Stateless LLM Providers #499
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This commit implements a comprehensive refactoring of the conversation history architecture to simplify the codebase and make message flow more transparent. Core Principle: - Agent._message_history is now the ONLY source of truth - Provider history (self.history) is diagnostic-only (write-only for debugging) - Fresh conversion from _message_history happens on every LLM API call - No more reading from provider history for API calls Key Changes: 1. Base Class (FastAgentLLM): - Added _convert_to_provider_format() method that combines templates + messages - Added abstract _convert_extended_messages_to_provider() method for providers - Templates are automatically prepended by _convert_to_provider_format() 2. All LLM Providers (Anthropic, OpenAI, Google, Bedrock): - Implemented _convert_extended_messages_to_provider() for each provider - Replaced self.history.get() calls with self._convert_to_provider_format() - Simplified history saving to diagnostic snapshots only (self.history.set()) - Removed manual template management from provider-specific methods - Simplified _apply_prompt_provider_specific() methods 3. OpenAI-Compatible Providers: - All providers that inherit from OpenAILLM automatically get updates - Verified Azure, DeepSeek, Groq, xAI, HuggingFace, etc. work correctly 4. Load History Fix: - Added load_history_into_agent() function in prompt_load.py - Updated interactive_prompt.py to use new function - No longer triggers LLM calls when loading history 5. Memory Class: - Added deprecation notices to Memory protocol and SimpleMemory.get() - Clarified that provider history is diagnostic-only - Memory.get() should not be called for API message construction 6. Tests: - Created integration tests for new architecture - Fixed PassthroughLLM to implement required abstract method - All existing tests pass Benefits: - Simpler architecture with single source of truth - No double-conversion or stale history issues - Easier to reason about template handling - Clear separation between conversation state and diagnostic snapshots - Fixes load_history bug (no unwanted LLM calls) Files Modified: - src/fast_agent/llm/fastagent_llm.py - src/fast_agent/llm/provider/anthropic/llm_anthropic.py - src/fast_agent/llm/provider/openai/llm_openai.py - src/fast_agent/llm/provider/google/llm_google_native.py - src/fast_agent/llm/provider/bedrock/llm_bedrock.py - src/fast_agent/llm/memory.py - src/fast_agent/llm/internal/passthrough.py - src/fast_agent/mcp/prompts/prompt_load.py - src/fast_agent/ui/interactive_prompt.py Files Added: - src/fast_agent/llm/provider/bedrock/multipart_converter_bedrock.py - tests/integration/history-architecture/test_history_architecture.py
Updates test stubs that inherit from FastAgentLLM to implement the new _convert_extended_messages_to_provider() abstract method: - StubLLM in test_prepare_arguments.py - Anthropic caching tests updated to use _message_history instead of provider history The caching tests now properly test the new architecture where messages come from _message_history via _convert_to_provider_format(). Note: These tests use MagicMock/patching which is against the preferred test style. Consider refactoring to use real implementations in the future.
Rewrote the caching tests to directly test the conversion method _convert_extended_messages_to_provider() instead of mocking the entire API call chain. Benefits: - Clean, isolated unit tests of the caching algorithm - No MagicMock or monkeypatching - Tests actual behavior of cache_control marker application - Easier to understand and maintain Tests now verify: - cache_mode='off' applies no cache_control - cache_mode='prompt' applies cache_control to templates only - cache_mode='auto' applies cache_control to templates - Message structure is preserved during conversion - Empty message lists are handled correctly - Templates-only scenarios work correctly
- Use model="passthrough" to avoid requiring API keys - Remove unused PromptMessageExtended import - Tests now verify history architecture without external dependencies
The agent wrapper from prompt_provider._agent() doesn't have _message_history directly - need to access agent_obj._llm
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This commit implements a comprehensive refactoring of the conversation history architecture to simplify the codebase and make message flow more transparent.
Core Principle:
Key Changes:
Base Class (FastAgentLLM):
All LLM Providers (Anthropic, OpenAI, Google, Bedrock):
OpenAI-Compatible Providers:
Load History Fix:
Memory Class:
Tests:
Benefits:
Files Modified:
Files Added: