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odysseus/src/tool_parsing.py

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"""
tool_parsing.py
Regex-based parsing of tool invocations from LLM response text.
Supports fenced code blocks, [TOOL_CALL] blocks, and XML-style <invoke> blocks.
"""
import ast
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import json
import logging
import re
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from typing import List, Optional
from src.agent_tools import ToolBlock, TOOL_TAGS
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Regex patterns
# ---------------------------------------------------------------------------
# Pattern 1: ```bash ... ``` fenced code blocks
_TOOL_BLOCK_RE = re.compile(
r"```(" + "|".join(TOOL_TAGS) + r")\s*\n([\s\S]*?)```",
re.IGNORECASE,
)
# Pattern 2: [TOOL_CALL] ... [/TOOL_CALL] blocks (some models use this format)
# Matches: {tool => "shell", args => {--command "ls -la"}} etc.
_TOOL_CALL_RE = re.compile(
r"\[TOOL_CALL\]\s*\{([\s\S]*?)\}\s*\[/TOOL_CALL\]",
re.IGNORECASE,
)
# Pattern 3: XML-style tool calls (minimax, some other models)
# <minimax:tool_call><invoke name="bash"><parameter name="command">...</parameter></invoke></minimax:tool_call>
# Also handles: <tool_call><invoke ...>, <function_call><invoke ...>, plain <invoke ...>
_XML_TOOL_CALL_RE = re.compile(
r"<(?:[\w]+:)?(?:tool_call|function_call)>\s*([\s\S]*?)</(?:[\w]+:)?(?:tool_call|function_call)>",
re.IGNORECASE,
)
_XML_INVOKE_RE = re.compile(
r'<invoke\s+name=["\'](\w+)["\']>\s*([\s\S]*?)</invoke>',
re.IGNORECASE,
)
_XML_PARAM_RE = re.compile(
r'<parameter\s+name=["\'](\w+)["\']>([\s\S]*?)</parameter>',
re.IGNORECASE,
)
# Pattern 4: <tool_code> blocks (MiniMax-M2.5 style)
# {tool => 'tool_name', args => '<param>value</param>'}
_TOOL_CODE_RE = re.compile(
r"<tool_code>\s*\{([\s\S]*?)\}\s*</tool_code>",
re.IGNORECASE,
)
# Pattern 5: DeepSeek DSML markup leaking into content. When deepseek
# models can't emit structured tool_calls (e.g. we sent no tool schemas
# that round, or the API didn't parse them), they fall back to raw
# markup using fullwidth-pipe delimiters:
# <||DSML||tool_calls>
# <||DSML||invoke name="web_search">
# <||DSML||parameter name="query" string="true">QUERY</||DSML||parameter>
# </||DSML||invoke>
# </||DSML||tool_calls>
# We normalize it into the standard <invoke>/<parameter> form so the
# existing XML parser + stripper handle it (parse → execute; strip →
# never show the garbage to the user). The pipe run is tolerant of
# fullwidth (U+FF5C) and ascii '|' in any count.
_DSML_PIPES = r"[||]+"
def _normalize_dsml(text: str) -> str:
if not isinstance(text, str):
return ""
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if "DSML" not in text:
return text
t = text
t = re.sub(rf"<\s*{_DSML_PIPES}\s*DSML\s*{_DSML_PIPES}\s*tool_calls\s*>", "<tool_call>", t, flags=re.IGNORECASE)
t = re.sub(rf"<\s*/\s*{_DSML_PIPES}\s*DSML\s*{_DSML_PIPES}\s*tool_calls\s*>", "</tool_call>", t, flags=re.IGNORECASE)
t = re.sub(rf"<\s*{_DSML_PIPES}\s*DSML\s*{_DSML_PIPES}\s*invoke\s+name=", "<invoke name=", t, flags=re.IGNORECASE)
t = re.sub(rf"<\s*/\s*{_DSML_PIPES}\s*DSML\s*{_DSML_PIPES}\s*invoke\s*>", "</invoke>", t, flags=re.IGNORECASE)
# parameter open tag — drop any extra attrs (e.g. string="true").
t = re.sub(rf'<\s*{_DSML_PIPES}\s*DSML\s*{_DSML_PIPES}\s*parameter\s+name=(["\'][^"\']+["\'])[^>]*>',
r"<parameter name=\1>", t, flags=re.IGNORECASE)
t = re.sub(rf"<\s*/\s*{_DSML_PIPES}\s*DSML\s*{_DSML_PIPES}\s*parameter\s*>", "</parameter>", t, flags=re.IGNORECASE)
return t
# Map model tool names to our tool types
_TOOL_NAME_MAP = {
"shell": "bash",
"bash": "bash",
"terminal": "bash",
"command": "bash",
"execute": "bash",
"run": "bash",
"python": "python",
"code": "python",
"search": "web_search",
"web_search": "web_search",
"websearch": "web_search",
"google_search": "web_search",
"google_search_retrieval": "web_search",
"google_search_grounding": "web_search",
"web_fetch": "web_fetch",
"webfetch": "web_fetch",
"fetch_url": "web_fetch",
"fetch": "web_fetch",
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"read": "read_file",
"read_file": "read_file",
"cat": "read_file",
"write": "write_file",
"write_file": "write_file",
"save": "write_file",
"document": "update_document",
"update_document": "update_document",
"create_document": "create_document",
"edit": "edit_document",
"edit_document": "edit_document",
"search_chats": "search_chats",
"search_conversations": "search_chats",
"find_chat": "search_chats",
"chat_with_model": "chat_with_model",
"ask_model": "chat_with_model",
"chat_model": "chat_with_model",
"create_session": "create_session",
"new_session": "create_session",
"list_sessions": "list_sessions",
"send_to_session": "send_to_session",
"message_session": "send_to_session",
"pipeline": "pipeline",
"chain": "pipeline",
"manage_session": "manage_session",
"session_control": "manage_session",
"manage_memory": "manage_memory",
"memory": "manage_memory",
"manage_tasks": "manage_tasks",
"tasks": "manage_tasks",
"schedule": "manage_tasks",
"list_models": "list_models",
"models": "list_models",
"available_models": "list_models",
"ui_control": "ui_control",
"ui": "ui_control",
"control": "ui_control",
"api_call": "api_call",
"api": "api_call",
"integration": "api_call",
"ask_teacher": "ask_teacher",
"teacher": "ask_teacher",
"manage_skills": "manage_skills",
"skills": "manage_skills",
"skill": "manage_skills",
"suggest_document": "suggest_document",
"suggest": "suggest_document",
"review_document": "suggest_document",
"manage_endpoints": "manage_endpoints",
"endpoints": "manage_endpoints",
"manage_mcp": "manage_mcp",
"mcp_servers": "manage_mcp",
"manage_webhooks": "manage_webhooks",
"webhooks": "manage_webhooks",
"manage_tokens": "manage_tokens",
"tokens": "manage_tokens",
"manage_documents": "manage_documents",
"documents": "manage_documents",
"manage_research": "manage_research",
"list_research": "manage_research",
"read_research": "manage_research",
"open_research": "manage_research",
"delete_research": "manage_research",
"manage_settings": "manage_settings",
"settings": "manage_settings",
"preferences": "manage_settings",
"manage_notes": "manage_notes",
"notes": "manage_notes",
"todo": "manage_notes",
"todos": "manage_notes",
}
_MISFENCED_WEB_TOOL_NAMES = {
"web_search": "web_search",
"websearch": "web_search",
"google_search": "web_search",
"google_search_retrieval": "web_search",
"google_search_grounding": "web_search",
"web_fetch": "web_fetch",
"webfetch": "web_fetch",
"fetch_url": "web_fetch",
}
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# ---------------------------------------------------------------------------
# Parsing functions
# ---------------------------------------------------------------------------
def _literal_string(value) -> Optional[str]:
"""Return a string from a small literal AST node, or None."""
try:
parsed = ast.literal_eval(value)
except (ValueError, SyntaxError, TypeError):
return None
if isinstance(parsed, str):
return parsed.strip()
if isinstance(parsed, list):
for item in parsed:
if isinstance(item, str) and item.strip():
return item.strip()
return None
def _parse_misfenced_web_lookup(content: str) -> Optional[ToolBlock]:
"""Recover simple web_search/web_fetch calls wrapped in python/bash fences.
Some local fenced-tool models write:
```python
web_search("latest python release")
```
That is an intended tool call, not Python code. Keep this intentionally
narrow: only a single bare function call to a known web tool alias converts.
"""
try:
module = ast.parse(content.strip(), mode="exec")
except SyntaxError:
return None
if len(module.body) != 1 or not isinstance(module.body[0], ast.Expr):
return None
call = module.body[0].value
if not isinstance(call, ast.Call) or not isinstance(call.func, ast.Name):
return None
mapped = _MISFENCED_WEB_TOOL_NAMES.get(call.func.id.lower())
if mapped not in ("web_search", "web_fetch"):
return None
if len(call.args) > 1:
return None
args = {}
if call.args:
key = "url" if mapped == "web_fetch" else "query"
value = _literal_string(call.args[0])
if not value:
return None
args[key] = value
allowed = {"query", "queries", "url", "time_filter", "freshness", "max_pages"}
for keyword in call.keywords:
if keyword.arg not in allowed:
return None
key = "query" if keyword.arg == "queries" else keyword.arg
value = _literal_string(keyword.value)
if value is not None:
args[key] = value
continue
try:
parsed = ast.literal_eval(keyword.value)
except (ValueError, SyntaxError, TypeError):
return None
if key == "max_pages" and isinstance(parsed, int):
args[key] = parsed
continue
return None
if mapped == "web_search":
query = args.get("query")
if not query:
return None
payload = {"query": query}
for key in ("time_filter", "freshness", "max_pages"):
if key in args:
payload[key] = args[key]
if len(payload) == 1:
return ToolBlock("web_search", query)
return ToolBlock("web_search", json.dumps(payload))
url = args.get("url")
if not url:
return None
return ToolBlock("web_fetch", url)
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def _parse_tool_call_block(raw: str) -> Optional[ToolBlock]:
"""Parse a [TOOL_CALL] block into a ToolBlock.
Handles formats like:
{tool => "shell", args => {--command "ls -la"}}
{tool: "shell", command: "ls -la"}
"""
# Try to extract tool name
tool_match = re.search(r'tool\s*(?:=>|:|=)\s*["\']?(\w+)["\']?', raw, re.IGNORECASE)
if not tool_match:
return None
tool_name = tool_match.group(1).lower()
# Fall back to the raw name when it's a real tool but not in the alias
# map, so known tools (e.g. manage_calendar) aren't silently dropped.
mapped = _TOOL_NAME_MAP.get(tool_name) or (tool_name if tool_name in TOOL_TAGS else None)
if not mapped:
return None
# Extract the command/content — try several patterns
content = None
# Pattern: --command "value" or --command 'value'
cmd_match = re.search(r'--command\s+["\'](.+?)["\']', raw, re.DOTALL)
if cmd_match:
content = cmd_match.group(1)
# Pattern: command => "value" or command: "value"
if not content:
cmd_match = re.search(r'command\s*(?:=>|:|=)\s*["\'](.+?)["\']', raw, re.DOTALL)
if cmd_match:
content = cmd_match.group(1)
# Pattern: args => {content} — extract everything inside the nested braces
if not content:
args_match = re.search(r'args\s*(?:=>|:|=)\s*\{([\s\S]*)\}', raw, re.DOTALL)
if args_match:
inner = args_match.group(1).strip()
# Strip quotes and key prefixes
inner = re.sub(r'^--?\w+\s+', '', inner)
inner = inner.strip('\'"')
if inner:
content = inner
# Pattern: query/path/code => "value"
if not content:
for key in ("query", "path", "code", "content", "text", "file"):
m = re.search(rf'{key}\s*(?:=>|:|=)\s*["\'](.+?)["\']', raw, re.DOTALL)
if m:
content = m.group(1)
break
# Last resort: take everything after the tool declaration
if not content:
rest = raw[tool_match.end():].strip()
rest = re.sub(r'^[,;]\s*', '', rest)
rest = rest.strip('{} \t\n\'"')
if rest:
content = rest
if content:
return ToolBlock(mapped, content.strip())
return None
def _parse_xml_invoke(inv_match) -> Optional[ToolBlock]:
"""Parse an <invoke name="tool"><parameter ...>...</parameter></invoke> match.
Delegates content-shaping to function_call_to_tool_block — the SAME
converter used for native function calls — so the full tool set (every
name in TOOL_TAGS, plus email + MCP tools) and the correct per-tool
content format are handled in ONE place. The previous version duplicated
a partial, hand-maintained tool-name map plus a `key: value` serializer:
any tool missing from that map (e.g. `manage_calendar`) was silently
dropped, and JSON-arg tools got an unparseable `k: v` blob. Both bugs
made deepseek's DSML `create_event` calls vanish with no execution.
"""
# Lowercase the tool name: models often emit capitalized invoke names
# (e.g. <invoke name="Bash">) and function_call_to_tool_block matches
# case-sensitively against the lowercase _TOOL_NAME_MAP / TOOL_TAGS, so a
# raw capitalized name would be silently dropped.
tool_name = inv_match.group(1).lower()
body = inv_match.group(2)
params = {}
for pm in _XML_PARAM_RE.finditer(body):
params[pm.group(1)] = pm.group(2).strip()
# Local import to avoid a circular import at module load.
from src.tool_schemas import function_call_to_tool_block
return function_call_to_tool_block(tool_name, json.dumps(params))
def _parse_tool_code_block(raw: str) -> Optional[ToolBlock]:
"""Parse a <tool_code>{tool => 'name', args => '...'}</tool_code> block (MiniMax style)."""
# Extract tool name
tool_match = re.search(r"tool\s*=>\s*['\"](\S+?)['\"]", raw)
if not tool_match:
return None
tool_name = tool_match.group(1).lower().replace('-', '_')
# Strip MCP prefixes like "mcp__server__" or "cli-mcp-server-"
for prefix in ("mcp__", "cli_mcp_server_", "desktop_commander_", "mcp_code_executor_"):
if tool_name.startswith(prefix):
tool_name = tool_name[len(prefix):]
break
mapped = _TOOL_NAME_MAP.get(tool_name)
# Extract args content
args_match = re.search(r"args\s*=>\s*['\"]?\s*([\s\S]*?)\s*['\"]?\s*$", raw, re.DOTALL)
args_body = args_match.group(1).strip().strip("'\"") if args_match else ""
# Parse XML params inside args (e.g. <command>ls</command>)
xml_params = {}
for pm in re.finditer(r"<(\w+)>([\s\S]*?)</\1>", args_body):
xml_params[pm.group(1)] = pm.group(2).strip()
# When the model gave structured params, hand them to the canonical
# converter (same as native calls + <invoke>) so the full tool set and
# correct per-tool content format apply — not a partial map + k:v blob.
if xml_params:
from src.tool_schemas import function_call_to_tool_block
block = function_call_to_tool_block(mapped or tool_name, json.dumps(xml_params))
if block:
return block
# No structured params: args_body is a raw single value (e.g. a bash
# command). Keep the freeform special-casing for the simple tools.
if mapped:
if mapped == "bash":
content = xml_params.get("command", args_body)
elif mapped == "python":
content = xml_params.get("code", args_body)
elif mapped == "web_search":
content = xml_params.get("query", args_body)
elif mapped == "web_fetch":
content = xml_params.get("url", args_body)
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elif mapped in ("read_file", "write_file"):
content = xml_params.get("path", xml_params.get("file_path", args_body))
else:
content = "\n".join(f"{k}: {v}" for k, v in xml_params.items()) if xml_params else args_body
if content:
return ToolBlock(mapped, content.strip())
elif tool_name and args_body:
# Unknown tool — try as MCP tool call
content = "\n".join(f"{k}: {v}" for k, v in xml_params.items()) if xml_params else args_body
return ToolBlock(tool_name, content.strip())
return None
fix(agent): stop treating illustrative Markdown fences as tool calls for native function-calling models (#3356) * fix(agent): stop executing illustrative Markdown fences as tool calls for native function-calling models _resolve_tool_blocks fell back to the textual parse_tool_blocks() fenced-block parser whenever a model produced no native tool_calls, regardless of whether that model has a reliable native function-calling channel. Native models (GPT/Claude/Grok/Qwen3/DeepSeek-V, etc. - _is_api_model true) commonly write illustrative ```bash/```python/```json examples in guide-only prose; the fallback parser matched these and executed them as real commands, sometimes looping for several rounds as the model tried to clarify with more examples (#3222). Restrict the textual fenced-block fallback to non-native models, which rely on it as their only tool-invocation channel. Native models are trusted to use their structured tool_calls channel for real invocations; when they don't emit one, a bare fence in their response is prose, not an action. The native tool_calls path itself is untouched. This sits one layer below #3088's guide-only policy enforcement: that PR blocks tool exposure/execution on explicit no-tools requests, while this fixes the parser so ordinary illustrative fences are never misread as calls in the first place, on any turn. * fix(agent): gate only the fenced-example pattern for native models, preserve DSML/invoke recovery and persistence _resolve_tool_blocks previously short-circuited the entire textual parser (tool_blocks = [] if is_api_model else parse_tool_blocks(...)) for native function-calling models with no native tool_calls. That also dropped Patterns 2-5 (explicit [TOOL_CALL]/<invoke>/<tool_code>/DSML markup leaked into content as text), which are real calls a model couldn't emit on its structured channel (e.g. DeepSeek-V falling back to DSML), not illustrative examples. parse_tool_blocks/strip_tool_blocks now take a skip_fenced flag that gates ONLY Pattern 1 (the fenced ```bash/```python/```json block matcher). _resolve_tool_blocks passes skip_fenced=is_api_model so fenced examples stop being executed for native models while [TOOL_CALL]/<invoke>/<tool_code>/DSML stay fully active and recoverable. cleaned_round mirrors the same gate when persisting round text, so an illustrative fence that wasn't executed isn't stripped from saved/reloaded history either (it was streaming once and then disappearing on reload).
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def parse_tool_blocks(text: str, skip_fenced: bool = False) -> List[ToolBlock]:
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"""Extract executable tool blocks from LLM response text.
Supports multiple formats:
1. ```bash ... ``` fenced code blocks (standard)
2. [TOOL_CALL] ... [/TOOL_CALL] blocks (some models)
3. XML-style <tool_call>/<invoke> blocks
4. <tool_code> blocks (MiniMax-M2.5 style)
5. DeepSeek DSML markup (normalized to <invoke> first)
fix(agent): stop treating illustrative Markdown fences as tool calls for native function-calling models (#3356) * fix(agent): stop executing illustrative Markdown fences as tool calls for native function-calling models _resolve_tool_blocks fell back to the textual parse_tool_blocks() fenced-block parser whenever a model produced no native tool_calls, regardless of whether that model has a reliable native function-calling channel. Native models (GPT/Claude/Grok/Qwen3/DeepSeek-V, etc. - _is_api_model true) commonly write illustrative ```bash/```python/```json examples in guide-only prose; the fallback parser matched these and executed them as real commands, sometimes looping for several rounds as the model tried to clarify with more examples (#3222). Restrict the textual fenced-block fallback to non-native models, which rely on it as their only tool-invocation channel. Native models are trusted to use their structured tool_calls channel for real invocations; when they don't emit one, a bare fence in their response is prose, not an action. The native tool_calls path itself is untouched. This sits one layer below #3088's guide-only policy enforcement: that PR blocks tool exposure/execution on explicit no-tools requests, while this fixes the parser so ordinary illustrative fences are never misread as calls in the first place, on any turn. * fix(agent): gate only the fenced-example pattern for native models, preserve DSML/invoke recovery and persistence _resolve_tool_blocks previously short-circuited the entire textual parser (tool_blocks = [] if is_api_model else parse_tool_blocks(...)) for native function-calling models with no native tool_calls. That also dropped Patterns 2-5 (explicit [TOOL_CALL]/<invoke>/<tool_code>/DSML markup leaked into content as text), which are real calls a model couldn't emit on its structured channel (e.g. DeepSeek-V falling back to DSML), not illustrative examples. parse_tool_blocks/strip_tool_blocks now take a skip_fenced flag that gates ONLY Pattern 1 (the fenced ```bash/```python/```json block matcher). _resolve_tool_blocks passes skip_fenced=is_api_model so fenced examples stop being executed for native models while [TOOL_CALL]/<invoke>/<tool_code>/DSML stay fully active and recoverable. cleaned_round mirrors the same gate when persisting round text, so an illustrative fence that wasn't executed isn't stripped from saved/reloaded history either (it was streaming once and then disappearing on reload).
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`skip_fenced`: when True, Pattern 1 (fenced ```bash/```python/```json code
blocks) is not matched at all. Native function-calling models (GPT/Claude/
Grok/Qwen3/DeepSeek-V, etc.) commonly write illustrative fenced examples in
prose; for those models we trust the structured tool_calls channel for real
invocations and treat a bare fence as display text rather than an action
(issue #3222). Patterns 2-5 — explicit [TOOL_CALL]/<invoke>/<tool_code>/DSML
markup that leaked into content as text — stay fully active regardless,
since that markup is never an illustrative example and dropping it would
silently lose real calls (e.g. DeepSeek-V falling back to DSML when it
can't emit structured tool_calls).
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"""
blocks = []
# Normalize DeepSeek DSML markup into standard <invoke> form so the
# XML patterns below catch it.
text = _normalize_dsml(text)
fix(agent): stop treating illustrative Markdown fences as tool calls for native function-calling models (#3356) * fix(agent): stop executing illustrative Markdown fences as tool calls for native function-calling models _resolve_tool_blocks fell back to the textual parse_tool_blocks() fenced-block parser whenever a model produced no native tool_calls, regardless of whether that model has a reliable native function-calling channel. Native models (GPT/Claude/Grok/Qwen3/DeepSeek-V, etc. - _is_api_model true) commonly write illustrative ```bash/```python/```json examples in guide-only prose; the fallback parser matched these and executed them as real commands, sometimes looping for several rounds as the model tried to clarify with more examples (#3222). Restrict the textual fenced-block fallback to non-native models, which rely on it as their only tool-invocation channel. Native models are trusted to use their structured tool_calls channel for real invocations; when they don't emit one, a bare fence in their response is prose, not an action. The native tool_calls path itself is untouched. This sits one layer below #3088's guide-only policy enforcement: that PR blocks tool exposure/execution on explicit no-tools requests, while this fixes the parser so ordinary illustrative fences are never misread as calls in the first place, on any turn. * fix(agent): gate only the fenced-example pattern for native models, preserve DSML/invoke recovery and persistence _resolve_tool_blocks previously short-circuited the entire textual parser (tool_blocks = [] if is_api_model else parse_tool_blocks(...)) for native function-calling models with no native tool_calls. That also dropped Patterns 2-5 (explicit [TOOL_CALL]/<invoke>/<tool_code>/DSML markup leaked into content as text), which are real calls a model couldn't emit on its structured channel (e.g. DeepSeek-V falling back to DSML), not illustrative examples. parse_tool_blocks/strip_tool_blocks now take a skip_fenced flag that gates ONLY Pattern 1 (the fenced ```bash/```python/```json block matcher). _resolve_tool_blocks passes skip_fenced=is_api_model so fenced examples stop being executed for native models while [TOOL_CALL]/<invoke>/<tool_code>/DSML stay fully active and recoverable. cleaned_round mirrors the same gate when persisting round text, so an illustrative fence that wasn't executed isn't stripped from saved/reloaded history either (it was streaming once and then disappearing on reload).
2026-06-08 17:25:28 -03:00
# Pattern 1: fenced code blocks (skipped when `skip_fenced` — see docstring).
if not skip_fenced:
for m in _TOOL_BLOCK_RE.finditer(text):
tag = m.group(1).lower()
content = m.group(2).strip()
if not content:
continue
# If a code block's content is an <invoke> XML call (some models wrap
# tool calls in ```python or ```xml fences), parse the invoke instead.
if '<invoke' in content:
for inv in _XML_INVOKE_RE.finditer(content):
block = _parse_xml_invoke(inv)
if block:
blocks.append(block)
# This fenced block is <invoke> markup, not literal code. Whether or
# not any call converted, never fall through to append the raw XML as
# a python/bash block — e.g. a hyphenated/namespaced tool name that
# _XML_INVOKE_RE's \w+ can't match would otherwise be executed as code.
continue
if tag in ("python", "bash"):
block = _parse_misfenced_web_lookup(content)
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if block:
blocks.append(block)
fix(agent): stop treating illustrative Markdown fences as tool calls for native function-calling models (#3356) * fix(agent): stop executing illustrative Markdown fences as tool calls for native function-calling models _resolve_tool_blocks fell back to the textual parse_tool_blocks() fenced-block parser whenever a model produced no native tool_calls, regardless of whether that model has a reliable native function-calling channel. Native models (GPT/Claude/Grok/Qwen3/DeepSeek-V, etc. - _is_api_model true) commonly write illustrative ```bash/```python/```json examples in guide-only prose; the fallback parser matched these and executed them as real commands, sometimes looping for several rounds as the model tried to clarify with more examples (#3222). Restrict the textual fenced-block fallback to non-native models, which rely on it as their only tool-invocation channel. Native models are trusted to use their structured tool_calls channel for real invocations; when they don't emit one, a bare fence in their response is prose, not an action. The native tool_calls path itself is untouched. This sits one layer below #3088's guide-only policy enforcement: that PR blocks tool exposure/execution on explicit no-tools requests, while this fixes the parser so ordinary illustrative fences are never misread as calls in the first place, on any turn. * fix(agent): gate only the fenced-example pattern for native models, preserve DSML/invoke recovery and persistence _resolve_tool_blocks previously short-circuited the entire textual parser (tool_blocks = [] if is_api_model else parse_tool_blocks(...)) for native function-calling models with no native tool_calls. That also dropped Patterns 2-5 (explicit [TOOL_CALL]/<invoke>/<tool_code>/DSML markup leaked into content as text), which are real calls a model couldn't emit on its structured channel (e.g. DeepSeek-V falling back to DSML), not illustrative examples. parse_tool_blocks/strip_tool_blocks now take a skip_fenced flag that gates ONLY Pattern 1 (the fenced ```bash/```python/```json block matcher). _resolve_tool_blocks passes skip_fenced=is_api_model so fenced examples stop being executed for native models while [TOOL_CALL]/<invoke>/<tool_code>/DSML stay fully active and recoverable. cleaned_round mirrors the same gate when persisting round text, so an illustrative fence that wasn't executed isn't stripped from saved/reloaded history either (it was streaming once and then disappearing on reload).
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continue
blocks.append(ToolBlock(tag, content))
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# Pattern 2: [TOOL_CALL] blocks (only if no fenced blocks found)
if not blocks:
for m in _TOOL_CALL_RE.finditer(text):
block = _parse_tool_call_block(m.group(1))
if block:
blocks.append(block)
# Pattern 3: XML-style <tool_call>/<invoke> blocks
if not blocks:
# Try wrapped: <tool_call><invoke ...>...</invoke></tool_call>
for m in _XML_TOOL_CALL_RE.finditer(text):
for inv in _XML_INVOKE_RE.finditer(m.group(1)):
block = _parse_xml_invoke(inv)
if block:
blocks.append(block)
# Try bare <invoke> without wrapper
if not blocks:
for inv in _XML_INVOKE_RE.finditer(text):
block = _parse_xml_invoke(inv)
if block:
blocks.append(block)
# Pattern 4: <tool_code> blocks (MiniMax-M2.5 style)
if not blocks:
for m in _TOOL_CODE_RE.finditer(text):
block = _parse_tool_code_block(m.group(1))
if block:
blocks.append(block)
return blocks
fix(agent): stop treating illustrative Markdown fences as tool calls for native function-calling models (#3356) * fix(agent): stop executing illustrative Markdown fences as tool calls for native function-calling models _resolve_tool_blocks fell back to the textual parse_tool_blocks() fenced-block parser whenever a model produced no native tool_calls, regardless of whether that model has a reliable native function-calling channel. Native models (GPT/Claude/Grok/Qwen3/DeepSeek-V, etc. - _is_api_model true) commonly write illustrative ```bash/```python/```json examples in guide-only prose; the fallback parser matched these and executed them as real commands, sometimes looping for several rounds as the model tried to clarify with more examples (#3222). Restrict the textual fenced-block fallback to non-native models, which rely on it as their only tool-invocation channel. Native models are trusted to use their structured tool_calls channel for real invocations; when they don't emit one, a bare fence in their response is prose, not an action. The native tool_calls path itself is untouched. This sits one layer below #3088's guide-only policy enforcement: that PR blocks tool exposure/execution on explicit no-tools requests, while this fixes the parser so ordinary illustrative fences are never misread as calls in the first place, on any turn. * fix(agent): gate only the fenced-example pattern for native models, preserve DSML/invoke recovery and persistence _resolve_tool_blocks previously short-circuited the entire textual parser (tool_blocks = [] if is_api_model else parse_tool_blocks(...)) for native function-calling models with no native tool_calls. That also dropped Patterns 2-5 (explicit [TOOL_CALL]/<invoke>/<tool_code>/DSML markup leaked into content as text), which are real calls a model couldn't emit on its structured channel (e.g. DeepSeek-V falling back to DSML), not illustrative examples. parse_tool_blocks/strip_tool_blocks now take a skip_fenced flag that gates ONLY Pattern 1 (the fenced ```bash/```python/```json block matcher). _resolve_tool_blocks passes skip_fenced=is_api_model so fenced examples stop being executed for native models while [TOOL_CALL]/<invoke>/<tool_code>/DSML stay fully active and recoverable. cleaned_round mirrors the same gate when persisting round text, so an illustrative fence that wasn't executed isn't stripped from saved/reloaded history either (it was streaming once and then disappearing on reload).
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def strip_tool_blocks(text: str, skip_fenced: bool = False) -> str:
"""Remove executable tool blocks from text for clean display.
`skip_fenced`: when True, fenced ```bash/```python/```json code blocks
(Pattern 1) are left intact instead of being stripped. This must mirror
whatever `skip_fenced` value `parse_tool_blocks` was called with for the
same response: if a fence wasn't executed as a tool call (because it's an
illustrative example from a native function-calling model), it shouldn't
vanish from the persisted/displayed text either — otherwise the example
streams once and then disappears on reload (issue #3222 follow-up).
Patterns 2-5 + DSML markup are always stripped, since that markup should
never reach the user regardless of whether it converted to a tool call.
"""
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# Normalize DSML first so its markup gets stripped by the <invoke>
# / <tool_call> removers below instead of leaking to the user.
text = _normalize_dsml(text)
fix(agent): stop treating illustrative Markdown fences as tool calls for native function-calling models (#3356) * fix(agent): stop executing illustrative Markdown fences as tool calls for native function-calling models _resolve_tool_blocks fell back to the textual parse_tool_blocks() fenced-block parser whenever a model produced no native tool_calls, regardless of whether that model has a reliable native function-calling channel. Native models (GPT/Claude/Grok/Qwen3/DeepSeek-V, etc. - _is_api_model true) commonly write illustrative ```bash/```python/```json examples in guide-only prose; the fallback parser matched these and executed them as real commands, sometimes looping for several rounds as the model tried to clarify with more examples (#3222). Restrict the textual fenced-block fallback to non-native models, which rely on it as their only tool-invocation channel. Native models are trusted to use their structured tool_calls channel for real invocations; when they don't emit one, a bare fence in their response is prose, not an action. The native tool_calls path itself is untouched. This sits one layer below #3088's guide-only policy enforcement: that PR blocks tool exposure/execution on explicit no-tools requests, while this fixes the parser so ordinary illustrative fences are never misread as calls in the first place, on any turn. * fix(agent): gate only the fenced-example pattern for native models, preserve DSML/invoke recovery and persistence _resolve_tool_blocks previously short-circuited the entire textual parser (tool_blocks = [] if is_api_model else parse_tool_blocks(...)) for native function-calling models with no native tool_calls. That also dropped Patterns 2-5 (explicit [TOOL_CALL]/<invoke>/<tool_code>/DSML markup leaked into content as text), which are real calls a model couldn't emit on its structured channel (e.g. DeepSeek-V falling back to DSML), not illustrative examples. parse_tool_blocks/strip_tool_blocks now take a skip_fenced flag that gates ONLY Pattern 1 (the fenced ```bash/```python/```json block matcher). _resolve_tool_blocks passes skip_fenced=is_api_model so fenced examples stop being executed for native models while [TOOL_CALL]/<invoke>/<tool_code>/DSML stay fully active and recoverable. cleaned_round mirrors the same gate when persisting round text, so an illustrative fence that wasn't executed isn't stripped from saved/reloaded history either (it was streaming once and then disappearing on reload).
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cleaned = text if skip_fenced else _TOOL_BLOCK_RE.sub('', text)
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cleaned = _TOOL_CALL_RE.sub('', cleaned)
cleaned = _XML_TOOL_CALL_RE.sub('', cleaned)
cleaned = _TOOL_CODE_RE.sub('', cleaned)
# Strip bare <invoke> blocks not wrapped in <tool_call>
cleaned = re.sub(r'<invoke\s+name=["\'].*?</invoke>', '', cleaned, flags=re.DOTALL | re.IGNORECASE)
cleaned = re.sub(r'\n{3,}', '\n\n', cleaned)
return cleaned.strip()