Add Consensus Tool for Multi-Model Perspective Gathering (#67)
* WIP Refactor resolving mode_names, should be done once at MCP call boundary Pass around model context instead Consensus tool allows one to get a consensus from multiple models, optionally assigning one a 'for' or 'against' stance to find nuanced responses. * Deduplication of model resolution, model_context should be available before reaching deeper parts of the code Improved abstraction when building conversations Throw programmer errors early * Guardrails Support for `model:option` format at MCP boundary so future tools can use additional options if needed instead of handling this only for consensus Model name now supports an optional ":option" for future use * Simplified async flow * Improved model for request to support natural language Simplified async flow * Improved model for request to support natural language Simplified async flow * Fix consensus tool async/sync patterns to match codebase standards CRITICAL FIXES: - Converted _get_consensus_responses from async to sync (matches other tools) - Converted store_conversation_turn from async to sync (add_turn is synchronous) - Removed unnecessary asyncio imports and sleep calls - Fixed ClosedResourceError in MCP protocol during long consensus operations PATTERN ALIGNMENT: - Consensus tool now follows same sync patterns as all other tools - Only execute() and prepare_prompt() are async (base class requirement) - All internal operations are synchronous like analyze, chat, debug, etc. TESTING: - MCP simulation test now passes: consensus_stance ✅ - Two-model consensus works correctly in ~35 seconds - Unknown stance handling defaults to neutral with warnings - All 9 unit tests pass (100% success rate) The consensus tool async patterns were anomalous in the codebase. This fix aligns it with the established synchronous patterns used by all other tools while maintaining full functionality. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * Fixed call order and added new test * Cleanup dead comments Docs for the new tool Improved tests --------- Co-authored-by: Claude <noreply@anthropic.com>
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simulator_tests/test_consensus_conversation.py
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simulator_tests/test_consensus_conversation.py
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#!/usr/bin/env python3
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"""
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Consensus Conversation Continuation Test
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Tests that the consensus tool properly handles conversation continuation
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and builds conversation context correctly when using continuation_id.
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"""
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import json
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import subprocess
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from .base_test import BaseSimulatorTest
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class TestConsensusConversation(BaseSimulatorTest):
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"""Test consensus tool conversation continuation functionality"""
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@property
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def test_name(self) -> str:
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return "consensus_conversation"
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@property
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def test_description(self) -> str:
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return "Test consensus tool conversation building and continuation"
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def get_docker_logs(self):
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"""Get Docker container logs"""
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try:
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result = subprocess.run(
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["docker", "logs", "--tail", "100", self.container_name], capture_output=True, text=True, timeout=30
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)
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if result.returncode == 0:
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return result.stdout.split("\n")
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else:
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self.logger.warning(f"Failed to get Docker logs: {result.stderr}")
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return []
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except Exception as e:
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self.logger.warning(f"Exception getting Docker logs: {e}")
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return []
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def run_test(self) -> bool:
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"""Test consensus conversation continuation"""
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try:
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self.logger.info("Testing consensus tool conversation continuation")
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# Setup test files for context
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self.setup_test_files()
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# Phase 1: Start conversation with chat tool (which properly creates continuation_id)
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self.logger.info("Phase 1: Starting conversation with chat tool")
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initial_response, continuation_id = self.call_mcp_tool(
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"chat",
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{
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"prompt": "Please use low thinking mode. I'm working on a web application and need advice on authentication. Can you look at this code?",
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"files": [self.test_files["python"]],
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"model": "local-llama",
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},
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)
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# Validate initial response
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if not initial_response:
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self.logger.error("Failed to get initial chat response")
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return False
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if not continuation_id:
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self.logger.error("Failed to get continuation_id from initial chat")
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return False
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self.logger.info(f"Initial chat response preview: {initial_response[:200]}...")
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self.logger.info(f"Got continuation_id: {continuation_id}")
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# Phase 2: Use consensus with continuation_id to test conversation building
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self.logger.info("Phase 2: Using consensus with continuation_id to test conversation building")
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consensus_response, _ = self.call_mcp_tool(
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"consensus",
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{
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"prompt": "Based on our previous discussion about authentication, I need expert consensus: Should we implement OAuth2 or stick with simple session-based auth?",
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"models": [
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{
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"model": "local-llama",
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"stance": "for",
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"stance_prompt": "Focus on OAuth2 benefits: security, scalability, and industry standards.",
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},
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{
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"model": "local-llama",
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"stance": "against",
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"stance_prompt": "Focus on OAuth2 complexity: implementation challenges and simpler alternatives.",
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},
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],
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"continuation_id": continuation_id,
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"model": "local-llama",
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},
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)
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# Validate consensus response
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if not consensus_response:
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self.logger.error("Failed to get consensus response with continuation_id")
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return False
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self.logger.info(f"Consensus response preview: {consensus_response[:300]}...")
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# Log the full response for debugging if it's not JSON
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if not consensus_response.startswith("{"):
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self.logger.error(f"Consensus response is not JSON. Full response: {consensus_response}")
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return False
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# Parse consensus response
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try:
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consensus_data = json.loads(consensus_response)
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except json.JSONDecodeError:
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self.logger.error(f"Failed to parse consensus response as JSON. Full response: {consensus_response}")
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return False
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if consensus_data.get("status") != "consensus_success":
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self.logger.error(f"Consensus failed with status: {consensus_data.get('status')}")
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if "error" in consensus_data:
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self.logger.error(f"Error: {consensus_data['error']}")
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return False
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# Phase 3: Check server logs for conversation building
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self.logger.info("Phase 3: Checking server logs for conversation building")
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# Check for conversation-related log entries
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logs = self.get_docker_logs()
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if not logs:
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self.logger.warning("Could not retrieve Docker logs for verification")
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else:
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# Look for conversation building indicators
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conversation_logs = [
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line
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for line in logs
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if any(
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keyword in line
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for keyword in [
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"CONVERSATION HISTORY",
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"continuation_id",
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"build_conversation_history",
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"ThreadContext",
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f"thread:{continuation_id}",
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]
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)
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]
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if conversation_logs:
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self.logger.info(f"Found {len(conversation_logs)} conversation-related log entries")
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# Show a few examples (truncated)
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for i, log in enumerate(conversation_logs[:3]):
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self.logger.info(f" Conversation log {i+1}: {log[:100]}...")
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else:
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self.logger.warning(
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"No conversation-related logs found (may indicate conversation not properly built)"
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)
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# Check for any ERROR entries related to consensus
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error_logs = [
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line
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for line in logs
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if "ERROR" in line
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and any(keyword in line for keyword in ["consensus", "conversation", continuation_id])
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]
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if error_logs:
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self.logger.error(f"Found {len(error_logs)} error logs related to consensus conversation:")
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for error in error_logs:
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self.logger.error(f" ERROR: {error}")
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return False
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# Phase 4: Verify response structure
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self.logger.info("Phase 4: Verifying consensus response structure")
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# Check that consensus has proper models_used
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models_used = consensus_data.get("models_used", [])
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if not models_used:
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self.logger.error("Consensus response missing models_used")
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return False
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# Check that we have responses
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responses = consensus_data.get("responses", [])
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if not responses:
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self.logger.error("Consensus response missing responses")
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return False
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# Verify at least one successful response
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successful_responses = [r for r in responses if r.get("status") == "success"]
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if not successful_responses:
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self.logger.error("No successful responses in consensus")
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return False
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self.logger.info(f"Consensus used models: {models_used}")
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self.logger.info(f"Consensus had {len(successful_responses)} successful responses")
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# Phase 5: Cross-tool continuation test
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self.logger.info("Phase 5: Testing cross-tool continuation from consensus")
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# Try to continue the conversation with a different tool
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chat_response, _ = self.call_mcp_tool(
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"chat",
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{
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"prompt": "Based on our consensus discussion about authentication, can you summarize the key points?",
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"continuation_id": continuation_id,
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"model": "local-llama",
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},
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)
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if not chat_response:
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self.logger.warning("Cross-tool continuation from consensus failed")
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# Don't fail the test for this - it's a bonus check
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else:
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self.logger.info("✓ Cross-tool continuation from consensus working")
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self.logger.info(f"Chat continuation preview: {chat_response[:200]}...")
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self.logger.info("✓ Consensus conversation continuation test completed successfully")
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return True
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except Exception as e:
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self.logger.error(f"Consensus conversation test failed with exception: {str(e)}")
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import traceback
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self.logger.error(f"Traceback: {traceback.format_exc()}")
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return False
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finally:
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self.cleanup_test_files()
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