CoreRasurae
227e2feece
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Implements the complete LLM agent tool-calling pipeline — the tool-calling path was broken in multiple ways: tools from agent config were not passed to the LLM, tool execution was single-pass (the model could not see tool results or make follow-up calls), tool errors were discarded, and shell/ python_exec tools were never registered. Key changes: - Multi-turn tool-call loop: passes tools to the LLM via ainvoke(tools=...) and re-invokes after each batch of ToolResults, with a configurable round limit (tool_max_rounds config / TOOL_MAX_ROUNDS env var, default 20). - Tool schema module (llm_tools.py): normalize_tool_entry() converts string tool names and config dicts to OpenAI function-calling format with full parameter schemas and LLM-facing guidance (max_chars for file_read, sandbox builtins for python_exec, etc.). - file_read enhancements: directory listing with file sizes and type indicators, max_chars truncation to prevent context overflow, shell command detection with redirect to shell tool, file-not-found recovery hints with parent directory listing. - python_exec sandbox: stdout capture so print() produces visible output, NameError guidance directing LLM to file_read/file_write instead of using open(). - shell/python_exec tool registration in builtin_tools when allow_shell / exec_python is enabled. - create_subprocess_shell for shell commands (was create_subprocess_exec, which blocks pipes/redirections/chains). - Tool error propagation: ExecutionError/ConfigurationError details included in ToolMessages for LLM self-correction. - Stuck-model recovery: injects synthesizing prompt when model exhausts tool rounds without producing content. - LLM provider error details preserved in ExecutionError messages instead of generic "LLM processing failed". - Empty message filtering in _prepare_conversation_history() prevents whitespace-only messages from polluting downstream conversation history in multi-agent pipelines. Closes #59
34 lines
1.6 KiB
Gherkin
34 lines
1.6 KiB
Gherkin
Feature: LLM Tools Coverage
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As a developer
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I want to cover remaining uncovered lines in llm_tools.py
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So that coverage reaches the target
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Scenario: normalize_tool_entry returns OpenAI-formatted dict as-is
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Given an llm_tools coverage test environment
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When I call normalize_tool_entry with OpenAI-formatted dict
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Then the result should be the same dict unchanged
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Scenario: normalize_tool_entry rejects dict missing valid name
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Given an llm_tools coverage test environment
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When I call normalize_tool_entry with dict missing name
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Then a ConfigurationError mentioning non-empty string 'name' is raised
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Scenario: normalize_tool_entry rejects dict with empty name
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Given an llm_tools coverage test environment
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When I call normalize_tool_entry with dict having empty name
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Then a ConfigurationError mentioning non-empty string 'name' is raised
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Scenario: normalize_tool_entry converts string tool name to OpenAI format
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Given an llm_tools coverage test environment
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When I call normalize_tool_entry with string "echo"
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Then the result should be OpenAI dict with name echo
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Scenario: normalize_tool_entry rejects empty string
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Given an llm_tools coverage test environment
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When I call normalize_tool_entry with empty string
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Then a ConfigurationError mentioning non-empty string is raised
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Scenario: normalize_tool_entry rejects non-str non-dict type
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Given an llm_tools coverage test environment
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When I call normalize_tool_entry with integer 42
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Then a ConfigurationError mentioning Invalid tool config entry is raised |