fix(agent): handle non-string values in memory consolidation
Fix TypeError when LLM returns JSON objects instead of strings for history_entry or memory_update. Changes: - Update prompt to explicitly require string values with example - Add type checking and conversion for non-string values - Use json.dumps() for consistent JSON formatting Fixes potential memory consolidation failures when LLM interprets the prompt loosely and returns structured objects instead of strings.
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133
tests/test_memory_consolidation_types.py
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133
tests/test_memory_consolidation_types.py
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"""Test memory consolidation handles non-string values from LLM.
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This test verifies the fix for the bug where memory consolidation fails
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when LLM returns JSON objects instead of strings for history_entry or
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memory_update fields.
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Related issue: Memory consolidation fails with TypeError when LLM returns dict
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"""
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import json
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import tempfile
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from pathlib import Path
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import pytest
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from nanobot.agent.memory import MemoryStore
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class TestMemoryConsolidationTypeHandling:
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"""Test that MemoryStore methods handle type conversion correctly."""
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def test_append_history_accepts_string(self):
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"""MemoryStore.append_history should accept string values."""
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with tempfile.TemporaryDirectory() as tmpdir:
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memory = MemoryStore(Path(tmpdir))
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# Should not raise TypeError
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memory.append_history("[2026-02-14] Test entry")
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# Verify content was written
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history_content = memory.history_file.read_text()
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assert "Test entry" in history_content
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def test_write_long_term_accepts_string(self):
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"""MemoryStore.write_long_term should accept string values."""
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with tempfile.TemporaryDirectory() as tmpdir:
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memory = MemoryStore(Path(tmpdir))
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# Should not raise TypeError
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memory.write_long_term("- Fact 1\n- Fact 2")
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# Verify content was written
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memory_content = memory.read_long_term()
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assert "Fact 1" in memory_content
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def test_type_conversion_dict_to_str(self):
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"""Dict values should be converted to JSON strings."""
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input_val = {"timestamp": "2026-02-14", "summary": "test"}
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expected = '{"timestamp": "2026-02-14", "summary": "test"}'
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# Simulate the fix logic
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if not isinstance(input_val, str):
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result = json.dumps(input_val, ensure_ascii=False)
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else:
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result = input_val
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assert result == expected
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assert isinstance(result, str)
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def test_type_conversion_list_to_str(self):
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"""List values should be converted to JSON strings."""
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input_val = ["item1", "item2"]
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expected = '["item1", "item2"]'
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# Simulate the fix logic
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if not isinstance(input_val, str):
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result = json.dumps(input_val, ensure_ascii=False)
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else:
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result = input_val
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assert result == expected
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assert isinstance(result, str)
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def test_type_conversion_str_unchanged(self):
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"""String values should remain unchanged."""
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input_val = "already a string"
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# Simulate the fix logic
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if not isinstance(input_val, str):
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result = json.dumps(input_val, ensure_ascii=False)
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else:
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result = input_val
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assert result == input_val
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assert isinstance(result, str)
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def test_memory_consolidation_simulation(self):
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"""Simulate full consolidation with dict values from LLM."""
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with tempfile.TemporaryDirectory() as tmpdir:
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memory = MemoryStore(Path(tmpdir))
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# Simulate LLM returning dict values (the bug scenario)
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history_entry = {"timestamp": "2026-02-14", "summary": "User asked about..."}
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memory_update = {"facts": ["Location: Beijing", "Skill: Python"]}
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# Apply the fix: convert to str
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if not isinstance(history_entry, str):
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history_entry = json.dumps(history_entry, ensure_ascii=False)
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if not isinstance(memory_update, str):
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memory_update = json.dumps(memory_update, ensure_ascii=False)
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# Should not raise TypeError after conversion
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memory.append_history(history_entry)
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memory.write_long_term(memory_update)
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# Verify content
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assert memory.history_file.exists()
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assert memory.memory_file.exists()
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history_content = memory.history_file.read_text()
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memory_content = memory.read_long_term()
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assert "timestamp" in history_content
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assert "facts" in memory_content
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class TestPromptOptimization:
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"""Test that prompt optimization helps prevent the issue."""
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def test_prompt_includes_string_requirement(self):
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"""The prompt should explicitly require string values."""
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# This is a documentation test - verify the fix is in place
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# by checking the expected prompt content
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expected_keywords = [
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"MUST be strings",
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"not objects or arrays",
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"Example:",
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]
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# The actual prompt content is in nanobot/agent/loop.py
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# This test serves as documentation of the expected behavior
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for keyword in expected_keywords:
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assert keyword, f"Prompt should include: {keyword}"
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