# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Tests for the safetensors agentic tool loop.
Covers the ``tool_call_parser`` helpers and the cumulative-text state machine in
``run_safetensors_tool_loop``, run against fake single-turn generators (no model
load). Edge cases: plain answers, JSON and XML tool-call forms, truncated/unclosed
calls, tool-result feedback, bad-JSON heal, duplicate-call short-circuit,
``__IMAGES__`` sentinel stripping, executor errors, cancel, and the iteration cap.
"""
import json
import threading
from typing import cast
import pytest
from core.inference import safetensors_agentic
from core.inference.safetensors_agentic import (
_coerce_arguments,
_detect_render_html_tool_start,
run_safetensors_tool_loop,
strip_tool_markup_streaming,
)
from core.inference.tool_call_parser import (
NUDGE_TOOL_CALLS_STATUS,
RAG_MAX_SEARCHES_PER_TURN,
has_tool_signal,
parse_tool_calls_from_text,
strip_tool_markup,
)
from state import tool_approvals
from state.tool_approvals import resolve_tool_decision
from utils.datasets import is_gpt_oss_model_name
# ────────────────────────────────────────────────────────────────────
# parse_tool_calls_from_text
# ────────────────────────────────────────────────────────────────────
class TestParser:
def test_json_tool_call(self):
text = '{"name":"web_search","arguments":{"query":"hello"}}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
tc = result[0]
assert tc["type"] == "function"
assert tc["function"]["name"] == "web_search"
# Arguments must always be a JSON string.
assert isinstance(tc["function"]["arguments"], str)
assert "hello" in tc["function"]["arguments"]
def test_json_tool_call_unclosed(self):
# No ; balanced-brace extractor must still close it.
text = '{"name":"python","arguments":{"code":"print(1)"}}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "python"
def test_json_tool_call_unclosed_requires_healing(self):
text = '{"name":"python","arguments":{"code":"print(1)"}}'
assert parse_tool_calls_from_text(text)[0]["function"]["name"] == "python"
assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
def test_gemma_native_tool_call(self):
text = '<|tool_call>call:terminal{command:"ls -la",workdir:"."}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "terminal"
args = json.loads(result[0]["function"]["arguments"])
assert args == {"command": "ls -la", "workdir": "."}
def test_gemma_native_tool_call_template_quotes(self):
text = '<|tool_call>call:web_search{query:<|"|>openai news<|"|>}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
assert json.loads(result[0]["function"]["arguments"]) == {"query": "openai news"}
def test_gemma_native_tool_call_template_quotes_escape_backslashes(self):
text = r'<|tool_call>call:ls{path:<|"|>C:\Users\wasim\repo<|"|>}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "ls"
assert json.loads(result[0]["function"]["arguments"]) == {"path": r"C:\Users\wasim\repo"}
def test_gemma_native_tool_call_hyphenated_argument_name(self):
text = '<|tool_call>call:mcp__srv__create-issue{issue-title:"Bug report"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "mcp__srv__create-issue"
assert json.loads(result[0]["function"]["arguments"]) == {"issue-title": "Bug report"}
def test_gemma_native_tool_call_keeps_braces_inside_string_value(self):
text = '<|tool_call>call:terminal{command:"echo {foo:bar}"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "terminal"
assert json.loads(result[0]["function"]["arguments"]) == {"command": "echo {foo:bar}"}
def test_gemma_native_tool_call_bare_string_values(self):
text = "<|tool_call>call:get_weather{location:Tokyo,unit:celsius}"
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert json.loads(result[0]["function"]["arguments"]) == {
"location": "Tokyo",
"unit": "celsius",
}
def test_xml_function_call(self):
text = "print('hi')"
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "python"
assert "print('hi')" in result[0]["function"]["arguments"]
def test_xml_param_preserves_leading_indentation(self):
import json
# Only the wrapping newline is trimmed; code-argument indentation survives.
text = (
"\n indented = 1\n more\n"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert json.loads(result[0]["function"]["arguments"]) == {
"code": " indented = 1\n more"
}
def test_xml_unclosed(self):
# Closing tags omitted; parser must still extract the value.
text = "ls -la"
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "terminal"
assert "ls -la" in result[0]["function"]["arguments"]
def test_xml_unclosed_requires_healing(self):
text = "ls -la"
assert parse_tool_calls_from_text(text)[0]["function"]["name"] == "terminal"
assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
def test_code_with_embedded_xml(self):
# A code parameter with a literal must not truncate: the
# parser uses end-of-body as the only boundary for single-param calls.
text = (
"html = ''\nprint('hi')"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert "print('hi')" in result[0]["function"]["arguments"]
def test_xml_param_preserves_leading_indentation(self):
# Only the wrapping newline is trimmed, so code-argument indentation survives (str.strip() destroyed it).
text = (
"\n indented = 1\n more\n"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert json.loads(result[0]["function"]["arguments"]) == {
"code": " indented = 1\n more"
}
def test_function_signal_inside_parameter_is_literal(self):
text = (
""
"print('')"
""
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "python"
assert "" in result[0]["function"]["arguments"]
def test_multiple_calls(self):
text = (
'{"name":"web_search","arguments":{"query":"a"}}'
'{"name":"web_search","arguments":{"query":"b"}}'
)
result = parse_tool_calls_from_text(text)
assert len(result) == 2
assert result[0]["function"]["name"] == "web_search"
assert result[1]["function"]["name"] == "web_search"
def test_bad_json_does_not_raise(self):
text = "{not valid json}"
result = parse_tool_calls_from_text(text)
# Bad JSON is dropped silently; caller can fall back to text.
assert result == []
def test_has_tool_signal(self):
assert has_tool_signal("blah x")
assert has_tool_signal("blah <|tool_call>call:terminal")
assert has_tool_signal("hi ...")
assert has_tool_signal("ok [TOOL_CALLS]web_search{...")
assert has_tool_signal("fine python[ARGS]{...")
assert not has_tool_signal("hello world")
def test_render_html_start_detector_uses_first_tool(self):
assert _detect_render_html_tool_start("")
assert _detect_render_html_tool_start(
'{"name":"render_html","arguments":{"code":""}'
)
assert not _detect_render_html_tool_start(
"''"
)
assert not _detect_render_html_tool_start(
'{"name":"python","arguments":{"code":""}}'
)
def test_render_html_start_detector_covers_mistral_and_rehearsal_forms(self):
# The provisional render-html card must fire for bracket-tag forms too, not only XML.
assert _detect_render_html_tool_start('[TOOL_CALLS]render_html{"code":""}')
assert _detect_render_html_tool_start('[TOOL_CALLS]render_html[ARGS]{"code":"x"}')
assert _detect_render_html_tool_start(
'[TOOL_CALLS] [{"name":"render_html","arguments":{}}]'
)
assert _detect_render_html_tool_start('render_html[ARGS]{"code":""}')
# A different first tool (or a prose mention with no JSON body) must not fire.
assert not _detect_render_html_tool_start('[TOOL_CALLS]web_search{"q":"x"}')
assert not _detect_render_html_tool_start('web_search[ARGS]{"q":"x"}')
assert not _detect_render_html_tool_start('python[ARGS]{"code":"render_html[ARGS]{}"}')
assert not _detect_render_html_tool_start("use render_html[ARGS] to render")
def test_render_html_start_detector_skips_think_block_rehearsal(self):
# A render_html rehearsed inside think must not fire the card; the outside-think call decides.
assert not _detect_render_html_tool_start(
'draft render_html[ARGS]{"code":"x"}python[ARGS]{"code":"print(1)"}'
)
assert not _detect_render_html_tool_start(
'[THINK]render_html[ARGS]{"code":"x"}[/THINK]web_search[ARGS]{"q":"y"}'
)
# A real render_html AFTER a rehearsed non-render_html inside think still fires.
assert _detect_render_html_tool_start(
'web_search[ARGS]{"q":"x"}render_html[ARGS]{"code":""}'
)
# A render_html rehearsed inside think with no real call after does not fire.
assert not _detect_render_html_tool_start('render_html[ARGS]{"code":"x"}')
def test_render_html_start_detector_reads_top_level_array_name(self):
# Array form: the name is the object's top-level ``"name"``, not an argument key.
assert not _detect_render_html_tool_start(
'[TOOL_CALLS] [{"arguments":{"name":"render_html"},"name":"python"}]'
)
assert _detect_render_html_tool_start(
'[TOOL_CALLS] [{"arguments":{"name":"python"},"name":"render_html"}]'
)
def test_strip_markup_closed(self):
text = "before {} after"
assert strip_tool_markup(text) == "before after"
text = 'before <|tool_call>call:terminal{command:"ls"} after'
assert strip_tool_markup(text) == "before after"
def test_strip_named_mistral_call_consumes_trailing_eos(self):
# The named ``[TOOL_CALLS]name{json}`` shape must eat the optional
# trailing ```` like the array shape, so the EOS marker is not left
# behind as visible content.
text = '[TOOL_CALLS]web_search{"query":"cats"}'
assert strip_tool_markup(text) == ""
text = '[TOOL_CALLS]web_search{"query":"cats"} and then'
assert strip_tool_markup(text) == " and then"
def test_strip_markup_unclosed_final(self):
text = "before {partial"
# final=True drops the trailing run.
assert strip_tool_markup(text, final = True) == "before"
# Without final=True the unclosed run is preserved.
assert "partial" in strip_tool_markup(text)
assert strip_tool_markup("before <|tool_call>call:terminal{", final = True) == "before"
def test_streaming_strip_respects_disabled_healing(self):
raw = 'before {"name":"web_search"'
assert strip_tool_markup_streaming(raw, auto_heal_tool_calls = False) == raw
assert strip_tool_markup_streaming(raw) == "before "
def test_streaming_strip_respects_disabled_healing_without_tool_protocol(self):
raw = 'before {"name":"web_search"'
assert strip_tool_markup_streaming(raw, auto_heal_tool_calls = False) == raw
assert (
strip_tool_markup_streaming(
raw,
auto_heal_tool_calls = False,
tool_protocol_active = True,
)
== "before "
)
# Mistral [TOOL_CALLS] bracket-tag.
def test_mistral_bracket_basic(self):
# Devstral / Mistral-Small fallback when bypassing native FC.
text = '[TOOL_CALLS]web_search{"query":"weather"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
assert isinstance(result[0]["function"]["arguments"], str)
assert "weather" in result[0]["function"]["arguments"]
def test_rehearsal_inside_unclosed_think_is_ignored(self):
"""Rehearsal-shaped markup inside an unclosed block must
not be executed as a real tool call. Mid-stream the
tag has not arrived yet, so the strip regex has to accept
end-of-string as a terminator. Regression for the Gemini
high-severity flag on this PR."""
text = 'I should call web_search[ARGS]{"query":"weather"} next to find the answer.'
result = parse_tool_calls_from_text(text)
# Inside an unclosed think block no calls are yielded.
assert result == []
def test_rehearsal_inside_unclosed_bracket_think_is_ignored(self):
text = '[THINK]planning to use python[ARGS]{"code":"print(1)"} but not yet.'
result = parse_tool_calls_from_text(text)
assert result == []
def test_rehearsal_after_closed_think_still_parsed(self):
text = 'planningpython[ARGS]{"code":"print(1)"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "python"
def test_rehearsal_inside_prefilled_think_is_ignored(self):
"""Reasoning models (Qwen3.5 enable_thinking) open in the PROMPT,
so generated content starts inside the thought and carries only a closing
. A call rehearsed in that leading thought must be skipped, while a
real call after the close still fires."""
text = 'planning web_search[ARGS]{"query":"draft"}python[ARGS]{"code":"print(1)"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "python"
def test_literal_close_think_in_leading_argument_not_prefill(self):
"""A literal inside a real leading call's arguments must not be
read as a prefilled-reasoning close (which would skip the call)."""
text = 'web_search[ARGS]{"query":"what is "}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
def test_stray_close_after_real_call_not_treated_as_prefill(self):
"""A real leading call followed by a stray and no further call is
a normal answer, not prefilled reasoning; the call must still fire (the
virtual span only applies when a real call follows the close)."""
text = 'Now web_search[ARGS]{"query":"x"} answer'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
def test_mistral_bracket_with_whitespace(self):
# Optional whitespace (incl. newlines) between the name and the opening brace.
text = '[TOOL_CALLS]python \n {"code":"print(1)"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "python"
assert "print(1)" in result[0]["function"]["arguments"]
def test_mistral_bracket_nested_json(self):
# Brace-balance scan handles nested objects and braces inside string literals.
text = '[TOOL_CALLS]web_search{"query":"a {nested} brace","opts":{"limit":5}}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
import json as _json
args = _json.loads(result[0]["function"]["arguments"])
assert args["query"] == "a {nested} brace"
assert args["opts"] == {"limit": 5}
def test_mistral_bracket_with_prose(self):
# Bracket-tag surrounded by prose is still recognised.
text = 'Sure, I will look that up.\n[TOOL_CALLS]web_search{"query":"weather"}\nCalling now.'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
def test_mistral_bracket_bad_json_dropped(self):
text = "[TOOL_CALLS]web_search{not valid}"
result = parse_tool_calls_from_text(text)
# No usable tool call; callers fall back to text.
assert result == []
def test_mistral_bracket_object_with_array_value(self):
# Args must be a JSON object; a dict wrapping an array value is accepted.
text = '[TOOL_CALLS]web_search{"opts":[1,2,3]}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
# Rehearsal syntax name[ARGS]{json}.
def test_rehearsal_basic(self):
text = 'python[ARGS]{"code":"print(1)"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "python"
assert "print(1)" in result[0]["function"]["arguments"]
def test_rehearsal_with_prose(self):
text = 'I should call the python tool. Like this: python[ARGS]{"code":"x = 1"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "python"
def test_rehearsal_bad_json_dropped(self):
text = "python[ARGS]{not valid json}"
result = parse_tool_calls_from_text(text)
assert result == []
def test_mistral_bracket_hyphenated_mcp_name(self):
# Dashed MCP names must be captured whole, not truncated at the first dash.
text = '[TOOL_CALLS]mcp__srv__list-issues{"q":"x"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "mcp__srv__list-issues"
def test_rehearsal_hyphenated_mcp_name(self):
text = 'mcp__srv__list-issues[ARGS]{"q":"x"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "mcp__srv__list-issues"
def test_streaming_strip_removes_partial_bracket_marker(self):
# A bracket tag streamed before its opening brace must strip on the final pass, not leak.
assert strip_tool_markup("answer [TOOL_CALLS]web_search", final = True) == "answer"
assert strip_tool_markup("text python[ARGS]", final = True) == "text"
# Non-final must keep the in-progress tag buffered (not yet stripped).
partial = "answer [TOOL_CALLS]web_search"
assert strip_tool_markup(partial, final = False) == partial
def test_strip_removes_two_level_nested_bracket_call_keeps_prose(self):
# Two-level-nested args must be removed whole; the balanced scan handles any depth.
text = 'before [TOOL_CALLS]search{"f":{"g":{"h":1}}} after'
assert strip_tool_markup(text, final = False) == "before after"
assert strip_tool_markup(text, final = True) == "before after"
def test_strip_removes_call_with_literal_think_in_argument(self):
# A literal think block inside arguments strips with the call, not as a reasoning block.
text = (
'{"name":"write","arguments":'
'{"text":"compare and tags"}}'
)
assert strip_tool_markup(text, final = True) == ""
def test_strip_preserves_real_think_but_strips_call_with_literal_think(self):
text = (
"planning ok "
'{"name":"w","arguments":{"t":"x"}} done'
)
out = strip_tool_markup(text, final = True)
assert "planning" in out
assert "" not in out and '"name"' not in out
assert "ok" in out and "done" in out
def test_prose_mentioning_args_marker_is_not_truncated(self):
# ``foo[ARGS] to the template`` is prose; the catch-all must not delete the sentence.
text = "Please pass foo[ARGS] to the template and continue reading."
assert strip_tool_markup(text, final = True) == text
def test_streaming_strip_handles_mistral_v11_call_id_args(self):
# The streaming strip uses the regex patterns directly, so they must cover the v11
# [CALL_ID]/[ARGS] metadata (aligned with the parser).
raw = 'before [TOOL_CALLS]web_search[CALL_ID]abc123[ARGS]{"q":"x"} after'
out = strip_tool_markup_streaming(raw)
assert "[TOOL_CALLS]" not in out and "[CALL_ID]" not in out and "[ARGS]" not in out
assert "before" in out and "after" in out
# pre-strip.
def test_think_block_stripped_before_xml(self):
# The think block is stripped before matching so the post-thinking call is recognised.
text = (
"I will use web_search to find the weather."
'{"name":"web_search","arguments":{"query":"sf"}}'
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
def test_think_block_stripped_before_bracket_tag(self):
text = 'Let me search for that.\n[TOOL_CALLS]web_search{"query":"weather"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
def test_uppercase_think_tag_stripped(self):
# Some templates use [THINK]...[/THINK] instead of .
text = '[THINK]planning my next call[/THINK][TOOL_CALLS]python{"code":"print(1)"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "python"
def test_think_block_hides_inner_tool_call(self):
# A call mentioned inside think is a rehearsal; the wrapper strip removes the inner markup.
text = (
"I might call "
'{"name":"web_search","arguments":{}} '
"but I am not sure\n"
"Let me just answer directly."
)
result = parse_tool_calls_from_text(text)
assert result == []
def test_think_literal_inside_real_tool_argument_is_preserved(self):
# A real call whose argument contains a literal think tag must not be corrupted.
text = (
'{"name":"write","arguments":'
'{"text":"compare and tags"}}'
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert json.loads(result[0]["function"]["arguments"])["text"] == (
"compare and tags"
)
def test_bracket_tag_argument_with_think_literal_is_preserved(self):
text = '[TOOL_CALLS]search{"q":"explain [THINK] blocks"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert json.loads(result[0]["function"]["arguments"])["q"] == "explain [THINK] blocks"
def test_real_call_after_think_with_rehearsal_inside(self):
# A rehearsal inside is skipped, but the real call after the close tag parses.
text = 'plan: search[ARGS]{"q":"x"}search[ARGS]{"q":"real"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert json.loads(result[0]["function"]["arguments"])["q"] == "real"
# XML takes precedence over bracket-tag.
def test_xml_wins_over_bracket(self):
# When a model emits both forms in one message, the XML form is canonical and wins.
text = (
'{"name":"primary","arguments":{}}[TOOL_CALLS]secondary{"k":"v"}'
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "primary"
# Strip patterns include bracket-tag and rehearsal.
def test_strip_bracket_tag_closed(self):
text = 'before [TOOL_CALLS]web_search{"q":"hi"} after'
assert "[TOOL_CALLS]" not in strip_tool_markup(text)
assert "before" in strip_tool_markup(text)
assert "after" in strip_tool_markup(text)
def test_strip_rehearsal_closed(self):
text = 'prose python[ARGS]{"code":"x"} more prose'
cleaned = strip_tool_markup(text)
assert "[ARGS]" not in cleaned
assert "prose" in cleaned
assert "more prose" in cleaned
def test_strip_bracket_tag_unclosed_final(self):
text = 'before [TOOL_CALLS]web_search{"q":"part'
# Final-mode strip drops the trailing unclosed run.
cleaned = strip_tool_markup(text, final = True)
assert "TOOL_CALLS" not in cleaned
assert cleaned == "before"
# Canonical Mistral array, v11 [CALL_ID], unified multi-call (PR review fixes).
def test_mistral_canonical_array_is_parsed(self):
# Canonical multi-call array: every call must parse (was dropped then deleted to EOS).
text = '[TOOL_CALLS] [{"name":"a","arguments":{"x":1}},{"name":"b","arguments":{"y":2}}]'
result = parse_tool_calls_from_text(text)
assert [c["function"]["name"] for c in result] == ["a", "b"]
assert json.loads(result[0]["function"]["arguments"]) == {"x": 1}
assert json.loads(result[1]["function"]["arguments"]) == {"y": 2}
def test_mistral_array_string_arguments_are_decoded(self):
# OpenAI-spec arguments arrive as a JSON string; decode to an object.
text = '[TOOL_CALLS] [{"name":"a","arguments":"{\\"x\\":1}"}]'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert json.loads(result[0]["function"]["arguments"]) == {"x": 1}
def test_mistral_array_scalar_string_argument_not_double_encoded(self):
# A bare scalar string argument in the Mistral array form must be kept
# raw, exactly like the path, so the downstream argument
# healer wraps ``weather`` into the single-string tool's key -- not
# ``"weather"`` with literal quotes from a redundant json.dumps.
array = parse_tool_calls_from_text(
'[TOOL_CALLS][{"name":"web_search","arguments":"weather"}]'
)
xml = parse_tool_calls_from_text(
'{"name":"web_search","arguments":"weather"}'
)
assert array[0]["function"]["arguments"] == xml[0]["function"]["arguments"] == "weather"
healed = _coerce_arguments(
array[0]["function"]["arguments"], heal = True, tool_name = "web_search"
)
assert healed == {"query": "weather"}
def test_mistral_array_strip_keeps_trailing_prose(self):
# The array form must be removed whole, not deleted to end-of-string.
text = 'answer [TOOL_CALLS] [{"name":"a","arguments":{}}] tail'
assert strip_tool_markup(text, final = True) == "answer tail"
def test_mistral_and_rehearsal_in_one_message_both_parse(self):
# A Mistral call and a rehearsal call together: both must parse.
text = '[TOOL_CALLS]a{"x":1} then b[ARGS]{"y":2}'
result = parse_tool_calls_from_text(text)
assert [c["function"]["name"] for c in result] == ["a", "b"]
def test_mistral_v11_call_id_is_not_the_function_name(self):
# v11 shape: the function name is ``name``, never the opaque call-id token.
result = parse_tool_calls_from_text('[TOOL_CALLS]get_weather[CALL_ID]abc123[ARGS]{"q":"x"}')
assert len(result) == 1
assert result[0]["function"]["name"] == "get_weather"
assert json.loads(result[0]["function"]["arguments"]) == {"q": "x"}
# v11 without a call-id parses the same name.
r2 = parse_tool_calls_from_text('[TOOL_CALLS]get_weather[ARGS]{"q":"y"}')
assert r2[0]["function"]["name"] == "get_weather"
def test_strip_preserves_rehearsal_inside_think(self):
# A rehearsal inside is reasoning; strip keeps it verbatim.
text = 'plan: search[ARGS]{"q":"x"} A'
out = strip_tool_markup(text, final = True)
assert out == text
assert "search[ARGS]" in out
def test_streaming_strip_preserves_rehearsal_inside_think(self):
# The streaming strip must also preserve a think rehearsal: a mid-stream strip shrinks
# then regrows the cumulative text (corrupts append-by-length consumers). Matches GGUF.
text = 'plan: search[ARGS]{"q":"x"} A'
assert strip_tool_markup_streaming(text) == text
assert strip_tool_markup_streaming(text, tool_protocol_active = True) == text
# An unclosed block during streaming is preserved too (the parser keeps it).
partial = 'plan: search[ARGS]{"q":"x"}'
assert strip_tool_markup_streaming(partial, tool_protocol_active = True) == partial
def test_streaming_strip_still_removes_real_call_outside_think(self):
# The think guard must not stop the streaming strip removing a call outside the block.
text = 'reason web_search[ARGS]{"q":"x"}'
out = strip_tool_markup_streaming(text, tool_protocol_active = True)
assert "web_search[ARGS]" not in out
assert "reason" in out
def test_strip_bracket_calls_is_linear(self):
# Many complete bracket calls must strip in ~linear time (was O(n^2) per match).
import time
text = '[TOOL_CALLS]f{"a":1}' * 4000 # ~80KB, 4000 complete calls
t0 = time.perf_counter()
out = strip_tool_markup(text, final = True)
elapsed = time.perf_counter() - t0
assert "[TOOL_CALLS]" not in out
assert elapsed < 1.0, f"strip took {elapsed * 1000:.0f}ms on 4000 bracket calls"
def test_streaming_strip_handles_nested_mistral_json(self):
# The non-greedy [TOOL_CALLS]name{...} pattern truncates nested JSON at the first }; the
# balanced helper must remove the whole call so no trailing brace leaks to the streaming ...
raw = 'ok [TOOL_CALLS]foo{"a":{"b":1}} tail'
out = strip_tool_markup_streaming(raw)
assert "[TOOL_CALLS]" not in out
assert "}" not in out
assert "ok " in out and "tail" in out
def test_streaming_strip_handles_nested_wrapperless_gemma(self):
# Same class of bug for the wrapper-less Gemma call:NAME{...} form with a
# nested object argument.
raw = "ok call:f{loc:{city:NYC},n:3} tail"
out = strip_tool_markup_streaming(raw)
assert "call:f" not in out
assert "}" not in out
assert "ok " in out and "tail" in out
def test_streaming_strip_keeps_prose_after_function_xml_with_literal_marker(self):
# A literal ```` in a value is data: the strip must close at the REAL
# ```` and keep trailing prose (the open-ended regex ate to EOF).
raw = (
"pref "
'print("") tail'
)
assert strip_tool_markup_streaming(raw) == "pref tail"
# Streaming and final strip agree on the visible text (final also trims).
assert strip_tool_markup_streaming(raw) == strip_tool_markup(raw, final = True)
def test_streaming_strip_drops_leading_magistral_reasoning(self):
# Magistral emits reasoning as a leading ``[THINK]...[/THINK]`` bracket block
# (not the ```` the reasoning channel renders). The streaming display
# strip must drop it so the raw chain-of-thought does not leak into the
# safetensors content; GGUF routes it to reasoning_content natively.
closed = "[THINK]Let me think. 2+2 is 4.[/THINK]The answer is 4."
assert strip_tool_markup_streaming(closed) == "The answer is 4."
assert strip_tool_markup_streaming(closed) == strip_tool_markup(closed, final = True)
# Unclosed mid-stream reasoning is held from the marker on (nothing leaks, and
# the cleaned text only grows as the answer streams in after ``[/THINK]``).
assert strip_tool_markup_streaming("[THINK]still thinking") == ""
assert strip_tool_markup_streaming("[THINK]r[/THINK]The") == "The"
assert strip_tool_markup_streaming("[THINK]r[/THINK]The answer") == "The answer"
# A non-leading ``[THINK]`` is ordinary prose and is left untouched.
assert strip_tool_markup_streaming("hi [THINK] later") == "hi [THINK] later"
class TestParserMultiFormat:
"""Shared-parser coverage: every family's emission maps to the same OpenAI shape."""
# Llama-3
def test_llama3_python_tag_dot_call(self):
# Llama-3 built-in tools: <|python_tag|>NAME.call(k="v", ...).
import json
text = '<|python_tag|>brave_search.call(query="weather in Tokyo")'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "brave_search"
args = json.loads(result[0]["function"]["arguments"])
assert args == {"query": "weather in Tokyo"}
def test_llama3_python_tag_dot_call_multi_arg(self):
import json
text = '<|python_tag|>get_weather.call(location="Tokyo", units="celsius", days=5)'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
args = json.loads(result[0]["function"]["arguments"])
assert args == {"location": "Tokyo", "units": "celsius", "days": 5}
def test_llama3_python_tag_json_form(self):
import json
text = '<|python_tag|>{"name":"web_search","parameters":{"query":"hi","n":5}}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
args = json.loads(result[0]["function"]["arguments"])
assert args == {"query": "hi", "n": 5}
def test_llama3_python_tag_json_form_with_eom(self):
# Llama-3 emits ``<|eom_id|>`` after the JSON; must not break parsing.
import json
text = '<|python_tag|>{"name":"python","parameters":{"code":"print(2+2)"}}<|eom_id|>'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
args = json.loads(result[0]["function"]["arguments"])
assert args == {"code": "print(2+2)"}
def test_llama3_strip_markup_final(self):
text = '<|python_tag|>brave_search.call(query="x")'
assert strip_tool_markup(text, final = True) == ""
def test_llama3_python_tag_json_form_non_scalar_args_skipped(self):
# Should NOT fabricate ``{"value": args}`` when the JSON form
# has a non-dict / non-string ``arguments`` value.
for bad in (
'<|python_tag|>{"name":"foo","arguments":42}',
'<|python_tag|>{"name":"foo","arguments":[1,2,3]}',
'<|python_tag|>{"name":"foo","arguments":null}',
'<|python_tag|>{"name":"foo","arguments":true}',
):
assert parse_tool_calls_from_text(bad) == [], bad
# ── Llama-3.2 bare JSON ``custom_tools`` ─────────────────────
def test_llama3_2_bare_json_parameters(self):
# Llama-3.2-Instruct emits bare JSON directly as content; no
# <|python_tag|> prefix per its training template.
import json
text = '{"name":"web_search","parameters":{"query":"Tokyo weather"}}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
args = json.loads(result[0]["function"]["arguments"])
assert args == {"query": "Tokyo weather"}
def test_llama3_2_bare_json_arguments_key(self):
import json
text = '{"name":"add","arguments":{"a":1,"b":2}}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
args = json.loads(result[0]["function"]["arguments"])
assert args == {"a": 1, "b": 2}
def test_llama3_2_bare_json_multi_call(self):
# Llama-3 may chain calls with ``; `` per training template.
text = '{"name":"a","parameters":{}}; {"name":"b","parameters":{}}'
result = parse_tool_calls_from_text(text)
assert len(result) == 2
assert result[0]["function"]["name"] == "a"
assert result[1]["function"]["name"] == "b"
def test_llama3_2_bare_json_with_eom_sentinel(self):
text = '{"name":"x","parameters":{"y":1}}<|eom_id|>'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "x"
def test_llama3_2_bare_json_leading_sentinel_skipped(self):
# Sometimes prior <|eot_id|> leaks into the next turn.
text = '<|eot_id|>{"name":"x","parameters":{}}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "x"
def test_llama3_2_bare_json_plain_prose_does_not_fire(self):
# Defensive: must NOT fire on plain assistant prose.
text = "Hello world, how are you today?"
assert parse_tool_calls_from_text(text) == []
def test_llama3_2_bare_json_embedded_in_prose_does_not_fire(self):
# Defensive: JSON embedded in prose must NOT fire (parser is
# strict about content STARTING with `{`).
text = 'The tool result was: {"name":"foo"}'
assert parse_tool_calls_from_text(text) == []
def test_llama3_2_bare_json_missing_name_does_not_fire(self):
text = '{"result":"ok","data":[1,2,3]}'
assert parse_tool_calls_from_text(text) == []
def test_llama3_2_bare_json_missing_args_does_not_fire(self):
text = '{"name":"x"}'
assert parse_tool_calls_from_text(text) == []
def test_llama3_2_bare_json_args_not_dict_does_not_fire(self):
text = '{"name":"x","parameters":42}'
assert parse_tool_calls_from_text(text) == []
def test_llama3_2_bare_json_string_parameters_does_not_fire(self):
# Llama-3 spec: parameters must be a dict. Prose like
# ``{"name":"foo","parameters":"a sentence"}`` must NOT trigger.
text = '{"name":"foo","parameters":"this is a sentence"}'
assert parse_tool_calls_from_text(text) == []
def test_llama3_2_bare_json_string_arguments_not_json_does_not_fire(self):
# OpenAI ``arguments`` may be a JSON-string of a dict, but a
# plain non-JSON string must not pass the guard.
text = '{"name":"foo","arguments":"not json"}'
assert parse_tool_calls_from_text(text) == []
def test_llama3_2_bare_json_string_arguments_json_dict_fires(self):
# OpenAI shape: arguments is a JSON-encoded string of a dict.
text = '{"name":"foo","arguments":"{\\"q\\":\\"x\\"}"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "foo"
# arguments stays as the original JSON-string.
assert result[0]["function"]["arguments"] == '{"q":"x"}'
def test_llama3_2_bare_json_string_arguments_json_non_dict_does_not_fire(self):
# JSON-string that parses to a list / scalar / null must NOT fire.
for bad in (
'{"name":"foo","arguments":"[1,2,3]"}',
'{"name":"foo","arguments":"\\"plain\\""}',
'{"name":"foo","arguments":"null"}',
'{"name":"foo","arguments":"42"}',
):
assert parse_tool_calls_from_text(bad) == [], bad
# Mistral pre-v11
def test_mistral_pre_v11_array(self):
import json
text = '[TOOL_CALLS] [{"name":"web_search","arguments":{"query":"hello"},"id":"abc"}]'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
# Mistral provides its own id; preserve it.
assert result[0]["id"] == "abc"
assert json.loads(result[0]["function"]["arguments"]) == {"query": "hello"}
def test_mistral_array_parameters_key_alias(self):
import json
# Array object keyed on ``parameters`` (not ``arguments``) must keep its
# payload, matching the JSON/XML paths and SGLang's base detector.
text = '[TOOL_CALLS] [{"name":"get_weather","parameters":{"city":"Paris"}}]'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "get_weather"
assert json.loads(result[0]["function"]["arguments"]) == {"city": "Paris"}
def test_mistral_pre_v11_array_multi(self):
text = (
'[TOOL_CALLS] [{"name":"a","arguments":{"x":1},"id":"id1"},'
'{"name":"b","arguments":{"y":2},"id":"id2"}]'
)
result = parse_tool_calls_from_text(text)
assert len(result) == 2
assert result[0]["function"]["name"] == "a"
assert result[1]["function"]["name"] == "b"
def test_mistral_pre_v11_unclosed_array(self):
# Closing ``]`` truncated -- parser must heal off individual objects.
text = '[TOOL_CALLS] [{"name":"web_search","arguments":{"q":"x"},"id":"id"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
# Mistral v11+
def test_mistral_v11_single(self):
# Magistral / Mistral Small 3.1: bare ``name{json}`` after trigger.
import json
text = '[TOOL_CALLS]add{"a":3.5,"b":4}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "add"
assert json.loads(result[0]["function"]["arguments"]) == {"a": 3.5, "b": 4}
def test_mistral_v11_parallel(self):
# v11+ parallel: ``[TOOL_CALLS]a{...}[TOOL_CALLS]b{...}``.
text = '[TOOL_CALLS]add{"a":1}[TOOL_CALLS]sub{"b":2}'
result = parse_tool_calls_from_text(text)
assert len(result) == 2
assert result[0]["function"]["name"] == "add"
assert result[1]["function"]["name"] == "sub"
def test_mistral_v11_with_args_marker(self):
# Ministral / Mistral Large 3: ``[TOOL_CALLS]name[ARGS]{json}``.
import json
text = '[TOOL_CALLS]add[ARGS]{"a":1,"b":2}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "add"
assert json.loads(result[0]["function"]["arguments"]) == {"a": 1, "b": 2}
def test_mistral_strip_markup_v11(self):
text = '[TOOL_CALLS]add{"a":1}'
assert strip_tool_markup(text, final = True) == ""
def test_mistral_call_id_form(self):
# Mistral Small 3.2: ``[TOOL_CALLS]name[CALL_ID][ARGS]{json}``.
# The ``[CALL_ID]`` segment must be skipped, not treated as a stop
# (llama.cpp test-chat.cpp:4785 parses this to one call).
import json
text = '[TOOL_CALLS]special_function[CALL_ID]123456789[ARGS]{"arg1": 1}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "special_function"
assert json.loads(result[0]["function"]["arguments"]) == {"arg1": 1}
def test_mistral_call_id_form_parallel(self):
text = (
'[TOOL_CALLS]special_function[CALL_ID]000000001[ARGS]{"arg1": 1}'
"[TOOL_CALLS]special_function_with_opt[CALL_ID]000000002"
'[ARGS]{"arg1": 1, "arg2": 2}'
)
result = parse_tool_calls_from_text(text)
assert len(result) == 2
assert result[0]["function"]["name"] == "special_function"
assert result[1]["function"]["name"] == "special_function_with_opt"
def test_mistral_call_id_form_stripped(self):
text = '[TOOL_CALLS]special_function[CALL_ID]123456789[ARGS]{"arg1": 1}'
assert strip_tool_markup(text, final = True) == ""
def test_mistral_think_reasoning_ignored(self):
# Magistral wraps reasoning in ``[THINK]...[/THINK]``. A ``[TOOL_CALLS]``
# inside the reasoning is chain-of-thought, not a real call; only the
# call after ``[/THINK]`` counts (llama.cpp test-chat.cpp:2285).
import json
text = (
'[THINK]Let me think about [TOOL_CALLS]fake[ARGS]{"x":1} '
'and more[/THINK][TOOL_CALLS]real_fn[ARGS]{"y":2}'
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "real_fn"
assert json.loads(result[0]["function"]["arguments"]) == {"y": 2}
def test_mistral_think_reasoning_no_real_call(self):
# Reasoning that merely mentions a tool call but does not emit one
# after ``[/THINK]`` yields no calls.
text = '[THINK]I might call [TOOL_CALLS]fake[ARGS]{"x":1}[/THINK]Done.'
assert parse_tool_calls_from_text(text) == []
def test_mistral_think_literal_in_argument_preserved(self):
# A literal ``[THINK]`` inside a real tool argument (after the call)
# must not be stripped or corrupt the parse.
import json
text = '[TOOL_CALLS]search[ARGS]{"q":"explain the [THINK] token"}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert json.loads(result[0]["function"]["arguments"]) == {"q": "explain the [THINK] token"}
# Gemma 4
def test_gemma4_simple_call(self):
import json
text = (
"<|tool_call>call:get_weather{"
'location:<|"|>Tokyo<|"|>,units:<|"|>celsius<|"|>}'
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "get_weather"
args = json.loads(result[0]["function"]["arguments"])
assert args == {"location": "Tokyo", "units": "celsius"}
def test_gemma4_with_primitives(self):
import json
text = (
"<|tool_call>call:set_pref{"
"enabled:true,attempts:5,threshold:1.5,nickname:null}"
)
result = parse_tool_calls_from_text(text)
args = json.loads(result[0]["function"]["arguments"])
assert args == {"enabled": True, "attempts": 5, "threshold": 1.5, "nickname": None}
def test_gemma4_nested_args(self):
# Gemma 4 nests dicts / lists with bare keys and ``<|"|>`` strings.
import json
text = (
"<|tool_call>call:search{"
'query:<|"|>foo<|"|>,filters:{site:<|"|>example.com<|"|>,recent:true},'
'tags:[<|"|>a<|"|>,<|"|>b<|"|>]}'
)
result = parse_tool_calls_from_text(text)
args = json.loads(result[0]["function"]["arguments"])
assert args["query"] == "foo"
assert args["filters"] == {"site": "example.com", "recent": True}
assert args["tags"] == ["a", "b"]
def test_gemma4_multi_call(self):
text = "<|tool_call>call:a{x:1}<|tool_call>call:b{y:2}"
result = parse_tool_calls_from_text(text)
assert len(result) == 2
assert result[0]["function"]["name"] == "a"
assert result[1]["function"]["name"] == "b"
def test_gemma4_unclosed_does_not_raise(self):
# Truncated mid-stream; must not raise.
text = '<|tool_call>call:foo{x:<|"|>bar<|"|>'
result = parse_tool_calls_from_text(text)
assert isinstance(result, list)
def test_gemma4_strip_markup_final(self):
text = "<|tool_call>call:foo{x:1}"
assert strip_tool_markup(text, final = True) == ""
# ── Gemma 4 wrapper-less (skip_special_tokens stripped) ───────────
def test_gemma4_bare_stripped_call(self):
# skip_special_tokens removes <|tool_call>/ and <|"|>,
# leaving a bare call:NAME{...} with an unquoted value.
import json
text = "call:web_search{query:weather in San Francisco right now}"
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
args = json.loads(result[0]["function"]["arguments"])
assert args == {"query": "weather in San Francisco right now"}
def test_gemma4_bare_code_with_commas(self):
# A code value with commas must not truncate at the first comma.
import json
text = (
"call:python{code:def f(n):\n a, b = 0, 1\n"
" for _ in range(2, n+1):\n a, b = b, a + b\n"
" return b\n\nprint(f(30))}"
)
result = parse_tool_calls_from_text(text)
assert result[0]["function"]["name"] == "python"
code = json.loads(result[0]["function"]["arguments"])["code"]
assert "a, b = 0, 1" in code and "print(f(30))" in code
def test_gemma4_bare_quotes_normalized(self):
# The same value quoted vs unquoted must parse identically so the
# agentic loop can collapse a looping model's repeated calls.
import json
a = parse_tool_calls_from_text('call:web_search{query:"foo bar"}')
b = parse_tool_calls_from_text("call:web_search{query:foo bar}")
assert json.loads(a[0]["function"]["arguments"]) == {"query": "foo bar"}
assert json.loads(a[0]["function"]["arguments"]) == json.loads(
b[0]["function"]["arguments"]
)
def test_gemma4_bare_multi_arg(self):
import json
text = "call:web_search{query:pytorch latest, url:https://pytorch.org}"
result = parse_tool_calls_from_text(text)
args = json.loads(result[0]["function"]["arguments"])
assert args == {"query": "pytorch latest", "url": "https://pytorch.org"}
def test_gemma4_bare_not_matched_in_prose(self):
# A word ending in "call:" must not trigger a bare tool call.
text = "I will recall:that the function{ } is helpful."
result = parse_tool_calls_from_text(text)
assert result == []
def test_gemma4_bare_strip_markup_final(self):
text = "Here you go: call:web_search{query:weather today}"
assert "call:web_search" not in strip_tool_markup(text, final = True)
# ── Cross-format sentinels ────────────────────────────────────
def test_all_markers_in_tool_xml_signals(self):
# Streaming buffer wakes up on every emission marker.
from core.inference.tool_call_parser import TOOL_XML_SIGNALS
for marker in (
"",
"",
"[TOOL_CALLS]",
"<|tool_call>",
):
assert marker in TOOL_XML_SIGNALS, f"streaming loop would not wake on {marker!r}"
def test_has_tool_signal_for_all_formats(self):
assert has_tool_signal('<|python_tag|>brave_search.call(q="x")')
assert has_tool_signal('[TOOL_CALLS] [{"name":"x"}]')
assert has_tool_signal('[TOOL_CALLS]add{"a":1}')
assert has_tool_signal("<|tool_call>call:foo{}")
# ────────────────────────────────────────────────────────────────────
# run_safetensors_tool_loop
# ────────────────────────────────────────────────────────────────────
def _fake_stream(chunks):
"""Build a single-turn generator that yields cumulative snapshots."""
def _gen(_messages):
acc = ""
for c in chunks:
acc += c
yield acc
return _gen
def _const_stream(text):
"""A single-turn generator that yields one cumulative snapshot."""
def _gen(_messages):
yield text
return _gen
class FakeExecuteTool:
"""Stand-in for ``core.inference.tools.execute_tool``."""
def __init__(self, results):
# ``results`` is a list of strings or RuntimeError instances.
self.results = list(results)
self.calls: list[tuple[str, dict]] = []
def __call__(
self,
name,
arguments,
*,
cancel_event = None,
timeout = None,
session_id = None,
thread_id = None,
rag_scope = None,
disable_sandbox = False,
):
self.calls.append((name, arguments))
result = self.results.pop(0) if self.results else "OK"
if isinstance(result, Exception):
raise result
return result
def _collect_events(generator, max_events = 200):
events = []
for ev in generator:
events.append(ev)
if len(events) >= max_events:
break
return events
def _make_loop(
*,
turns,
exec_results = None,
**kwargs,
):
"""Build a configured loop with a multi-turn fake generator.
``turns`` is a list of chunk-lists; iteration N yields chunks from ``turns[N]``.
"""
turn_iter = iter(turns)
def _gen(_messages):
try:
chunks = next(turn_iter)
except StopIteration:
return
acc = ""
for c in chunks:
acc += c
yield acc
exec_fn = FakeExecuteTool(exec_results or [])
return run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "hi"}],
tools = [
{"type": "function", "function": {"name": "web_search"}},
{"type": "function", "function": {"name": "python"}},
{"type": "function", "function": {"name": "terminal"}},
],
execute_tool = exec_fn,
**kwargs,
), exec_fn
class TestParserDeepSeek:
"""DeepSeek R1 / V3 / V3.1 coverage. Markers use full-width pipes
(U+FF5C) and lower-one-eighth-block (U+2581). R1 wraps args in a
Markdown ``` ```json ``` ``` fence; V3 / V3.1 emit bare JSON."""
def test_r1_simple_call_with_code_fence(self):
import json as _json
text = (
"<｜tool▁calls▁begin｜>"
"<｜tool▁call▁begin｜>function"
"<｜tool▁sep｜>special_function\n"
"```json\n"
'{"arg1": 1}\n'
"```"
"<｜tool▁call▁end｜>"
"<｜tool▁calls▁end｜>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "special_function"
assert _json.loads(result[0]["function"]["arguments"]) == {"arg1": 1}
def test_r1_short_form_outer_marker(self):
# llama.cpp accepts ``<｜tool▁calls｜>`` as the short-form opener.
import json as _json
text = (
"<｜tool▁calls｜>function"
"<｜tool▁sep｜>get_time\n"
"```json\n"
'{"city": "Paris"}\n'
"```"
"<｜tool▁call▁end｜>"
"<｜tool▁calls▁end｜>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "get_time"
def test_v3_1_bare_json(self):
# V3 / V3.1 omit the ``function`` prefix and the code fence.
import json as _json
text = (
"<｜tool▁calls▁begin｜>"
"<｜tool▁call▁begin｜>get_time"
"<｜tool▁sep｜>"
'{"city": "Tokyo"}'
"<｜tool▁call▁end｜>"
"<｜tool▁calls▁end｜>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "get_time"
assert _json.loads(result[0]["function"]["arguments"]) == {"city": "Tokyo"}
def test_v3_1_multi_call_shares_envelope(self):
# Parallel calls share one outer envelope; each inner call has
# its own ``<｜tool▁call▁begin｜>...<｜tool▁call▁end｜>``.
text = (
"<｜tool▁calls▁begin｜>"
"<｜tool▁call▁begin｜>get_time"
"<｜tool▁sep｜>"
'{"city": "Paris"}'
"<｜tool▁call▁end｜>"
"<｜tool▁call▁begin｜>get_weather"
"<｜tool▁sep｜>"
'{"city": "Paris"}'
"<｜tool▁call▁end｜>"
"<｜tool▁calls▁end｜>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 2
assert result[0]["function"]["name"] == "get_time"
assert result[1]["function"]["name"] == "get_weather"
def test_v3_1_with_reasoning(self):
# Reasoning ... precedes the tool block.
text = (
"I'm thinking\n"
"<｜tool▁calls▁begin｜>"
"<｜tool▁call▁begin｜>get_time"
"<｜tool▁sep｜>"
'{"city": "Tokyo"}'
"<｜tool▁call▁end｜>"
"<｜tool▁calls▁end｜>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "get_time"
def test_v3_1_strict_rejects_unclosed_envelope(self):
# Envelope truncated mid-stream (no <｜tool▁calls▁end｜>): healed by
# default, rejected with Auto-Heal off.
text = '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{"city": "Tokyo"}'
assert len(parse_tool_calls_from_text(text)) == 1
assert parse_tool_calls_from_text(text, allow_incomplete = False) == []
def test_v3_1_multi_call_recovers_when_first_end_marker_missing(self):
# First inner call omits its <｜tool▁call▁end｜>; the second must still be parsed.
text = (
"<｜tool▁calls▁begin｜>"
"<｜tool▁call▁begin｜>get_time"
"<｜tool▁sep｜>"
'{"city": "Paris"}'
"<｜tool▁call▁begin｜>get_weather"
"<｜tool▁sep｜>"
'{"city": "Paris"}'
"<｜tool▁call▁end｜>"
"<｜tool▁calls▁end｜>"
)
result = parse_tool_calls_from_text(text)
assert [c["function"]["name"] for c in result] == ["get_time", "get_weather"]
def test_v3_1_strict_recovers_after_missing_call_end(self):
# Strict mode (Auto-Heal off): the FIRST inner call is missing its <｜tool▁call▁end｜>
# terminator, so it is skipped -- but the parser must keep scanning and still return the ...
text = (
"<｜tool▁calls▁begin｜>"
"<｜tool▁call▁begin｜>get_weather"
"<｜tool▁sep｜>"
'{"city": "SF"}'
"<｜tool▁call▁begin｜>get_time"
"<｜tool▁sep｜>"
'{"tz": "PST"}'
"<｜tool▁call▁end｜>"
"<｜tool▁calls▁end｜>"
)
# Auto-Heal keeps both; strict skips the truncated first, keeps the second.
assert [c["function"]["name"] for c in parse_tool_calls_from_text(text)] == [
"get_weather",
"get_time",
]
strict = parse_tool_calls_from_text(text, allow_incomplete = False)
assert [c["function"]["name"] for c in strict] == ["get_time"]
def test_r1_strict_recovers_after_missing_close_fence(self):
# R1 form.
text = (
"<｜tool▁calls▁begin｜>"
"function<｜tool▁sep｜>get_weather\n```json\n"
'{"city": "SF"}'
"function<｜tool▁sep｜>get_time\n```json\n"
'{"tz": "PST"}'
"\n```<｜tool▁call▁end｜>"
"<｜tool▁calls▁end｜>"
)
strict = parse_tool_calls_from_text(text, allow_incomplete = False)
assert [c["function"]["name"] for c in strict] == ["get_time"]
def test_deepseek_strip_markup(self):
text = (
"before "
"<｜tool▁calls▁begin｜>"
"<｜tool▁call▁begin｜>foo"
"<｜tool▁sep｜>"
"{}"
"<｜tool▁call▁end｜>"
"<｜tool▁calls▁end｜>"
" after"
)
assert strip_tool_markup(text, final = True) == "before after"
def test_deepseek_signal_wakes_streaming(self):
# The streaming buffer state machine must wake on the DeepSeek opener so the rest of the
# section is drained instead of leaked.
text = "<｜tool▁calls▁begin｜>..."
assert has_tool_signal(text)
def test_deepseek_short_opener_is_stripped(self):
# The short ``<｜tool▁calls｜>`` opener is parsed, so its markup must also be stripped (the
# strip patterns used to require ...calls_begin and left the short-opener markup leaking to ...
text = (
"before "
"<｜tool▁calls｜>"
"<｜tool▁call▁begin｜>foo"
"<｜tool▁sep｜>"
"{}"
"<｜tool▁call▁end｜>"
"<｜tool▁calls▁end｜>"
" after"
)
assert strip_tool_markup(text, final = True) == "before after"
class TestParserGLM:
"""GLM 4.5 / 4.6 / 4.7 coverage. Marker collides with Qwen's
```` but the body shape is XML kv pairs instead of JSON,
so the dispatch order keeps both formats working."""
def test_glm_simple_call(self):
import json as _json
text = (
"web_search\n"
"query\n"
"weather Tokyo\n"
""
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
args = _json.loads(result[0]["function"]["arguments"])
# Strings come through raw; the parser does not double-quote.
assert args == {"query": "weather Tokyo"}
def test_glm_mixed_types_decode_correctly(self):
# Per the chat_template.jinja, strings are emitted raw and non-strings are JSON-encoded.
import json as _json
text = (
"complex_function\n"
"name\nJohn Doe\n"
"age\n30\n"
"active\ntrue\n"
"score\n95.5\n"
""
)
result = parse_tool_calls_from_text(text)
args = _json.loads(result[0]["function"]["arguments"])
assert args == {"name": "John Doe", "age": 30, "active": True, "score": 95.5}
def test_glm_multi_call_back_to_back(self):
# GLM emits parallel calls as consecutive ``...
# `` blocks with no outer envelope.
text = (
"a\nx\n1\n"
"b\ny\n2\n"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 2
assert result[0]["function"]["name"] == "a"
assert result[1]["function"]["name"] == "b"
def test_glm_unclosed_tool_call_does_not_lose_value(self):
# Truncated mid-stream (no ) -- the parser must
# still surface what it found rather than dropping the call.
text = "web_search\nquery\npartial"
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
def test_glm_does_not_break_qwen_path(self):
# Real Qwen emission must still be parsed by the Qwen branch,
# not silently misrouted to GLM (the marker is shared).
text = '{"name":"web_search","arguments":{"q":"x"}}'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
def test_glm_strip_markup(self):
text = (
"before "
"a\nx\n1\n"
" after"
)
assert strip_tool_markup(text, final = True) == "before after"
def test_glm_zero_arg_inline_call(self):
# GLM 4.7 emits a no-argument call inline as ``name`` (name followed
# straight by the close tag, no \n / ).
import json as _json
text = "get_current_date"
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "get_current_date"
assert _json.loads(result[0]["function"]["arguments"]) == {}
def test_glm_zero_arg_call_in_parallel_batch(self):
# A no-arg call alongside a normal one must not make either vanish.
text = (
"get_current_date"
"get_weather\ncity\n"
"Tokyo"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 2
assert result[0]["function"]["name"] == "get_current_date"
assert result[1]["function"]["name"] == "get_weather"
def test_glm_string_value_whitespace_preserved(self):
# The template emits string args verbatim, so significant leading / trailing whitespace
# (code, diffs) must survive.
import json as _json
text = (
"run\ncode\n"
" indented code "
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
args = _json.loads(result[0]["function"]["arguments"])
assert args == {"code": " indented code "}
class TestParserKimi:
"""Kimi K2 / Moonshot coverage. ASCII pipes only (NOT full-width).
Name arrives as ``functions.NAME:IDX``; the parser strips the
prefix and the index to recover the bare callable name while
preserving the full id for round-trip rendering."""
def test_kimi_simple_call(self):
import json as _json
text = (
"<|tool_calls_section_begin|>"
"<|tool_call_begin|>functions.special_function:0"
"<|tool_call_argument_begin|>"
'{"arg1": 1}'
"<|tool_call_end|>"
"<|tool_calls_section_end|>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
# Bare name recovered; full id preserved verbatim.
assert result[0]["function"]["name"] == "special_function"
assert result[0]["id"] == "functions.special_function:0"
assert _json.loads(result[0]["function"]["arguments"]) == {"arg1": 1}
def test_outer_tool_call_with_embedded_kimi_marker_parses_outer(self):
# A Qwen/Hermes whose argument contains literal Kimi markup (a user asking
# about that syntax) must execute the OUTER call, not the embedded marker via the ...
text = (
'{"name":"web_search","arguments":{"query":'
'"explain <|tool_call_begin|>functions.evil:0'
'<|tool_call_argument_begin|>{}<|tool_call_end|>"}}'
""
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
def test_genuine_kimi_call_without_envelope_still_parses(self):
# Control: a real Kimi call with no leading envelope must
# still go through the pre-pass.
text = (
"<|tool_calls_section_begin|>"
"<|tool_call_begin|>functions.web_search:0"
'<|tool_call_argument_begin|>{"query":"x"}<|tool_call_end|>'
"<|tool_calls_section_end|>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "web_search"
def test_kimi_multi_call_with_index(self):
# Multiple consecutive calls inside a single section, each
# with its own monotonically incrementing ``:IDX``.
text = (
"<|tool_calls_section_begin|>"
"<|tool_call_begin|>functions.read_file:0"
"<|tool_call_argument_begin|>"
'{"path":"a"}'
"<|tool_call_end|>"
"<|tool_call_begin|>functions.web_search:1"
"<|tool_call_argument_begin|>"
'{"query":"x"}'
"<|tool_call_end|>"
"<|tool_calls_section_end|>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 2
assert result[0]["function"]["name"] == "read_file"
assert result[0]["id"].endswith(":0")
assert result[1]["function"]["name"] == "web_search"
assert result[1]["id"].endswith(":1")
def test_kimi_dotted_name_keeps_full_dotted_name(self):
# A dotted Kimi id keeps its FULL name after stripping only the ``functions.`` prefix and
# ``:idx`` suffix -- matching current vLLM ...
text = (
"<|tool_calls_section_begin|>"
"<|tool_call_begin|>a.b.c:2"
"<|tool_call_argument_begin|>"
"{}"
"<|tool_call_end|>"
"<|tool_calls_section_end|>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "a.b.c"
def test_kimi_dotted_mcp_name_with_functions_prefix(self):
# ``functions.mcp.server-list:0`` must resolve to ``mcp.server-list``
# (only the ``functions.`` prefix and ``:idx`` are removed).
text = (
"<|tool_calls_section_begin|>"
"<|tool_call_begin|>functions.mcp.server-list:0"
"<|tool_call_argument_begin|>"
"{}"
"<|tool_call_end|>"
"<|tool_calls_section_end|>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "mcp.server-list"
def test_kimi_multi_call_recovers_when_first_end_marker_missing(self):
# First call omits its <|tool_call_end|>; the second must still parse.
text = (
"<|tool_calls_section_begin|>"
"<|tool_call_begin|>functions.read_file:0"
"<|tool_call_argument_begin|>"
'{"path":"a"}'
"<|tool_call_begin|>functions.web_search:1"
"<|tool_call_argument_begin|>"
'{"query":"x"}'
"<|tool_call_end|>"
"<|tool_calls_section_end|>"
)
result = parse_tool_calls_from_text(text)
assert [c["function"]["name"] for c in result] == ["read_file", "web_search"]
def test_kimi_handles_unclosed_section(self):
# End marker missing -- the parser must still extract the call.
text = (
"<|tool_calls_section_begin|>"
"<|tool_call_begin|>functions.foo:0"
"<|tool_call_argument_begin|>"
'{"a":1}'
"<|tool_call_end|>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "foo"
def test_kimi_strip_markup(self):
text = (
"before "
"<|tool_calls_section_begin|>"
"<|tool_call_begin|>functions.x:0"
"<|tool_call_argument_begin|>"
"{}"
"<|tool_call_end|>"
"<|tool_calls_section_end|>"
" after"
)
assert strip_tool_markup(text, final = True) == "before after"
def test_kimi_signal_wakes_streaming(self):
text = "<|tool_calls_section_begin|>..."
assert has_tool_signal(text)
def test_kimi_call_without_section_wrapper(self):
# llama.cpp makes the ``<|tool_calls_section_begin|>`` wrapper optional -- Kimi K2 can emit
# a bare ``<|tool_call_begin|>`` call.
import json as _json
text = (
"<|tool_call_begin|>functions.execute_command:0"
"<|tool_call_argument_begin|>"
'{"cmd":"ls"}'
"<|tool_call_end|>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "execute_command"
assert _json.loads(result[0]["function"]["arguments"]) == {"cmd": "ls"}
def test_kimi_malformed_json_recovers_later_calls(self):
# A call with malformed / truncated JSON must not drop the valid calls that follow it in
# the same section (the bad call is skipped, the good one is recovered).
import json as _json
text = (
"<|tool_calls_section_begin|>"
"<|tool_call_begin|>functions.a:0"
'<|tool_call_argument_begin|>{"city":"Beijing"' # missing closing brace
"<|tool_call_end|>"
"<|tool_call_begin|>functions.b:1"
'<|tool_call_argument_begin|>{"city":"Shanghai"}'
"<|tool_call_end|>"
"<|tool_calls_section_end|>"
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "b"
assert _json.loads(result[0]["function"]["arguments"]) == {"city": "Shanghai"}
class TestParserCrossFormatRouting:
"""Ensure the per-format dispatch order doesn't misroute any
family. Real emissions for each new family + every old family
must still parse correctly when intermixed."""
def test_dispatch_routes_each_family_correctly(self):
cases = [
(
"Qwen",
'{"name":"a","arguments":{"x":1}}',
"a",
),
(
"DeepSeek V3.1",
"<｜tool▁calls▁begin｜>"
"<｜tool▁call▁begin｜>get_time"
"<｜tool▁sep｜>"
'{"city":"Tokyo"}'
"<｜tool▁call▁end｜>"
"<｜tool▁calls▁end｜>",
"get_time",
),
(
"GLM",
"web_search\n"
"q\nx\n"
"",
"web_search",
),
(
"Kimi",
"<|tool_calls_section_begin|>"
"<|tool_call_begin|>functions.add:0"
"<|tool_call_argument_begin|>"
'{"a":1}'
"<|tool_call_end|>"
"<|tool_calls_section_end|>",
"add",
),
]
for label, text, expected_name in cases:
result = parse_tool_calls_from_text(text)
assert len(result) == 1, f"{label}: parser missed the call"
assert (
result[0]["function"]["name"] == expected_name
), f"{label}: got {result[0]['function']['name']!r}, expected {expected_name!r}"
def test_all_new_markers_in_tool_xml_signals(self):
# The safetensors / MLX streaming buffer must wake on every supported emission marker --
# otherwise the BUFFERING state leaks tool content to the user before parse.
from core.inference.tool_call_parser import TOOL_XML_SIGNALS
for marker in (
"<｜tool▁calls▁begin｜>",
"<｜tool▁call▁begin｜>",
"<|tool_calls_section_begin|>",
"<|tool_call_begin|>",
):
assert marker in TOOL_XML_SIGNALS, f"streaming loop would not wake on {marker!r}"
def test_active_tools_are_passed_to_single_turn_after_render_html_success():
captured_tool_names: list[list[str]] = []
exec_fn = FakeExecuteTool(["Rendered HTML canvas."])
def fake_single_turn(_messages, *, active_tools = None):
captured_tool_names.append(
[
(tool.get("function") or {}).get("name")
for tool in (active_tools or [])
if (tool.get("function") or {}).get("name")
]
)
if len(captured_tool_names) == 1:
yield '{"name":"render_html","arguments":{"code":"one"}}'
else:
yield "Done."
events = _collect_events(
run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "make html"}],
tools = [
{"type": "function", "function": {"name": "render_html"}},
{"type": "function", "function": {"name": "web_search"}},
],
execute_tool = exec_fn,
max_tool_iterations = 3,
)
)
assert exec_fn.calls == [("render_html", {"code": "one"})]
assert captured_tool_names == [["render_html", "web_search"], ["web_search"]]
assert any(event.get("type") == "content" and event.get("text") == "Done." for event in events)
def test_spent_one_shot_rehearsal_repeat_is_detected_not_blank_continuation():
# A spent one-shot (render_html) stays in the ORIGINAL tool list; detection is gated on
# that list (matching the strip gate) so a re-emitted repeat is drained and routed to the
# repeat no-op instead of stripped into a blank continuation.
exec_fn = FakeExecuteTool(["Rendered HTML canvas."])
turns = iter(
[
[
'{"name":"render_html","arguments":{"code":"one"}}'
],
['render_html[ARGS]{"code":"two"}'], # spent one-shot rehearsal
["The chart is above."],
]
)
def gen(_messages, *, active_tools = None):
try:
chunks = next(turns)
except StopIteration:
return
acc = ""
for c in chunks:
acc += c
yield acc
events = _collect_events(
run_safetensors_tool_loop(
single_turn = gen,
messages = [{"role": "user", "content": "make a chart"}],
tools = [
{"type": "function", "function": {"name": "render_html"}},
{"type": "function", "function": {"name": "web_search"}},
],
execute_tool = exec_fn,
max_tool_iterations = 5,
)
)
contents = [e["text"] for e in events if e["type"] == "content"]
# render_html ran exactly once; the repeat was a no-op, not a second execution.
assert exec_fn.calls == [("render_html", {"code": "one"})], exec_fn.calls
# The loop continued past the repeat to the real answer (not a blank continuation).
assert any("The chart is above." in t for t in contents), contents
# The raw rehearsal markup never leaked as visible content.
assert not any("render_html[ARGS]" in t for t in contents), contents
def test_rehearsal_call_name_is_not_streamed_before_args():
# A rehearsal whose name and [ARGS] arrive together must drain, not stream the bare name.
loop, exec_fn = _make_loop(
turns = [['web_search[ARGS]{"query":"cats"}'], ["Found."]],
exec_results = ["RESULT"],
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any("web_search" in t for t in contents), contents
def test_rehearsal_call_name_split_before_args_is_not_streamed():
# Finding 5: name and [ARGS] in separate chunks -- the bare name is held until [ARGS] arrives.
loop, exec_fn = _make_loop(
turns = [["web_search", '[ARGS]{"query":"cats"}'], ["Found."]],
exec_results = ["RESULT"],
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any("web_search" in t for t in contents), contents
def test_plain_word_matching_no_tool_still_streams():
# The prefix guard must not swallow prose: a non-tool bare word streams.
loop, _exec = _make_loop(
turns = [["weather", " is nice today."]],
max_tool_iterations = 1,
)
events = _collect_events(loop)
contents = "".join(e["text"] for e in events if e["type"] == "content")
assert "weather is nice today." in contents, contents
def test_rehearsal_name_after_prose_in_streaming_is_not_streamed():
# After prose has streamed (STREAMING state), a split rehearsal name must still be held.
loop, exec_fn = _make_loop(
turns = [
# _make_loop accumulates these deltas into cumulative snapshots.
["Let me think. ", "I will search ", "web_search", '[ARGS]{"query":"cats"}'],
["Found."],
],
exec_results = ["RESULT"],
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any("web_search" in t for t in contents), contents
def test_rehearsal_name_after_prose_same_chunk_in_streaming_is_not_streamed():
# Prose then ``web_search[ARGS]{...}`` in one chunk: the boundary is pulled back over the name.
loop, exec_fn = _make_loop(
turns = [
["Sure. ", 'now web_search[ARGS]{"query":"cats"}'],
["Found."],
],
exec_results = ["RESULT"],
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any("web_search" in t for t in contents), contents
def test_initial_buffer_flush_holds_split_rehearsal_name():
# First flush out of BUFFERING applies the same trailing-name hold as STREAMING.
loop, exec_fn = _make_loop(
turns = [["I will use python", '[ARGS]{"code":"print(1)"}'], ["done"]],
exec_results = ["RESULT"],
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == [("python", {"code": "print(1)"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any("python" in t for t in contents), contents
def test_think_rehearsal_streams_monotonically_and_keeps_reasoning():
# A think rehearsal streams the same text the final strip keeps: cumulative content is
# monotonically non-decreasing and ends with the markup intact.
loop, exec_fn = _make_loop(
turns = [["plan ", 'search[ARGS]{"q":"x"}', " visible"]],
max_tool_iterations = 1,
)
events = _collect_events(loop)
contents = [e["text"] for e in events if e["type"] == "content"]
assert exec_fn.calls == [], exec_fn.calls
assert all(len(b) >= len(a) for a, b in zip(contents, contents[1:])), contents
final = contents[-1] if contents else ""
assert 'search[ARGS]{"q":"x"}' in final, contents
assert "visible" in final, contents
def test_plain_answer_ending_with_tool_name_word_is_preserved():
# End-of-stream flush: a plain answer ending on a tool-name word is prose, not dropped.
loop, exec_fn = _make_loop(
turns = [["I think ", "you should ", "web_search"]],
max_tool_iterations = 1,
)
events = _collect_events(loop)
assert exec_fn.calls == [], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert any(t.rstrip().endswith("web_search") for t in contents), contents
def test_long_tool_name_split_rehearsal_is_not_capped_and_executes():
# Finding 10/11: an MCP name longer than the buffer cap, split before [ARGS], is still
# held (self-bounding prefix); no leak and the call executes.
from core.inference.safetensors_agentic import _MAX_BUFFER_CHARS
name = "mcp__github__create_pull_request"
assert len(name) >= _MAX_BUFFER_CHARS, len(name)
exec_fn = FakeExecuteTool(["RESULT"])
_turns = iter([[name, name + '[ARGS]{"x":1}'], ["done"]])
def st(_messages, active_tools = None):
yield from next(_turns)
events = _collect_events(
run_safetensors_tool_loop(
single_turn = st,
messages = [{"role": "user", "content": "go"}],
tools = [{"type": "function", "function": {"name": name}}],
execute_tool = exec_fn,
max_tool_iterations = 2,
)
)
assert exec_fn.calls == [(name, {"x": 1})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any(name in t for t in contents), contents
def test_unrestricted_mode_split_rehearsal_name_is_not_streamed():
# Finding 6: unrestricted mode treats any bare identifier as a possible rehearsal NAME.
exec_fn = FakeExecuteTool(["RESULT"])
_turns = iter([["web_search", 'web_search[ARGS]{"q":"x"}'], ["done"]])
def st(_messages, active_tools = None):
yield from next(_turns)
events = _collect_events(
run_safetensors_tool_loop(
single_turn = st,
messages = [{"role": "user", "content": "go"}],
tools = [], # unrestricted
execute_tool = exec_fn,
max_tool_iterations = 2,
)
)
assert exec_fn.calls == [("web_search", {"q": "x"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any("web_search" in t for t in contents), contents
def test_unrestricted_mode_split_after_bracket_is_not_streamed():
# Unrestricted mode: a chunk split right after ``NAME[`` is still held (parity with the
# restricted-mode startswith hold).
exec_fn = FakeExecuteTool(["RESULT"])
_turns = iter([["web_search[", 'web_search[ARGS]{"q":"x"}'], ["done"]])
def st(_messages, active_tools = None):
yield from next(_turns)
events = _collect_events(
run_safetensors_tool_loop(
single_turn = st,
messages = [{"role": "user", "content": "go"}],
tools = [], # unrestricted
execute_tool = exec_fn,
max_tool_iterations = 2,
)
)
assert exec_fn.calls == [("web_search", {"q": "x"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any("web_search[" in t for t in contents), contents
def test_unrestricted_mode_plain_prose_still_streams():
# The unrestricted hold releases a held identifier once the rest of the sentence follows.
def st(_messages, active_tools = None):
for snap in ("Hello", "Hello there friend."):
yield snap
events = _collect_events(
run_safetensors_tool_loop(
single_turn = st,
messages = [{"role": "user", "content": "hi"}],
tools = [],
execute_tool = FakeExecuteTool([]),
max_tool_iterations = 1,
)
)
contents = "".join(e["text"] for e in events if e["type"] == "content")
assert "Hello there friend." in contents, contents
def test_safety_net_honors_disabled_auto_heal_for_late_incomplete_call():
# A late call caught by the safety net: an unclosed ```` heals only with Auto-Heal on;
# off, the safety net must not pass ``allow_incomplete=True`` and execute a truncated call.
prose = "Sure, let me look that up for you right now. "
incomplete = '{"name":"web_search","arguments":{"query":"weather in Sydney"}}'
loop_off, exec_off = _make_loop(
turns = [[prose, incomplete], ["Final answer."]],
exec_results = ["RESULT"],
auto_heal_tool_calls = False,
max_tool_iterations = 3,
)
events_off = _collect_events(loop_off)
assert exec_off.calls == [], "disabled Auto-Heal must not execute a healed incomplete call"
assert not [e for e in events_off if e.get("type") == "tool_start"]
loop_on, exec_on = _make_loop(
turns = [[prose, incomplete], ["Final answer."]],
exec_results = ["RESULT"],
auto_heal_tool_calls = True,
max_tool_iterations = 3,
)
_collect_events(loop_on)
assert exec_on.calls == [("web_search", {"query": "weather in Sydney"})], exec_on.calls
def test_bare_json_tool_call_is_not_streamed_as_content():
# Llama-3.2 ``custom_tools`` bare form ``{"name":..,"parameters":..}`` carries no
# XML signal. The loop must BUFFER it until the object closes and execute it via
# the safety net, never leaking the raw JSON to streaming clients as content.
bare = '{"name":"web_search","parameters":{"query":"cats"}}'
loop, exec_fn = _make_loop(
turns = [[bare], ["Here are the results."]],
exec_results = ["RESULT"],
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any('"name"' in t or "web_search" in t for t in contents), contents
assert any("Here are the results." in t for t in contents)
def test_ordinary_json_with_name_key_is_shown_not_treated_as_tool_call():
# Markerless JSON whose "name" is not an enabled tool (e.g. a person record
# ``{"name":"Alice",...}``) must be shown as the answer, not misread as a call
# to a disabled tool and dropped. _make_loop enables web_search/python/terminal.
answer = '{"name":"Alice","parameters":{"age":30}}'
loop, exec_fn = _make_loop(turns = [[answer]], max_tool_iterations = 1)
events = _collect_events(loop)
assert exec_fn.calls == [], exec_fn.calls
contents = "".join(e["text"] for e in events if e["type"] == "content")
assert "Alice" in contents, contents
def test_bare_json_tool_call_split_across_chunks_is_not_streamed():
# Same as above but the bare object arrives split mid-key, so the buffer is
# held open across chunks before it balances.
loop, exec_fn = _make_loop(
turns = [
['{"name":"web_', 'search","parameters":{"query":"cats"}}'],
["Done."],
],
exec_results = ["RESULT"],
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any('"name"' in t or "web_search" in t for t in contents), contents
def test_gemma_wrapperless_call_is_not_streamed_as_content():
# Gemma 4 wrapper-less ``call:NAME{...}`` has no XML signal; the loop must hold
# it (BUFFERING) and execute it, never streaming the raw call text.
loop, exec_fn = _make_loop(
turns = [["call:web_search{query:cats}"], ["Found."]],
exec_results = ["RESULT"],
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any("call:web_search" in t for t in contents), contents
def test_gemma_wrapperless_call_with_whitespace_is_suppressed_when_streamed():
# Gemma may emit ``call : NAME{...}`` with whitespace around the colon, split across stream
# chunks.
loop, exec_fn = _make_loop(
turns = [["call", " : ", "web_search", "{query:cats}"], ["Found."]],
exec_results = ["RESULT"],
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any("call" in t for t in contents), contents
def test_long_gemma_tool_name_is_not_streamed_as_content():
# A tool name longer than the small buffer cap (OpenAI 64 chars, MCP longer)
# must still be held: the ``call:NAME`` prefix keeps buffering until ``{``
# instead of leaking ``call:longname`` as visible text.
long_name = "mcp__github__list_repository_issues" # 35 chars
turns = iter([list('call:%s{repo:"octo/hello"}' % long_name), ["Done."]])
def _gen(_messages):
try:
chunks = next(turns)
except StopIteration:
return
acc = ""
for c in chunks:
acc += c
yield acc
exec_fn = FakeExecuteTool(["RESULT"])
loop = run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "hi"}],
tools = [{"type": "function", "function": {"name": long_name}}],
execute_tool = exec_fn,
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == [(long_name, {"repo": "octo/hello"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any("call:" in t for t in contents), contents
def test_leading_json_answer_is_not_dropped():
# A leading ``{...}`` that is NOT a tool call must still surface as content:
# the bare-JSON hold can only ever delay it to end-of-object, never drop it.
obj = '{"answer": 42, "note": "done"}'
loop, exec_fn = _make_loop(
turns = [[obj]],
exec_results = [],
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == []
contents = [e["text"] for e in events if e["type"] == "content"]
assert any('"answer"' in t for t in contents), contents
def _reprompt_loop(*, auto_heal_tool_calls):
"""Drive one restricted tool with an intent-only first turn to exercise the nudge; returns conversations and events."""
captured: list[list] = []
def fake_single_turn(messages, active_tools = None):
captured.append(list(messages))
if len(captured) == 1:
yield "I'll search for that now." # forward-looking intent, no call
else:
yield "Final answer."
exec_fn = FakeExecuteTool([])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "find X"}],
tools = [{"type": "function", "function": {"name": "search_knowledge_base"}}],
execute_tool = exec_fn,
auto_heal_tool_calls = auto_heal_tool_calls,
# Unsloth always nudges (always-on for the Unsloth inference paths); the
# API opts in per request. Model the Unsloth caller here.
nudge_tool_calls = True,
max_tool_iterations = 3,
)
)
return captured, events
def test_reprompt_names_only_active_tools_not_hardcoded():
# The plan-without-action nudge must name the tools actually enabled, never the
# old hardcoded ``web_search``/``python`` (which a restricted set would reject).
captured, _events = _reprompt_loop(auto_heal_tool_calls = True)
assert len(captured) >= 2, "intent prose should have triggered a re-prompt turn"
reprompt = captured[1][-1]
assert reprompt["role"] == "user"
assert "search_knowledge_base" in reprompt["content"]
assert "web_search" not in reprompt["content"]
assert "python" not in reprompt["content"]
def test_reprompt_stops_when_the_retry_restates_the_stall():
"""A nudge answered with the same text has not worked; do not spend the budget."""
captured: list[list] = []
stall = "I'll search for that now."
def fake_single_turn(messages, active_tools = None):
captured.append(list(messages))
yield stall # same forward-looking intent every time
exec_fn = FakeExecuteTool([])
_events = _collect_events(
run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "find X"}],
tools = [{"type": "function", "function": {"name": "search_knowledge_base"}}],
execute_tool = exec_fn,
auto_heal_tool_calls = True,
nudge_tool_calls = True,
max_tool_iterations = 3,
)
)
# One nudge, then the repeat guard stops it: two generations, not MAX_ACT_REPROMPTS + 1.
assert len(captured) == 2, captured
def test_reprompt_is_announced_on_the_status_channel():
# The re-prompted turn is hidden, so the badge is the only sign of life.
# Blank still comes first: the route resets its text cursor only on that.
_captured, events = _reprompt_loop(auto_heal_tool_calls = True)
statuses = [e["text"] for e in events if e["type"] == "status"]
assert NUDGE_TOOL_CALLS_STATUS in statuses
index = statuses.index(NUDGE_TOOL_CALLS_STATUS)
# index > 0 matters: at 0, statuses[-1] wraps to the terminal clear.
assert index > 0 and statuses[index - 1] == ""
assert statuses[-1] == ""
def test_reprompt_status_absent_without_a_nudge():
_captured, events = _reprompt_loop(auto_heal_tool_calls = False)
statuses = [e["text"] for e in events if e["type"] == "status"]
assert NUDGE_TOOL_CALLS_STATUS not in statuses
def test_reprompt_suppressed_when_auto_heal_disabled():
# With Auto-Heal off the safetensors nudge must stay silent for backend parity
# with the GGUF loop, so only the single initial generation runs.
captured, events = _reprompt_loop(auto_heal_tool_calls = False)
assert len(captured) == 1, captured
contents = [e["text"] for e in events if e["type"] == "content"]
assert any("search for that" in t for t in contents)
class TestLoopBasic:
def test_plain_answer(self):
# No tool XML; loop should yield content then status="".
loop, _exec = _make_loop(
turns = [["Hello", " world", "!"]],
exec_results = [],
)
events = _collect_events(loop)
contents = [e for e in events if e["type"] == "content"]
statuses = [e for e in events if e["type"] == "status"]
assert contents, "expected at least one content event"
# Final cumulative content must contain the answer.
final_text = contents[-1]["text"]
assert "Hello world!" in final_text
assert statuses and statuses[-1]["text"] == ""
def test_single_tool_then_answer(self):
loop, exec_fn = _make_loop(
turns = [
# Tool call only.
[
'{"name":"web_search",',
'"arguments":{"query":"weather"}}',
"",
],
# Final answer.
["The ", "weather is ", "sunny."],
],
exec_results = ["Sunny and 22C"],
)
events = _collect_events(loop)
kinds = [e["type"] for e in events]
assert "tool_start" in kinds
assert "tool_end" in kinds
# Tool was called with the parsed arguments.
assert exec_fn.calls == [("web_search", {"query": "weather"})]
tool_start = next(e for e in events if e["type"] == "tool_start")
assert tool_start["tool_name"] == "web_search"
tool_end = next(e for e in events if e["type"] == "tool_end")
assert tool_end["result"] == "Sunny and 22C"
contents = [e for e in events if e["type"] == "content"]
assert contents and "sunny" in contents[-1]["text"].lower()
def test_function_xml_form(self):
loop, exec_fn = _make_loop(
turns = [
["print(1)"],
["Result: 1"],
],
exec_results = ["1\n"],
)
events = _collect_events(loop)
assert exec_fn.calls == [("python", {"code": "print(1)"})]
contents = [e for e in events if e["type"] == "content"]
assert "Result: 1" in contents[-1]["text"]
def test_llama3_python_tag_form(self):
# The agentic loop must recognise Llama-3's <|python_tag|>
# marker, drain the rest of the turn, and execute the call.
loop, exec_fn = _make_loop(
turns = [
[
"<|python_tag|>web_search.call(",
'query="weather in Tokyo"',
")",
],
["The weather is sunny."],
],
exec_results = ["Sunny, 22C"],
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "weather in Tokyo"})]
contents = [e for e in events if e["type"] == "content"]
assert "sunny" in contents[-1]["text"].lower()
def test_llama3_bare_json_form_fires_tool(self):
# Llama-3.1 / 3.2 emit a bare-JSON tool call
# ``{"name":..,"parameters":..}`` with NO XML signal. The loop's
# safety-net parse must still fire the tool instead of treating the
# turn as "planned without calling tools" and re-prompting the model
# into giving up. Regression for the has_tool_signal gate that
# dropped these; GGUF's llama-server parses them natively.
loop, exec_fn = _make_loop(
turns = [
['{"name": "web_search", "parameters": {"query": "weather in SF"}}'],
["The weather is sunny."],
],
exec_results = ["Sunny, 18C"],
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "weather in SF"})]
contents = [e for e in events if e["type"] == "content"]
assert "sunny" in contents[-1]["text"].lower()
def test_mistral_pre_v11_form(self):
# Pre-v11 Mistral emission: ``[TOOL_CALLS] [{...}]``.
loop, exec_fn = _make_loop(
turns = [
[
'[TOOL_CALLS] [{"name":"web_search",',
'"arguments":{"query":"hi"},"id":"abc"}]',
],
["done"],
],
exec_results = ["ok"],
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "hi"})]
# Mistral-provided ids must propagate to tool_start events.
tool_start = next(e for e in events if e["type"] == "tool_start")
assert tool_start["tool_call_id"] == "abc"
def test_mistral_v11_form(self):
# v11+ Mistral emission: bare ``name{json}`` after the trigger.
loop, exec_fn = _make_loop(
turns = [
['[TOOL_CALLS]web_search{"query":"hi"}'],
["done"],
],
exec_results = ["ok"],
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "hi"})]
def test_gemma4_form(self):
# Gemma 4 emission: ``<|tool_call>call:NAME{...}``.
loop, exec_fn = _make_loop(
turns = [
[
"<|tool_call>call:web_search{",
'query:<|"|>weather<|"|>',
"}",
],
["sunny"],
],
exec_results = ["Sunny, 22C"],
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "weather"})]
def test_deepseek_v3_1_form(self):
# DeepSeek V3.1 emission inside the agentic loop -- the buffer state machine must wake on
# ``<｜tool▁calls▁begin｜>`` and the parser must extract the V3.1 bare-JSON body.
loop, exec_fn = _make_loop(
turns = [
[
"<｜tool▁calls▁begin｜>",
"<｜tool▁call▁begin｜>web_search",
"<｜tool▁sep｜>",
'{"query":"Tokyo weather"}',
"<｜tool▁call▁end｜>",
"<｜tool▁calls▁end｜>",
],
["The weather is sunny."],
],
exec_results = ["Sunny, 22C"],
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "Tokyo weather"})]
contents = [e for e in events if e["type"] == "content"]
assert contents and "sunny" in contents[-1]["text"].lower()
def test_glm_form(self):
# GLM 4.x emission: ``NAME\n...``.
loop, exec_fn = _make_loop(
turns = [
[
"web_search\n",
"query\n",
"Tokyo\n",
"",
],
["found"],
],
exec_results = ["..."],
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "Tokyo"})]
def test_kimi_form(self):
# Kimi K2 emission ``<|tool_calls_section_begin|>...``.
loop, exec_fn = _make_loop(
turns = [
[
"<|tool_calls_section_begin|>",
"<|tool_call_begin|>functions.web_search:0",
"<|tool_call_argument_begin|>",
'{"query":"Tokyo"}',
"<|tool_call_end|>",
"<|tool_calls_section_end|>",
],
["done"],
],
exec_results = ["..."],
)
events = _collect_events(loop)
# The bare name must reach execute_tool, even though the model
# emitted ``functions.web_search:0`` as the formatted id.
assert exec_fn.calls == [("web_search", {"query": "Tokyo"})]
# tool_start carries the original full id so the conversation
# roundtrip can replay it verbatim.
tool_start = next(e for e in events if e["type"] == "tool_start")
assert tool_start["tool_call_id"] == "functions.web_search:0"
def test_render_html_emits_provisional_tool_start(self):
exec_fn = FakeExecuteTool(["Rendered HTML canvas."])
turn_iter = iter(
[
[
"",
"",
"Hi",
],
["Done."],
]
)
def _gen(_messages):
chunks = next(turn_iter)
acc = ""
for chunk in chunks:
acc += chunk
yield acc
loop = run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "make html"}],
tools = [{"type": "function", "function": {"name": "render_html"}}],
execute_tool = exec_fn,
)
events = _collect_events(loop)
tool_starts = [e for e in events if e["type"] == "tool_start"]
assert len(tool_starts) == 2
assert tool_starts[0]["tool_name"] == "render_html"
assert tool_starts[0]["arguments"] == {}
assert tool_starts[1]["tool_name"] == "render_html"
assert "" in tool_starts[1]["arguments"]["code"]
assert exec_fn.calls[0][0] == "render_html"
assert "" in exec_fn.calls[0][1]["code"]
def test_render_html_confirmation_gate_suppresses_early_provisional(self, monkeypatch):
"""When a human confirmation gate is active, render_html must not surface
an early provisional tool_start: that card (keyed by tool_call_id, no
approval) would show the tool 'running' before the user approves. The
gated real tool_start is the first signal the UI receives instead."""
monkeypatch.setattr(safetensors_agentic, "new_approval_id", lambda: "approval-rh")
monkeypatch.setattr(safetensors_agentic, "begin_tool_decision", lambda *_a, **_k: object())
monkeypatch.setattr(safetensors_agentic, "wait_tool_decision", lambda *_a, **_k: "allow")
exec_fn = FakeExecuteTool(["Rendered HTML canvas."])
turn_iter = iter(
[
[
"",
"",
"Hi",
],
["Done."],
]
)
def _gen(_messages):
chunks = next(turn_iter)
acc = ""
for chunk in chunks:
acc += chunk
yield acc
loop = run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "make html"}],
tools = [{"type": "function", "function": {"name": "render_html"}}],
execute_tool = exec_fn,
confirm_tool_calls = True,
# Unset defaults to "auto", which only gates render_html when it
# reaches the network, so this static canvas would not prompt.
permission_mode = "ask",
session_id = "sess",
max_tool_iterations = 3,
)
events = _collect_events(loop)
tool_starts = [e for e in events if e["type"] == "tool_start"]
# No early provisional (empty-args) card while confirmation is pending.
assert [e for e in tool_starts if e.get("arguments") == {}] == []
# The real, gated tool_start still surfaces with the full arguments.
real = [e for e in tool_starts if e.get("arguments", {}).get("code")]
assert len(real) == 1
assert real[0].get("awaiting_confirmation") is True
assert "" in real[0]["arguments"]["code"]
assert exec_fn.calls[0][0] == "render_html"
def test_render_html_bypass_permissions_keeps_early_provisional(self, monkeypatch):
"""bypass_permissions wins over the confirm gate, so the early provisional
card is preserved (no human approval is required)."""
exec_fn = FakeExecuteTool(["Rendered HTML canvas."])
turn_iter = iter(
[
[
"",
"",
"Hi",
],
["Done."],
]
)
def _gen(_messages):
chunks = next(turn_iter)
acc = ""
for chunk in chunks:
acc += chunk
yield acc
loop = run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "make html"}],
tools = [{"type": "function", "function": {"name": "render_html"}}],
execute_tool = exec_fn,
confirm_tool_calls = True,
bypass_permissions = True,
session_id = "sess",
max_tool_iterations = 3,
)
events = _collect_events(loop)
tool_starts = [e for e in events if e["type"] == "tool_start"]
assert len(tool_starts) == 2
assert tool_starts[0]["arguments"] == {}
assert "" in tool_starts[1]["arguments"]["code"]
def test_render_html_auto_mode_static_runs_without_prompt(self):
"""permission_mode="auto" ships confirm_tool_calls=true. render_html is no
longer unconditionally safe (a networked canvas must ask), so its early
provisional card is suppressed under the confirm gate; a static canvas is
still classified safe and runs without an approval prompt."""
exec_fn = FakeExecuteTool(["Rendered HTML canvas."])
turn_iter = iter(
[
[
"",
"",
"Hi",
],
["Done."],
]
)
def _gen(_messages):
chunks = next(turn_iter)
acc = ""
for chunk in chunks:
acc += chunk
yield acc
loop = run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "make html"}],
tools = [{"type": "function", "function": {"name": "render_html"}}],
execute_tool = exec_fn,
confirm_tool_calls = True,
permission_mode = "auto",
session_id = "sess",
max_tool_iterations = 3,
)
events = _collect_events(loop)
tool_starts = [e for e in events if e["type"] == "tool_start"]
# No early provisional card under the auto confirm gate; just the real call.
assert len(tool_starts) == 1
assert tool_starts[0]["tool_name"] == "render_html"
assert "" in tool_starts[0]["arguments"]["code"]
# A static canvas is classified safe, so it runs without an approval gate.
assert tool_starts[0].get("awaiting_confirmation") in (False, None)
def test_render_html_provisional_card_closed_on_generator_exception(self):
"""If the model generator raises mid-stream after a provisional render_html
card was surfaced, the loop must close that card as errored before the
exception propagates, so the UI never leaves a tool spinning forever."""
exec_fn = FakeExecuteTool([])
def _gen(_messages):
acc = ""
for chunk in ["", ""]:
acc += chunk
yield acc
raise RuntimeError("model pipeline exploded")
loop = run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "make html"}],
tools = [{"type": "function", "function": {"name": "render_html"}}],
execute_tool = exec_fn,
)
collected: list[dict] = []
raised = False
try:
for event in loop:
collected.append(event)
except RuntimeError as exc:
raised = True
assert "exploded" in str(exc)
assert raised
provisional = [
e for e in collected if e["type"] == "tool_start" and e.get("arguments") == {}
]
assert len(provisional) == 1
# The provisional card is closed (as an error) before the exception
# propagates, so it never dangles.
closing = [
e
for e in collected
if e["type"] == "tool_end" and e.get("tool_call_id") == provisional[0]["tool_call_id"]
]
assert len(closing) == 1
assert "Error" in (closing[0].get("result") or "")
def test_python_tool_containing_render_html_signal_does_not_emit_provisional_start(self):
loop, exec_fn = _make_loop(
turns = [
[
"",
"print('')",
"",
],
["Done."],
],
exec_results = ["ok"],
)
events = _collect_events(loop)
tool_starts = [e for e in events if e["type"] == "tool_start"]
assert len(tool_starts) == 1
assert tool_starts[0]["tool_name"] == "python"
assert exec_fn.calls == [("python", {"code": "print('')"})]
def test_render_html_rehearsed_in_think_block_emits_no_provisional_start(self):
# BUG B: a render_html rehearsed inside think before a real python call must not emit a
# provisional render_html card; only the outside-think call fires.
exec_fn = FakeExecuteTool(["ok"])
turn_iter = iter(
[
[
'draft render_html[ARGS]{"code":"x"}',
'python[ARGS]{"code":"print(1)"}',
],
["Done."],
]
)
def _gen(_messages):
chunks = next(turn_iter)
acc = ""
for chunk in chunks:
acc += chunk
yield acc
loop = run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "run code"}],
tools = [
{"type": "function", "function": {"name": "render_html"}},
{"type": "function", "function": {"name": "python"}},
],
execute_tool = exec_fn,
)
events = _collect_events(loop)
tool_starts = [e for e in events if e["type"] == "tool_start"]
assert [e["tool_name"] for e in tool_starts] == ["python"], tool_starts
assert exec_fn.calls == [("python", {"code": "print(1)"})]
def test_render_html_success_blocks_second_canvas_call(self):
exec_fn = FakeExecuteTool(["Rendered HTML canvas."])
turn_iter = iter(
[
[
'{"name":"render_html",',
'"arguments":{"code":"one"}}',
],
[
'{"name":"render_html",',
'"arguments":{"code":"two"}}',
],
["Done."],
]
)
def _gen(_messages):
chunks = next(turn_iter)
acc = ""
for chunk in chunks:
acc += chunk
yield acc
loop = run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "make html"}],
tools = [{"type": "function", "function": {"name": "render_html"}}],
execute_tool = exec_fn,
)
events = _collect_events(loop)
tool_starts = [e for e in events if e["type"] == "tool_start"]
assert exec_fn.calls == [("render_html", {"code": "one"})]
assert [e["arguments"] for e in tool_starts] == [{}, {"code": "one"}]
def test_truncated_unclosed_tool_call(self):
loop, exec_fn = _make_loop(
turns = [
# No ; balanced-brace parser still succeeds because
# the JSON itself is balanced.
['{"name":"web_search","arguments":{"query":"x"}}'],
["done"],
],
exec_results = ["result"],
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "x"})]
def test_bad_json_healed_to_query(self):
# Non-JSON string arguments heal to {"query": ...} under auto_heal_tool_calls.
loop, exec_fn = _make_loop(
turns = [
# ``arguments`` is a string _coerce_arguments can't parse, so heal runs.
['{"name":"web_search","arguments":"hello world"}'],
["ok"],
],
exec_results = ["..."],
)
events = _collect_events(loop)
assert exec_fn.calls and exec_fn.calls[0][0] == "web_search"
assert exec_fn.calls[0][1] == {"query": "hello world"}
class TestLoopBehaviour:
def test_duplicate_tool_call_internal_noop(self):
captured_messages: list[list[dict]] = []
turns = iter(
[
['{"name":"web_search","arguments":{"query":"x"}}'],
['{"name":"web_search","arguments":{"query":"x"}}'],
["final"],
]
)
def fake_single_turn(messages):
captured_messages.append([dict(message) for message in messages])
chunks = next(turns)
acc = ""
for chunk in chunks:
acc += chunk
yield acc
exec_fn = FakeExecuteTool(["search-result-1"])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "hi"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
execute_tool = exec_fn,
max_tool_iterations = 3,
)
)
assert exec_fn.calls == [("web_search", {"query": "x"})]
assert [e["tool_call_id"] for e in events if e["type"] == "tool_end"] == ["call_0"]
assert not [
e
for e in events
if e.get("tool_call_id") == "call_1" and e.get("type") in {"tool_start", "tool_end"}
]
duplicate_nudges = [
message
for message in captured_messages[-1]
if message.get("role") == "user"
and "already completed successfully" in message.get("content", "")
]
assert len(duplicate_nudges) == 1
def test_same_turn_duplicate_does_not_drop_later_parallel_call(self):
# Turn 1 runs search(x). Turn 2's batch is [search(x) duplicate, python]:
# the duplicate is a no-op, but python after it must still run, and the
# no-op nudge must land after python's result rather than splitting it.
captured_messages: list[list[dict]] = []
turns = iter(
[
['{"name":"web_search","arguments":{"query":"x"}}'],
[
'{"name":"web_search","arguments":{"query":"x"}}'
'{"name":"python","arguments":{"code":"print(1)"}}'
],
["final"],
]
)
def fake_single_turn(messages, active_tools = None):
captured_messages.append([dict(m) for m in messages])
chunks = next(turns)
acc = ""
for chunk in chunks:
acc += chunk
yield acc
exec_fn = FakeExecuteTool(["search-x", "py-result"])
_collect_events(
run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "hi"}],
tools = [
{"type": "function", "function": {"name": "web_search"}},
{"type": "function", "function": {"name": "python"}},
],
execute_tool = exec_fn,
max_tool_iterations = 4,
)
)
# Turn-1 search and turn-2 python both ran; the turn-2 duplicate search did not.
assert exec_fn.calls == [
("web_search", {"query": "x"}),
("python", {"code": "print(1)"}),
]
conv = captured_messages[-1]
turn2 = [m for m in conv if m.get("role") == "assistant" and m.get("tool_calls")][-1]
assert [tc["function"]["name"] for tc in turn2["tool_calls"]] == ["python"]
after = conv[conv.index(turn2) + 1 :]
assert after[0]["role"] == "tool" and after[0]["content"] == "py-result"
assert after[1]["role"] == "user" # deferred duplicate nudge, after the result
assert after[1]["content"].startswith(
"One earlier request to call tool 'web_search' in this batch was not executed"
)
assert "previous tool request" not in after[1]["content"].lower()
def test_duplicate_tool_call_internal_noop_allows_distinct_followup_tool(self):
captured_messages: list[list[dict]] = []
captured_tool_names: list[list[str]] = []
turns = iter(
[
['{"name":"web_search","arguments":{"query":"x"}}'],
['{"name":"web_search","arguments":{"query":"x"}}'],
['{"name":"python","arguments":{"code":"print(1)"}}'],
["final"],
]
)
def fake_single_turn(messages, active_tools = None):
captured_messages.append([dict(message) for message in messages])
captured_tool_names.append(
[
tool["function"]["name"]
for tool in (active_tools or [])
if tool.get("function", {}).get("name")
]
)
chunks = next(turns)
acc = ""
for chunk in chunks:
acc += chunk
yield acc
exec_fn = FakeExecuteTool(["search-result-1", "python-result"])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "hi"}],
tools = [
{"type": "function", "function": {"name": "web_search"}},
{"type": "function", "function": {"name": "python"}},
],
execute_tool = exec_fn,
max_tool_iterations = 4,
)
)
assert exec_fn.calls == [
("web_search", {"query": "x"}),
("python", {"code": "print(1)"}),
]
assert [e["tool_call_id"] for e in events if e["type"] == "tool_end"] == [
"call_0",
"call_2",
]
assert not [
e
for e in events
if e.get("tool_call_id") == "call_1" and e.get("type") in {"tool_start", "tool_end"}
]
duplicate_nudges = [
message
for message in captured_messages[2]
if message.get("role") == "user"
and "already completed successfully" in message.get("content", "")
]
assert len(duplicate_nudges) == 1
assert captured_tool_names[2] == ["web_search", "python"]
def test_duplicate_noop_does_not_consume_budget_at_small_cap(self):
# A duplicate/disabled no-op turn is a correction turn and must NOT spend the
# caller's tool budget, so with max_tool_iterations=2 the model can still make a
# DISTINCT valid call after repeating one. Only turns that actually execute a
# tool count -- matching the GGUF loop. (The budget used to be charged per
# non-re-prompt iteration, so the duplicate burned the second slot and the third
# turn was sent with no tools, dropping the ``python`` call.)
captured_tool_names: list[list[str]] = []
turns = iter(
[
['{"name":"web_search","arguments":{"query":"x"}}'],
['{"name":"web_search","arguments":{"query":"x"}}'],
['{"name":"python","arguments":{"code":"print(1)"}}'],
["final"],
]
)
def fake_single_turn(messages, active_tools = None):
captured_tool_names.append(
[
tool["function"]["name"]
for tool in (active_tools or [])
if tool.get("function", {}).get("name")
]
)
chunks = next(turns)
acc = ""
for chunk in chunks:
acc += chunk
yield acc
exec_fn = FakeExecuteTool(["search-result", "python-result"])
_collect_events(
run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "hi"}],
tools = [
{"type": "function", "function": {"name": "web_search"}},
{"type": "function", "function": {"name": "python"}},
],
execute_tool = exec_fn,
max_tool_iterations = 2,
)
)
# Both distinct tools execute; the repeated call in between did not cost a slot.
assert exec_fn.calls == [
("web_search", {"query": "x"}),
("python", {"code": "print(1)"}),
]
# The turn after the duplicate still offered tools (budget not yet spent).
assert captured_tool_names[2] == ["web_search", "python"]
def test_repeated_duplicate_noop_transitions_to_final_attempt(self):
captured_tool_names: list[list[str]] = []
turns = iter(
[
['{"name":"web_search","arguments":{"query":"x"}}'],
['{"name":"web_search","arguments":{"query":"x"}}'],
['{"name":"web_search","arguments":{"query":"x"}}'],
["final from first result"],
]
)
def fake_single_turn(messages, active_tools = None):
captured_tool_names.append(
[
(tool.get("function") or {}).get("name")
for tool in (active_tools or [])
if (tool.get("function") or {}).get("name")
]
)
chunks = next(turns)
acc = ""
for chunk in chunks:
acc += chunk
yield acc
exec_fn = FakeExecuteTool(["search-result"])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "hi"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
execute_tool = exec_fn,
max_tool_iterations = 10,
)
)
assert exec_fn.calls == [("web_search", {"query": "x"})]
assert [
event.get("tool_call_id") for event in events if event.get("type") == "tool_end"
] == ["call_0"]
assert captured_tool_names[-1] == []
assert any(
event.get("type") == "content" and "final from first result" in event.get("text", "")
for event in events
)
def test_kb_search_capped_per_turn(self):
# Paraphrased KB searches differ by args (dup guard misses them); the
# per-turn cap stops the runaway re-search loop.
n = RAG_MAX_SEARCHES_PER_TURN
queries = [f"paraphrase {i}" for i in range(n + 1)]
turns = [
[
'{"name":"search_knowledge_base",'
f'"arguments":{{"query":"{q}"}}}}'
]
for q in queries
] + [["final answer"]]
turn_iter = iter(turns)
def _gen(_messages):
try:
chunks = next(turn_iter)
except StopIteration:
return
acc = ""
for c in chunks:
acc += c
yield acc
exec_fn = FakeExecuteTool([f"chunk-{i}" for i in range(n)])
loop = run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "hi"}],
tools = [{"type": "function", "function": {"name": "search_knowledge_base"}}],
execute_tool = exec_fn,
)
events = _collect_events(loop)
assert len(exec_fn.calls) == n
assert all(c[0] == "search_knowledge_base" for c in exec_fn.calls)
tool_end_events = [e for e in events if e["type"] == "tool_end"]
assert len(tool_end_events) == n + 1
assert "do not search again" in tool_end_events[n]["result"].lower()
def test_image_sentinel_stripped_from_model_feed(self):
# The image sentinel is stripped before the next turn, but tool_end still
# carries the raw result for the UI.
loop, exec_fn = _make_loop(
turns = [
['{"name":"python","arguments":{"code":"plot()"}}'],
["see chart"],
],
exec_results = ["chart\n__IMAGES__:/tmp/chart.png"],
)
events = _collect_events(loop)
tool_end = next(e for e in events if e["type"] == "tool_end")
assert "__IMAGES__" in tool_end["result"]
def test_image_sentinel_stripped_with_leading_marker(self):
# Sentinel at start (no newline) must not leak to the model.
from core.inference import safetensors_agentic as _sa
captured: list[list[dict]] = []
def fake_single_turn(messages, **_kw):
captured.append([dict(m) for m in messages])
if len(captured) == 1:
yield '{"name":"python","arguments":{"code":"plot()"}}'
else:
yield "done"
events = list(
_sa.run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "plot please"}],
tools = [{"function": {"name": "python"}}],
execute_tool = lambda *_a, **_kw: "__IMAGES__:/tmp/x.png",
cancel_event = threading.Event(),
max_tool_iterations = 3,
auto_heal_tool_calls = True,
)
)
# The model's second turn must not see "__IMAGES__".
assert len(captured) >= 2
tool_msgs = [m for m in captured[1] if m.get("role") == "tool"]
assert tool_msgs, "no tool message reached the model"
for tm in tool_msgs:
assert "__IMAGES__" not in tm["content"], f"sentinel leaked to model: {tm['content']!r}"
def test_image_sentinel_stripped_with_multiple_markers(self):
# Consecutive sentinels: cut at the first, nothing leaks.
from core.inference import safetensors_agentic as _sa
captured: list[list[dict]] = []
def fake_single_turn(messages, **_kw):
captured.append([dict(m) for m in messages])
if len(captured) == 1:
yield '{"name":"python","arguments":{"code":"plot()"}}'
else:
yield "done"
multi = "panel\n__IMAGES__:/tmp/a.png\n__IMAGES__:/tmp/b.png"
events = list(
_sa.run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "plot please"}],
tools = [{"function": {"name": "python"}}],
execute_tool = lambda *_a, **_kw: multi,
cancel_event = threading.Event(),
max_tool_iterations = 3,
auto_heal_tool_calls = True,
)
)
tool_msgs = [m for m in captured[1] if m.get("role") == "tool"]
assert tool_msgs
for tm in tool_msgs:
assert "__IMAGES__" not in tm["content"], f"second sentinel leaked: {tm['content']!r}"
assert tm["content"] == "panel", f"expected payload-only 'panel', got {tm['content']!r}"
def test_tool_execution_error_is_emitted_but_loop_continues(self):
loop, exec_fn = _make_loop(
turns = [
['{"name":"web_search","arguments":{"query":"x"}}'],
["sorry, that failed"],
],
exec_results = ["Error: network unreachable"],
)
events = _collect_events(loop)
tool_end = next(e for e in events if e["type"] == "tool_end")
assert tool_end["result"].startswith("Error")
# The loop must still emit a content event after the failure.
contents = [e for e in events if e["type"] == "content"]
assert contents
def test_exception_in_executor_does_not_raise(self):
loop, exec_fn = _make_loop(
turns = [
['{"name":"web_search","arguments":{"query":"x"}}'],
["recovered"],
],
exec_results = [RuntimeError("boom")],
)
events = _collect_events(loop)
tool_end = next(e for e in events if e["type"] == "tool_end")
assert "boom" in tool_end["result"]
class TestLoopRePrompt:
"""Plan-without-action re-prompt parity with GGUF: nudge instead of terminating, up to ``MAX_ACT_REPROMPTS`` extra slots. Unsloth always nudges, so these drive the loop with ``nudge_tool_calls=True``."""
def test_reasoning_intent_does_not_reprompt_a_visible_answer(self):
generations = 0
def _gen(_messages, active_tools = None):
nonlocal generations
generations += 1
yield (
"Let me prepare the requested summary carefully."
"This is the final visible answer."
)
exec_fn = FakeExecuteTool([])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "summarize this"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
execute_tool = exec_fn,
nudge_tool_calls = True,
)
)
assert generations == 1
assert exec_fn.calls == []
contents = [e["text"] for e in events if e["type"] == "content"]
assert contents[-1].endswith("This is the final visible answer.")
def test_prefilled_reasoning_intent_does_not_reprompt_a_visible_answer(self):
generations = 0
def _gen(_messages, active_tools = None):
nonlocal generations
generations += 1
yield "Let me prepare the requested summary carefully.This is the final visible answer."
exec_fn = FakeExecuteTool([])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "summarize this"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
execute_tool = exec_fn,
nudge_tool_calls = True,
reasoning_prefilled = True,
)
)
assert generations == 1
assert exec_fn.calls == []
contents = [e["text"] for e in events if e["type"] == "content"]
assert contents[-1].endswith("This is the final visible answer.")
def test_prefilled_reasoning_with_reemitted_think_does_not_reprompt(self):
generations = 0
def _gen(_messages, active_tools = None):
nonlocal generations
generations += 1
yield (
"Let me prepare the requested summary carefully."
"more private planningThis is the final visible answer."
)
exec_fn = FakeExecuteTool([])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "summarize this"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
execute_tool = exec_fn,
nudge_tool_calls = True,
reasoning_prefilled = True,
)
)
assert generations == 1
assert exec_fn.calls == []
contents = [e["text"] for e in events if e["type"] == "content"]
assert contents[-1].endswith("This is the final visible answer.")
def test_prefilled_reasoning_with_later_think_does_not_reprompt(self):
generations = 0
def _gen(_messages, active_tools = None):
nonlocal generations
generations += 1
yield (
"private prefilled planning"
"Let me prepare the requested summary carefully."
"This is the final visible answer."
)
exec_fn = FakeExecuteTool([])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "summarize this"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
execute_tool = exec_fn,
nudge_tool_calls = True,
reasoning_prefilled = True,
)
)
assert generations == 1
assert exec_fn.calls == []
contents = [e["text"] for e in events if e["type"] == "content"]
assert contents[-1].endswith("This is the final visible answer.")
def test_reasoning_only_intent_still_reprompts_and_uses_a_tool(self):
loop, exec_fn = _make_loop(
turns = [
["Let me search for that."],
['{"name":"web_search","arguments":{"query":"cats"}}'],
["Here is the answer."],
],
exec_results = ["result"],
nudge_tool_calls = True,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "cats"})]
contents = [e["text"] for e in events if e["type"] == "content"]
assert contents[-1] == "Here is the answer."
def test_prefilled_no_close_reasoning_intent_still_reprompts(self):
loop, exec_fn = _make_loop(
turns = [
["I need more context.Let me search for that."],
['{"name":"web_search","arguments":{"query":"cats"}}'],
["Here is the answer."],
],
exec_results = ["result"],
nudge_tool_calls = True,
reasoning_prefilled = True,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "cats"})]
contents = [e["text"] for e in events if e["type"] == "content"]
assert contents[-1] == "Here is the answer."
def test_prefilled_reasoning_prefix_is_kept_for_reasoning_only_reprompt(self):
loop, exec_fn = _make_loop(
turns = [
["Let me search for that.checking details"],
['{"name":"web_search","arguments":{"query":"cats"}}'],
["Here is the answer."],
],
exec_results = ["result"],
nudge_tool_calls = True,
reasoning_prefilled = True,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "cats"})]
contents = [e["text"] for e in events if e["type"] == "content"]
assert contents[-1] == "Here is the answer."
def test_reprompt_history_uses_visible_intent_text(self):
captured: list[list[dict]] = []
def _gen(messages, active_tools = None):
captured.append([dict(message) for message in messages])
if len(captured) == 1:
yield "private planning detailsLet me search for that."
elif len(captured) == 2:
yield '{"name":"web_search","arguments":{"query":"cats"}}'
else:
yield "Here is the answer."
exec_fn = FakeExecuteTool(["result"])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "find cats"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
execute_tool = exec_fn,
nudge_tool_calls = True,
)
)
assert exec_fn.calls == [("web_search", {"query": "cats"})]
assert captured[1][1] == {"role": "assistant", "content": "Let me search for that."}
contents = [e["text"] for e in events if e["type"] == "content"]
assert contents[-1] == "Here is the answer."
def test_intent_signal_triggers_reprompt(self):
# Turn 1: intent signal, no tool call.
# Turn 2 (re-prompt): proper tool call -> executes.
# Turn 3: final answer.
loop, exec_fn = _make_loop(
turns = [
["Let me search for that."],
['{"name":"web_search","arguments":{"query":"sky color"}}'],
["The sky is blue."],
],
exec_results = ["Blue (Rayleigh scattering)"],
nudge_tool_calls = True,
)
events = _collect_events(loop)
# web_search must have been called once (after the re-prompt).
assert exec_fn.calls == [("web_search", {"query": "sky color"})]
contents = [e for e in events if e["type"] == "content"]
assert contents and "blue" in contents[-1]["text"].lower()
def test_intent_signal_without_tools_does_not_reprompt(self):
# Same intent signal but no tools enabled -- must NOT re-prompt.
loop, exec_fn = _make_loop(
turns = [["Let me think about that for a moment."]],
exec_results = [],
)
# _make_loop hard-codes three tools; rebuild without tools.
from core.inference.safetensors_agentic import run_safetensors_tool_loop
def _gen(_messages):
yield "Let me think about that for a moment."
exec_fn = FakeExecuteTool([])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "hi"}],
tools = [],
execute_tool = exec_fn,
)
)
assert exec_fn.calls == []
contents = [e for e in events if e["type"] == "content"]
assert contents and "think" in contents[-1]["text"].lower()
def test_direct_answer_does_not_trigger_reprompt(self):
# Plain answer with no intent words: do NOT re-prompt.
loop, exec_fn = _make_loop(
turns = [["4"]],
exec_results = [],
)
events = _collect_events(loop)
assert exec_fn.calls == []
contents = [e for e in events if e["type"] == "content"]
assert contents and contents[-1]["text"].strip() == "4"
def test_max_reprompts_capped(self):
# Model keeps stalling with intent -- after MAX_ACT_REPROMPTS re-prompts
# the loop must give up rather than burn forever.
turns = [["Let me search for that."]] * 6 # well over the cap
loop, exec_fn = _make_loop(
turns = turns,
exec_results = [],
nudge_tool_calls = True,
)
events = _collect_events(loop, max_events = 500)
# No tool ever ran, but the loop terminated cleanly.
assert exec_fn.calls == []
statuses = [e for e in events if e["type"] == "status"]
assert statuses and statuses[-1]["text"] == ""
def test_short_intent_below_buffer_threshold_triggers_reprompt(self):
# Short emission that never exits BUFFERING (< 32 chars + no
# marker prefix). The unified buffer-end path must still
# trigger the intent re-prompt, not silently terminate.
loop, exec_fn = _make_loop(
turns = [
["Let me check."],
['{"name":"web_search","arguments":{"query":"x"}}'],
["found"],
],
exec_results = ["..."],
nudge_tool_calls = True,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "x"})]
def test_reprompt_does_not_consume_tool_budget(self):
# max_tool_iterations=1: one re-prompt, then one real tool call,
# then the budget-exhausted final answer must still fire. If the
# re-prompt ate the slot the tool call would never run.
loop, exec_fn = _make_loop(
turns = [
# 1. Intent stall (re-prompt).
["Let me search for that."],
# 2. Real tool call (uses the budget slot).
['{"name":"web_search","arguments":{"query":"weather"}}'],
# 3. Budget exhausted -> nudged final answer.
["Final: it is sunny"],
],
exec_results = ["sunny"],
max_tool_iterations = 1,
nudge_tool_calls = True,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "weather"})]
contents = [e for e in events if e["type"] == "content"]
assert contents and "sunny" in contents[-1]["text"].lower()
class TestLoopCanonicalHealKey:
"""Per-tool canonical heal key (``code``/``command``/``query``), mirroring GGUF."""
def test_python_bare_string_heals_to_code(self):
loop, exec_fn = _make_loop(
turns = [
['{"name":"python","arguments":"print(1)"}'],
["done"],
],
exec_results = ["1\n"],
)
events = _collect_events(loop)
# The bare string must heal to {"code": "print(1)"}, not
# {"query": ...}, so the python sandbox actually executes it.
assert exec_fn.calls == [("python", {"code": "print(1)"})]
def test_terminal_bare_string_heals_to_command(self):
loop, exec_fn = _make_loop(
turns = [
['{"name":"terminal","arguments":"ls -la"}'],
["done"],
],
exec_results = ["..."],
)
events = _collect_events(loop)
assert exec_fn.calls == [("terminal", {"command": "ls -la"})]
def test_unknown_tool_bare_string_heals_to_query(self):
loop, exec_fn = _make_loop(
turns = [
['{"name":"web_search","arguments":"hello"}'],
["ok"],
],
exec_results = ["..."],
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "hello"})]
class TestGGUFSafetensorsHealingParity:
"""Pin GGUF vs safetensors/MLX loop parity so a regression on either side breaks CI."""
def test_gguf_imports_shared_signal_markers(self):
# The GGUF BUFFERING state machine must wake on every emission
# marker the shared parser knows -- otherwise Llama-3 / Mistral
# / Gemma 4 emissions slip past as plain prose when the
# llama-server structured channel fails.
import inspect
from core.inference.llama_cpp import LlamaCppBackend
src = inspect.getsource(LlamaCppBackend.generate_chat_completion_with_tools)
assert "_SHARED_TOOL_XML_SIGNALS" in src, (
"GGUF agentic loop must reuse the shared TOOL_XML_SIGNALS "
"tuple so it wakes on all five emission formats"
)
def test_gguf_uses_shared_strip_helper(self):
# The GGUF stream-cleanup function must delegate to the shared
# strip_tool_markup so closed-pair markup is removed for every
# emission family (Llama-3 <|python_tag|>, Mistral [TOOL_CALLS],
# Gemma 4 <|tool_call>...).
import inspect
from core.inference.llama_cpp import LlamaCppBackend
src = inspect.getsource(LlamaCppBackend.generate_chat_completion_with_tools)
assert (
"_shared_strip_tool_markup" in src
), "GGUF stream cleanup must delegate to the shared strip_tool_markup helper"
def test_gguf_uses_canonical_heal_keys(self):
# GGUF and safetensors heal a bare-string ``arguments`` to the same
# per-tool canonical key -- ``code`` for python, ``command`` for
# terminal, ``query`` for everything else. The mapping is centralised in
# the shared ToolLoopController (both backends route bare-string args
# through ``coerce_tool_arguments``), so the two paths cannot drift.
from core.inference.tool_loop_controller import (
_CANONICAL_HEAL_ARG,
coerce_tool_arguments,
)
assert _CANONICAL_HEAL_ARG["python"] == "code"
assert _CANONICAL_HEAL_ARG["terminal"] == "command"
assert coerce_tool_arguments("print(1)", heal = True, tool_name = "python").arguments == {
"code": "print(1)"
}
assert coerce_tool_arguments("ls -la", heal = True, tool_name = "terminal").arguments == {
"command": "ls -la"
}
assert coerce_tool_arguments("weather", heal = True, tool_name = "web_search").arguments == {
"query": "weather"
}
def test_intent_regex_matches_same_phrases_as_gguf(self):
# The intent re-prompt regex is now a single shared source of truth
# (tool_call_parser.INTENT_SIGNAL) consumed by both the GGUF and the
# safetensors/MLX loops, so behaviour is identical on Mac and Linux.
# Both backends must resolve to that one shared helper.
from core.inference.llama_cpp import (
_is_short_intent_without_action as gguf_fn,
)
from core.inference.safetensors_agentic import (
is_short_intent_without_action as sf_fn,
)
from core.inference.tool_call_parser import (
INTENT_SIGNAL as shared_re,
is_short_intent_without_action as shared_fn,
)
assert gguf_fn is shared_fn and sf_fn is shared_fn
for phrase in (
"I'll search for that",
"I will look it up",
"Let me check",
"I am going to call the tool",
"First, I will explore",
"First, let's search the web",
"First, let us search the web",
# Imperative plans carry no pronoun; an action verb is enough.
"First, search the web for the latest release notes.",
"First, check the documentation.",
"First, analyze the attached data",
"The first step is to search the web",
"First, my plan is to search the web.",
"First: search the web for release notes.",
"First - search the web for release notes.",
"First \u2013 search the web for release notes.",
"First, our approach is to check the docs.",
"Here's my plan",
"Now I need to call web_search",
# The "let me know" exemption is scoped to "let me", not all direct intent.
"I will know the answer after I search the web",
):
assert shared_re.search(phrase), f"missed {phrase!r}"
assert shared_fn(phrase), f"helper missed {phrase!r}"
for plain in (
"4",
"Hello!",
"The sky is blue.",
"I can help with that.",
"I should mention",
"Let's go.",
# Negated intent is a refusal, not a plan: neither backend may
# force a tool-call re-prompt on it.
"I will not search the web for that.",
"I'll never call that tool.",
# Hands control back rather than announcing an action.
"Let me know if you need anything else.",
"First, the answer is 42",
"First, the result is 3.",
"First, it is 42",
"First, my answer is 42",
"The first line is blank.",
# Ordinal prose, not a plan.
"First place went to Alice",
"First class is available",
# Advice to the user, not work for this turn.
"First, install the package.",
):
assert not shared_re.search(plain), f"wrongly fired on {plain!r}"
assert not shared_fn(plain), f"helper wrongly fired on {plain!r}"
def test_max_reprompts_equal_on_both_backends(self):
# Both loops draw the cap from the shared constant, so they stay equal.
from core.inference.llama_cpp import _MAX_REPROMPTS as gguf_cap
from core.inference.safetensors_agentic import MAX_ACT_REPROMPTS as sf_cap
from core.inference.tool_call_parser import MAX_ACT_REPROMPTS as shared_cap
assert gguf_cap == sf_cap == shared_cap
def test_reprompt_repeat_keeps_punctuation_bearing_terms(self):
# Stripping all non-word chars collapsed "C++" and "C#" to "c", so different
# plans compared equal and the retry lost its nudge.
from core.inference.tool_call_parser import is_reprompt_repeat
assert not is_reprompt_repeat("I will search for C#.", "I will search for C++.")
# A leading mark is part of the term too.
assert not is_reprompt_repeat("I will search for .NET", "I will search for NET")
def test_reprompt_repeat_respects_word_order(self):
# Set overlap scores a reordered query as identical, so the comparison is
# sequence-based.
from core.inference.tool_call_parser import is_reprompt_repeat
assert not is_reprompt_repeat(
"I will search for dogs not cats", "I will search for cats not dogs"
)
assert is_reprompt_repeat(
"I will search for cats not dogs", "I will search for cats not dogs"
)
assert is_reprompt_repeat("I will search for C++!", "I will search for C++.")
def test_reprompt_repeat_keeps_a_changed_query_token(self):
# One corrected token in a long plan is a new attempt; at the old 0.85 bar it
# scored ~0.87 and cost the model its remaining nudge.
from core.inference.tool_call_parser import is_reprompt_repeat
before = "I will search the web for the latest CUDA version 12.4 driver release notes"
after = "I will search the web for the latest CUDA version 12.5 driver release notes"
assert not is_reprompt_repeat(after, before)
assert is_reprompt_repeat(before, before)
def test_reprompt_repeat_keeps_standalone_operator_tokens(self):
# A marks-only token stripped to nothing, so a bounded correction compared
# equal to the unbounded original.
from core.inference.tool_call_parser import is_reprompt_repeat, is_reprompt_restatement
loose = "Now I think the value is 5"
bounded = "Now I think the value is < 5"
assert not is_reprompt_repeat(bounded, loose)
assert not is_reprompt_restatement(bounded, loose)
def test_reprompt_repeat_keeps_a_changed_token_in_a_long_plan(self):
# Every similarity ratio is length-dependent: one changed token scored 0.98
# across 54 tokens, so long corrected plans lost their nudge.
from core.inference.tool_call_parser import is_reprompt_repeat
words = [f"token{index}" for index in range(54)]
corrected = list(words)
corrected[20] = "revised"
assert not is_reprompt_repeat(" ".join(corrected), " ".join(words))
assert is_reprompt_repeat(" ".join(words), " ".join(words))
def test_reprompt_repeat_keeps_articles_that_name_a_target(self):
# "The Who" and "Who" are different searches, so articles are not filler.
from core.inference.tool_call_parser import is_reprompt_repeat
assert not is_reprompt_repeat(
"I will search for The Who discography",
"I will search for Who discography",
)
def test_reprompt_repeat_keeps_filler_words_that_name_a_target(self):
# No word is reliably filler: dropping "ok"/"the" to absorb rewording also
# absorbed the search target. Reordered filler now reads as a new attempt,
# which costs one nudge out of the cap and never strands a plan.
from core.inference.tool_call_parser import is_reprompt_repeat
assert not is_reprompt_repeat(
"I will search for OK Go discography",
"I will search for Go discography",
)
assert not is_reprompt_repeat(
"I will now summarize the findings",
"I will summarize the findings now",
)
def test_reprompt_repeat_detects_restated_answers(self):
# A nudge answered with the same text again has not worked; stop there.
from core.inference.tool_call_parser import is_reprompt_repeat
same = "I will summarize what I found."
assert is_reprompt_repeat(same, same)
assert is_reprompt_repeat("I WILL summarize what I found!", same)
assert is_reprompt_repeat(
"The summary is ready, please let me know if you need anything else",
"The summary is ready. Please let me know if you need anything else!",
)
# No previous text, or genuinely different progress, keeps the nudge.
assert not is_reprompt_repeat(same, "")
assert not is_reprompt_repeat("Tokyo is 18C and cloudy right now.", same)
# Short texts must not collide on incidental word overlap.
assert not is_reprompt_repeat("Let me check.", "Let me search.")
class TestLoopControl:
def test_cancel_event_breaks_loop(self):
cancel = threading.Event()
cancel.set()
# With cancel set, the loop bails before invoking execute_tool.
exec_fn = FakeExecuteTool([])
events = list(
run_safetensors_tool_loop(
single_turn = _const_stream(
'{"name":"web_search","arguments":{"query":"x"}}'
),
messages = [{"role": "user", "content": "hi"}],
tools = [],
execute_tool = exec_fn,
cancel_event = cancel,
)
)
assert events == []
assert exec_fn.calls == []
def test_max_iterations_caps_loop(self):
# The loop stops after max_tool_iterations even if the model keeps
# asking for tools, then emits a final-attempt round.
loop, exec_fn = _make_loop(
turns = [
# Tool call (executes once).
['{"name":"web_search","arguments":{"query":"a"}}'],
# Model gives a final answer when nudged.
["here is the final answer"],
],
exec_results = ["result"],
max_tool_iterations = 1,
)
events = _collect_events(loop)
contents = [e for e in events if e["type"] == "content"]
# Final content must contain the final answer.
assert contents and "final answer" in contents[-1]["text"]
class TestStatusFormatting:
def test_status_for_known_tools(self):
# Call the private helper directly to verify status formatting.
assert (
safetensors_agentic._status_for_tool("web_search", {"query": "abc"}) == "Searching: abc"
)
assert (
safetensors_agentic._status_for_tool("web_search", {"url": "https://www.example.com/x"})
== "Reading: example.com"
)
assert safetensors_agentic._status_for_tool("python", {"code": "x = 1"}).startswith(
"Running Python:"
)
assert safetensors_agentic._status_for_tool("terminal", {"command": "ls"}).startswith(
"Running:"
)
assert safetensors_agentic._status_for_tool("unknown_tool", {}).startswith("Calling:")
class TestProseMentioningToolCall:
def test_assistant_prose_with_literal_tool_call_text_survives(self):
# Regression: prose that mentions a literal ```` (no real call)
# must surface in full, not be stripped past the marker.
loop, exec_fn = _make_loop(
turns = [
# A real tool call so the loop advances a turn.
['{"name":"web_search","arguments":{"query":"x"}}'],
# Prose that mentions the literal text.
["the docs say means an LLM tool call wrapper"],
],
exec_results = ["result"],
)
events = _collect_events(loop)
contents = [e for e in events if e["type"] == "content"]
assert contents, "expected at least one content event"
final = contents[-1]["text"]
assert (
"LLM tool" in final
), f"prose mentioning should not be truncated; got {final!r}"
def test_tool_result_with_tool_call_text_does_not_retrigger(self):
# A literal ```` in the tool result must not re-trigger: the
# loop parses only model output, so exactly one call.
loop, exec_fn = _make_loop(
turns = [
['{"name":"web_search","arguments":{"query":"x"}}'],
["the docs mention wrappers"],
],
exec_results = ["Page text: appears here in the docs"],
)
events = _collect_events(loop)
assert len(exec_fn.calls) == 1
class TestChatTemplateHelper:
"""Cover the dependency-light helper used by InferenceBackend."""
def setup_method(self):
from core.inference.chat_template_helpers import (
apply_chat_template_for_generation,
)
self.apply = apply_chat_template_for_generation
class _Tok:
def __init__(self, accepted):
self.accepted = accepted
self.call_count = 0
self.last_kwargs = None
def apply_chat_template(
self,
messages,
*,
tokenize = False,
add_generation_prompt = True,
**kw,
):
self.call_count += 1
unknown = set(kw) - self.accepted
if unknown:
raise TypeError(f"unexpected kwargs: {sorted(unknown)}")
self.last_kwargs = dict(kw)
return "PROMPT"
def test_richest_call_wins_when_template_supports_all(self):
tok = self._Tok({"tools", "enable_thinking"})
self.apply(tok, [], tools = [{}], enable_thinking = True)
assert tok.call_count == 1
assert tok.last_kwargs is not None
assert "tools" in tok.last_kwargs
assert "enable_thinking" in tok.last_kwargs
def test_falls_back_when_template_rejects_reasoning_kwarg(self):
tok = self._Tok({"tools"})
self.apply(tok, [], tools = [{}], enable_thinking = True)
assert tok.call_count >= 2
assert tok.last_kwargs == {"tools": [{}]}
def test_falls_back_to_bare_call(self):
tok = self._Tok(set())
self.apply(tok, [], tools = [{}], enable_thinking = True)
assert tok.last_kwargs == {}
def test_jinja_error_propagates(self):
class Boom:
def apply_chat_template(self, *a, **kw):
raise ValueError("jinja: missing var")
with pytest.raises(ValueError):
self.apply(Boom(), [])
def test_no_kwargs_single_call(self):
tok = self._Tok(set())
self.apply(tok, [])
assert tok.call_count == 1
# ────────────────────────────────────────────────────────────────────
# Guardrails (allowlist, budget, streaming-leak, dedup, id offset,
# auto_heal=False, canonical healed-arg key)
# ────────────────────────────────────────────────────────────────────
class TestGuardrails:
def test_disabled_tool_is_not_executed(self):
captured_messages: list[list[dict]] = []
def fake_single_turn(messages):
captured_messages.append([dict(message) for message in messages])
if len(captured_messages) == 1:
yield '{"name":"terminal","arguments":{"command":"echo bypass"}}'
else:
yield "final"
exec_fn = FakeExecuteTool([])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "hi"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
execute_tool = exec_fn,
max_tool_iterations = 2,
)
)
assert exec_fn.calls == []
assert not [event for event in events if event.get("type") in {"tool_start", "tool_end"}]
disabled_nudges = [
message
for message in captured_messages[-1]
if message.get("role") == "user" and "not enabled" in message.get("content", "")
]
assert len(disabled_nudges) == 1
def test_empty_tools_list_means_allow_all_in_core_loop(self):
turns = iter(
[
['{"name":"python","arguments":{"code":"print(1)"}}'],
["done"],
]
)
def fake_single_turn(_messages, active_tools = None):
assert active_tools == []
acc = ""
for chunk in next(turns):
acc += chunk
yield acc
exec_fn = FakeExecuteTool(["OK"])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "hi"}],
tools = [],
execute_tool = exec_fn,
max_tool_iterations = 2,
)
)
assert exec_fn.calls == [("python", {"code": "print(1)"})]
assert any(event.get("type") == "tool_end" for event in events)
def test_max_iterations_zero_executes_no_tools(self):
loop, exec_fn = _make_loop(
turns = [['{"name":"web_search","arguments":{"query":"x"}}']],
exec_results = ["OK"],
max_tool_iterations = 0,
)
events = _collect_events(loop)
assert exec_fn.calls == []
assert events and events[-1] == {"type": "status", "text": ""}
def test_streaming_clips_before_tool_signal_no_leak(self):
loop, exec_fn = _make_loop(
turns = [
[
"I will look this up. ",
"Some more prose that's long enough to leave the buffer. ",
'{"name":"web_search","arguments":{"query":"x"}}',
],
["all done"],
],
exec_results = ["weather: sunny"],
max_tool_iterations = 2,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "x"})]
for e in events:
if e["type"] == "content":
assert "" not in e["text"]
assert "web_search" not in e["text"]
def test_auto_heal_disabled_still_parses_valid_tool_call(self):
loop, exec_fn = _make_loop(
turns = [
['{"name":"web_search","arguments":{"query":"x"}}'],
["done"],
],
exec_results = ["OK"],
auto_heal_tool_calls = False,
max_tool_iterations = 2,
)
_collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "x"})]
def test_confirm_tool_calls_close_after_prompt_cleans_slot(self, monkeypatch):
approval_id = "approval-close-sf"
monkeypatch.setattr(safetensors_agentic, "new_approval_id", lambda: approval_id)
loop, exec_fn = _make_loop(
turns = [['{"name":"python","arguments":{"code":"print(1)"}}']],
exec_results = ["OK"],
confirm_tool_calls = True,
# Unset defaults to "auto", which would not prompt this safe call.
permission_mode = "ask",
session_id = "sess",
max_tool_iterations = 1,
)
with tool_approvals._lock:
tool_approvals._pending.clear()
try:
assert next(loop)["type"] == "status"
start = next(loop)
assert start["type"] == "tool_start"
assert start["approval_id"] == approval_id
with tool_approvals._lock:
assert approval_id in tool_approvals._pending
finally:
loop.close()
with tool_approvals._lock:
assert approval_id not in tool_approvals._pending
assert resolve_tool_decision(approval_id, "allow", session_id = "sess") is False
assert exec_fn.calls == []
def test_confirm_tool_calls_skips_rag_autoinject(self, monkeypatch):
def fail_autoinject(*_args, **_kwargs):
raise AssertionError("RAG autoinject must not run before approval")
monkeypatch.setattr("core.inference.tools.build_rag_autoinject", fail_autoinject)
loop, exec_fn = _make_loop(
turns = [["plain answer"]],
confirm_tool_calls = True,
# "ask" gates every call so autoinject waits; the companion test
# below covers "auto", where the safe retrieval never gates.
permission_mode = "ask",
rag_scope = {"thread_id": "t1"},
)
events = _collect_events(loop)
assert any(e.get("type") == "content" and e.get("text") == "plain answer" for e in events)
assert exec_fn.calls == []
def test_auto_mode_still_runs_rag_autoinject(self, monkeypatch):
# "auto" sends confirm_tool_calls=true so unsafe calls gate, but the
# safe search_knowledge_base retrieval never gates, so autoinject must
# still run (unlike ask mode above).
ran = {"called": False}
def fake_autoinject(*_args, **_kwargs):
ran["called"] = True
return None
monkeypatch.setattr("core.inference.tools.build_rag_autoinject", fake_autoinject)
loop, _exec_fn = _make_loop(
turns = [["plain answer"]],
confirm_tool_calls = True,
permission_mode = "auto",
rag_scope = {"thread_id": "t1"},
)
_collect_events(loop)
assert ran["called"] is True
def test_auto_heal_disabled_preserves_xml_on_final_no_tools_pass(self):
turns = iter(
[
['{"name":"web_search","arguments":{"query":"x"}}'],
['{"name":"web_search","arguments":{"query":"literal"}}'],
]
)
def fake_single_turn(_messages, active_tools = None):
acc = ""
for chunk in next(turns):
acc += chunk
yield acc
exec_fn = FakeExecuteTool(["OK"])
events = _collect_events(
run_safetensors_tool_loop(
single_turn = fake_single_turn,
messages = [{"role": "user", "content": "show literal"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
execute_tool = exec_fn,
max_tool_iterations = 1,
auto_heal_tool_calls = False,
)
)
assert exec_fn.calls == [("web_search", {"query": "x"})]
assert any(
event.get("type") == "content" and "" in event.get("text", "")
for event in events
)
def test_auto_heal_disabled_does_not_repair_unclosed_tool_call(self):
loop, exec_fn = _make_loop(
turns = [
['{"name":"web_search","arguments":{"query":"x"}}'],
],
exec_results = ["OK"],
auto_heal_tool_calls = False,
max_tool_iterations = 1,
)
events = _collect_events(loop)
assert exec_fn.calls == []
assert any(
event.get("type") == "content" and "" in event.get("text", "")
for event in events
)
def test_auto_heal_enabled_strips_unparseable_xml_tool_call(self):
loop, exec_fn = _make_loop(
turns = [["{not valid json}"]],
exec_results = ["OK"],
auto_heal_tool_calls = True,
max_tool_iterations = 1,
)
events = _collect_events(loop)
assert exec_fn.calls == []
assert not any(
event.get("type") == "content" and "" in event.get("text", "")
for event in events
)
def test_non_consecutive_duplicate_is_short_circuited(self):
loop, exec_fn = _make_loop(
turns = [
['{"name":"web_search","arguments":{"query":"A"}}'],
['{"name":"web_search","arguments":{"query":"B"}}'],
['{"name":"web_search","arguments":{"query":"A"}}'],
["final"],
],
exec_results = ["res-A", "res-B"],
max_tool_iterations = 4,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "A"}), ("web_search", {"query": "B"})]
assert [
event.get("tool_call_id") for event in events if event.get("type") == "tool_end"
] == ["call_0", "call_1"]
assert not [
event
for event in events
if event.get("tool_call_id") == "call_2"
and event.get("type") in {"tool_start", "tool_end"}
]
def test_same_turn_duplicate_is_short_circuited(self):
loop, exec_fn = _make_loop(
turns = [
[
'{"name":"web_search","arguments":{"query":"A"}}'
'{"name":"web_search","arguments":{"query":"A"}}'
],
["final"],
],
exec_results = ["res-A"],
max_tool_iterations = 2,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "A"})]
assert [
event.get("tool_call_id") for event in events if event.get("type") == "tool_end"
] == ["call_0"]
assert not [
event
for event in events
if event.get("tool_call_id") == "call_1"
and event.get("type") in {"tool_start", "tool_end"}
]
def test_same_turn_distinct_calls_are_capped(self):
# >_MAX_TOOL_CALLS_PER_TURN DISTINCT calls in one turn must be capped so a runaway turn
# cannot fan out into many executions (the GGUF path is held back by llama-server's lazy ...
from core.inference.safetensors_agentic import _MAX_TOOL_CALLS_PER_TURN
n = _MAX_TOOL_CALLS_PER_TURN + 4
turn = "".join(
'{"name":"web_search","arguments":{"query":"q%d"}}' % i
for i in range(n)
)
loop, exec_fn = _make_loop(
turns = [[turn], ["final"]],
exec_results = ["r"] * n,
max_tool_iterations = 2,
)
_collect_events(loop)
assert len(exec_fn.calls) == _MAX_TOOL_CALLS_PER_TURN
# The first N distinct queries executed, in document order.
assert [a["query"] for _name, a in exec_fn.calls] == [
"q%d" % i for i in range(_MAX_TOOL_CALLS_PER_TURN)
]
def test_coerce_string_args_python_uses_code_key(self):
assert _coerce_arguments("print(1)", heal = True, tool_name = "python") == {"code": "print(1)"}
def test_coerce_string_args_terminal_uses_command_key(self):
assert _coerce_arguments("ls -la", heal = True, tool_name = "terminal") == {"command": "ls -la"}
def test_tool_call_ids_unique_across_loop_iterations(self):
loop, _exec = _make_loop(
turns = [
['{"name":"web_search","arguments":{"query":"A"}}'],
['{"name":"web_search","arguments":{"query":"B"}}'],
["done"],
],
exec_results = ["A", "B"],
max_tool_iterations = 3,
)
events = _collect_events(loop)
ids = [e["tool_call_id"] for e in events if e["type"] == "tool_start"]
assert len(ids) == 2 and ids[0] != ids[1]
# ────────────────────────────────────────────────────────────────────
# Shared gpt-oss name detector
# ────────────────────────────────────────────────────────────────────
class TestGptOssNameDetection:
def test_substring_match(self):
assert is_gpt_oss_model_name("unsloth/gpt-oss-20b") is True
def test_negative_known_non_oss_model(self):
assert is_gpt_oss_model_name("meta-llama/Llama-3.1-8B-Instruct") is False
def test_empty_or_none_returns_false(self):
assert is_gpt_oss_model_name("") is False
assert is_gpt_oss_model_name(cast(str, None)) is False
# ────────────────────────────────────────────────────────────────────
# Plan-without-action re-prompt (GGUF loop parity)
# ────────────────────────────────────────────────────────────────────
class TestPlanWithoutActionReprompt:
def test_short_intent_is_reprompted_and_tool_executes(self):
loop, exec_fn = _make_loop(
turns = [
["I'll search the web for that."],
['{"name":"web_search","arguments":{"query":"cats"}}'],
["Here is the final answer."],
],
exec_results = ["result-1"],
nudge_tool_calls = True,
)
events = _collect_events(loop)
assert [c[0] for c in exec_fn.calls] == ["web_search"]
texts = [e["text"] for e in events if e["type"] == "content"]
assert any("Here is the final answer." in t for t in texts)
def test_reprompt_fires_up_to_the_cap(self):
# GGUF parity: a persistently stalling model is re-prompted up to
# MAX_ACT_REPROMPTS times, then the last stall is surrendered as the
# final answer and no further turn is generated.
from core.inference.tool_call_parser import MAX_ACT_REPROMPTS
# Distinct stalls: identical ones stop at the repeat guard, never reaching the cap.
stalls = [f"Let me look into detail {i} first." for i in range(MAX_ACT_REPROMPTS)]
stall = stalls[-1]
turns = [["I'll search the web for that."]]
turns += [[s] for s in stalls]
turns += [["SHOULD NOT APPEAR"]]
generations = {"count": 0}
turn_iter = iter(turns)
def _gen(_messages):
generations["count"] += 1
try:
chunks = next(turn_iter)
except StopIteration:
return
acc = ""
for c in chunks:
acc += c
yield acc
exec_fn = FakeExecuteTool([])
loop = run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "hi"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
execute_tool = exec_fn,
nudge_tool_calls = True,
)
events = _collect_events(loop)
assert exec_fn.calls == []
# One initial turn plus exactly MAX_ACT_REPROMPTS re-prompted turns.
assert generations["count"] == MAX_ACT_REPROMPTS + 1
texts = [e["text"] for e in events if e["type"] == "content"]
assert any(stall in t for t in texts)
assert not any("SHOULD NOT APPEAR" in t for t in texts)
def test_long_prose_answer_is_not_reprompted(self):
long_answer = "I'll keep explaining the details of the topic. " * 60
loop, exec_fn = _make_loop(
turns = [
[long_answer],
["SHOULD NOT APPEAR"],
],
nudge_tool_calls = True,
)
events = _collect_events(loop)
assert exec_fn.calls == []
texts = [e["text"] for e in events if e["type"] == "content"]
assert not any("SHOULD NOT APPEAR" in t for t in texts)
def test_disabled_auto_heal_is_not_reprompted(self):
loop, exec_fn = _make_loop(
turns = [
["I'll search the web for that."],
["SHOULD NOT APPEAR"],
],
auto_heal_tool_calls = False,
nudge_tool_calls = True,
)
events = _collect_events(loop)
assert exec_fn.calls == []
texts = [e["text"] for e in events if e["type"] == "content"]
assert any("I'll search the web for that." in t for t in texts)
assert not any("SHOULD NOT APPEAR" in t for t in texts)
def test_explicit_nudge_off_is_not_reprompted(self):
loop, exec_fn = _make_loop(
turns = [
["I'll search the web for that."],
["SHOULD NOT APPEAR"],
],
nudge_tool_calls = False,
)
events = _collect_events(loop)
assert exec_fn.calls == []
texts = [e["text"] for e in events if e["type"] == "content"]
assert any("I'll search the web for that." in t for t in texts)
assert not any("SHOULD NOT APPEAR" in t for t in texts)
def test_omitted_nudge_flag_is_not_reprompted(self):
# The retry is new on this loop: API callers who do not send the flag
# must keep today's behavior. Unsloth opts in explicitly.
loop, exec_fn = _make_loop(
turns = [
["I'll search the web for that."],
["SHOULD NOT APPEAR"],
],
)
events = _collect_events(loop)
assert exec_fn.calls == []
texts = [e["text"] for e in events if e["type"] == "content"]
assert any("I'll search the web for that." in t for t in texts)
assert not any("SHOULD NOT APPEAR" in t for t in texts)
def test_rag_autoinject_counts_as_executed_tool(self, monkeypatch):
# Autoinject already ran a KB search outside the controller; a short
# post-retrieval intent must not trigger a spurious re-prompt.
import core.inference.tools as tools_mod
def fake_autoinject(conversation, rag_scope):
return {
"events": [
{"type": "tool_start", "tool_name": "search_knowledge_base"},
{"type": "tool_end", "tool_name": "search_knowledge_base"},
],
"messages": [{"role": "tool", "content": "kb result"}],
}
monkeypatch.setattr(tools_mod, "build_rag_autoinject", fake_autoinject)
loop, exec_fn = _make_loop(
turns = [
["I'll search the docs."],
["SHOULD NOT APPEAR"],
],
nudge_tool_calls = True,
)
events = _collect_events(loop)
assert exec_fn.calls == []
assert any(e.get("type") == "tool_start" for e in events)
texts = [e["text"] for e in events if e["type"] == "content"]
assert any("I'll search the docs." in t for t in texts)
assert not any("SHOULD NOT APPEAR" in t for t in texts)
def test_no_reprompt_after_a_denied_tool_confirmation(self, monkeypatch):
# An explicit user denial must not be answered with a nudge to call
# the tool again (which would raise another confirmation prompt).
monkeypatch.setattr(safetensors_agentic, "new_approval_id", lambda: "appr-1")
monkeypatch.setattr(safetensors_agentic, "begin_tool_decision", lambda *_a, **_k: object())
monkeypatch.setattr(safetensors_agentic, "wait_tool_decision", lambda *_a, **_k: "deny")
loop, exec_fn = _make_loop(
turns = [
['{"name":"web_search","arguments":{"query":"cats"}}'],
["I'll search again."],
["SHOULD NOT APPEAR"],
],
confirm_tool_calls = True,
# Only "ask" gates the always-safe web_search, so the deny path runs.
permission_mode = "ask",
session_id = "sess",
nudge_tool_calls = True,
)
events = _collect_events(loop)
assert exec_fn.calls == []
texts = [e["text"] for e in events if e["type"] == "content"]
assert any("I'll search again." in t for t in texts)
assert not any("SHOULD NOT APPEAR" in t for t in texts)
def test_no_reprompt_after_a_tool_already_executed(self):
loop, exec_fn = _make_loop(
turns = [
['{"name":"web_search","arguments":{"query":"cats"}}'],
["Now I'll refine the search."],
["SHOULD NOT APPEAR"],
],
exec_results = ["result-1"],
nudge_tool_calls = True,
)
events = _collect_events(loop)
assert [c[0] for c in exec_fn.calls] == ["web_search"]
texts = [e["text"] for e in events if e["type"] == "content"]
assert not any("SHOULD NOT APPEAR" in t for t in texts)
# Routes-level python_tag strip (multi-line; stop on next sentinel)
class TestRoutesPythonTagStrip:
"""``_TOOL_XML_RE`` must consume multi-line code, embedded JSON, and bare ``<`` (earlier ``[^\n<]*`` / ``[^\n]*`` revisions leaked tails); the streaming route-level strip is the regression-prone path."""
def _strip(self, text: str) -> str:
# Import inside the test so a routes-module import error does
# not blow up the entire test file at collection time.
from routes.inference import _strip_tool_xml
return _strip_tool_xml(text)
def test_single_line_python_tag_stripped(self):
# Floor: the original 5620 single-line behaviour still works.
text = '<|python_tag|>brave_search.call(query="weather")'
assert self._strip(text) == ""
def test_python_tag_with_less_than_in_code(self):
# 5615 regression: literal ``<`` inside code must NOT terminate
# the strip early.
text = '<|python_tag|>python.call(code="if x < 10: pass")'
assert self._strip(text) == ""
def test_python_tag_multiline_code_stripped(self):
# 5620 round-1 regression: multi-line code's second line leaked.
text = '<|python_tag|>python.call(code="line1\nline2\nline3")'
assert self._strip(text) == ""
def test_python_tag_multiline_with_less_than(self):
# Combined: multi-line code AND literal ``<`` in code.
text = (
'<|python_tag|>python.call(code="for i in range(10):\n if i < 5:\n print(i)")'
)
assert self._strip(text) == ""
def test_python_tag_stops_at_eom_sentinel(self):
# Strip stops at the next Llama-3 ``<|`` sentinel so any
# trailing assistant content survives.
text = '<|python_tag|>python.call(code="multi\nline")<|eom_id|>final answer text'
assert self._strip(text) == "<|eom_id|>final answer text"
def test_python_tag_stops_at_eot_sentinel(self):
text = '<|python_tag|>brave_search.call(query="x")<|eot_id|>after'
assert self._strip(text) == "<|eot_id|>after"
def test_python_tag_json_form_multiline_stripped(self):
# The JSON form of python_tag with newlines inside string args.
text = '<|python_tag|>{"name":"python","parameters":{"code":"a = 1\nb = 2\nprint(a+b)"}}'
assert self._strip(text) == ""
def test_python_tag_with_eom_then_trailing_python_tag(self):
# Two python_tag emissions back-to-back across a sentinel: both
# should strip independently.
text = (
'<|python_tag|>brave_search.call(query="a")'
"<|eom_id|>"
'<|python_tag|>python.call(code="x=1")'
)
# ``<|eom_id|>`` between the two strips remains; both
# python_tag blocks are fully consumed.
assert self._strip(text) == "<|eom_id|>"
# Robustness fixes uncovered while validating against vLLM / sglang.
class TestParserRobustness:
def test_tool_call_json_accepts_parameters_key(self):
# Hermes wrapper around a Llama-3.2 bare-JSON object that uses
# ``parameters`` instead of ``arguments``. The bare-JSON and
# python_tag paths already accept both keys; this path now does
# too. Was extracting name only and silently dropping the args.
import json
text = '\n{"name": "search", "parameters": {"q": "ramen"}}\n'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "search"
assert json.loads(result[0]["function"]["arguments"]) == {"q": "ramen"}
def test_function_xml_attribute_form(self):
# MiniCPM-5 / MiniMax-M2 attribute syntax:
# ``v``.
import json
text = 'Tokyo'
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "get_weather"
assert json.loads(result[0]["function"]["arguments"]) == {"city": "Tokyo"}
def test_function_xml_attribute_form_multi_param(self):
import json
text = (
''
'Tokyo'
'celsius'
""
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
args = json.loads(result[0]["function"]["arguments"])
assert args == {"city": "Tokyo", "unit": "celsius"}
def test_function_xml_legacy_equals_form_still_works(self):
# Regression guard: the old ``v``
# syntax must keep parsing after the regex broadening.
import json
text = "Tokyo"
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "get_weather"
assert json.loads(result[0]["function"]["arguments"]) == {"city": "Tokyo"}
def test_function_attribute_form_has_tool_signal(self):
# The standalone ```` attribute form must flip
# the streaming buffer; otherwise the end-of-turn safety-net parse in
# the agentic loop is gated off and the real call is dropped.
assert has_tool_signal('') is True
def test_function_attribute_form_strip_markup(self):
# The attribute form must also be stripped from displayed text, like
# the legacy ```` form.
text = 'result X'
assert strip_tool_markup(text, final = True) == "result"
def test_llama3_chat_template_round_trip(self):
# Meta's official Llama-3.x chat template prefixes every
# assistant turn with
# ``<|start_header_id|>assistant<|end_header_id|>\n\n``. The
# sentinel-strip in ``_parse_llama3_bare_json`` must reach past
# the role label to the JSON body, else every round-tripped
# tool call in history silently drops.
import json
text = (
"<|start_header_id|>assistant<|end_header_id|>\n\n"
'{"name": "get_weather", "parameters": {"city": "Tokyo"}}'
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "get_weather"
assert json.loads(result[0]["function"]["arguments"]) == {"city": "Tokyo"}
def test_llama3_round_trip_all_roles(self):
# Same logic must work for every role the chat template inserts.
import json
for role in ("assistant", "user", "system", "tool", "ipython"):
text = (
f"<|start_header_id|>{role}<|end_header_id|>\n\n"
'{"name": "f", "parameters": {"x": 1}}'
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1, f"failed for role={role}"
assert json.loads(result[0]["function"]["arguments"]) == {"x": 1}
def test_llama3_round_trip_with_eot_prefix(self):
# Prior assistant turn closes with ``<|eot_id|>``, then the
# new header opens. Both sentinels + the role must be consumed.
import json
text = (
"<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
'{"name": "f", "parameters": {}}'
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert result[0]["function"]["name"] == "f"
def test_function_xml_followed_by_prose(self):
# Models routinely follow a tool call with explanatory prose.
# Body must terminate at ```` even without a
# ```` wrapper, else trailing prose leaks into the
# last parameter value.
import json
text = (
""
"Tokyo"
"\n\nHere is what I found."
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert json.loads(result[0]["function"]["arguments"]) == {"city": "Tokyo"}
def test_function_attribute_xml_followed_by_prose(self):
# Same expectation for the MiniCPM-5 attribute form.
import json
text = (
''
'Tokyo'
"\n\nLet me know if you need anything else."
)
result = parse_tool_calls_from_text(text)
assert len(result) == 1
assert json.loads(result[0]["function"]["arguments"]) == {"city": "Tokyo"}
def test_render_with_native_template_returns_render_only_when_tools_emitted():
# The native-template fallback re-renders with the model's repo template when an override drops
# the tools schema.
from types import SimpleNamespace
from core.inference.chat_template_helpers import render_native_template
messages = [{"role": "user", "content": "hi"}]
tools = [{"type": "function", "function": {"name": "web_search"}}]
model_info = {
"native_chat_template": "TPL",
"tokenizer": SimpleNamespace(chat_template = "OVERRIDE"),
}
def emitting(tokenizer, msgs, *, tools, **_kw):
body = "".join(m["content"] for m in msgs)
return body + ("|TOOLS=" + ",".join(t["function"]["name"] for t in tools) if tools else "")
def ignoring(tokenizer, msgs, *, tools, **_kw):
return "".join(m["content"] for m in msgs) # never reflects tools
out = render_native_template(
model_info = dict(model_info),
active_model_name = "x",
messages = messages,
tools = tools,
apply_fn = emitting,
)
assert out == "hi|TOOLS=web_search"
# The native template must be restored on the live tokenizer after probing.
assert model_info["tokenizer"].chat_template == "OVERRIDE"
assert (
render_native_template(
model_info = dict(model_info),
active_model_name = "x",
messages = messages,
tools = tools,
apply_fn = ignoring,
)
is None
)
# No tokenizer and no processor -> return None instead of an AttributeError.
no_tok = {"native_chat_template": "TPL"}
assert (
render_native_template(
model_info = no_tok,
active_model_name = "x",
messages = messages,
tools = tools,
apply_fn = emitting,
)
is None
)
def test_render_with_native_template_does_not_mutate_shared_tokenizer():
# The shared tokenizer must never carry the temporary native template, even mid-render: this
# runs outside the generation lock, so a concurrent request could otherwise render with the ...
from types import SimpleNamespace
from core.inference.chat_template_helpers import render_native_template
shared = SimpleNamespace(chat_template = "OVERRIDE")
seen = []
def capture(tokenizer, msgs, *, tools, **_kw):
seen.append((tokenizer is shared, shared.chat_template))
body = "".join(m["content"] for m in msgs)
return body + ("|T" if tools else "")
model_info = {"native_chat_template": "TPL", "tokenizer": shared}
render_native_template(
model_info = model_info,
active_model_name = "x",
messages = [{"role": "user", "content": "hi"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
apply_fn = capture,
)
# Rendering happened on a copy, and the shared tokenizer stayed "OVERRIDE"
# throughout (never the temporary "TPL").
assert seen and all(not is_shared for is_shared, _ in seen)
assert all(tpl == "OVERRIDE" for _, tpl in seen)
assert shared.chat_template == "OVERRIDE"
def test_native_template_loads_from_base_model_for_lora(monkeypatch):
# For a LoRA adapter the chat template lives on the base model; active_model_name
# is the adapter id and may ship no template. The loader must read base_model.
from types import SimpleNamespace
import transformers
from core.inference.chat_template_helpers import render_native_template
captured = {}
def fake_from_pretrained(name, *args, **kwargs):
captured["source"] = name
return SimpleNamespace(chat_template = "BASE_TPL")
monkeypatch.setattr(transformers.AutoTokenizer, "from_pretrained", fake_from_pretrained)
def emitting(tokenizer, msgs, *, tools, **_kw):
body = "".join(m["content"] for m in msgs)
return body + ("|T" if tools else "")
model_info = {
"base_model": "base/model-id",
"tokenizer": SimpleNamespace(chat_template = "OVERRIDE"),
}
out = render_native_template(
model_info = model_info,
active_model_name = "adapter/path",
messages = [{"role": "user", "content": "hi"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
apply_fn = emitting,
)
assert captured["source"] == "base/model-id"
assert out == "hi|T"
def test_render_with_native_template_fallback_swaps_when_override_drops_tools():
# The shared gate (used by the transformers and MLX backends): when the live render is
# identical with and without tools, re-render with the native template and return it.
from types import SimpleNamespace
from core.inference.chat_template_helpers import render_with_native_template_fallback
messages = [{"role": "user", "content": "hi"}]
tools = [{"type": "function", "function": {"name": "web_search"}}]
# apply_fn that IGNORES tools -> live render drops the schema.
def ignoring(tokenizer, msgs, *, tools, **_kw):
return "".join(m["content"] for m in msgs)
model_info = {
"native_chat_template": "TPL",
"tokenizer": SimpleNamespace(chat_template = "OVERRIDE"),
}
# Native render emits the tools, so the fallback swaps to it.
def native_emits(tokenizer, msgs, *, tools, **_kw):
body = "".join(m["content"] for m in msgs)
return body + ("|TOOLS" if tools else "")
out = render_with_native_template_fallback(
formatted_prompt = ignoring(None, messages, tools = tools),
tokenizer = SimpleNamespace(),
model_info = dict(model_info),
active_model_name = "x",
messages = messages,
tools = tools,
apply_fn = lambda tok, msgs, *, tools, **kw: (
native_emits(tok, msgs, tools = tools)
if getattr(tok, "chat_template", None) == "TPL"
else ignoring(tok, msgs, tools = tools)
),
)
assert out == "hi|TOOLS", out
def test_render_with_native_template_fallback_keeps_prompt_when_tools_emitted():
# Live render already differs with vs without tools -> no fallback, returned
# unchanged. Also a no-tools call is a passthrough.
from types import SimpleNamespace
from core.inference.chat_template_helpers import render_with_native_template_fallback
messages = [{"role": "user", "content": "hi"}]
tools = [{"type": "function", "function": {"name": "web_search"}}]
def emitting(tokenizer, msgs, *, tools, **_kw):
body = "".join(m["content"] for m in msgs)
return body + ("|T" if tools else "")
kept = render_with_native_template_fallback(
formatted_prompt = emitting(None, messages, tools = tools),
tokenizer = SimpleNamespace(),
model_info = {"native_chat_template": "TPL", "tokenizer": SimpleNamespace()},
active_model_name = "x",
messages = messages,
tools = tools,
apply_fn = emitting,
)
assert kept == "hi|T", kept
# No tools -> passthrough (native template never consulted).
passthrough = render_with_native_template_fallback(
formatted_prompt = "hi",
tokenizer = SimpleNamespace(),
model_info = {},
active_model_name = "x",
messages = messages,
tools = None,
apply_fn = emitting,
)
assert passthrough == "hi"
def test_render_with_native_template_fallback_keeps_prompt_when_no_tools_probe_raises():
# A template that REQUIRES tools can raise on the no-tools probe.
from types import SimpleNamespace
from core.inference.chat_template_helpers import render_with_native_template_fallback
messages = [{"role": "user", "content": "hi"}]
tools = [{"type": "function", "function": {"name": "web_search"}}]
def raises_without_tools(tokenizer, msgs, *, tools, **_kw):
if not tools:
raise RuntimeError("template requires tools")
return "".join(m["content"] for m in msgs) + "|T"
out = render_with_native_template_fallback(
formatted_prompt = "hi|T",
tokenizer = SimpleNamespace(),
model_info = {"native_chat_template": "TPL", "tokenizer": SimpleNamespace()},
active_model_name = "x",
messages = messages,
tools = tools,
apply_fn = raises_without_tools,
)
assert out == "hi|T", out
def test_truncated_bare_json_at_eof_is_not_leaked():
# Stream ends mid bare-JSON object: the held fragment must be dropped at the
# EOF resolver, not flushed as plain assistant content (GGUF parity).
loop, _exec = _make_loop(
turns = [['{"name":"web_search","parameters":{"query":"weather in S']],
max_tool_iterations = 1,
)
events = _collect_events(loop)
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any('"name"' in t for t in contents), contents
def test_oversized_bare_json_call_is_not_leaked_and_executes():
# A bare-JSON call whose arguments exceed _MAX_BARE_JSON_BUFFER must DRAIN
# (suppress) rather than stream the raw JSON prefix, and still execute once
# the full object is parsed by the safety net.
from core.inference.safetensors_agentic import _MAX_BARE_JSON_BUFFER
big = "A" * (_MAX_BARE_JSON_BUFFER + 5000)
full = '{"name":"python","parameters":{"code":"' + big + '"}}'
chunks = [full[i : i + 2000] for i in range(0, len(full), 2000)]
loop, exec_fn = _make_loop(turns = [chunks, ["done"]], exec_results = ["OK"], max_tool_iterations = 2)
events = _collect_events(loop)
contents = [e["text"] for e in events if e["type"] == "content"]
assert not any(t.lstrip().startswith('{"name') for t in contents), contents[:1]
assert exec_fn.calls and exec_fn.calls[0][0] == "python"
assert len(exec_fn.calls[0][1].get("code", "")) > _MAX_BARE_JSON_BUFFER
def test_oversized_plain_json_answer_still_streams():
# A giant plain JSON answer (no "name" key) is NOT a tool call and must still
# stream -- the oversized DRAIN route is gated on a "name" key.
from core.inference.safetensors_agentic import _MAX_BARE_JSON_BUFFER
big = "A" * (_MAX_BARE_JSON_BUFFER + 5000)
full = '{"result":"' + big + '"}'
chunks = [full[i : i + 2000] for i in range(0, len(full), 2000)]
loop, _exec = _make_loop(turns = [chunks], max_tool_iterations = 1)
events = _collect_events(loop)
contents = "".join(e["text"] for e in events if e["type"] == "content")
assert '"result"' in contents
def test_oversized_disabled_name_json_answer_still_streams():
# A giant still-open JSON answer whose "name" is NOT an enabled tool must stream:
# the oversized DRAIN branch was gated only on the presence of a "name" key, so a
# large ordinary record ({"name":"Alice",...}) was drained instead of shown.
from core.inference.safetensors_agentic import _MAX_BARE_JSON_BUFFER
big = "A" * (_MAX_BARE_JSON_BUFFER + 5000)
answer = '{"name":"Alice","parameters":{"bio":"' + big # never closes
chunks = [answer[i : i + 2000] for i in range(0, len(answer), 2000)]
loop, exec_fn = _make_loop(turns = [chunks], max_tool_iterations = 1)
events = _collect_events(loop)
assert exec_fn.calls == [], exec_fn.calls
contents = "".join(e["text"] for e in events if e["type"] == "content")
assert "Alice" in contents, contents[:80]
def test_truncated_disabled_name_json_is_shown_at_eof():
# A truncated ordinary JSON answer whose name is not an enabled tool, held to EOF,
# must be shown -- the EOF bare-JSON DRAIN branch was gated only on a "name" key.
truncated = '{"name":"Alice","parameters":{"age":'
loop, exec_fn = _make_loop(turns = [[truncated]], max_tool_iterations = 1)
events = _collect_events(loop)
assert exec_fn.calls == [], exec_fn.calls
contents = "".join(e["text"] for e in events if e["type"] == "content")
assert "Alice" in contents, contents
def test_truncated_plain_json_with_nested_enabled_name_is_visible():
# A truncated ordinary JSON answer with a NESTED ``"name"`` matching an enabled
# tool ({"result":{"name":"web_search",...) must be shown, not suppressed: the
# gate now extracts the TOP-LEVEL name only, so the nested field is just data.
loop, exec_fn = _make_loop(
turns = [['{"result":{"name":"web_search","age":']],
max_tool_iterations = 1,
)
events = _collect_events(loop)
assert exec_fn.calls == []
contents = "".join(e["text"] for e in events if e["type"] == "content")
assert '"result"' in contents and "web_search" in contents, contents
def test_bare_json_call_not_replayed_in_next_turn_content():
# After a complete bare-JSON call executes, the assistant content fed to the
# next turn must not contain the raw call (next-turn contamination).
captured: list[list[dict]] = []
exec_fn = FakeExecuteTool(["RESULT"])
def st(messages, active_tools = None):
captured.append([dict(m) for m in messages])
if len(captured) == 1:
yield '{"name":"web_search","parameters":{"query":"cats"}}'
else:
yield "Found."
_collect_events(
run_safetensors_tool_loop(
single_turn = st,
messages = [{"role": "user", "content": "cats"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
execute_tool = exec_fn,
max_tool_iterations = 3,
)
)
assert len(captured) >= 2, captured
asst = [m for m in captured[1] if m.get("role") == "assistant"]
assert asst and not any('"name"' in (m.get("content") or "") for m in asst), asst
if __name__ == "__main__":
pytest.main([__file__, "-v"])
def test_streaming_strip_keeps_bare_args_before_think_block():
# F3: a bare ``foo[ARGS]`` before a think block is prose; EOS-anchored tail arms run only
# on the last segment.
text = "Please pass foo[ARGS] pause to the template."
out = strip_tool_markup_streaming(text, tool_protocol_active = True)
assert out == text
def test_streaming_strip_still_removes_complete_call_before_think_block():
# A complete bracket call before a think block still strips in the non-last segment.
text = 'go web_search[ARGS]{"q":"x"} z done'
out = strip_tool_markup_streaming(text, tool_protocol_active = True)
assert "web_search[ARGS]" not in out
assert "z" in out
assert "go" in out and "done" in out
def test_prose_args_marker_before_real_call_does_not_drain_the_prose():
# F5: an inactive ``foo[ARGS]`` in prose is not a call boundary; the prose streams in
# full and the later real call still executes.
loop, exec_fn = _make_loop(
turns = [
["Intro ", "foo[ARGS] syntax. ", 'web_search[ARGS]{"query":"cats"}'],
["Cats are great."],
],
exec_results = ["RESULT"],
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == [("web_search", {"query": "cats"})], exec_fn.calls
contents = [e["text"] for e in events if e["type"] == "content"]
# The prose between the bogus marker and the real call must survive.
assert any("foo[ARGS] syntax." in t for t in contents), contents
# The real call markup is never shown as content.
assert not any("web_search[ARGS]" in t for t in contents), contents
def test_inactive_name_args_with_body_is_not_parsed_into_disabled_noop():
# BUG A: a prose answer with an inactive ``foo[ARGS]{...}`` is not drained into a
# disabled no-op extra turn; the [ARGS] checks are name-gated.
turns = [['foo[ARGS]{"x":1} is just syntax.']]
turn_calls: list[int] = []
def _gen(_messages):
turn_calls.append(1)
chunks = turns[len(turn_calls) - 1] if len(turn_calls) <= len(turns) else []
acc = ""
for chunk in chunks:
acc += chunk
yield acc
exec_fn = FakeExecuteTool([])
loop = run_safetensors_tool_loop(
single_turn = _gen,
messages = [{"role": "user", "content": "explain"}],
tools = [{"type": "function", "function": {"name": "web_search"}}],
execute_tool = exec_fn,
max_tool_iterations = 3,
)
events = _collect_events(loop)
assert exec_fn.calls == [], exec_fn.calls
assert not any(e["type"] in ("tool_start", "tool_end") for e in events), events
# Exactly one generation turn -- no disabled ``foo`` no-op re-prompt.
assert len(turn_calls) == 1, turn_calls
contents = [e["text"] for e in events if e["type"] == "content"]
assert any("is just syntax." in t for t in contents), contents
class TestEnabledToolNameGate:
"""The safetensors loop passes the active tool names into parse/strip so the
ambiguous bare-rehearsal ``NAME[ARGS]{json}`` is treated as a call only when NAME
is an active tool (#5704). Without the gate an inactive ``foo[ARGS]{...}`` in prose
was parsed into a disabled no-op call and stripped from the visible text."""
def _names(self, calls):
return [c["function"]["name"] for c in calls]
def test_parse_inactive_rehearsal_does_not_swallow_active_call(self):
text = 'foo[ARGS]{"a":1} web_search[ARGS]{"query":"cats"}'
calls = parse_tool_calls_from_text(text, enabled_tool_names = {"web_search"})
assert self._names(calls) == ["web_search"]
assert json.loads(calls[0]["function"]["arguments"]) == {"query": "cats"}
def test_parse_inactive_rehearsal_alone_is_prose(self):
assert (
parse_tool_calls_from_text('foo[ARGS]{"a":1}', enabled_tool_names = {"web_search"}) == []
)
def test_streaming_strip_keeps_inactive_rehearsal(self):
raw = 'answer foo[ARGS]{"x":1} tail'
assert strip_tool_markup_streaming(raw, enabled_tool_names = {"web_search"}) == raw
def test_streaming_strip_removes_active_rehearsal(self):
raw = 'answer web_search[ARGS]{"q":1} tail'
out = strip_tool_markup_streaming(raw, enabled_tool_names = {"web_search"})
assert "web_search[ARGS]" not in out
assert out == "answer tail"
def test_final_strip_keeps_inactive_rehearsal(self):
text = 'foo[ARGS]{"x":1} is just syntax.'
assert strip_tool_markup(text, final = True, enabled_tool_names = {"web_search"}) == text
def test_gate_none_preserves_legacy_strip_and_parse(self):
text = 'foo[ARGS]{"x":1} tail'
assert self._names(parse_tool_calls_from_text(text)) == ["foo"]
assert strip_tool_markup_streaming(text) == " tail"
def test_drain_truncated_enabled_name_json_preserved_when_auto_heal_disabled():
# F3: with Auto-Heal OFF, a truncated ENABLED-name bare-JSON fragment that did
# not parse must stay visible (disabled-Auto-Heal contract: malformed markup is
# preserved), matching the XML strip in the same drain branch. With Auto-Heal ON
# the same fragment is suppressed.
trunc = '{"name":"web_search","parameters":{"query":"weather'
off, exec_off = _make_loop(turns = [[trunc]], max_tool_iterations = 1, auto_heal_tool_calls = False)
events_off = _collect_events(off)
assert exec_off.calls == [], exec_off.calls
contents_off = "".join(e["text"] for e in events_off if e["type"] == "content")
assert "web_search" in contents_off, contents_off
on, exec_on = _make_loop(turns = [[trunc]], max_tool_iterations = 1, auto_heal_tool_calls = True)
events_on = _collect_events(on)
assert exec_on.calls == [], exec_on.calls
contents_on = "".join(e["text"] for e in events_on if e["type"] == "content")
assert "web_search" not in contents_on, contents_on
def test_looks_like_enabled_bare_json_accepts_function_alias():
# The safetensors buffering gate must recognise the "function" bare-JSON alias
# the parser accepts, so a truncated/complete {"function":} call is
# buffered/healed instead of streaming as visible content.
from core.inference.safetensors_agentic import _looks_like_enabled_bare_json
enabled = {"web_search"}
assert _looks_like_enabled_bare_json(
'{"function":"web_search","parameters":{"q":"x"}}', enabled
)
# A non-tool "function" value is an ordinary JSON answer -> not gated.
assert not _looks_like_enabled_bare_json('{"function":"Alice","parameters":{}}', enabled)
class TestFalseAlarmMarkerProse:
def test_leading_marker_prose_streams_intact(self):
# An answer that starts with a literal marker is a false alarm: the
# drain finds no calls and the full prose must reach the client.
text = "[TOOL_CALLS] is the Mistral tool marker. More prose after."
loop, exec_fn = _make_loop(turns = [[text]])
events = _collect_events(loop)
assert exec_fn.calls == []
texts = [e["text"] for e in events if e["type"] == "content"]
assert texts and texts[-1] == text
def test_chained_bare_json_calls_not_replayed_in_history(self):
# Both chained calls execute; the kept content (next-turn assistant
# history) must not contain the second call's raw JSON.
chained = (
'{"name":"web_search","parameters":{"q":"first"}};'
'{"name":"python","parameters":{"code":"x"}}'
)
convs = []
turn_iter = iter([[chained], ["Final answer."]])
def gen(messages, active_tools = None):
convs.append([dict(m) for m in messages])
try:
chunks = next(turn_iter)
except StopIteration:
return
acc = ""
for c in chunks:
acc += c
yield acc
exec_fn = FakeExecuteTool(["r1", "r2"])
loop = run_safetensors_tool_loop(
single_turn = gen,
messages = [{"role": "user", "content": "hi"}],
tools = [
{"type": "function", "function": {"name": "web_search"}},
{"type": "function", "function": {"name": "python"}},
],
execute_tool = exec_fn,
)
_collect_events(loop)
assert [c[0] for c in exec_fn.calls] == ["web_search", "python"]
assistant = next(m for m in convs[1] if m["role"] == "assistant")
assert '"python"' not in (assistant.get("content") or "")
def test_both_tool_loops_say_they_are_waiting_for_approval():
"""A gated call must not report "Running" in either loop.
The GGUF loop was fixed first and the safetensors one was missed, so the
badge counted up "Running ..." against a prompt nobody had answered yet.
Asserted on the source so the two paths cannot drift apart again.
"""
import ast
import os
backend = os.path.join(os.path.dirname(__file__), "..")
for name in ("core/inference/safetensors_agentic.py", "core/inference/llama_cpp.py"):
with open(os.path.join(backend, name), encoding = "utf-8") as f:
tree = ast.parse(f.read())
calls = [
node
for node in ast.walk(tree)
if isinstance(node, ast.Call)
and isinstance(node.func, ast.Name)
and node.func.id == "awaiting_approval_status"
]
assert calls, f"{name} still announces a gated tool call as running"