Migration from Docker to Standalone Python Server (#73)
* Migration from docker to standalone server Migration handling Fixed tests Use simpler in-memory storage Support for concurrent logging to disk Simplified direct connections to localhost * Migration from docker / redis to standalone script Updated tests Updated run script Fixed requirements Use dotenv Ask if user would like to install MCP in Claude Desktop once Updated docs * More cleanup and references to docker removed * Cleanup * Comments * Fixed tests * Fix GitHub Actions workflow for standalone Python architecture - Install requirements-dev.txt for pytest and testing dependencies - Remove Docker setup from simulation tests (now standalone) - Simplify linting job to use requirements-dev.txt - Update simulation tests to run directly without Docker Fixes unit test failures in CI due to missing pytest dependency. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * Remove simulation tests from GitHub Actions - Removed simulation-tests job that makes real API calls - Keep only unit tests (mocked, no API costs) and linting - Simulation tests should be run manually with real API keys - Reduces CI costs and complexity GitHub Actions now only runs: - Unit tests (569 tests, all mocked) - Code quality checks (ruff, black) 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * Fixed tests * Fixed tests --------- Co-authored-by: Claude <noreply@anthropic.com>
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@@ -3,15 +3,29 @@ Conversation Memory for AI-to-AI Multi-turn Discussions
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This module provides conversation persistence and context reconstruction for
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stateless MCP (Model Context Protocol) environments. It enables multi-turn
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conversations between Claude and Gemini by storing conversation state in Redis
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conversations between Claude and Gemini by storing conversation state in memory
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across independent request cycles.
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CRITICAL ARCHITECTURAL REQUIREMENT:
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This conversation memory system is designed for PERSISTENT MCP SERVER PROCESSES.
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It uses in-memory storage that persists only within a single Python process.
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⚠️ IMPORTANT: This system will NOT work correctly if MCP tool calls are made
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as separate subprocess invocations (each subprocess starts with empty memory).
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WORKING SCENARIO: Claude Desktop with persistent MCP server process
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FAILING SCENARIO: Simulator tests calling server.py as individual subprocesses
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Root cause of test failures: Each subprocess call loses the conversation
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state from previous calls because memory is process-specific, not shared
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across subprocess boundaries.
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ARCHITECTURE OVERVIEW:
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The MCP protocol is inherently stateless - each tool request is independent
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with no memory of previous interactions. This module bridges that gap by:
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1. Creating persistent conversation threads with unique UUIDs
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2. Storing complete conversation context (turns, files, metadata) in Redis
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2. Storing complete conversation context (turns, files, metadata) in memory
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3. Reconstructing conversation history when tools are called with continuation_id
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4. Supporting cross-tool continuation - seamlessly switch between different tools
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while maintaining full conversation context and file references
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@@ -35,9 +49,9 @@ Key Features:
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most recent file context is preserved when token limits require exclusions.
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- Automatic turn limiting (20 turns max) to prevent runaway conversations
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- Context reconstruction for stateless request continuity
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- Redis-based persistence with automatic expiration (3 hour TTL)
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- In-memory persistence with automatic expiration (3 hour TTL)
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- Thread-safe operations for concurrent access
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- Graceful degradation when Redis is unavailable
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- Graceful degradation when storage is unavailable
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DUAL PRIORITIZATION STRATEGY (Files & Conversations):
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The conversation memory system implements sophisticated prioritization for both files and
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@@ -187,26 +201,16 @@ class ThreadContext(BaseModel):
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initial_context: dict[str, Any] # Original request parameters
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def get_redis_client():
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def get_storage():
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"""
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Get Redis client from environment configuration
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Creates a Redis client using the REDIS_URL environment variable.
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Defaults to localhost:6379/0 if not specified.
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Get in-memory storage backend for conversation persistence.
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Returns:
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redis.Redis: Configured Redis client with decode_responses=True
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Raises:
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ValueError: If redis package is not installed
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InMemoryStorage: Thread-safe in-memory storage backend
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"""
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try:
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import redis
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from .storage_backend import get_storage_backend
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redis_url = os.getenv("REDIS_URL", "redis://localhost:6379/0")
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return redis.from_url(redis_url, decode_responses=True)
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except ImportError:
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raise ValueError("redis package required. Install with: pip install redis")
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return get_storage_backend()
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def create_thread(tool_name: str, initial_request: dict[str, Any], parent_thread_id: Optional[str] = None) -> str:
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@@ -251,10 +255,10 @@ def create_thread(tool_name: str, initial_request: dict[str, Any], parent_thread
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initial_context=filtered_context,
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)
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# Store in Redis with configurable TTL to prevent indefinite accumulation
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client = get_redis_client()
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# Store in memory with configurable TTL to prevent indefinite accumulation
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storage = get_storage()
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key = f"thread:{thread_id}"
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client.setex(key, CONVERSATION_TIMEOUT_SECONDS, context.model_dump_json())
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storage.setex(key, CONVERSATION_TIMEOUT_SECONDS, context.model_dump_json())
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logger.debug(f"[THREAD] Created new thread {thread_id} with parent {parent_thread_id}")
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@@ -263,7 +267,7 @@ def create_thread(tool_name: str, initial_request: dict[str, Any], parent_thread
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def get_thread(thread_id: str) -> Optional[ThreadContext]:
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"""
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Retrieve thread context from Redis
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Retrieve thread context from in-memory storage
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Fetches complete conversation context for cross-tool continuation.
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This is the core function that enables tools to access conversation
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@@ -278,22 +282,22 @@ def get_thread(thread_id: str) -> Optional[ThreadContext]:
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Security:
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- Validates UUID format to prevent injection attacks
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- Handles Redis connection failures gracefully
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- Handles storage connection failures gracefully
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- No error information leakage on failure
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"""
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if not thread_id or not _is_valid_uuid(thread_id):
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return None
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try:
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client = get_redis_client()
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storage = get_storage()
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key = f"thread:{thread_id}"
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data = client.get(key)
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data = storage.get(key)
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if data:
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return ThreadContext.model_validate_json(data)
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return None
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except Exception:
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# Silently handle errors to avoid exposing Redis details
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# Silently handle errors to avoid exposing storage details
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return None
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@@ -313,8 +317,7 @@ def add_turn(
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Appends a new conversation turn to an existing thread. This is the core
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function for building conversation history and enabling cross-tool
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continuation. Each turn preserves the tool and model that generated it,
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and tracks file reception order using atomic Redis counters.
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continuation. Each turn preserves the tool and model that generated it.
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Args:
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thread_id: UUID of the conversation thread
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@@ -333,7 +336,7 @@ def add_turn(
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Failure cases:
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- Thread doesn't exist or expired
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- Maximum turn limit reached
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- Redis connection failure
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- Storage connection failure
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Note:
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- Refreshes thread TTL to configured timeout on successful update
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@@ -370,14 +373,14 @@ def add_turn(
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context.turns.append(turn)
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context.last_updated_at = datetime.now(timezone.utc).isoformat()
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# Save back to Redis and refresh TTL
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# Save back to storage and refresh TTL
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try:
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client = get_redis_client()
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storage = get_storage()
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key = f"thread:{thread_id}"
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client.setex(key, CONVERSATION_TIMEOUT_SECONDS, context.model_dump_json()) # Refresh TTL to configured timeout
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storage.setex(key, CONVERSATION_TIMEOUT_SECONDS, context.model_dump_json()) # Refresh TTL to configured timeout
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return True
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except Exception as e:
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logger.debug(f"[FLOW] Failed to save turn to Redis: {type(e).__name__}")
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logger.debug(f"[FLOW] Failed to save turn to storage: {type(e).__name__}")
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return False
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@@ -591,11 +594,9 @@ def _plan_file_inclusion_by_size(all_files: list[str], max_file_tokens: int) ->
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for file_path in all_files:
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try:
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from utils.file_utils import estimate_file_tokens, translate_path_for_environment
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from utils.file_utils import estimate_file_tokens
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translated_path = translate_path_for_environment(file_path)
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if os.path.exists(translated_path) and os.path.isfile(translated_path):
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if os.path.exists(file_path) and os.path.isfile(file_path):
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# Use centralized token estimation for consistency
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estimated_tokens = estimate_file_tokens(file_path)
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@@ -613,7 +614,7 @@ def _plan_file_inclusion_by_size(all_files: list[str], max_file_tokens: int) ->
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else:
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files_to_skip.append(file_path)
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# More descriptive message for missing files
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if not os.path.exists(translated_path):
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if not os.path.exists(file_path):
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logger.debug(
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f"[FILES] Skipping {file_path} - file no longer exists (may have been moved/deleted since conversation)"
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)
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@@ -724,7 +725,7 @@ def build_conversation_history(context: ThreadContext, model_context=None, read_
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Performance Characteristics:
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- O(n) file collection with newest-first prioritization
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- Intelligent token budgeting prevents context window overflow
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- Redis-based persistence with automatic TTL management
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- In-memory persistence with automatic TTL management
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- Graceful degradation when files are inaccessible or too large
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"""
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# Get the complete thread chain
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@@ -851,10 +852,7 @@ def build_conversation_history(context: ThreadContext, model_context=None, read_
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except Exception as e:
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# More descriptive error handling for missing files
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try:
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from utils.file_utils import translate_path_for_environment
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translated_path = translate_path_for_environment(file_path)
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if not os.path.exists(translated_path):
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if not os.path.exists(file_path):
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logger.info(
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f"File no longer accessible for conversation history: {file_path} - file was moved/deleted since conversation (marking as excluded)"
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)
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