add: LFM2/2.5 Tool Parser

[jbuchananr]

Purpose

Currently LFM models tool calling functionality outputs into assistant content response since they do not have a dedicated parser.

The current behavior of a tool call:

"choices": [
      {
        "index": 0,
        "message": {
          "role": "assistant",
          "content": "<|tool_call_start|>[get_current_temperature(location=\"Tokyo\")] <|tool_call_end|>", <---------------------------------------- content should be empty
          "refusal": null,
          "annotations": null,
          "audio": null,
          "function_call": null,
          "tool_calls": [], <--------------------------------- tool call should be here
          "reasoning": null
        },
        "logprobs": null,
        "finish_reason": "stop",
        "stop_reason": null,
        "token_ids": null
      }
    ],

With this change we introduce the lfm2 tool call parser that can be used with --tool-call-parser lfm2 allowing with LFM2 and LFM2.5 models tool calls to be parsed and outputted in the correct format.

Test Plan

• Added unit tests tests/tool_parsers/test_lfm2_tool_parser.py
• Tested tool calling with the following models: LiquidAI/LFM2-2.6B and LiquidAI/LFM2.5-1.2B-Instruct

Test Result

============================= test session starts ==============================
platform linux -- Python 3.12.10, pytest-9.0.3, pluggy-1.6.0 -- /usr/local/bin/python
cachedir: .pytest_cache
rootdir: /root
plugins: anyio-4.13.0
collecting ... collected 19 items

tests/tool_parsers/test_lfm2_tool_parser.py::test_no_tool_call[True] PASSED [  5%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_no_tool_call[False] PASSED [ 10%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[simple_streaming] PASSED [ 15%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[simple_nonstreaming] PASSED [ 21%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[more_types_streaming] PASSED [ 26%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[more_types_nonstreaming] PASSED [ 31%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[parameterless_streaming] PASSED [ 36%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[parameterless_nonstreaming] PASSED [ 42%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[empty_dict_streaming] PASSED [ 47%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[empty_dict_nonstreaming] PASSED [ 52%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[empty_list_streaming] PASSED [ 57%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[empty_list_nonstreaming] PASSED [ 63%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[escaped_string_streaming] PASSED [ 68%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[escaped_string_nonstreaming] PASSED [ 73%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[parallel_calls_streaming] PASSED [ 78%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[parallel_calls_nonstreaming] PASSED [ 84%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_tool_call[content_after_tool_call_nonstreaming] PASSED [ 89%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_streaming_tool_call_with_large_steps PASSED [ 94%]
tests/tool_parsers/test_lfm2_tool_parser.py::test_regex_timeout_handling[False] PASSED [100%]

---

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
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• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

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[gemini-code-assist]

Code Review

This pull request introduces the Lfm2ToolParser to support LiquidAI LFM2/LFM2.5 models, which generate pythonic tool calls within specific sentinel tokens. The implementation includes comprehensive test cases and supports both standard and streaming tool call extraction. A high-severity security issue was identified regarding the TOOL_CALL_REGEX, which is vulnerable to Regular Expression Denial of Service (ReDoS) due to complex nested quantifiers. It is recommended to simplify this regex, as the code already performs robust validation using ast.parse.

[tianshu-Michael-yu]

@jbuchananr Thanks for adding this. The parser looks directionally right to me, especially reusing the existing pythonic AST helpers for LFM's <|tool_call_start|>[...]<|tool_call_end|> format. I think there is one streaming edge case that should be fixed before merge.

extract_tool_calls_streaming appears to drop content when the tool block and trailing assistant text arrive in the same streaming delta. For example, if a single delta/current text contains:

<|tool_call_start|>[get_weather(city="Paris")]<|tool_call_end|>
Done.

the parser emits the tool call but does not emit Done. as content. I reproduced this locally with run_tool_extraction_streaming(..., model_deltas=[full_text]): the reconstructed content was empty while the tool call was parsed correctly. Token-by-token streaming happens to work because the trailing text arrives after prev_tool_call_arr has been set, but streaming parsers should not depend on that chunk boundary.

The relevant logic is around if TOOL_CALL_END in current_text and self.prev_tool_call_arr:. In the single-delta case, prev_tool_call_arr is still unset when the end token and trailing text first appear, so the function goes down the tool parsing path and returns only DeltaMessage(tool_calls=...).

A similar boundary issue exists for text before the tool call if the prefix and <|tool_call_start|> arrive in the same delta, e.g.:

Let me check. <|tool_call_start|>[get_weather(city="Paris")]<|tool_call_end|>

Let me check. is not streamed as content in that case.

Could you add streaming tests where the full tool block plus leading/trailing content are passed as one delta, and update the parser to preserve that content, either in the same DeltaMessage or a subsequent one?

[jbuchananr]

Updated with streaming tests and tested locally. @tianshu-Michael-yu

[tianshu-Michael-yu]

Updated with streaming tests and tested locally. @tianshu-Michael-yu

@chaunceyjiang can you take a look? If ok, then approve this?

[chaunceyjiang]

LGTM.

I haven’t tested it locally; I assume you’ve already tested it on your side.

[mergify]

Hi @jbuchananr, the pre-commit checks have failed. Please run:

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[mergify]

Hi @jbuchananr, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

Tip

Is mypy failing?

mypy is run differently in CI. If the failure is related to this check, please use the following command to run it locally:

# For mypy (substitute "3.10" with the failing version if needed)
pre-commit run --hook-stage manual mypy-3.10

[jbuchananr]

Yes tested extensively locally, @chaunceyjiang let me know if anything else is required. Thanks!