initial commit

.env.example

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+# ==================== NVIDIA NIM Config ====================
+NVIDIA_NIM_API_KEY="nvapi-..."
+NVIDIA_NIM_BASE_URL="https://integrate.api.nvidia.com/v1"
+
+# ==================== Model Mapping ====================
+# Claude Sonnet/Opus -> BIG_MODEL, Claude Haiku -> SMALL_MODEL
+BIG_MODEL="moonshotai/kimi-k2-thinking"
+SMALL_MODEL="moonshotai/kimi-k2-thinking"
+
+# ==================== Rate Limiting ====================
+NVIDIA_NIM_RATE_LIMIT=40        # Requests per window
+NVIDIA_NIM_RATE_WINDOW=60       # Window in seconds (40 req/min = free tier)
+
+# ==================== Fast Prefix Detection ====================
+# Skip LLM call for Claude Code bash command prefix classification
+FAST_PREFIX_DETECTION=true
+
+# ==================== Optional NIM Parameters ====================
+# These are passed to the NIM API if set
+
+# Core parameters
+NVIDIA_NIM_TEMPERATURE=1.0
+NVIDIA_NIM_TOP_P=1.0
+NVIDIA_NIM_TOP_K=-1
+NVIDIA_NIM_MAX_TOKENS=81920
+NVIDIA_NIM_PRESENCE_PENALTY=0.0
+NVIDIA_NIM_FREQUENCY_PENALTY=0.0
+
+# Advanced parameters
+NVIDIA_NIM_MIN_P=0.0
+NVIDIA_NIM_REPETITION_PENALTY=1.0
+NVIDIA_NIM_SEED=
+NVIDIA_NIM_STOP=
+
+# Flag parameters (true/false)
+NVIDIA_NIM_PARALLEL_TOOL_CALLS=true
+NVIDIA_NIM_RETURN_TOKENS_AS_TOKEN_IDS=false
+NVIDIA_NIM_INCLUDE_STOP_STR_IN_OUTPUT=false
+NVIDIA_NIM_IGNORE_EOS=false
+
+NVIDIA_NIM_MIN_TOKENS=0
+NVIDIA_NIM_CHAT_TEMPLATE=""
+NVIDIA_NIM_REQUEST_ID=""
+
+# Thinking/Reasoning Parameters (passed via extra_body)
+NVIDIA_NIM_REASONING_EFFORT=high
+NVIDIA_NIM_INCLUDE_REASONING=true

.gitignore

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+# Environment variables
+.env
+
+# Python
+__pycache__/
+.mypy_cache/
+.pytest_cache/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual environments
+venv/
+env/
+ENV/
+.venv/
+
+# Logs
+*.log
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+.claude
+.serena

.python-version

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+3.10

README.md

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+# cc-nim
+
+Use **Claude Code CLI for free** with NVIDIA NIM's free unlimited 40 reqs/s API. This lightweight proxy converts Claude Code's Anthropic API requests to NVIDIA NIM format.
+
+## Quick Start
+
+### 1. Get Your Free NVIDIA API Key
+
+1. Visit [build.nvidia.com/settings/api-keys](https://build.nvidia.com/settings/api-keys)
+2. Sign in or create a free NVIDIA account
+3. Generate a new API key (`nvapi-...`)
+
+### 2. Install & Configure
+
+```bash
+git clone https://github.com/Alishahryar1/cc-nim.git
+cd cc-nim
+
+cp .env.example .env
+```
+
+Edit `.env`:
+
+```dotenv
+NVIDIA_NIM_API_KEY=nvapi-your-key-here
+BIG_MODEL=moonshotai/kimi-k2-instruct
+SMALL_MODEL=moonshotai/kimi-k2-instruct
+```
+
+### 3. Run
+
+**Terminal 1 - Start the proxy:**
+
+```bash
+uv run uvicorn server:app --host 0.0.0.0 --port 8082
+```
+
+**Terminal 2 - Run Claude Code:**
+
+```bash
+ANTHROPIC_BASE_URL=http://localhost:8082 claude
+```
+
+That's it! Claude Code now uses NVIDIA NIM for free.
+
+## Available Models
+
+See [`nvidia_nim_models.json`](nvidia_nim_models.json) for the full list of supported models.
+
+Popular choices:
+
+- `moonshotai/kimi-k2.5`
+- `z-ai/glm4.7`
+- `minimaxai/minimax-m2.1`
+- `mistralai/devstral-2-123b-instruct-2512`
+
+Browse all models at [build.nvidia.com](https://build.nvidia.com/explore/discover)
+
+### Updating the Model List
+
+To update `nvidia_nim_models.json` with the latest models from NVIDIA NIM, run the following command:
+```bash
+curl "https://integrate.api.nvidia.com/v1/models" > nvidia_nim_models.json
+```
+
+## Configuration
+
+| Variable                 | Description                    | Default                               |
+| ------------------------ | ------------------------------ | ------------------------------------- |
+| `NVIDIA_NIM_API_KEY`     | Your NVIDIA API key            | required                              |
+| `BIG_MODEL`              | Model for Sonnet/Opus requests | `moonshotai/kimi-k2-thinking`         |
+| `SMALL_MODEL`            | Model for Haiku requests       | `moonshotai/kimi-k2-thinking`         |
+| `NVIDIA_NIM_BASE_URL`    | NIM endpoint                   | `https://integrate.api.nvidia.com/v1` |
+| `NVIDIA_NIM_RATE_LIMIT`  | Requests per window            | `40`                                  |
+| `NVIDIA_NIM_RATE_WINDOW` | Rate limit window (seconds)    | `60`                                  |
+
+See [`.env.example`](.env.example) for all supported parameters.
+
+## Adding Your Own Provider
+
+Extend `BaseProvider` in `providers/` to add support for other APIs:
+
+```python
+from providers.base import BaseProvider, ProviderConfig
+
+class MyProvider(BaseProvider):
+    async def complete(self, request):
+        # Make API call, return raw JSON
+        pass
+
+    async def stream_response(self, request, input_tokens=0):
+        # Yield Anthropic SSE format events
+        pass
+
+    def convert_response(self, response_json, original_request):
+        # Convert to Anthropic response format
+        pass
+```

providers/__init__.py

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+"""Providers package - implement your own provider by extending BaseProvider."""
+
+from .base import BaseProvider, ProviderConfig
+from .nvidia_nim import NvidiaNimProvider
+
+__all__ = ["BaseProvider", "ProviderConfig", "NvidiaNimProvider"]

providers/base.py

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+"""Base provider interface - extend this to implement your own provider."""
+
+from abc import ABC, abstractmethod
+from typing import Any, AsyncIterator, Optional
+from pydantic import BaseModel
+
+
+class ProviderConfig(BaseModel):
+    """Configuration for a provider."""
+    api_key: str
+    base_url: Optional[str] = None
+    rate_limit: Optional[int] = None
+    rate_window: int = 60
+
+
+class BaseProvider(ABC):
+    """Base class for all providers. Extend this to add your own."""
+
+    def __init__(self, config: ProviderConfig):
+        self.config = config
+
+    @abstractmethod
+    async def complete(self, request: Any) -> dict:
+        """Make a non-streaming completion request. Returns raw JSON response."""
+        pass
+
+    @abstractmethod
+    async def stream_response(self, request: Any, input_tokens: int = 0) -> AsyncIterator[str]:
+        """Stream response in Anthropic SSE format."""
+        pass
+
+    @abstractmethod
+    def convert_response(self, response_json: dict, original_request: Any) -> Any:
+        """Convert provider response to Anthropic format."""
+        pass

providers/nvidia_nim.py

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+"""NVIDIA NIM provider - converts Anthropic format to OpenAI format for NIM."""
+
+import logging
+import os
+import json
+import uuid
+from typing import Dict, Any, AsyncIterator
+
+import httpx
+
+from .base import BaseProvider, ProviderConfig
+from .utils import (
+    SlidingWindowRateLimiter,
+    SSEBuilder,
+    map_stop_reason,
+    ThinkTagParser,
+    HeuristicToolParser,
+    ContentType,
+    extract_think_content,
+    extract_reasoning_from_delta,
+    AnthropicToOpenAIConverter,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class NvidiaNimProvider(BaseProvider):
+    """NVIDIA NIM provider using direct httpx requests."""
+
+    def __init__(self, config: ProviderConfig):
+        super().__init__(config)
+        self._api_key = config.api_key or os.getenv("NVIDIA_NIM_API_KEY", "")
+        self._base_url = (
+            config.base_url
+            or os.getenv("NVIDIA_NIM_BASE_URL", "https://integrate.api.nvidia.com/v1")
+        ).rstrip("/")
+        self._nim_params = self._load_nim_params()
+        self._rate_limiter = SlidingWindowRateLimiter(
+            rate_limit=config.rate_limit or 40,
+            window_seconds=config.rate_window or 60,
+        )
+        self._client = httpx.AsyncClient(
+            timeout=httpx.Timeout(300.0, connect=30.0, read=60.0),
+            limits=httpx.Limits(max_connections=100, max_keepalive_connections=20),
+        )
+
+    def _load_nim_params(self) -> Dict[str, Any]:
+        """Load NIM-specific parameters from environment."""
+        params: Dict[str, Any] = {}
+        if val := os.getenv("NVIDIA_NIM_TEMPERATURE"):
+            params["temperature"] = float(val)
+        if val := os.getenv("NVIDIA_NIM_TOP_P"):
+            params["top_p"] = float(val)
+        if val := os.getenv("NVIDIA_NIM_MAX_TOKENS"):
+            params["max_tokens"] = int(val)
+        return params
+
+    def _build_request_body(self, request_data: Any, stream: bool = False) -> dict:
+        """Build OpenAI-format request body from Anthropic request."""
+        messages = AnthropicToOpenAIConverter.convert_messages(request_data.messages)
+
+        # Add system prompt
+        if request_data.system:
+            system_msg = AnthropicToOpenAIConverter.convert_system_prompt(
+                request_data.system
+            )
+            if system_msg:
+                messages.insert(0, system_msg)
+
+        body = {
+            "model": request_data.model,
+            "messages": messages,
+            "max_tokens": request_data.max_tokens,
+            "stream": stream,
+        }
+
+        if request_data.temperature is not None:
+            body["temperature"] = request_data.temperature
+        if request_data.top_p is not None:
+            body["top_p"] = request_data.top_p
+        if request_data.stop_sequences:
+            body["stop"] = request_data.stop_sequences
+        if request_data.tools:
+            body["tools"] = AnthropicToOpenAIConverter.convert_tools(request_data.tools)
+
+        # Handle thinking/reasoning mode
+        extra_body = request_data.extra_body.copy() if request_data.extra_body else {}
+        if request_data.thinking and getattr(request_data.thinking, "enabled", True):
+            extra_body.setdefault("thinking", {"type": "enabled"})
+            extra_body.setdefault("reasoning_split", True)
+
+        body.update(extra_body)
+
+        # Apply NIM defaults
+        for key, val in self._nim_params.items():
+            if key not in body:
+                body[key] = val
+
+        return body
+
+    async def stream_response(
+        self, request: Any, input_tokens: int = 0
+    ) -> AsyncIterator[str]:
+        """Stream response in Anthropic SSE format."""
+        await self._rate_limiter.acquire()
+
+        body = self._build_request_body(request, stream=True)
+        headers = {
+            "Authorization": f"Bearer {self._api_key}",
+            "Content-Type": "application/json",
+            "Accept": "text/event-stream",
+        }
+
+        message_id = f"msg_{uuid.uuid4()}"
+        sse = SSEBuilder(message_id, request.model, input_tokens)
+        think_parser = ThinkTagParser()
+        heuristic_parser = HeuristicToolParser()
+
+        yield sse.message_start()
+
+        finish_reason = None
+        usage_info = None
+        error_occurred = False
+        error_message = ""
+
+        try:
+            async for chunk_json in self._stream_chunks(headers, body):
+                # Process metadata
+                if "usage" in chunk_json:
+                    usage_info = chunk_json["usage"]
+
+                if "choices" not in chunk_json or not chunk_json["choices"]:
+                    continue
+
+                choice = chunk_json["choices"][0]
+                delta = choice.get("delta", {})
+
+                if choice.get("finish_reason"):
+                    finish_reason = choice["finish_reason"]
+
+                # Handle reasoning content from delta
+                reasoning = extract_reasoning_from_delta(delta)
+                if reasoning:
+                    for event in sse.ensure_thinking_block():
+                        yield event
+                    yield sse.emit_thinking_delta(reasoning)
+
+                # Handle text content with think tag and heuristic tool parsing
+                if delta.get("content"):
+                    for chunk in think_parser.feed(delta["content"]):
+                        if chunk.type == ContentType.THINKING:
+                            for event in sse.ensure_thinking_block():
+                                yield event
+                            yield sse.emit_thinking_delta(chunk.content)
+                        else:
+                            # Pass non-thinking text through heuristic tool parser
+                            filtered_text, detected_tools = heuristic_parser.feed(
+                                chunk.content
+                            )
+
+                            # Emit filtered text if any
+                            if filtered_text:
+                                for event in sse.ensure_text_block():
+                                    yield event
+                                yield sse.emit_text_delta(filtered_text)
+
+                            # Emit detected heuristic tool calls
+                            for tool_use in detected_tools:
+                                for event in sse.close_content_blocks():
+                                    yield event
+
+                                block_idx = sse.blocks.allocate_index()
+                                yield sse.content_block_start(
+                                    block_idx,
+                                    "tool_use",
+                                    id=tool_use["id"],
+                                    name=tool_use["name"],
+                                )
+                                yield sse.content_block_delta(
+                                    block_idx,
+                                    "input_json_delta",
+                                    json.dumps(tool_use["input"]),
+                                )
+                                yield sse.content_block_stop(block_idx)
+
+                # Handle native tool calls
+                if delta.get("tool_calls"):
+                    for event in sse.close_content_blocks():
+                        yield event
+                    for tc in delta["tool_calls"]:
+                        for event in self._process_tool_call(tc, sse):
+                            yield event
+
+        except Exception as e:
+            logger.error(f"Streaming error: {e}")
+            error_occurred = True
+            error_message = str(e)
+
+        # Handle errors
+        if error_occurred:
+            for event in sse.emit_error(error_message):
+                yield event
+
+        # Flush remaining content from parsers
+        remaining = think_parser.flush()
+        if remaining:
+            if remaining.type == ContentType.THINKING:
+                for event in sse.ensure_thinking_block():
+                    yield event
+                yield sse.emit_thinking_delta(remaining.content)
+            else:
+                for event in sse.ensure_text_block():
+                    yield event
+                yield sse.emit_text_delta(remaining.content)
+
+        # Flush heuristic tool calls
+        for tool_use in heuristic_parser.flush():
+            for event in sse.close_content_blocks():
+                yield event
+
+            block_idx = sse.blocks.allocate_index()
+            yield sse.content_block_start(
+                block_idx,
+                "tool_use",
+                id=tool_use["id"],
+                name=tool_use["name"],
+            )
+            yield sse.content_block_delta(
+                block_idx,
+                "input_json_delta",
+                json.dumps(tool_use["input"]),
+            )
+            yield sse.content_block_stop(block_idx)
+
+        # Close all blocks
+        for event in sse.close_all_blocks():
+            yield event
+
+        # Final events
+        output_tokens = (
+            usage_info.get("completion_tokens", 0)
+            if usage_info
+            else sse.estimate_output_tokens()
+        )
+        yield sse.message_delta(map_stop_reason(finish_reason), output_tokens)
+        yield sse.message_stop()
+        yield sse.done()
+
+    async def _stream_chunks(self, headers: dict, body: dict):
+        """Generator that yields parsed SSE data chunks from the API."""
+        async with self._client.stream(
+            "POST", f"{self._base_url}/chat/completions", headers=headers, json=body
+        ) as response:
+            if response.status_code != 200:
+                error_text = await response.aread()
+                raise Exception(
+                    f"API error {response.status_code}: {error_text.decode('utf-8', errors='replace')}"
+                )
+
+            buffer = ""
+            async for chunk in response.aiter_text():
+                buffer += chunk
+                while "\n\n" in buffer:
+                    event_end = buffer.index("\n\n")
+                    event_data = buffer[:event_end]
+                    buffer = buffer[event_end + 2 :]
+
+                    parsed = self._parse_sse_event(event_data)
+                    if parsed is not None:
+                        yield parsed
+
+            # Flush remaining non-empty buffer
+            if buffer.strip():
+                parsed = self._parse_sse_event(buffer)
+                if parsed is not None:
+                    yield parsed
+
+    def _parse_sse_event(self, event_data: str) -> Any:
+        """Parse a single SSE event, return None if invalid/done."""
+        if not event_data.strip():
+            return None
+
+        for line in event_data.splitlines():
+            line = line.strip()
+            if line.startswith("data:"):
+                data_content = line[5:].lstrip()
+                if data_content == "[DONE]":
+                    return None
+                try:
+                    return json.loads(data_content)
+                except json.JSONDecodeError:
+                    logger.debug(f"JSON decode failed for SSE data: {data_content}")
+                    return None
+        return None
+
+    def _process_tool_call(self, tc: dict, sse: SSEBuilder):
+        """Process a single tool call delta and yield SSE events."""
+        tc_index = tc.get("index", 0)
+        if tc_index < 0:
+            tc_index = len(sse.blocks.tool_indices)
+
+        # Update accumulated name if present
+        fn_delta = tc.get("function", {})
+        if fn_delta.get("name") is not None:
+            sse.blocks.tool_names[tc_index] = (
+                sse.blocks.tool_names.get(tc_index, "") + fn_delta["name"]
+            )
+
+        # Check if we should start the tool block
+        if tc_index not in sse.blocks.tool_indices:
+            name = sse.blocks.tool_names.get(tc_index, "")
+            # Only start if name is non-empty or we have an ID (start of tool call)
+            if name or tc.get("id"):
+                tool_id = tc.get("id") or f"tool_{uuid.uuid4()}"
+                yield sse.start_tool_block(tc_index, tool_id, name)
+                sse.blocks.tool_started[tc_index] = True
+        elif not sse.blocks.tool_started.get(tc_index) and sse.blocks.tool_names.get(
+            tc_index
+        ):
+            # Block index exists (due to ID in previous chunk) but not started due to empty name
+            tool_id = f"tool_{uuid.uuid4()}"  # Should ideally reuse ID if we saved it
+            name = sse.blocks.tool_names[tc_index]
+            yield sse.start_tool_block(tc_index, tool_id, name)
+            sse.blocks.tool_started[tc_index] = True
+
+        args = fn_delta.get("arguments", "")
+        if args:
+            # Ensure block is started before emitting args (with a fallback name if still empty)
+            if not sse.blocks.tool_started.get(tc_index):
+                tool_id = tc.get("id") or f"tool_{uuid.uuid4()}"
+                name = sse.blocks.tool_names.get(tc_index, "tool_call") or "tool_call"
+                yield sse.start_tool_block(tc_index, tool_id, name)
+                sse.blocks.tool_started[tc_index] = True
+
+            yield sse.emit_tool_delta(tc_index, args)
+
+    async def complete(self, request: Any) -> dict:
+        """Make a non-streaming completion request."""
+        await self._rate_limiter.acquire()
+
+        body = self._build_request_body(request, stream=False)
+        headers = {
+            "Authorization": f"Bearer {self._api_key}",
+            "Content-Type": "application/json",
+        }
+
+        response = await self._client.post(
+            f"{self._base_url}/chat/completions", headers=headers, json=body
+        )
+        response.raise_for_status()
+        return response.json()
+
+    def convert_response(self, response_json: dict, original_request: Any) -> dict:
+        """Convert OpenAI response to Anthropic format."""
+        choice = response_json["choices"][0]
+        message = choice["message"]
+        content = []
+
+        # Extract reasoning from various sources
+        reasoning = message.get("reasoning_content")
+        if not reasoning:
+            reasoning_details = message.get("reasoning_details")
+            if reasoning_details and isinstance(reasoning_details, list):
+                reasoning = "\n".join(
+                    item.get("text", "")
+                    for item in reasoning_details
+                    if isinstance(item, dict)
+                )
+
+        if reasoning:
+            content.append({"type": "thinking", "thinking": reasoning})
+
+        # Extract text content (with think tag handling)
+        if message.get("content"):
+            raw_content = message["content"]
+            if isinstance(raw_content, str):
+                if not reasoning:
+                    think_content, raw_content = extract_think_content(raw_content)
+                    if think_content:
+                        content.append({"type": "thinking", "thinking": think_content})
+                if raw_content:
+                    content.append({"type": "text", "text": raw_content})
+            elif isinstance(raw_content, list):
+                for item in raw_content:
+                    if isinstance(item, dict) and item.get("type") == "text":
+                        content.append(item)
+
+        # Extract tool calls
+        if message.get("tool_calls"):
+            for tc in message["tool_calls"]:
+                try:
+                    args = json.loads(tc["function"]["arguments"])
+                except Exception:
+                    args = tc["function"].get("arguments", {})
+                content.append(
+                    {
+                        "type": "tool_use",
+                        "id": tc["id"],
+                        "name": tc["function"]["name"],
+                        "input": args,
+                    }
+                )
+
+        if not content:
+            content.append({"type": "text", "text": ""})
+            # Mistral models in NIM (like Devstral) require content to be non-empty
+            # even in sequences of messages.
+            content.append({"type": "text", "text": " "})
+
+        usage = response_json.get("usage", {})
+
+        return {
+            "id": response_json.get("id", f"msg_{uuid.uuid4()}"),
+            "type": "message",
+            "role": "assistant",
+            "model": original_request.model,
+            "content": content,
+            "stop_reason": map_stop_reason(choice.get("finish_reason")),
+            "stop_sequence": None,
+            "usage": {
+                "input_tokens": usage.get("prompt_tokens", 0),
+                "output_tokens": usage.get("completion_tokens", 0),
+                "cache_creation_input_tokens": 0,
+                "cache_read_input_tokens": 0,
+            },
+        }

providers/utils/__init__.py

@@ -0,0 +1,33 @@
+"""Utility modules for providers."""
+
+from .rate_limiter import SlidingWindowRateLimiter
+from .sse_builder import SSEBuilder, ContentBlockManager, map_stop_reason
+from .think_parser import (
+    ThinkTagParser,
+    ContentType,
+    ContentChunk,
+    extract_think_content,
+    extract_reasoning_from_delta,
+)
+from .heuristic_tool_parser import HeuristicToolParser
+from .message_converter import (
+    AnthropicToOpenAIConverter,
+    get_block_attr,
+    get_block_type,
+)
+
+__all__ = [
+    "SlidingWindowRateLimiter",
+    "SSEBuilder",
+    "ContentBlockManager",
+    "map_stop_reason",
+    "ThinkTagParser",
+    "HeuristicToolParser",
+    "ContentType",
+    "ContentChunk",
+    "extract_think_content",
+    "extract_reasoning_from_delta",
+    "AnthropicToOpenAIConverter",
+    "get_block_attr",
+    "get_block_type",
+]

providers/utils/heuristic_tool_parser.py

@@ -0,0 +1,174 @@
+import re
+import logging
+import uuid
+from enum import Enum
+from typing import List, Dict, Any, Tuple
+
+logger = logging.getLogger(__name__)
+
+
+class ParserState(Enum):
+    TEXT = 1
+    MATCHING_FUNCTION = 2
+    PARSING_PARAMETERS = 3
+
+
+class HeuristicToolParser:
+    """
+    Stateful parser that detects raw text tool calls in the format:
+    ● <function=Name><parameter=key>value</parameter>...
+
+    This is used as a fallback for models that emit tool calls as text
+    instead of using the structured API.
+    """
+
+    def __init__(self):
+        self.state = ParserState.TEXT
+        self.buffer = ""
+        self.current_tool_id = None
+        self.current_function_name = None
+        self.current_parameters = {}
+
+        # Regex patterns
+        self.func_start_pattern = re.compile(r"●\s*<function=([^>]+)>")
+        self.param_pattern = re.compile(
+            r"<parameter=([^>]+)>(.*?)(?:</parameter>|$)", re.DOTALL
+        )
+
+    def feed(self, text: str) -> Tuple[str, List[Dict[str, Any]]]:
+        """
+        Feed text into the parser.
+        Returns a tuple of (filtered_text, detected_tool_calls).
+
+        filtered_text: Text that should be passed through as normal message content.
+        detected_tool_calls: List of Anthropic-format tool_use blocks.
+        """
+        self.buffer += text
+        detected_tools = []
+        filtered_output = ""
+
+        while True:
+            if self.state == ParserState.TEXT:
+                # Look for the trigger character
+                if "●" in self.buffer:
+                    idx = self.buffer.find("●")
+                    filtered_output += self.buffer[:idx]
+                    self.buffer = self.buffer[idx:]
+                    self.state = ParserState.MATCHING_FUNCTION
+                else:
+                    filtered_output += self.buffer
+                    self.buffer = ""
+                    break
+
+            if self.state == ParserState.MATCHING_FUNCTION:
+                # We need enough buffer to match the function tag
+                # e.g. "● <function=Grep>"
+                match = self.func_start_pattern.search(self.buffer)
+                if match:
+                    self.current_function_name = match.group(1).strip()
+                    self.current_tool_id = f"toolu_heuristic_{uuid.uuid4().hex[:8]}"
+                    self.current_parameters = {}
+
+                    # Consume the function start from buffer
+                    self.buffer = self.buffer[match.end() :]
+                    self.state = ParserState.PARSING_PARAMETERS
+                    logger.debug(
+                        f"Heuristic bypass: Detected start of tool call '{self.current_function_name}'"
+                    )
+                else:
+                    # If we have "●" but not the full tag yet, wait for more data
+                    # Unless the buffer has grown too large without a match
+                    if len(self.buffer) > 100:
+                        # Probably not a tool call, treat as text
+                        filtered_output += self.buffer[0]
+                        self.buffer = self.buffer[1:]
+                        self.state = ParserState.TEXT
+                    else:
+                        break
+
+            if self.state == ParserState.PARSING_PARAMETERS:
+                # Look for parameters. We look for </parameter> to know a param is complete.
+                # Or wait for another <parameter or the end of the text if it seems complete.
+
+                # If we see a newline followed by anything other than <parameter or spaces,
+                # we might be done with the tool call.
+
+                finished_tool_call = False
+
+                # Check if we have any complete parameters
+                while True:
+                    param_match = self.param_pattern.search(self.buffer)
+                    if param_match and "</parameter>" in param_match.group(0):
+                        key = param_match.group(1).strip()
+                        val = param_match.group(2).strip()
+                        self.current_parameters[key] = val
+                        self.buffer = self.buffer[param_match.end() :]
+                    else:
+                        break
+
+                # Heuristic for completion:
+                # 1. We have at least one param and we see a character that doesn't belong to the format
+                # 2. Significant pause (not handled here, handled by caller via flush if needed)
+                # 3. Another ● character (start of NEXT tool call)
+
+                if "●" in self.buffer:
+                    # Next tool call starting or something else, close current
+                    finished_tool_call = True
+                elif (
+                    len(self.buffer) > 0
+                    and not self.buffer.strip().startswith("<")
+                    and not self.buffer.lstrip().startswith("<")
+                ):
+                    # We have text that doesn't look like a tag, and we already parsed some or are in param state
+                    # Let's see if we have trailing param starts
+                    if "<parameter=" not in self.buffer:
+                        finished_tool_call = True
+
+                if finished_tool_call:
+                    # Emit the tool call
+                    detected_tools.append(
+                        {
+                            "type": "tool_use",
+                            "id": self.current_tool_id,
+                            "name": self.current_function_name,
+                            "input": self.current_parameters,
+                        }
+                    )
+                    logger.debug(
+                        f"Heuristic bypass: Emitting tool call '{self.current_function_name}' with {len(self.current_parameters)} params"
+                    )
+                    self.state = ParserState.TEXT
+                    # Continue loop to process remaining buffer
+                else:
+                    break
+
+        return filtered_output, detected_tools
+
+    def flush(self) -> List[Dict[str, Any]]:
+        """
+        Flush any remaining tool calls in the buffer.
+        """
+        detected_tools = []
+        if self.state == ParserState.PARSING_PARAMETERS:
+            # Try to extract any partial parameters remaining in buffer
+            # Even without </parameter>
+            partial_matches = re.finditer(
+                r"<parameter=([^>]+)>(.*)$", self.buffer, re.DOTALL
+            )
+            for m in partial_matches:
+                key = m.group(1).strip()
+                val = m.group(2).strip()
+                self.current_parameters[key] = val
+
+            detected_tools.append(
+                {
+                    "type": "tool_use",
+                    "id": self.current_tool_id,
+                    "name": self.current_function_name,
+                    "input": self.current_parameters,
+                }
+            )
+            self.state = ParserState.TEXT
+            self.buffer = ""
+
+        return detected_tools

providers/utils/message_converter.py

@@ -0,0 +1,165 @@
+"""Message and tool format converters."""
+
+import json
+from typing import Any, Dict, List, Optional
+
+
+def get_block_attr(block: Any, attr: str, default: Any = None) -> Any:
+    """Get attribute from object or dict."""
+    if hasattr(block, attr):
+        return getattr(block, attr)
+    if isinstance(block, dict):
+        return block.get(attr, default)
+    return default
+
+
+def get_block_type(block: Any) -> Optional[str]:
+    """Get block type from object or dict."""
+    return get_block_attr(block, "type")
+
+
+class AnthropicToOpenAIConverter:
+    """Converts Anthropic message format to OpenAI format."""
+
+    @staticmethod
+    def convert_messages(messages: List[Any]) -> List[Dict[str, Any]]:
+        """Convert a list of Anthropic messages to OpenAI format."""
+        result = []
+
+        for msg in messages:
+            role = msg.role
+            content = msg.content
+
+            if isinstance(content, str):
+                result.append({"role": role, "content": content})
+            elif isinstance(content, list):
+                if role == "assistant":
+                    result.extend(
+                        AnthropicToOpenAIConverter._convert_assistant_message(content)
+                    )
+                elif role == "user":
+                    result.extend(
+                        AnthropicToOpenAIConverter._convert_user_message(content)
+                    )
+            else:
+                result.append({"role": role, "content": str(content)})
+
+        return result
+
+    @staticmethod
+    def _convert_assistant_message(content: List[Any]) -> List[Dict[str, Any]]:
+        """Convert assistant message blocks."""
+        text_parts = []
+        tool_calls = []
+        reasoning_parts = []
+
+        for block in content:
+            block_type = get_block_type(block)
+
+            if block_type == "text":
+                text_parts.append(get_block_attr(block, "text", ""))
+            elif block_type == "thinking":
+                reasoning_parts.append(get_block_attr(block, "thinking", ""))
+            elif block_type == "tool_use":
+                tool_input = get_block_attr(block, "input", {})
+                tool_calls.append(
+                    {
+                        "id": get_block_attr(block, "id"),
+                        "type": "function",
+                        "function": {
+                            "name": get_block_attr(block, "name"),
+                            "arguments": json.dumps(tool_input)
+                            if isinstance(tool_input, dict)
+                            else str(tool_input),
+                        },
+                    }
+                )
+
+        # Merge everything into content for NIM/Mistral compatibility
+        # Anthropic 'thinking' blocks are converted to <thought> tags
+        actual_content = []
+        if reasoning_parts:
+            # Join reasoning parts and handle as a separate block
+            reasoning_str = "\n".join(reasoning_parts)
+            actual_content.append(f"<think>\n{reasoning_str}\n</think>")
+
+        if text_parts:
+            actual_content.append("\n".join(text_parts))
+
+        content_str = "\n\n".join(actual_content)
+
+        # Ensure content is never an empty string for assistant messages
+        # NIM (especially Mistral models) requires non-empty content if there are no tool calls
+        if not content_str and not tool_calls:
+            content_str = " "
+
+        msg: Dict[str, Any] = {
+            "role": "assistant",
+            "content": content_str,
+        }
+        if tool_calls:
+            msg["tool_calls"] = tool_calls
+
+        return [msg]
+
+    @staticmethod
+    def _convert_user_message(content: List[Any]) -> List[Dict[str, Any]]:
+        """Convert user message blocks (including tool results)."""
+        result = []
+        text_parts = []
+
+        for block in content:
+            block_type = get_block_type(block)
+
+            if block_type == "text":
+                text_parts.append(get_block_attr(block, "text", ""))
+            elif block_type == "tool_result":
+                tool_content = get_block_attr(block, "content", "")
+                if isinstance(tool_content, list):
+                    tool_content = "\n".join(
+                        item.get("text", str(item))
+                        if isinstance(item, dict)
+                        else str(item)
+                        for item in tool_content
+                    )
+                result.append(
+                    {
+                        "role": "tool",
+                        "tool_call_id": get_block_attr(block, "tool_use_id"),
+                        "content": str(tool_content) if tool_content else "",
+                    }
+                )
+
+        if text_parts:
+            result.append({"role": "user", "content": "\n".join(text_parts)})
+
+        return result
+
+    @staticmethod
+    def convert_tools(tools: List[Any]) -> List[Dict[str, Any]]:
+        """Convert Anthropic tools to OpenAI format."""
+        return [
+            {
+                "type": "function",
+                "function": {
+                    "name": tool.name,
+                    "description": tool.description or "",
+                    "parameters": tool.input_schema,
+                },
+            }
+            for tool in tools
+        ]
+
+    @staticmethod
+    def convert_system_prompt(system: Any) -> Optional[Dict[str, str]]:
+        """Convert Anthropic system prompt to OpenAI format."""
+        if isinstance(system, str):
+            return {"role": "system", "content": system}
+        elif isinstance(system, list):
+            text_parts = []
+            for block in system:
+                if get_block_type(block) == "text":
+                    text_parts.append(get_block_attr(block, "text", ""))
+            if text_parts:
+                return {"role": "system", "content": "\n\n".join(text_parts).strip()}
+        return None

providers/utils/rate_limiter.py

@@ -0,0 +1,72 @@
+"""Reusable async rate limiter with sliding window."""
+
+import asyncio
+import time
+import threading
+from collections import deque
+from typing import Deque
+
+
+class SlidingWindowRateLimiter:
+    """
+    Async rate limiter using sliding window algorithm.
+
+    Thread-safe for use across multiple async contexts.
+    """
+
+    def __init__(
+        self,
+        rate_limit: int = 40,
+        window_seconds: int = 60,
+        max_retries: int = 120,
+    ):
+        self.rate_limit = rate_limit
+        self.window_seconds = window_seconds
+        self.max_retries = max_retries
+        self._timestamps: Deque[float] = deque()
+        self._lock = threading.Condition()
+
+    async def acquire(self) -> None:
+        """
+        Acquire a rate limit slot, waiting if necessary.
+
+        Uses exponential backoff up to max_retries attempts.
+        """
+        for _ in range(self.max_retries):
+            now = time.time()
+
+            with self._lock:
+                # Remove expired timestamps
+                while self._timestamps and now - self._timestamps[0] > self.window_seconds:
+                    self._timestamps.popleft()
+
+                # Check if we can proceed
+                if len(self._timestamps) < self.rate_limit:
+                    self._timestamps.append(now)
+                    return
+
+                # Calculate wait time
+                wait_time = self._timestamps[0] + self.window_seconds - now
+
+            if wait_time <= 0:
+                continue
+
+            await asyncio.sleep(min(wait_time, 1.0))
+
+        # Fallback: allow request after max retries
+        with self._lock:
+            self._timestamps.append(time.time())
+
+    def reset(self) -> None:
+        """Clear all timestamps."""
+        with self._lock:
+            self._timestamps.clear()
+
+    @property
+    def current_count(self) -> int:
+        """Current number of requests in the window."""
+        now = time.time()
+        with self._lock:
+            while self._timestamps and now - self._timestamps[0] > self.window_seconds:
+                self._timestamps.popleft()
+            return len(self._timestamps)

providers/utils/sse_builder.py

@@ -0,0 +1,260 @@
+"""SSE event builder for Anthropic-format streaming responses."""
+
+import json
+from dataclasses import dataclass, field
+from typing import Optional, Dict, Any, Iterator, List
+
+
+# Map OpenAI finish_reason to Anthropic stop_reason
+STOP_REASON_MAP = {
+    "stop": "end_turn",
+    "length": "max_tokens",
+    "tool_calls": "tool_use",
+    "content_filter": "stop_sequence",
+}
+
+
+def map_stop_reason(openai_reason: Optional[str]) -> str:
+    """Map OpenAI finish_reason to Anthropic stop_reason."""
+    return (
+        STOP_REASON_MAP.get(openai_reason, "end_turn") if openai_reason else "end_turn"
+    )
+
+
+@dataclass
+class ContentBlockManager:
+    """Manages content block indices and state."""
+
+    next_index: int = 0
+    thinking_index: int = -1
+    text_index: int = -1
+    thinking_started: bool = False
+    text_started: bool = False
+    tool_indices: Dict[int, int] = field(default_factory=dict)
+    tool_contents: Dict[int, str] = field(default_factory=dict)
+    tool_names: Dict[int, str] = field(default_factory=dict)
+    tool_started: Dict[int, bool] = field(default_factory=dict)
+
+    def allocate_index(self) -> int:
+        """Allocate and return the next block index."""
+        idx = self.next_index
+        self.next_index += 1
+        return idx
+
+
+class SSEBuilder:
+    """Builder for Anthropic SSE streaming events."""
+
+    def __init__(self, message_id: str, model: str, input_tokens: int = 0):
+        self.message_id = message_id
+        self.model = model
+        self.input_tokens = input_tokens
+        self.blocks = ContentBlockManager()
+        self._accumulated_text = ""
+        self._accumulated_reasoning = ""
+
+    def _format_event(self, event_type: str, data: Dict[str, Any]) -> str:
+        """Format as SSE string."""
+        return f"event: {event_type}\ndata: {json.dumps(data)}\n\n"
+
+    # Message lifecycle events
+    def message_start(self) -> str:
+        """Generate message_start event."""
+        return self._format_event(
+            "message_start",
+            {
+                "type": "message_start",
+                "message": {
+                    "id": self.message_id,
+                    "type": "message",
+                    "role": "assistant",
+                    "content": [],
+                    "model": self.model,
+                    "stop_reason": None,
+                    "stop_sequence": None,
+                    "usage": {"input_tokens": self.input_tokens, "output_tokens": 1},
+                },
+            },
+        )
+
+    def message_delta(self, stop_reason: str, output_tokens: int) -> str:
+        """Generate message_delta event with stop reason."""
+        return self._format_event(
+            "message_delta",
+            {
+                "type": "message_delta",
+                "delta": {"stop_reason": stop_reason, "stop_sequence": None},
+                "usage": {"output_tokens": output_tokens},
+            },
+        )
+
+    def message_stop(self) -> str:
+        """Generate message_stop event."""
+        return self._format_event("message_stop", {"type": "message_stop"})
+
+    def done(self) -> str:
+        """Generate [DONE] marker."""
+        return "[DONE]\n\n"
+
+    # Content block events
+    def content_block_start(self, index: int, block_type: str, **kwargs) -> str:
+        """Generate content_block_start event."""
+        content_block: Dict[str, Any] = {"type": block_type}
+        if block_type == "thinking":
+            content_block["thinking"] = kwargs.get("thinking", "")
+        elif block_type == "text":
+            content_block["text"] = kwargs.get("text", "")
+        elif block_type == "tool_use":
+            content_block["id"] = kwargs.get("id", "")
+            content_block["name"] = kwargs.get("name", "")
+            content_block["input"] = kwargs.get("input", {})
+
+        return self._format_event(
+            "content_block_start",
+            {
+                "type": "content_block_start",
+                "index": index,
+                "content_block": content_block,
+            },
+        )
+
+    def content_block_delta(self, index: int, delta_type: str, content: str) -> str:
+        """Generate content_block_delta event."""
+        delta: Dict[str, Any] = {"type": delta_type}
+        if delta_type == "thinking_delta":
+            delta["thinking"] = content
+        elif delta_type == "text_delta":
+            delta["text"] = content
+        elif delta_type == "input_json_delta":
+            delta["partial_json"] = content
+
+        return self._format_event(
+            "content_block_delta",
+            {
+                "type": "content_block_delta",
+                "index": index,
+                "delta": delta,
+            },
+        )
+
+    def content_block_stop(self, index: int) -> str:
+        """Generate content_block_stop event."""
+        return self._format_event(
+            "content_block_stop",
+            {
+                "type": "content_block_stop",
+                "index": index,
+            },
+        )
+
+    # High-level helpers for thinking blocks
+    def start_thinking_block(self) -> str:
+        """Start a thinking block, allocating index."""
+        self.blocks.thinking_index = self.blocks.allocate_index()
+        self.blocks.thinking_started = True
+        return self.content_block_start(self.blocks.thinking_index, "thinking")
+
+    def emit_thinking_delta(self, content: str) -> str:
+        """Emit thinking content delta."""
+        self._accumulated_reasoning += content
+        return self.content_block_delta(
+            self.blocks.thinking_index, "thinking_delta", content
+        )
+
+    def stop_thinking_block(self) -> str:
+        """Stop the current thinking block."""
+        self.blocks.thinking_started = False
+        return self.content_block_stop(self.blocks.thinking_index)
+
+    # High-level helpers for text blocks
+    def start_text_block(self) -> str:
+        """Start a text block, allocating index."""
+        self.blocks.text_index = self.blocks.allocate_index()
+        self.blocks.text_started = True
+        return self.content_block_start(self.blocks.text_index, "text")
+
+    def emit_text_delta(self, content: str) -> str:
+        """Emit text content delta."""
+        self._accumulated_text += content
+        return self.content_block_delta(self.blocks.text_index, "text_delta", content)
+
+    def stop_text_block(self) -> str:
+        """Stop the current text block."""
+        self.blocks.text_started = False
+        return self.content_block_stop(self.blocks.text_index)
+
+    # High-level helpers for tool blocks
+    def start_tool_block(self, tool_index: int, tool_id: str, name: str) -> str:
+        """Start a tool_use block."""
+        block_idx = self.blocks.allocate_index()
+        self.blocks.tool_indices[tool_index] = block_idx
+        self.blocks.tool_contents[tool_index] = ""
+        return self.content_block_start(block_idx, "tool_use", id=tool_id, name=name)
+
+    def emit_tool_delta(self, tool_index: int, partial_json: str) -> str:
+        """Emit tool input delta."""
+        self.blocks.tool_contents[tool_index] += partial_json
+        block_idx = self.blocks.tool_indices[tool_index]
+        return self.content_block_delta(block_idx, "input_json_delta", partial_json)
+
+    def stop_tool_block(self, tool_index: int) -> str:
+        """Stop a tool block."""
+        block_idx = self.blocks.tool_indices[tool_index]
+        return self.content_block_stop(block_idx)
+
+    # State management helpers
+    def ensure_thinking_block(self) -> Iterator[str]:
+        """Ensure a thinking block is started, closing text block if needed."""
+        if self.blocks.text_started:
+            yield self.stop_text_block()
+        if not self.blocks.thinking_started:
+            yield self.start_thinking_block()
+
+    def ensure_text_block(self) -> Iterator[str]:
+        """Ensure a text block is started, closing thinking block if needed."""
+        if self.blocks.thinking_started:
+            yield self.stop_thinking_block()
+        if not self.blocks.text_started:
+            yield self.start_text_block()
+
+    def close_content_blocks(self) -> Iterator[str]:
+        """Close thinking and text blocks (before tool calls)."""
+        if self.blocks.thinking_started:
+            yield self.stop_thinking_block()
+        if self.blocks.text_started:
+            yield self.stop_text_block()
+
+    def close_all_blocks(self) -> Iterator[str]:
+        """Close all open blocks (thinking, text, tools)."""
+        if self.blocks.thinking_started:
+            yield self.stop_thinking_block()
+        if self.blocks.text_started:
+            yield self.stop_text_block()
+        for tool_index in list(self.blocks.tool_indices.keys()):
+            yield self.stop_tool_block(tool_index)
+
+    # Error handling
+    def emit_error(self, error_message: str) -> Iterator[str]:
+        """Emit an error as a text block."""
+        error_index = self.blocks.allocate_index()
+        yield self.content_block_start(error_index, "text")
+        yield self.content_block_delta(error_index, "text_delta", error_message)
+        yield self.content_block_stop(error_index)
+
+    # Accumulated content access
+    @property
+    def accumulated_text(self) -> str:
+        """Get accumulated text content."""
+        return self._accumulated_text
+
+    @property
+    def accumulated_reasoning(self) -> str:
+        """Get accumulated reasoning content."""
+        return self._accumulated_reasoning
+
+    def estimate_output_tokens(self) -> int:
+        """Estimate output tokens from accumulated content."""
+        text_tokens = len(self._accumulated_text) // 4
+        reasoning_tokens = len(self._accumulated_reasoning) // 4
+        tool_tokens = len(self.blocks.tool_indices) * 50
+        return text_tokens + reasoning_tokens + tool_tokens

providers/utils/think_parser.py

@@ -0,0 +1,174 @@
+"""Think tag parser for extracting reasoning content from responses."""
+
+import re
+from dataclasses import dataclass
+from typing import Optional, Tuple, Iterator, Any
+from enum import Enum
+
+
+class ContentType(Enum):
+    """Type of content chunk."""
+
+    TEXT = "text"
+    THINKING = "thinking"
+
+
+@dataclass
+class ContentChunk:
+    """A chunk of parsed content."""
+
+    type: ContentType
+    content: str
+
+
+class ThinkTagParser:
+    """
+    Streaming parser for <think>...</think> tags.
+
+    Handles partial tags at chunk boundaries by buffering.
+    """
+
+    OPEN_TAG = "<think>"
+    CLOSE_TAG = "</think>"
+    OPEN_TAG_LEN = 7
+    CLOSE_TAG_LEN = 8
+
+    def __init__(self):
+        self._buffer: str = ""
+        self._in_think_tag: bool = False
+
+    @property
+    def in_think_mode(self) -> bool:
+        """Whether currently inside a think tag."""
+        return self._in_think_tag
+
+    def feed(self, content: str) -> Iterator[ContentChunk]:
+        """
+        Feed content and yield parsed chunks.
+
+        Handles partial tags by buffering content near potential tag boundaries.
+        """
+        self._buffer += content
+
+        while self._buffer:
+            if not self._in_think_tag:
+                chunk = self._parse_outside_think()
+                if chunk:
+                    yield chunk
+                else:
+                    break
+            else:
+                chunk = self._parse_inside_think()
+                if chunk:
+                    yield chunk
+                else:
+                    break
+
+    def _parse_outside_think(self) -> Optional[ContentChunk]:
+        """Parse content outside think tags."""
+        think_start = self._buffer.find(self.OPEN_TAG)
+
+        if think_start == -1:
+            # No tag found - check for partial at end
+            if len(self._buffer) > self.OPEN_TAG_LEN - 1:
+                # Check if buffer ends with start of a potential tag
+                if self._buffer[-(self.OPEN_TAG_LEN - 1) :].startswith("<"):
+                    emit = self._buffer[: -(self.OPEN_TAG_LEN - 1)]
+                    self._buffer = self._buffer[-(self.OPEN_TAG_LEN - 1) :]
+                else:
+                    emit = self._buffer
+                    self._buffer = ""
+                if emit:
+                    return ContentChunk(ContentType.TEXT, emit)
+            return None
+        else:
+            # Found <think> tag
+            pre_think = self._buffer[:think_start]
+            self._buffer = self._buffer[think_start + self.OPEN_TAG_LEN :]
+            self._in_think_tag = True
+            if pre_think:
+                return ContentChunk(ContentType.TEXT, pre_think)
+            # Continue parsing inside think tag
+            return self._parse_inside_think()
+
+    def _parse_inside_think(self) -> Optional[ContentChunk]:
+        """Parse content inside think tags."""
+        think_end = self._buffer.find(self.CLOSE_TAG)
+
+        if think_end == -1:
+            # No closing tag - check for partial at end
+            if len(self._buffer) > self.CLOSE_TAG_LEN - 1:
+                # Check if buffer ends with start of a potential closing tag
+                if self._buffer[-(self.CLOSE_TAG_LEN - 1) :].startswith("<"):
+                    emit = self._buffer[: -(self.CLOSE_TAG_LEN - 1)]
+                    self._buffer = self._buffer[-(self.CLOSE_TAG_LEN - 1) :]
+                else:
+                    emit = self._buffer
+                    self._buffer = ""
+                if emit:
+                    return ContentChunk(ContentType.THINKING, emit)
+            return None
+        else:
+            # Found </think> tag
+            thinking_content = self._buffer[:think_end]
+            self._buffer = self._buffer[think_end + self.CLOSE_TAG_LEN :]
+            self._in_think_tag = False
+            if thinking_content:
+                return ContentChunk(ContentType.THINKING, thinking_content)
+            # Continue parsing outside think tag
+            return self._parse_outside_think()
+
+    def flush(self) -> Optional[ContentChunk]:
+        """Flush any remaining buffered content."""
+        if self._buffer:
+            chunk_type = (
+                ContentType.THINKING if self._in_think_tag else ContentType.TEXT
+            )
+            content = self._buffer
+            self._buffer = ""
+            return ContentChunk(chunk_type, content)
+        return None
+
+    def reset(self):
+        """Reset parser state."""
+        self._buffer = ""
+        self._in_think_tag = False
+
+
+def extract_think_content(text: str) -> Tuple[Optional[str], str]:
+    """
+    Extract thinking content from text (non-streaming).
+
+    Returns: (thinking_content, remaining_text)
+    """
+    think_pattern = re.compile(r"<think>(.*?)</think>", re.DOTALL)
+    matches = think_pattern.findall(text)
+
+    if matches:
+        thinking = "\n".join(matches)
+        remaining = think_pattern.sub("", text).strip()
+        return thinking, remaining
+
+    return None, text
+
+
+def extract_reasoning_from_delta(delta: Any) -> Optional[str]:
+    """
+    Extract reasoning content from an OpenAI delta object.
+
+    Checks both 'reasoning_content' and 'reasoning_details' fields.
+    """
+    if isinstance(delta, dict):
+        reasoning = delta.get("reasoning_content")
+        if reasoning:
+            return reasoning
+
+        reasoning_details = delta.get("reasoning_details")
+        if reasoning_details and isinstance(reasoning_details, list):
+            return "".join(
+                item.get("text", "")
+                for item in reasoning_details
+                if isinstance(item, dict)
+            )
+
+    return None

pyproject.toml

@@ -0,0 +1,13 @@
+[project]
+name = "cc-nim"
+version = "2.0.0"
+description = "Proxy that translates Claude Code CLI (Anthropic API) to NVIDIA NIM"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "fastapi[standard]>=0.115.11",
+    "uvicorn>=0.34.0",
+    "httpx>=0.25.0",
+    "pydantic>=2.0.0",
+    "python-dotenv>=1.0.0",
+]

server.py

@@ -0,0 +1,362 @@
+"""
+Anthropic API Proxy - Barebones NVIDIA NIM Implementation
+
+This server acts as a proxy between Anthropic API requests and NVIDIA NIM,
+using direct httpx calls without any external LLM libraries.
+"""
+
+from fastapi import FastAPI, Request, HTTPException
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel, field_validator
+from typing import List, Dict, Any, Optional, Union, Literal
+import os
+import json
+import logging
+import sys
+import uvicorn
+from dotenv import load_dotenv
+from contextlib import asynccontextmanager
+
+# Load environment variables
+load_dotenv()
+
+# Configure logging
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s - %(levelname)s - %(message)s",
+    handlers=[logging.FileHandler("server.log", encoding="utf-8", mode="w")],
+)
+logger = logging.getLogger(__name__)
+
+logging.getLogger("uvicorn").setLevel(logging.WARNING)
+logging.getLogger("uvicorn.access").setLevel(logging.WARNING)
+logging.getLogger("uvicorn.error").setLevel(logging.WARNING)
+
+# =============================================================================
+# Models
+# =============================================================================
+
+BIG_MODEL = os.getenv("BIG_MODEL", "moonshotai/kimi-k2-instruct")
+SMALL_MODEL = os.getenv("SMALL_MODEL", "moonshotai/kimi-k2-instruct")
+
+
+class ContentBlockText(BaseModel):
+    type: Literal["text"]
+    text: str
+
+
+class ContentBlockImage(BaseModel):
+    type: Literal["image"]
+    source: Dict[str, Any]
+
+
+class ContentBlockToolUse(BaseModel):
+    type: Literal["tool_use"]
+    id: str
+    name: str
+    input: Dict[str, Any]
+
+
+class ContentBlockToolResult(BaseModel):
+    type: Literal["tool_result"]
+    tool_use_id: str
+    content: Union[str, List[Dict[str, Any]], Dict[str, Any], List[Any], Any]
+
+
+class ContentBlockThinking(BaseModel):
+    type: Literal["thinking"]
+    thinking: str
+
+
+class SystemContent(BaseModel):
+    type: Literal["text"]
+    text: str
+
+
+class Message(BaseModel):
+    role: Literal["user", "assistant"]
+    content: Union[
+        str,
+        List[Union[ContentBlockText, ContentBlockImage, ContentBlockToolUse, ContentBlockToolResult, ContentBlockThinking]],
+    ]
+    reasoning_content: Optional[str] = None
+
+
+class Tool(BaseModel):
+    name: str
+    description: Optional[str] = None
+    input_schema: Dict[str, Any]
+
+
+class ThinkingConfig(BaseModel):
+    enabled: bool = True
+
+
+class MessagesRequest(BaseModel):
+    model: str
+    max_tokens: int
+    messages: List[Message]
+    system: Optional[Union[str, List[SystemContent]]] = None
+    stop_sequences: Optional[List[str]] = None
+    stream: Optional[bool] = False
+    temperature: Optional[float] = 1.0
+    top_p: Optional[float] = None
+    top_k: Optional[int] = None
+    metadata: Optional[Dict[str, Any]] = None
+    tools: Optional[List[Tool]] = None
+    tool_choice: Optional[Dict[str, Any]] = None
+    thinking: Optional[ThinkingConfig] = None
+    extra_body: Optional[Dict[str, Any]] = None
+    original_model: Optional[str] = None
+
+    @field_validator("model")
+    @classmethod
+    def validate_model_field(cls, v, info):
+        original_model = v
+        clean_v = v
+        for prefix in ["anthropic/", "openai/", "gemini/"]:
+            if clean_v.startswith(prefix):
+                clean_v = clean_v[len(prefix):]
+                break
+
+        if "haiku" in clean_v.lower():
+            new_model = SMALL_MODEL
+        elif "sonnet" in clean_v.lower() or "opus" in clean_v.lower():
+            new_model = BIG_MODEL
+        else:
+            new_model = v
+
+        if new_model != original_model:
+            logger.debug(f"MODEL MAPPING: '{original_model}' -> '{new_model}'")
+
+        if isinstance(info.data, dict):
+            info.data["original_model"] = original_model
+
+        return new_model
+
+
+class TokenCountRequest(BaseModel):
+    model: str
+    messages: List[Message]
+    system: Optional[Union[str, List[SystemContent]]] = None
+    tools: Optional[List[Tool]] = None
+    thinking: Optional[ThinkingConfig] = None
+    tool_choice: Optional[Dict[str, Any]] = None
+
+    @field_validator("model")
+    @classmethod
+    def validate_model_field(cls, v, info):
+        clean_v = v
+        for prefix in ["anthropic/", "openai/", "gemini/"]:
+            if clean_v.startswith(prefix):
+                clean_v = clean_v[len(prefix):]
+                break
+
+        if "haiku" in clean_v.lower():
+            return SMALL_MODEL
+        elif "sonnet" in clean_v.lower() or "opus" in clean_v.lower():
+            return BIG_MODEL
+        return v
+
+
+class TokenCountResponse(BaseModel):
+    input_tokens: int
+
+
+class Usage(BaseModel):
+    input_tokens: int
+    output_tokens: int
+    cache_creation_input_tokens: int = 0
+    cache_read_input_tokens: int = 0
+
+
+class MessagesResponse(BaseModel):
+    id: str
+    model: str
+    role: Literal["assistant"] = "assistant"
+    content: List[Union[ContentBlockText, ContentBlockToolUse, ContentBlockThinking, Dict[str, Any]]]
+    type: Literal["message"] = "message"
+    stop_reason: Optional[Literal["end_turn", "max_tokens", "stop_sequence", "tool_use"]] = None
+    stop_sequence: Optional[str] = None
+    usage: Usage
+
+
+# =============================================================================
+# Provider
+# =============================================================================
+
+from providers.nvidia_nim import NvidiaNimProvider, ProviderConfig
+
+provider_config = ProviderConfig(
+    api_key=os.getenv("NVIDIA_NIM_API_KEY", ""),
+    base_url=os.getenv("NVIDIA_NIM_BASE_URL", "https://integrate.api.nvidia.com/v1"),
+    rate_limit=int(os.getenv("NVIDIA_NIM_RATE_LIMIT", "40")),
+    rate_window=int(os.getenv("NVIDIA_NIM_RATE_WINDOW", "60")),
+)
+provider = NvidiaNimProvider(provider_config)
+
+# =============================================================================
+# FastAPI App
+# =============================================================================
+
+FAST_PREFIX_DETECTION = os.getenv("FAST_PREFIX_DETECTION", "true").lower() == "true"
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    logger.info("Server starting up...")
+    yield
+    logger.info("Server shutting down...")
+    if hasattr(provider, "_client"):
+        await provider._client.aclose()
+
+
+app = FastAPI(title="Claude Code Proxy", version="2.0.0", lifespan=lifespan)
+
+
+def extract_command_prefix(command: str) -> str:
+    import shlex
+
+    if "`" in command or "$(" in command:
+        return "command_injection_detected"
+
+    try:
+        parts = shlex.split(command)
+        if not parts:
+            return "none"
+
+        env_prefix = []
+        cmd_start = 0
+        for i, part in enumerate(parts):
+            if "=" in part and not part.startswith("-"):
+                env_prefix.append(part)
+                cmd_start = i + 1
+            else:
+                break
+
+        if cmd_start >= len(parts):
+            return "none"
+
+        cmd_parts = parts[cmd_start:]
+        if not cmd_parts:
+            return "none"
+
+        first_word = cmd_parts[0]
+        two_word_commands = {"git", "npm", "docker", "kubectl", "cargo", "go", "pip", "yarn"}
+
+        if first_word in two_word_commands and len(cmd_parts) > 1:
+            second_word = cmd_parts[1]
+            if not second_word.startswith("-"):
+                return f"{first_word} {second_word}"
+            return first_word
+        return first_word if not env_prefix else " ".join(env_prefix) + " " + first_word
+
+    except ValueError:
+        return command.split()[0] if command.split() else "none"
+
+
+def is_prefix_detection_request(request_data: MessagesRequest) -> tuple[bool, str]:
+    if len(request_data.messages) != 1 or request_data.messages[0].role != "user":
+        return False, ""
+
+    msg = request_data.messages[0]
+    content = ""
+    if isinstance(msg.content, str):
+        content = msg.content
+    elif isinstance(msg.content, list):
+        for block in msg.content:
+            if hasattr(block, "text"):
+                content += block.text
+
+    if "<policy_spec>" in content and "Command:" in content:
+        try:
+            cmd_start = content.rfind("Command:") + len("Command:")
+            return True, content[cmd_start:].strip()
+        except:
+            pass
+
+    return False, ""
+
+
+def get_token_count(messages, system=None, tools=None) -> int:
+    total_chars = 0
+
+    if system:
+        if isinstance(system, str):
+            total_chars += len(system)
+        elif isinstance(system, list):
+            for block in system:
+                if hasattr(block, "text"):
+                    total_chars += len(block.text)
+
+    for msg in messages:
+        if isinstance(msg.content, str):
+            total_chars += len(msg.content)
+        elif isinstance(msg.content, list):
+            for block in msg.content:
+                if hasattr(block, "text"):
+                    total_chars += len(block.text)
+                elif hasattr(block, "thinking"):
+                    total_chars += len(block.thinking)
+
+    if tools:
+        for tool in tools:
+            total_chars += len(tool.name) + len(tool.description or "") + len(json.dumps(tool.input_schema))
+
+    return max(1, total_chars // 4)
+
+
+@app.post("/v1/messages")
+async def create_message(request_data: MessagesRequest, raw_request: Request):
+    try:
+        if FAST_PREFIX_DETECTION:
+            is_prefix_req, command = is_prefix_detection_request(request_data)
+            if is_prefix_req:
+                import uuid
+                return MessagesResponse(
+                    id=f"msg_{uuid.uuid4()}",
+                    model=request_data.model,
+                    content=[{"type": "text", "text": extract_command_prefix(command)}],
+                    stop_reason="end_turn",
+                    usage=Usage(input_tokens=100, output_tokens=5),
+                )
+
+        logger.info(f"Request: model={request_data.model}, messages={len(request_data.messages)}, stream={request_data.stream}")
+
+        if request_data.stream:
+            input_tokens = get_token_count(request_data.messages, request_data.system, request_data.tools)
+            return StreamingResponse(
+                provider.stream_response(request_data, input_tokens=input_tokens),
+                media_type="text/event-stream",
+                headers={"X-Accel-Buffering": "no", "Cache-Control": "no-cache", "Connection": "keep-alive"},
+            )
+        else:
+            response_json = await provider.complete(request_data)
+            return provider.convert_response(response_json, request_data)
+
+    except Exception as e:
+        import traceback
+        logger.error(f"Error: {str(e)}\n{traceback.format_exc()}")
+        raise HTTPException(status_code=getattr(e, "status_code", 500), detail=str(e))
+
+
+@app.post("/v1/messages/count_tokens")
+async def count_tokens(request_data: TokenCountRequest):
+    try:
+        return TokenCountResponse(input_tokens=get_token_count(request_data.messages, request_data.system, request_data.tools))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/")
+async def root():
+    return {"status": "ok", "provider": "nvidia_nim", "big_model": BIG_MODEL, "small_model": SMALL_MODEL}
+
+
+@app.get("/health")
+async def health():
+    return {"status": "healthy"}
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8082, log_level="debug")

uv.lock

@@ -0,0 +1,754 @@
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