Add support for Atropos Agentic RL environments (requires branch tool_call_support in Atropos atm)

- Added new environments for reinforcement learning, including `HermesSweEnv` for software engineering tasks and `TerminalTestEnv` for inline testing.
- Introduced `ToolContext` for unrestricted access to tools during reward computation.
- Updated `.gitignore` to exclude `wandb/` directory.
- Enhanced `README.md` with detailed architecture and usage instructions for Atropos environments.
- Added configuration files for SWE and terminal test environments to streamline setup.
- Removed unnecessary compiled Python files from `__pycache__`.
This commit is contained in:
teknium 2026-02-07 09:17:16 +00:00
parent ac79725923
commit 07b615e96e
30 changed files with 2851 additions and 965 deletions

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"""
Tool Call Parser Registry
Client-side parsers that extract structured tool_calls from raw model output text.
Used in Phase 2 (VLLM server type) where ManagedServer's /generate endpoint returns
raw text without tool call parsing.
Each parser is a standalone reimplementation of the corresponding VLLM parser's
non-streaming extract_tool_calls() logic. No VLLM dependency -- only standard library
(re, json, uuid) and openai types.
Usage:
from environments.tool_call_parsers import get_parser
parser = get_parser("hermes")
content, tool_calls = parser.parse(raw_model_output)
# content = text with tool call markup stripped
# tool_calls = list of ChatCompletionMessageToolCall objects, or None
"""
import logging
from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Tuple, Type
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
)
logger = logging.getLogger(__name__)
# Type alias for parser return value
ParseResult = Tuple[Optional[str], Optional[List[ChatCompletionMessageToolCall]]]
class ToolCallParser(ABC):
"""
Base class for tool call parsers.
Each parser knows how to extract structured tool_calls from a specific
model family's raw output text format.
"""
@abstractmethod
def parse(self, text: str) -> ParseResult:
"""
Parse raw model output text for tool calls.
Args:
text: Raw decoded text from the model's completion
Returns:
Tuple of (content, tool_calls) where:
- content: text with tool call markup stripped (the message 'content' field),
or None if the entire output was tool calls
- tool_calls: list of ChatCompletionMessageToolCall objects,
or None if no tool calls were found
"""
raise NotImplementedError
# Global parser registry: name -> parser class
PARSER_REGISTRY: Dict[str, Type[ToolCallParser]] = {}
def register_parser(name: str):
"""
Decorator to register a parser class under a given name.
Usage:
@register_parser("hermes")
class HermesToolCallParser(ToolCallParser):
...
"""
def decorator(cls: Type[ToolCallParser]) -> Type[ToolCallParser]:
PARSER_REGISTRY[name] = cls
return cls
return decorator
def get_parser(name: str) -> ToolCallParser:
"""
Get a parser instance by name.
Args:
name: Parser name (e.g., "hermes", "mistral", "llama3_json")
Returns:
Instantiated parser
Raises:
KeyError: If parser name is not found in registry
"""
if name not in PARSER_REGISTRY:
available = sorted(PARSER_REGISTRY.keys())
raise KeyError(
f"Tool call parser '{name}' not found. Available parsers: {available}"
)
return PARSER_REGISTRY[name]()
def list_parsers() -> List[str]:
"""Return sorted list of registered parser names."""
return sorted(PARSER_REGISTRY.keys())
# Import all parser modules to trigger registration via @register_parser decorators
# Each module registers itself when imported
from environments.tool_call_parsers.hermes_parser import HermesToolCallParser # noqa: E402, F401
from environments.tool_call_parsers.longcat_parser import LongcatToolCallParser # noqa: E402, F401
from environments.tool_call_parsers.mistral_parser import MistralToolCallParser # noqa: E402, F401
from environments.tool_call_parsers.llama_parser import LlamaToolCallParser # noqa: E402, F401
from environments.tool_call_parsers.qwen_parser import QwenToolCallParser # noqa: E402, F401
from environments.tool_call_parsers.deepseek_v3_parser import DeepSeekV3ToolCallParser # noqa: E402, F401
from environments.tool_call_parsers.deepseek_v3_1_parser import DeepSeekV31ToolCallParser # noqa: E402, F401
from environments.tool_call_parsers.kimi_k2_parser import KimiK2ToolCallParser # noqa: E402, F401
from environments.tool_call_parsers.glm45_parser import Glm45ToolCallParser # noqa: E402, F401
from environments.tool_call_parsers.glm47_parser import Glm47ToolCallParser # noqa: E402, F401
from environments.tool_call_parsers.qwen3_coder_parser import Qwen3CoderToolCallParser # noqa: E402, F401

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"""
DeepSeek V3.1 tool call parser.
Similar to V3 but with a slightly different format:
<toolcallbegin>function_name<toolsep>arguments<toolcallend>
Note: V3 has type+name before the separator, V3.1 has name before and args after.
Based on VLLM's DeepSeekV31ToolParser.extract_tool_calls()
"""
import re
import uuid
from typing import List, Optional
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
Function,
)
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
@register_parser("deepseek_v3_1")
@register_parser("deepseek_v31")
class DeepSeekV31ToolCallParser(ToolCallParser):
"""
Parser for DeepSeek V3.1 tool calls.
Slightly different regex than V3: function_name comes before the separator,
arguments come after (no type field, no json code block wrapper).
"""
START_TOKEN = "<tool▁calls▁begin>"
# Regex captures: function_name, function_arguments
PATTERN = re.compile(
r"<tool▁call▁begin>(?P<function_name>.*?)<tool▁sep>(?P<function_arguments>.*?)<tool▁call▁end>"
)
def parse(self, text: str) -> ParseResult:
if self.START_TOKEN not in text:
return text, None
try:
matches = self.PATTERN.findall(text)
if not matches:
return text, None
tool_calls: List[ChatCompletionMessageToolCall] = []
for match in matches:
func_name, func_args = match
tool_calls.append(
ChatCompletionMessageToolCall(
id=f"call_{uuid.uuid4().hex[:8]}",
type="function",
function=Function(
name=func_name.strip(),
arguments=func_args.strip(),
),
)
)
if not tool_calls:
return text, None
content = text[: text.find(self.START_TOKEN)].strip()
return content if content else None, tool_calls
except Exception:
return text, None

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"""
DeepSeek V3 tool call parser.
Format uses special unicode tokens:
<toolcallsbegin>
<toolcallbegin>type<toolsep>function_name
```json
{"arg": "value"}
```
<toolcallend>
<toolcallsend>
Based on VLLM's DeepSeekV3ToolParser.extract_tool_calls()
"""
import re
import uuid
from typing import List, Optional
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
Function,
)
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
@register_parser("deepseek_v3")
class DeepSeekV3ToolCallParser(ToolCallParser):
"""
Parser for DeepSeek V3 tool calls.
Uses special unicode tokens with fullwidth angle brackets and block elements.
Extracts type, function name, and JSON arguments from the structured format.
"""
START_TOKEN = "<tool▁calls▁begin>"
# Regex captures: type, function_name, function_arguments
PATTERN = re.compile(
r"<tool▁call▁begin>(?P<type>.*)<tool▁sep>(?P<function_name>.*)\n```json\n(?P<function_arguments>.*)\n```<tool▁call▁end>"
)
def parse(self, text: str) -> ParseResult:
if self.START_TOKEN not in text:
return text, None
try:
matches = self.PATTERN.findall(text)
if not matches:
return text, None
tool_calls: List[ChatCompletionMessageToolCall] = []
for match in matches:
tc_type, func_name, func_args = match
tool_calls.append(
ChatCompletionMessageToolCall(
id=f"call_{uuid.uuid4().hex[:8]}",
type="function",
function=Function(
name=func_name.strip(),
arguments=func_args.strip(),
),
)
)
if not tool_calls:
return text, None
# Content is everything before the tool calls section
content = text[: text.find(self.START_TOKEN)].strip()
return content if content else None, tool_calls
except Exception:
return text, None

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"""
GLM 4.5 (GLM-4-MoE) tool call parser.
Format uses custom arg_key/arg_value tags rather than standard JSON:
<tool_call>function_name
<arg_key>param1</arg_key><arg_value>value1</arg_value>
<arg_key>param2</arg_key><arg_value>value2</arg_value>
</tool_call>
Values are deserialized using json.loads -> ast.literal_eval -> raw string fallback.
Based on VLLM's Glm4MoeModelToolParser.extract_tool_calls()
"""
import ast
import json
import re
import uuid
from typing import Any, Dict, List, Optional
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
Function,
)
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
def _deserialize_value(value: str) -> Any:
"""
Try to deserialize a string value to its native Python type.
Attempts json.loads, then ast.literal_eval, then returns raw string.
"""
try:
return json.loads(value)
except (json.JSONDecodeError, TypeError):
pass
try:
return ast.literal_eval(value)
except (ValueError, SyntaxError, TypeError):
pass
return value
@register_parser("glm45")
class Glm45ToolCallParser(ToolCallParser):
"""
Parser for GLM 4.5 (GLM-4-MoE) tool calls.
Uses <tool_call>...</tool_call> tags with <arg_key>/<arg_value> pairs
instead of standard JSON arguments.
"""
FUNC_CALL_REGEX = re.compile(r"<tool_call>.*?</tool_call>", re.DOTALL)
FUNC_DETAIL_REGEX = re.compile(r"<tool_call>([^\n]*)\n(.*)</tool_call>", re.DOTALL)
FUNC_ARG_REGEX = re.compile(
r"<arg_key>(.*?)</arg_key>\s*<arg_value>(.*?)</arg_value>", re.DOTALL
)
START_TOKEN = "<tool_call>"
def parse(self, text: str) -> ParseResult:
if self.START_TOKEN not in text:
return text, None
try:
matched_calls = self.FUNC_CALL_REGEX.findall(text)
if not matched_calls:
return text, None
tool_calls: List[ChatCompletionMessageToolCall] = []
for match in matched_calls:
detail = self.FUNC_DETAIL_REGEX.search(match)
if not detail:
continue
func_name = detail.group(1).strip()
func_args_raw = detail.group(2)
# Parse arg_key/arg_value pairs
pairs = self.FUNC_ARG_REGEX.findall(func_args_raw) if func_args_raw else []
arg_dict: Dict[str, Any] = {}
for key, value in pairs:
arg_key = key.strip()
arg_val = _deserialize_value(value.strip())
arg_dict[arg_key] = arg_val
tool_calls.append(
ChatCompletionMessageToolCall(
id=f"call_{uuid.uuid4().hex[:8]}",
type="function",
function=Function(
name=func_name,
arguments=json.dumps(arg_dict, ensure_ascii=False),
),
)
)
if not tool_calls:
return text, None
content = text[: text.find(self.START_TOKEN)].strip()
return content if content else None, tool_calls
except Exception:
return text, None

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"""
GLM 4.7 tool call parser.
Same as GLM 4.5 but with slightly different regex patterns.
The tool_call tags may wrap differently and arg parsing handles
newlines between key/value pairs.
Based on VLLM's Glm47MoeModelToolParser (extends Glm4MoeModelToolParser).
"""
import re
from environments.tool_call_parsers import ParseResult, register_parser
from environments.tool_call_parsers.glm45_parser import Glm45ToolCallParser
@register_parser("glm47")
class Glm47ToolCallParser(Glm45ToolCallParser):
"""
Parser for GLM 4.7 tool calls.
Extends GLM 4.5 with updated regex patterns.
"""
def __init__(self):
super().__init__()
# GLM 4.7 uses a slightly different detail regex that includes
# the <tool_call> wrapper and optional arg_key content
self.FUNC_DETAIL_REGEX = re.compile(
r"<tool_call>(.*?)(<arg_key>.*?)?</tool_call>", re.DOTALL
)
# GLM 4.7 handles newlines between arg_key and arg_value tags
self.FUNC_ARG_REGEX = re.compile(
r"<arg_key>(.*?)</arg_key>(?:\\n|\s)*<arg_value>(.*?)</arg_value>",
re.DOTALL,
)

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"""
Hermes tool call parser.
Format: <tool_call>{"name": "func", "arguments": {...}}</tool_call>
Based on VLLM's Hermes2ProToolParser.extract_tool_calls()
"""
import json
import re
import uuid
from typing import List, Optional, Tuple
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
Function,
)
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
@register_parser("hermes")
class HermesToolCallParser(ToolCallParser):
"""
Parser for Hermes-format tool calls.
Matches <tool_call>...</tool_call> tags containing JSON with "name" and "arguments".
Also handles unclosed <tool_call> at end-of-string (truncated generation).
"""
# Matches both closed and unclosed tool_call tags
PATTERN = re.compile(
r"<tool_call>\s*(.*?)\s*</tool_call>|<tool_call>\s*(.*)", re.DOTALL
)
def parse(self, text: str) -> ParseResult:
if "<tool_call>" not in text:
return text, None
try:
matches = self.PATTERN.findall(text)
if not matches:
return text, None
tool_calls: List[ChatCompletionMessageToolCall] = []
for match in matches:
# match is a tuple: (closed_content, unclosed_content)
raw_json = match[0] if match[0] else match[1]
if not raw_json.strip():
continue
tc_data = json.loads(raw_json)
tool_calls.append(
ChatCompletionMessageToolCall(
id=f"call_{uuid.uuid4().hex[:8]}",
type="function",
function=Function(
name=tc_data["name"],
arguments=json.dumps(
tc_data.get("arguments", {}), ensure_ascii=False
),
),
)
)
if not tool_calls:
return text, None
# Content is everything before the first <tool_call> tag
content = text[: text.find("<tool_call>")].strip()
return content if content else None, tool_calls
except Exception:
return text, None

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"""
Kimi K2 tool call parser.
Format:
<|tool_calls_section_begin|>
<|tool_call_begin|>function_id:0<|tool_call_argument_begin|>{"arg": "val"}<|tool_call_end|>
<|tool_calls_section_end|>
The function_id format is typically "functions.func_name:index" or "func_name:index".
Based on VLLM's KimiK2ToolParser.extract_tool_calls()
"""
import re
import uuid
from typing import List, Optional
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
Function,
)
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
@register_parser("kimi_k2")
class KimiK2ToolCallParser(ToolCallParser):
"""
Parser for Kimi K2 tool calls.
Uses section begin/end tokens wrapping individual tool call begin/end tokens.
The tool_call_id contains the function name (after last dot, before colon).
"""
# Support both singular and plural variants
START_TOKENS = [
"<|tool_calls_section_begin|>",
"<|tool_call_section_begin|>",
]
# Regex captures: tool_call_id (e.g., "functions.get_weather:0"), function_arguments
PATTERN = re.compile(
r"<\|tool_call_begin\|>\s*(?P<tool_call_id>[^<]+:\d+)\s*"
r"<\|tool_call_argument_begin\|>\s*"
r"(?P<function_arguments>(?:(?!<\|tool_call_begin\|>).)*?)\s*"
r"<\|tool_call_end\|>",
re.DOTALL,
)
def parse(self, text: str) -> ParseResult:
# Check for any variant of the start token
has_start = any(token in text for token in self.START_TOKENS)
if not has_start:
return text, None
try:
matches = self.PATTERN.findall(text)
if not matches:
return text, None
tool_calls: List[ChatCompletionMessageToolCall] = []
for match in matches:
function_id, function_args = match
# Extract function name from ID format: "functions.get_weather:0" -> "get_weather"
function_name = function_id.split(":")[0].split(".")[-1]
tool_calls.append(
ChatCompletionMessageToolCall(
id=function_id, # Preserve the original ID format
type="function",
function=Function(
name=function_name,
arguments=function_args.strip(),
),
)
)
if not tool_calls:
return text, None
# Content is everything before the tool calls section
earliest_start = len(text)
for token in self.START_TOKENS:
idx = text.find(token)
if idx >= 0 and idx < earliest_start:
earliest_start = idx
content = text[:earliest_start].strip()
return content if content else None, tool_calls
except Exception:
return text, None

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"""
Llama 3.x / 4 tool call parser.
Format: The model outputs JSON objects with "name" and "arguments" (or "parameters") keys.
May be preceded by <|python_tag|> token. Supports multiple JSON objects separated
by content or semicolons.
Based on VLLM's Llama3JsonToolParser.extract_tool_calls()
"""
import json
import re
import uuid
from typing import List, Optional
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
Function,
)
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
@register_parser("llama3_json")
@register_parser("llama4_json")
class LlamaToolCallParser(ToolCallParser):
"""
Parser for Llama 3.x and 4 JSON-format tool calls.
Finds JSON objects containing "name" + ("arguments" or "parameters") keys.
Uses Python's json.JSONDecoder.raw_decode for robust extraction of
JSON objects from mixed text.
"""
BOT_TOKEN = "<|python_tag|>"
# Regex to find the start of potential JSON objects
JSON_START = re.compile(r"\{")
def parse(self, text: str) -> ParseResult:
# Quick check: need either the bot token or a JSON brace
if self.BOT_TOKEN not in text and "{" not in text:
return text, None
try:
decoder = json.JSONDecoder()
tool_calls: List[ChatCompletionMessageToolCall] = []
end_index = -1 # Track where the last parsed JSON ended
for match in self.JSON_START.finditer(text):
start = match.start()
# Skip if this brace is inside a previously parsed JSON object
if start <= end_index:
continue
try:
obj, json_end = decoder.raw_decode(text[start:])
end_index = start + json_end
# Must have "name" and either "arguments" or "parameters"
name = obj.get("name")
args = obj.get("arguments", obj.get("parameters"))
if not name or args is None:
continue
# Normalize arguments to JSON string
if isinstance(args, dict):
args = json.dumps(args, ensure_ascii=False)
elif not isinstance(args, str):
args = json.dumps(args, ensure_ascii=False)
tool_calls.append(
ChatCompletionMessageToolCall(
id=f"call_{uuid.uuid4().hex[:8]}",
type="function",
function=Function(name=name, arguments=args),
)
)
except (json.JSONDecodeError, KeyError, ValueError):
continue
if not tool_calls:
return text, None
# Content is everything before the first tool call JSON
# Find where the first tool call starts in the text
first_tc_start = text.find("{")
if self.BOT_TOKEN in text:
first_tc_start = text.find(self.BOT_TOKEN)
content = text[:first_tc_start].strip() if first_tc_start > 0 else None
return content, tool_calls
except Exception:
return text, None

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"""
Longcat Flash Chat tool call parser.
Same as Hermes but uses <longcat_tool_call> tags instead of <tool_call>.
Based on VLLM's LongcatFlashToolParser (extends Hermes2ProToolParser).
"""
import json
import re
import uuid
from typing import List, Optional
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
Function,
)
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
@register_parser("longcat")
class LongcatToolCallParser(ToolCallParser):
"""
Parser for Longcat Flash Chat tool calls.
Identical logic to Hermes, just different tag names.
"""
PATTERN = re.compile(
r"<longcat_tool_call>\s*(.*?)\s*</longcat_tool_call>|<longcat_tool_call>\s*(.*)",
re.DOTALL,
)
def parse(self, text: str) -> ParseResult:
if "<longcat_tool_call>" not in text:
return text, None
try:
matches = self.PATTERN.findall(text)
if not matches:
return text, None
tool_calls: List[ChatCompletionMessageToolCall] = []
for match in matches:
raw_json = match[0] if match[0] else match[1]
if not raw_json.strip():
continue
tc_data = json.loads(raw_json)
tool_calls.append(
ChatCompletionMessageToolCall(
id=f"call_{uuid.uuid4().hex[:8]}",
type="function",
function=Function(
name=tc_data["name"],
arguments=json.dumps(
tc_data.get("arguments", {}), ensure_ascii=False
),
),
)
)
if not tool_calls:
return text, None
content = text[: text.find("<longcat_tool_call>")].strip()
return content if content else None, tool_calls
except Exception:
return text, None

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"""
Mistral tool call parser.
Supports two formats depending on tokenizer version:
- Pre-v11: content[TOOL_CALLS] [{"name": ..., "arguments": {...}}, ...]
- v11+: content[TOOL_CALLS]tool_name1{"arg": "val"}[TOOL_CALLS]tool_name2{"arg": "val"}
Based on VLLM's MistralToolParser.extract_tool_calls()
The [TOOL_CALLS] token is the bot_token used by Mistral models.
"""
import json
import re
import uuid
from typing import List, Optional
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
Function,
)
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
def _generate_mistral_id() -> str:
"""Mistral tool call IDs are 9-char alphanumeric strings."""
import random
import string
return "".join(random.choices(string.ascii_letters + string.digits, k=9))
@register_parser("mistral")
class MistralToolCallParser(ToolCallParser):
"""
Parser for Mistral-format tool calls.
Detects format by checking if the content after [TOOL_CALLS] starts with '['
(pre-v11 JSON array) or with a tool name (v11+ format).
"""
# The [TOOL_CALLS] token -- may appear as different strings depending on tokenizer
BOT_TOKEN = "[TOOL_CALLS]"
# Fallback regex for pre-v11 format when JSON parsing fails
TOOL_CALL_REGEX = re.compile(r"\[?\s*(\{.*?\})\s*\]?", re.DOTALL)
def parse(self, text: str) -> ParseResult:
if self.BOT_TOKEN not in text:
return text, None
try:
parts = text.split(self.BOT_TOKEN)
content = parts[0].strip()
raw_tool_calls = parts[1:]
# Detect format: if the first raw part starts with '[', it's pre-v11
first_raw = raw_tool_calls[0].strip() if raw_tool_calls else ""
is_pre_v11 = first_raw.startswith("[") or first_raw.startswith("{")
tool_calls: List[ChatCompletionMessageToolCall] = []
if not is_pre_v11:
# v11+ format: [TOOL_CALLS]tool_name{args}[TOOL_CALLS]tool_name2{args2}
for raw in raw_tool_calls:
raw = raw.strip()
if not raw or "{" not in raw:
continue
brace_idx = raw.find("{")
tool_name = raw[:brace_idx].strip()
args_str = raw[brace_idx:]
tool_calls.append(
ChatCompletionMessageToolCall(
id=_generate_mistral_id(),
type="function",
function=Function(name=tool_name, arguments=args_str),
)
)
else:
# Pre-v11 format: [TOOL_CALLS] [{"name": ..., "arguments": {...}}]
try:
parsed = json.loads(first_raw)
if isinstance(parsed, dict):
parsed = [parsed]
for tc in parsed:
args = tc.get("arguments", {})
if isinstance(args, dict):
args = json.dumps(args, ensure_ascii=False)
tool_calls.append(
ChatCompletionMessageToolCall(
id=_generate_mistral_id(),
type="function",
function=Function(
name=tc["name"], arguments=args
),
)
)
except json.JSONDecodeError:
# Fallback regex extraction
match = self.TOOL_CALL_REGEX.findall(first_raw)
if match:
for raw_json in match:
try:
tc = json.loads(raw_json)
args = tc.get("arguments", {})
if isinstance(args, dict):
args = json.dumps(args, ensure_ascii=False)
tool_calls.append(
ChatCompletionMessageToolCall(
id=_generate_mistral_id(),
type="function",
function=Function(
name=tc["name"], arguments=args
),
)
)
except (json.JSONDecodeError, KeyError):
continue
if not tool_calls:
return text, None
return content if content else None, tool_calls
except Exception:
return text, None

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"""
Qwen3-Coder tool call parser.
Format uses XML-style nested tags:
<tool_call>
<function=function_name>
<parameter=param_name>value</parameter>
<parameter=param_name2>value2</parameter>
</function>
</tool_call>
Parameters are extracted from <parameter=name>value</parameter> tags and
type-converted using the schema if available, otherwise treated as strings.
Based on VLLM's Qwen3CoderToolParser.extract_tool_calls()
"""
import ast
import json
import re
import uuid
from typing import Any, Dict, List, Optional
from openai.types.chat.chat_completion_message_tool_call import (
ChatCompletionMessageToolCall,
Function,
)
from environments.tool_call_parsers import ParseResult, ToolCallParser, register_parser
def _try_convert_value(value: str) -> Any:
"""
Try to convert a parameter value string to a native Python type.
Handles null, numbers, booleans, JSON objects/arrays, and falls back to string.
"""
stripped = value.strip()
# Handle null
if stripped.lower() == "null":
return None
# Try JSON first (handles objects, arrays, strings, numbers, booleans)
try:
return json.loads(stripped)
except (json.JSONDecodeError, TypeError):
pass
# Try Python literal eval (handles tuples, etc.)
try:
return ast.literal_eval(stripped)
except (ValueError, SyntaxError, TypeError):
pass
# Return as string
return stripped
@register_parser("qwen3_coder")
class Qwen3CoderToolCallParser(ToolCallParser):
"""
Parser for Qwen3-Coder XML-format tool calls.
Uses nested XML tags: <tool_call><function=name><parameter=key>val</parameter></function></tool_call>
"""
START_TOKEN = "<tool_call>"
FUNCTION_PREFIX = "<function="
# Find complete tool_call blocks (or unclosed at end)
TOOL_CALL_REGEX = re.compile(
r"<tool_call>(.*?)</tool_call>|<tool_call>(.*?)$", re.DOTALL
)
# Find function blocks within a tool_call
FUNCTION_REGEX = re.compile(
r"<function=(.*?)</function>|<function=(.*)$", re.DOTALL
)
# Find parameter blocks within a function
PARAMETER_REGEX = re.compile(
r"<parameter=(.*?)(?:</parameter>|(?=<parameter=)|(?=</function>)|$)",
re.DOTALL,
)
def _parse_function_call(self, function_str: str) -> Optional[ChatCompletionMessageToolCall]:
"""Parse a single <function=name>...</function> block into a ToolCall."""
try:
# Extract function name: everything before the first '>'
gt_idx = function_str.index(">")
func_name = function_str[:gt_idx].strip()
params_str = function_str[gt_idx + 1:]
# Extract parameters
param_dict: Dict[str, Any] = {}
for match_text in self.PARAMETER_REGEX.findall(params_str):
if ">" not in match_text:
continue
eq_idx = match_text.index(">")
param_name = match_text[:eq_idx].strip()
param_value = match_text[eq_idx + 1:]
# Clean up whitespace
if param_value.startswith("\n"):
param_value = param_value[1:]
if param_value.endswith("\n"):
param_value = param_value[:-1]
param_dict[param_name] = _try_convert_value(param_value)
return ChatCompletionMessageToolCall(
id=f"call_{uuid.uuid4().hex[:24]}",
type="function",
function=Function(
name=func_name,
arguments=json.dumps(param_dict, ensure_ascii=False),
),
)
except (ValueError, IndexError):
return None
def parse(self, text: str) -> ParseResult:
if self.FUNCTION_PREFIX not in text:
return text, None
try:
# Find all tool_call blocks
tc_matches = self.TOOL_CALL_REGEX.findall(text)
raw_blocks = [m[0] if m[0] else m[1] for m in tc_matches]
# Fallback: if no tool_call tags, try the whole text
if not raw_blocks:
raw_blocks = [text]
# Find function blocks within each tool_call
function_strs: List[str] = []
for block in raw_blocks:
func_matches = self.FUNCTION_REGEX.findall(block)
function_strs.extend(m[0] if m[0] else m[1] for m in func_matches)
if not function_strs:
return text, None
# Parse each function call
tool_calls: List[ChatCompletionMessageToolCall] = []
for func_str in function_strs:
tc = self._parse_function_call(func_str)
if tc is not None:
tool_calls.append(tc)
if not tool_calls:
return text, None
# Content before tool calls
first_tc = text.find(self.START_TOKEN)
if first_tc < 0:
first_tc = text.find(self.FUNCTION_PREFIX)
content = text[:first_tc].strip() if first_tc > 0 else None
return content, tool_calls
except Exception:
return text, None

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"""
Qwen 2.5 tool call parser.
Uses the same <tool_call> format as Hermes.
Registered as a separate parser name for clarity when using --tool-parser=qwen.
"""
from environments.tool_call_parsers import register_parser
from environments.tool_call_parsers.hermes_parser import HermesToolCallParser
@register_parser("qwen")
class QwenToolCallParser(HermesToolCallParser):
"""
Parser for Qwen 2.5 tool calls.
Same <tool_call>{"name": ..., "arguments": ...}</tool_call> format as Hermes.
"""
pass # Identical format -- inherits everything from Hermes