Modular Client Abstraction for Various Coding Assistants / Agents

[lukehinds]

Summary

We have numerous coding assistant clients (e.g., Cline, CoPilot, Continue, Aider, and others in the pipeline) that sometimes conform to a common standard (e.g., OpenAI) but often introduce unique deviations in our code in how they serialize input and process streams. To accommodate these differences cleanly, we need a modular “client providers” system—similar to our modular providers system—to identify each client (potentially via user-agent or other metadata) and apply client-specific logic.

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Background & Motivation

• Current Issue: Each new coding assistant has its own peculiarities (e.g., prompt structure or stream handling (start end delimiters), which forces us to write ad hoc parsing or abstraction logic in code. This approach becomes unscalable and error-prone as we add more assistants.
• User-Agent Challenges: While user-agents could be a natural way to detect the client, some VS Code extensions or tools send generic UAs (e.g., “Node/16.x”), preventing a straightforward identification.
• Goal: A dedicated, modular mechanism that helps us detect and handle each client’s unique prompt/stream format seamlessly without duplicating effort.

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Requirements

1. Client Identification Logic

   • Implement a robust strategy to detect which client is sending the request. Potential approaches include:
     • User-Agent inspection (if reliably set).
     • Custom header (e.g., X-Coding-Assistant: cline).
     • A fallback or heuristic if no identifying header is present.
   • Provide a clear, documented flow for adding new identification rules or hooking into existing ones.

2. Modular Architecture

   • Similar to our current “providers” model, implement a “client providers” system where each coding assistant (Cline, CoPilot, Continue, Aider, etc.) has its own plugin/module.
   • Each module should define:
     • Serialization/Deserialization logic for prompts and responses.
     • Any specialized stream handling (if they differ from a standard JSON or SSE approach).
     • Additional domain-specific logic (e.g., unique callback endpoints, chunked streaming format, etc.).

3. Fallback Handling

   • If a request can’t be matched to any known client, default to a “generic” handler.
   • Ensure an error or warning is logged so we can track unknown or unconfigured clients.

4. Scalability

   • Provide an interface or configuration file where new client definitions can be added quickly without disrupting existing logic.
   • Ensure minimal duplication or branching logic by centralizing common functions and code patterns.

5. Testing & Validation

   • Include unit tests for each known client provider to ensure correct prompt parsing, streaming, and response formatting.
   • Provide integration tests that simulate real requests from each client.

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Proposed Implementation Steps

1. Client Detection

   • Introduce a new “client detection” middleware or function that inspects incoming requests.
   • Attempt to match on:
     • Custom HTTP header (preferred).
     • Known user-agent substrings.
     • Other request attributes (optional fallback).

2. Client Modules

   • Create a new directory or code structure (e.g., clients/) akin to our “providers/” directory.
   • For each client, define:
     • detect() (optional) if the client includes a unique pattern in the request.
     • parseRequest() to handle the request body and convert it into a standardized internal format.
     • processStream() if streaming differences exist.
     • serializeResponse() to convert the internal response format back into the client’s expected format.

3. Generic Handler

   • Implement a fallback or “generic” client module for requests that don’t match any known signature.
   • Log a warning or notify the admin that a new/unsupported client might be in use.

4. Refactor Existing Code

   • Move any ad hoc logic for clients (Cline, CoPilot, Continue, etc.) into their respective modules.
   • Replace inline checks for user-agents with the new detection mechanism.

5. Documentation & Examples

   • Provide clear instructions on how to add a new client provider.
   • Show example configurations for known clients.

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Acceptance Criteria

1. Clean Abstraction: Each known assistant (Cline, CoPilot, Continue, Aider, etc.) has its own well-defined module or class.
2. Consistent Detection: Incoming requests are reliably matched to the correct client or fall back to a generic handler.
3. Reduced Code Duplication: Common functions (e.g., stream reading/writing) are shared or inherited, rather than re-implemented.
4. Robust Logging: Unknown or unhandled clients trigger clear logs for troubleshooting.
5. Ease of Extension: New clients can be onboarded by adding a single new module plus some minimal config.