name": fn["name"],
+                        "description": fn.get("description", ""),
+                        "parameters": fn.get("parameters", {}),
+                    }
+                )
+            elif "name" in tool:
+                responses_tools.append(
+                    {
+                        "type": "function",
+                        "name": tool["name"],
+                        "description": tool.get("description", ""),
+                        "parameters": tool.get("parameters", {}),
+                    }
+                )
+        return responses_tools
+
+    def _call_codex_api(self, messages: list[BaseMessage], tools: list[dict] | None = None) -> dict:
+        """Call the Codex Responses API and return the completed response."""
+        instructions, input_items = self._convert_messages(messages)
+
+        payload = {
+            "model": self.model,
+            "instructions": instructions,
+            "input": input_items,
+            "store": False,
+            "stream": True,
+            "reasoning": {"effort": self.reasoning_effort, "summary": "detailed"} if self.reasoning_effort != "none" else {"effort": "none"},
+        }
+
+        if tools:
+            payload["tools"] = self._convert_tools(tools)
+
+        headers = {
+            "Authorization": f"Bearer {self._access_token}",
+            "ChatGPT-Account-ID": self._account_id,
+            "Content-Type": "application/json",
+            "Accept": "text/event-stream",
+            "originator": "codex_cli_rs",
+        }
+
+        last_error = None
+        for attempt in range(1, self.retry_max_attempts + 1):
+            try:
+                return self._stream_response(headers, payload)
+            except httpx.HTTPStatusError as e:
+                last_error = e
+                if e.response.status_code in (429, 500, 529):
+                    if attempt >= self.retry_max_attempts:
+                        raise
+                    wait_ms = 2000 * (1 << (attempt - 1))
+                    logger.warning(f"Codex API error {e.response.status_code}, retrying {attempt}/{self.retry_max_attempts} after {wait_ms}ms")
+                    time.sleep(wait_ms / 1000)
+                else:
+                    raise
+            except Exception:
+                raise
+
+        raise last_error
+
+    def _stream_response(self, headers: dict, payload: dict) -> dict:
+        """Stream SSE from Codex API and collect the final response."""
+        completed_response = None
+
+        with httpx.Client(timeout=300) as client:
+            with client.stream("POST", f"{CODEX_BASE_URL}/responses", headers=headers, json=payload) as resp:
+                resp.raise_for_status()
+                for line in resp.iter_lines():
+                    data = self._parse_sse_data_line(line)
+                    if data and data.get("type") == "response.completed":
+                        completed_response = data["response"]
+
+        if not completed_response:
+            raise RuntimeError("Codex API stream ended without response.completed event")
+
+        return completed_response
+
+    @staticmethod
+    def _parse_sse_data_line(line: str) -> dict[str, Any] | None:
+        """Parse a data line from the SSE stream, skipping terminal markers."""
+        if not line.startswith("data:"):
+            return None
+
+        raw_data = line[5:].strip()
+        if not raw_data or raw_data == "[DONE]":
+            return None
+
+        try:
+            data = json.loads(raw_data)
+        except json.JSONDecodeError:
+            logger.debug(f"Skipping non-JSON Codex SSE frame: {raw_data}")
+            return None
+
+        return data if isinstance(data, dict) else None
+
+    def _parse_tool_call_arguments(self, output_item: dict[str, Any]) -> tuple[dict[str, Any] | None, dict[str, Any] | None]:
+        """Parse function-call arguments, surfacing malformed payloads safely."""
+        raw_arguments = output_item.get("arguments", "{}")
+        if isinstance(raw_arguments, dict):
+            return raw_arguments, None
+
+        normalized_arguments = raw_arguments or "{}"
+        try:
+            parsed_arguments = json.loads(normalized_arguments)
+        except (TypeError, json.JSONDecodeError) as exc:
+            return None, {
+                "type": "invalid_tool_call",
+                "name": output_item.get("name"),
+                "args": str(raw_arguments),
+                "id": output_item.get("call_id"),
+                "error": f"Failed to parse tool arguments: {exc}",
+            }
+
+        if not isinstance(parsed_arguments, dict):
+            return None, {
+                "type": "invalid_tool_call",
+                "name": output_item.get("name"),
+                "args": str(raw_arguments),
+                "id": output_item.get("call_id"),
+                "error": "Tool arguments must decode to a JSON object.",
+            }
+
+        return parsed_arguments, None
+
+    def _parse_response(self, response: dict) -> ChatResult:
+        """Parse Codex Responses API response into LangChain ChatResult."""
+        content = ""
+        tool_calls = []
+        invalid_tool_calls = []
+        reasoning_content = ""
+
+        for output_item in response.get("output", []):
+            if output_item.get("type") == "reasoning":
+                # Extract reasoning summary text
+                for summary_item in output_item.get("summary", []):
+                    if isinstance(summary_item, dict) and summary_item.get("type") == "summary_text":
+                        reasoning_content += summary_item.get("text", "")
+                    elif isinstance(summary_item, str):
+                        reasoning_content += summary_item
+            elif output_item.get("type") == "message":
+                for part in output_item.get("content", []):
+                    if part.get("type") == "output_text":
+                        content += part.get("text", "")
+            elif output_item.get("type") == "function_call":
+                parsed_arguments, invalid_tool_call = self._parse_tool_call_arguments(output_item)
+                if invalid_tool_call:
+                    invalid_tool_calls.append(invalid_tool_call)
+                    continue
+
+                tool_calls.append(
+                    {
+                        "name": output_item["name"],
+                        "args": parsed_arguments or {},
+                        "id": output_item.get("call_id", ""),
+                        "type": "tool_call",
+                    }
+                )
+
+        usage = response.get("usage", {})
+        additional_kwargs = {}
+        if reasoning_content:
+            additional_kwargs["reasoning_content"] = reasoning_content
+
+        message = AIMessage(
+            content=content,
+            tool_calls=tool_calls if tool_calls else [],
+            invalid_tool_calls=invalid_tool_calls,
+            additional_kwargs=additional_kwargs,
+            response_metadata={
+                "model": response.get("model", self.model),
+                "usage": usage,
+            },
+        )
+
+        return ChatResult(
+            generations=[ChatGeneration(message=message)],
+            llm_output={
+                "token_usage": {
+                    "prompt_tokens": usage.get("input_tokens", 0),
+                    "completion_tokens": usage.get("output_tokens", 0),
+                    "total_tokens": usage.get("total_tokens", 0),
+                },
+                "model_name": response.get("model", self.model),
+            },
+        )
+
+    def _generate(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """Generate a response using Codex Responses API."""
+        tools = kwargs.get("tools", None)
+        response = self._call_codex_api(messages, tools=tools)
+        return self._parse_response(response)
+
+    def bind_tools(self, tools: list, **kwargs: Any) -> Any:
+        """Bind tools for function calling."""
+        from langchain_core.runnables import RunnableBinding
+        from langchain_core.tools import BaseTool
+        from langchain_core.utils.function_calling import convert_to_openai_function
+
+        formatted_tools = []
+        for tool in tools:
+            if isinstance(tool, BaseTool):
+                try:
+                    fn = convert_to_openai_function(tool)
+                    formatted_tools.append(
+                        {
+                            "type": "function",
+                            "name": fn["name"],
+                            "description": fn.get("description", ""),
+                            "parameters": fn.get("parameters", {}),
+                        }
+                    )
+                except Exception:
+                    formatted_tools.append(
+                        {
+                            "type": "function",
+                            "name": tool.name,
+                            "description": tool.description,
+                            "parameters": {"type": "object", "properties": {}},
+                        }
+                    )
+            elif isinstance(tool, dict):
+                if "function" in tool:
+                    fn = tool["function"]
+                    formatted_tools.append(
+                        {
+                            "type": "function",
+                            "name": fn["name"],
+                            "description": fn.get("description", ""),
+                            "parameters": fn.get("parameters", {}),
+                        }
+                    )
+                else:
+                    formatted_tools.append(tool)
+
+        return RunnableBinding(bound=self, kwargs={"tools": formatted_tools}, **kwargs)

backend/tests/test_cli_auth_providers.py

@@ -0,0 +1,151 @@
+from __future__ import annotations
+
+import json
+
+import pytest
+from langchain_core.messages import HumanMessage, SystemMessage
+
+from deerflow.models.claude_provider import ClaudeChatModel
+from deerflow.models.credential_loader import CodexCliCredential
+from deerflow.models.openai_codex_provider import CodexChatModel
+
+
+def test_codex_provider_rejects_non_positive_retry_attempts():
+    with pytest.raises(ValueError, match="retry_max_attempts must be >= 1"):
+        CodexChatModel(retry_max_attempts=0)
+
+
+def test_codex_provider_requires_credentials(monkeypatch):
+    monkeypatch.setattr(CodexChatModel, "_load_codex_auth", lambda self: None)
+
+    with pytest.raises(ValueError, match="Codex CLI credential not found"):
+        CodexChatModel()
+
+
+def test_codex_provider_concatenates_multiple_system_messages(monkeypatch):
+    monkeypatch.setattr(
+        CodexChatModel,
+        "_load_codex_auth",
+        lambda self: CodexCliCredential(access_token="token", account_id="acct"),
+    )
+
+    model = CodexChatModel()
+    instructions, input_items = model._convert_messages(
+        [
+            SystemMessage(content="First system prompt."),
+            SystemMessage(content="Second system prompt."),
+            HumanMessage(content="Hello"),
+        ]
+    )
+
+    assert instructions == "First system prompt.\n\nSecond system prompt."
+    assert input_items == [{"role": "user", "content": "Hello"}]
+
+
+def test_codex_provider_flattens_structured_text_blocks(monkeypatch):
+    monkeypatch.setattr(
+        CodexChatModel,
+        "_load_codex_auth",
+        lambda self: CodexCliCredential(access_token="token", account_id="acct"),
+    )
+
+    model = CodexChatModel()
+    instructions, input_items = model._convert_messages(
+        [
+            HumanMessage(content=[{"type": "text", "text": "Hello from blocks"}]),
+        ]
+    )
+
+    assert instructions == "You are a helpful assistant."
+    assert input_items == [{"role": "user", "content": "Hello from blocks"}]
+
+
+def test_claude_provider_rejects_non_positive_retry_attempts():
+    with pytest.raises(ValueError, match="retry_max_attempts must be >= 1"):
+        ClaudeChatModel(model="claude-sonnet-4-6", retry_max_attempts=0)
+
+
+def test_codex_provider_skips_terminal_sse_markers(monkeypatch):
+    monkeypatch.setattr(
+        CodexChatModel,
+        "_load_codex_auth",
+        lambda self: CodexCliCredential(access_token="token", account_id="acct"),
+    )
+
+    model = CodexChatModel()
+
+    assert model._parse_sse_data_line("data: [DONE]") is None
+    assert model._parse_sse_data_line("event: response.completed") is None
+
+
+def test_codex_provider_skips_non_json_sse_frames(monkeypatch):
+    monkeypatch.setattr(
+        CodexChatModel,
+        "_load_codex_auth",
+        lambda self: CodexCliCredential(access_token="token", account_id="acct"),
+    )
+
+    model = CodexChatModel()
+
+    assert model._parse_sse_data_line("data: not-json") is None
+
+
+def test_codex_provider_marks_invalid_tool_call_arguments(monkeypatch):
+    monkeypatch.setattr(
+        CodexChatModel,
+        "_load_codex_auth",
+        lambda self: CodexCliCredential(access_token="token", account_id="acct"),
+    )
+
+    model = CodexChatModel()
+    result = model._parse_response(
+        {
+            "model": "gpt-5.4",
+            "output": [
+                {
+                    "type": "function_call",
+                    "name": "bash",
+                    "arguments": "{invalid",
+                    "call_id": "tc-1",
+                }
+            ],
+            "usage": {},
+        }
+    )
+
+    message = result.generations[0].message
+    assert message.tool_calls == []
+    assert len(message.invalid_tool_calls) == 1
+    assert message.invalid_tool_calls[0]["type"] == "invalid_tool_call"
+    assert message.invalid_tool_calls[0]["name"] == "bash"
+    assert message.invalid_tool_calls[0]["args"] == "{invalid"
+    assert message.invalid_tool_calls[0]["id"] == "tc-1"
+    assert "Failed to parse tool arguments" in message.invalid_tool_calls[0]["error"]
+
+
+def test_codex_provider_parses_valid_tool_arguments(monkeypatch):
+    monkeypatch.setattr(
+        CodexChatModel,
+        "_load_codex_auth",
+        lambda self: CodexCliCredential(access_token="token", account_id="acct"),
+    )
+
+    model = CodexChatModel()
+    result = model._parse_response(
+        {
+            "model": "gpt-5.4",
+            "output": [
+                {
+                    "type": "function_call",
+                    "name": "bash",
+                    "arguments": json.dumps({"cmd": "pwd"}),
+                    "call_id": "tc-1",
+                }
+            ],
+            "usage": {},
+        }
+    )
+
+    assert result.generations[0].message.tool_calls == [
+        {"name": "bash", "args": {"cmd": "pwd"}, "id": "tc-1", "type": "tool_call"}
+    ]

backend/tests/test_credential_loader.py

@@ -0,0 +1,156 @@
+import json
+import os
+
+from deerflow.models.credential_loader import (
+    load_claude_code_credential,
+    load_codex_cli_credential,
+)
+
+
+def _clear_claude_code_env(monkeypatch) -> None:
+    for env_var in (
+        "CLAUDE_CODE_OAUTH_TOKEN",
+        "ANTHROPIC_AUTH_TOKEN",
+        "CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR",
+        "CLAUDE_CODE_CREDENTIALS_PATH",
+    ):
+        monkeypatch.delenv(env_var, raising=False)
+
+
+def test_load_claude_code_credential_from_direct_env(monkeypatch):
+    _clear_claude_code_env(monkeypatch)
+    monkeypatch.setenv("CLAUDE_CODE_OAUTH_TOKEN", "  sk-ant-oat01-env  ")
+
+    cred = load_claude_code_credential()
+
+    assert cred is not None
+    assert cred.access_token == "sk-ant-oat01-env"
+    assert cred.refresh_token == ""
+    assert cred.source == "claude-cli-env"
+
+
+def test_load_claude_code_credential_from_anthropic_auth_env(monkeypatch):
+    _clear_claude_code_env(monkeypatch)
+    monkeypatch.setenv("ANTHROPIC_AUTH_TOKEN", "sk-ant-oat01-anthropic-auth")
+
+    cred = load_claude_code_credential()
+
+    assert cred is not None
+    assert cred.access_token == "sk-ant-oat01-anthropic-auth"
+    assert cred.source == "claude-cli-env"
+
+
+def test_load_claude_code_credential_from_file_descriptor(monkeypatch):
+    _clear_claude_code_env(monkeypatch)
+
+    read_fd, write_fd = os.pipe()
+    try:
+        os.write(write_fd, b"sk-ant-oat01-fd")
+        os.close(write_fd)
+        monkeypatch.setenv("CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR", str(read_fd))
+
+        cred = load_claude_code_credential()
+    finally:
+        os.close(read_fd)
+
+    assert cred is not None
+    assert cred.access_token == "sk-ant-oat01-fd"
+    assert cred.refresh_token == ""
+    assert cred.source == "claude-cli-fd"
+
+
+def test_load_claude_code_credential_from_override_path(tmp_path, monkeypatch):
+    _clear_claude_code_env(monkeypatch)
+    cred_path = tmp_path / "claude-credentials.json"
+    cred_path.write_text(
+        json.dumps(
+            {
+                "claudeAiOauth": {
+                    "accessToken": "sk-ant-oat01-test",
+                    "refreshToken": "sk-ant-ort01-test",
+                    "expiresAt": 4_102_444_800_000,
+                }
+            }
+        )
+    )
+    monkeypatch.setenv("CLAUDE_CODE_CREDENTIALS_PATH", str(cred_path))
+
+    cred = load_claude_code_credential()
+
+    assert cred is not None
+    assert cred.access_token == "sk-ant-oat01-test"
+    assert cred.refresh_token == "sk-ant-ort01-test"
+    assert cred.source == "claude-cli-file"
+
+
+def test_load_claude_code_credential_ignores_directory_path(tmp_path, monkeypatch):
+    _clear_claude_code_env(monkeypatch)
+    cred_dir = tmp_path / "claude-creds-dir"
+    cred_dir.mkdir()
+    monkeypatch.setenv("CLAUDE_CODE_CREDENTIALS_PATH", str(cred_dir))
+
+    assert load_claude_code_credential() is None
+
+
+def test_load_claude_code_credential_falls_back_to_default_file_when_override_is_invalid(tmp_path, monkeypatch):
+    _clear_claude_code_env(monkeypatch)
+    monkeypatch.setenv("HOME", str(tmp_path))
+
+    cred_dir = tmp_path / "claude-creds-dir"
+    cred_dir.mkdir()
+    monkeypatch.setenv("CLAUDE_CODE_CREDENTIALS_PATH", str(cred_dir))
+
+    default_path = tmp_path / ".claude" / ".credentials.json"
+    default_path.parent.mkdir()
+    default_path.write_text(
+        json.dumps(
+            {
+                "claudeAiOauth": {
+                    "accessToken": "sk-ant-oat01-default",
+                    "refreshToken": "sk-ant-ort01-default",
+                    "expiresAt": 4_102_444_800_000,
+                }
+            }
+        )
+    )
+
+    cred = load_claude_code_credential()
+
+    assert cred is not None
+    assert cred.access_token == "sk-ant-oat01-default"
+    assert cred.refresh_token == "sk-ant-ort01-default"
+    assert cred.source == "claude-cli-file"
+
+
+def test_load_codex_cli_credential_supports_nested_tokens_shape(tmp_path, monkeypatch):
+    auth_path = tmp_path / "auth.json"
+    auth_path.write_text(
+        json.dumps(
+            {
+                "tokens": {
+                    "access_token": "codex-access-token",
+                    "account_id": "acct_123",
+                }
+            }
+        )
+    )
+    monkeypatch.setenv("CODEX_AUTH_PATH", str(auth_path))
+
+    cred = load_codex_cli_credential()
+
+    assert cred is not None
+    assert cred.access_token == "codex-access-token"
+    assert cred.account_id == "acct_123"
+    assert cred.source == "codex-cli"
+
+
+def test_load_codex_cli_credential_supports_legacy_top_level_shape(tmp_path, monkeypatch):
+    auth_path = tmp_path / "auth.json"
+    auth_path.write_text(json.dumps({"access_token": "legacy-access-token"}))
+    monkeypatch.setenv("CODEX_AUTH_PATH", str(auth_path))
+
+    cred = load_codex_cli_credential()
+
+    assert cred is not None
+    assert cred.access_token == "legacy-access-token"
+    assert cred.account_id == ""

backend/tests/test_model_factory.py

@@ -9,6 +9,7 @@ from deerflow.config.app_config import AppConfig
 from deerflow.config.model_config import ModelConfig
 from deerflow.config.sandbox_config import SandboxConfig
 from deerflow.models import factory as factory_module
+from deerflow.models import openai_codex_provider as codex_provider_module
 
 # ---------------------------------------------------------------------------
 # Helpers
@@ -30,6 +31,7 @@ def _make_model(
     supports_reasoning_effort: bool = False,
     when_thinking_enabled: dict | None = None,
     thinking: dict | None = None,
+    max_tokens: int | None = None,
 ) -> ModelConfig:
     return ModelConfig(
         name=name,
@@ -37,6 +39,7 @@ def _make_model(
         description=None,
         use=use,
         model=name,
+        max_tokens=max_tokens,
         supports_thinking=supports_thinking,
         supports_reasoning_effort=supports_reasoning_effort,
         when_thinking_enabled=when_thinking_enabled,
@@ -500,6 +503,96 @@ def test_openai_compatible_provider_multiple_models(monkeypatch):
     assert captured.get("model") == "MiniMax-M2.5-highspeed"
 
 
+# ---------------------------------------------------------------------------
+# Codex provider reasoning_effort mapping
+# ---------------------------------------------------------------------------
+
+
+class FakeCodexChatModel(FakeChatModel):
+    pass
+
+
+def test_codex_provider_disables_reasoning_when_thinking_disabled(monkeypatch):
+    cfg = _make_app_config(
+        [
+            _make_model(
+                "codex",
+                use="deerflow.models.openai_codex_provider:CodexChatModel",
+                supports_thinking=True,
+                supports_reasoning_effort=True,
+            )
+        ]
+    )
+    _patch_factory(monkeypatch, cfg, model_class=FakeCodexChatModel)
+    monkeypatch.setattr(codex_provider_module, "CodexChatModel", FakeCodexChatModel)
+
+    FakeChatModel.captured_kwargs = {}
+    factory_module.create_chat_model(name="codex", thinking_enabled=False)
+
+    assert FakeChatModel.captured_kwargs.get("reasoning_effort") == "none"
+
+
+def test_codex_provider_preserves_explicit_reasoning_effort(monkeypatch):
+    cfg = _make_app_config(
+        [
+            _make_model(
+                "codex",
+                use="deerflow.models.openai_codex_provider:CodexChatModel",
+                supports_thinking=True,
+                supports_reasoning_effort=True,
+            )
+        ]
+    )
+    _patch_factory(monkeypatch, cfg, model_class=FakeCodexChatModel)
+    monkeypatch.setattr(codex_provider_module, "CodexChatModel", FakeCodexChatModel)
+
+    FakeChatModel.captured_kwargs = {}
+    factory_module.create_chat_model(name="codex", thinking_enabled=True, reasoning_effort="high")
+
+    assert FakeChatModel.captured_kwargs.get("reasoning_effort") == "high"
+
+
+def test_codex_provider_defaults_reasoning_effort_to_medium(monkeypatch):
+    cfg = _make_app_config(
+        [
+            _make_model(
+                "codex",
+                use="deerflow.models.openai_codex_provider:CodexChatModel",
+                supports_thinking=True,
+                supports_reasoning_effort=True,
+            )
+        ]
+    )
+    _patch_factory(monkeypatch, cfg, model_class=FakeCodexChatModel)
+    monkeypatch.setattr(codex_provider_module, "CodexChatModel", FakeCodexChatModel)
+
+    FakeChatModel.captured_kwargs = {}
+    factory_module.create_chat_model(name="codex", thinking_enabled=True)
+
+    assert FakeChatModel.captured_kwargs.get("reasoning_effort") == "medium"
+
+
+def test_codex_provider_strips_unsupported_max_tokens(monkeypatch):
+    cfg = _make_app_config(
+        [
+            _make_model(
+                "codex",
+                use="deerflow.models.openai_codex_provider:CodexChatModel",
+                supports_thinking=True,
+                supports_reasoning_effort=True,
+                max_tokens=4096,
+            )
+        ]
+    )
+    _patch_factory(monkeypatch, cfg, model_class=FakeCodexChatModel)
+    monkeypatch.setattr(codex_provider_module, "CodexChatModel", FakeCodexChatModel)
+
+    FakeChatModel.captured_kwargs = {}
+    factory_module.create_chat_model(name="codex", thinking_enabled=True)
+
+    assert "max_tokens" not in FakeChatModel.captured_kwargs
+    
+    
 def test_openai_responses_api_settings_are_passed_to_chatopenai(monkeypatch):
     model = ModelConfig(
         name="gpt-5-responses",