Status of gRPC, Tool Bridge, and Variables Implementation

• Date: 2025-07-23
• Author: AI Assistant
• Version: 1.0

1. Overall Status

The project has successfully implemented the foundational layers of the unified gRPC bridge, as outlined in the specifications. The core variable system (Stage 1) is nearly complete on both the Elixir and Python sides, providing a robust, type-safe, and versioned state management system. The bidirectional tool bridge (Stage 2) is partially implemented, with mechanisms for registration and discovery in place, but the core remote execution logic is incomplete. Real-time streaming features (Stage 3) are defined in the protocol but are not yet implemented in the server handlers.

The architecture is sound, with a clear separation of concerns between the stateful Elixir SessionStore and stateless Python workers, a comprehensive protobuf definition, and a rich Python SessionContext API. The remaining work focuses on activating the streaming capabilities and completing the tool execution dispatch logic.

2. Detailed Status by Feature

2.1. Variables System

The variable system is the most complete feature set, aligning closely with the Stage 1 and Stage 2 specifications.

✅ Complete

• Elixir Core:
  • A comprehensive Snakepit.Bridge.Variables.Variable struct with versioning, metadata, and optimization status fields.
  • A complete type system in snakepit/lib/bridge/variables/types/ supporting float, integer, string, boolean, choice, module, embedding, and tensor. Each type includes validation and constraint checking.
  • The Snakepit.Bridge.SessionStore is fully equipped with variable management logic, including CRUD operations, batch get_variables/update_variables, and an index for name-based lookups.
• Python Client:
  • The snakepit_bridge.session_context.SessionContext provides a rich, Pythonic API for all variable operations.
  • Features intelligent caching with TTL, multiple access patterns (__getitem__, .v namespace), VariableProxy for lazy access, and a batch_updates context manager.
  • A matching snakepit_bridge.serialization.TypeSerializer ensures cross-language type consistency.
• gRPC Protocol:
  • All necessary RPCs for variable CRUD and batch operations (RegisterVariable, GetVariable, SetVariable, GetVariables, etc.) are defined in snakepit_bridge.proto and implemented in the Elixir BridgeServer.

❌ To Do

1. Implement WatchVariables Streaming: The WatchVariables RPC is defined in the protocol but the handler in snakepit/grpc/bridge_server.ex raises an :unimplemented error. The corresponding Python client-side logic in SessionContext is also a placeholder. This is the primary missing piece for Stage 3 reactivity.
2. Implement Advanced Features (Stage 4): The protocol defines RPCs for dependencies, optimization, and history (AddDependency, StartOptimization, GetVariableHistory, RollbackVariable), but the underlying SessionStore logic and gRPC handlers are not yet implemented.

2.2. Tool Bridge

The tool bridge provides the mechanisms for bidirectional tool execution. The registration and discovery parts are complete, but the execution dispatch logic is a key missing piece.

✅ Complete

• Elixir Core:
  • Snakepit.Bridge.ToolRegistry provides a robust GenServer for registering both local (Elixir) and remote (Python) tools, storing them with their associated worker_id.
  • Logic for exposing Elixir tools to Python (list_exposed_elixir_tools) is in place.
• Python Client & Adapters:
  • The snakepit_bridge.base_adapter.BaseAdapter and @tool decorator provide a clean way for Python workers to define and register their tools.
  • The SessionContext has implemented methods (get_exposed_elixir_tools, call_elixir_tool) for Python to discover and execute Elixir tools.
• gRPC Protocol:
  • RPCs for RegisterTools, GetExposedElixirTools, and ExecuteElixirTool are defined and fully implemented on the Elixir server, enabling the bidirectional flow.

❌ To Do

1. Implement Remote Tool Dispatch: The ExecuteTool handler in snakepit/grpc/bridge_server.ex currently only executes local Elixir tools. It needs to be extended to:
   • Look up the tool in ToolRegistry.
   • If the tool is :remote, identify the correct worker_id.
   • Dispatch an ExecuteTool request to that specific Python worker’s gRPC server. This requires the Elixir side to have a gRPC client connection to each worker.
2. Implement ExecuteStreamingTool: The handler for streaming tools in snakepit/grpc/bridge_server.ex is a stub. This needs to be implemented to forward the request to the correct Python worker and then proxy the resulting stream back to the original Elixir caller.

2.3. gRPC Core & Streaming

The core infrastructure is solid, but the streaming capabilities are not yet activated on the server side.

✅ Complete

• Protocol Definition: The snakepit_bridge.proto file is comprehensive, using a BridgeService and defining messages for all planned stages.
• Client Implementation: The Elixir gRPC client (snakepit/lib/grpc/client_impl.ex) has implemented functions for both unary and streaming RPCs.
• Server Foundation: The Elixir BridgeServer and Python grpc_server.py are functional and handle unary requests correctly.
• Process Management: The Elixir GRPCWorker reliably starts, monitors, and cleans up the Python gRPC server process.

❌ To Do

1. Implement Server-Side Streaming Handlers: This is the most significant remaining task.
   • WatchVariables in BridgeServer: Implement the logic to subscribe to the ObserverManager (from Stage 3 specs) and push updates to the client stream.
   • ExecuteStreamingTool in BridgeServer: Implement the logic to dispatch the call to the appropriate Python worker and proxy the stream of ToolChunk messages back.
   • ExecuteStreamingTool in Python grpc_server.py: The Python gRPC handler needs to be able to handle a tool that returns a generator/iterator and stream the yielded chunks back to Elixir.

3. Work Remaining (Roadmap)

The following is a prioritized list of tasks to achieve full functionality for the unified bridge.

Priority 1: Complete Core Tool & Streaming Execution

1. Implement Remote Tool Dispatch in BridgeServer:

   • Modify the ExecuteTool handler in snakepit/grpc/bridge_server.ex.
   • Use ToolRegistry.get_tool/2 to find the tool’s worker_id.
   • The GRPCWorker needs to manage a gRPC channel to its Python process, or a central dispatcher needs to manage connections to all workers.
   • Forward the ExecuteToolRequest to the correct worker.

2. Implement ExecuteStreamingTool Handler (Elixir & Python):

   • Elixir BridgeServer: Implement the ExecuteStreamingTool handler to forward the request to the Python worker (similar to the above) and proxy the stream response.
   • Python grpc_server.py: Implement the ExecuteStreamingTool handler. It should call the adapter’s tool, check if it returns a generator, and then yield ToolChunk messages for each item.

Priority 2: Enable Real-Time Variable Watching

1. Implement ObserverManager: Create the ObserverManager GenServer as specified in Stage 3 to decouple SessionStore from stream management.
2. Update SessionStore: Integrate ObserverManager.notify_observers into the variable update logic.
3. Implement WatchVariables Handler: Implement the WatchVariables gRPC handler in BridgeServer. It should subscribe the stream to the ObserverManager and push updates.
4. Implement Python Client Stream Consumption: Finalize the async for implementation in SessionContext.watch_variables to consume the stream from Elixir.

Priority 3: Implement Advanced Stage 4 Features

1. Dependencies: Implement the DependencyGraph and integrate it into SessionStore. Add the AddDependency RPC handler.
2. Optimization: Add optimization_status logic to Variable and SessionStore. Implement the StartOptimization and StopOptimization RPC handlers.
3. History & Versioning: Extend the Variable struct and SessionStore to maintain a history of changes. Implement the GetVariableHistory and RollbackVariable RPC handlers.