santosh-refactor.md

santosh-refactor.md

Santosh - Assignment 15 - A2A

🧭 TL;DR Feedback – Simplifying & Aligning with A2A Client Patterns

Great job — your code works and shows deep understanding of streaming, chunk parsing, and A2A context handling. To simplify and align with the official A2A client patterns (like test_client.py), focus on these refinements:

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🔹 1. Separate Concerns

• Move all A2A-specific logic into a helper module (a2a_adapter.py).
• Keep your LangGraph node (call_a2a_server) lightweight — just call the adapter and update state.
• This mirrors the clean layering in the example client.

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🔹 2. Use Typed Models Instead of Generic JSON

• Use the built-in Pydantic models (e.g., chunk.root.result.artifact.parts) instead of generic .get() chains.
• Avoid converting everything to dicts; rely on model_dump() only for debugging or logging.
• The SDK already normalizes streaming and non-streaming responses for you.

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🔹 3. Simplify the Streaming Loop

• Follow the example’s pattern:

  async for chunk in client.send_message_streaming(request):
      data = chunk.model_dump(mode='json', exclude_none=True)
      result = data.get('result') or data.get('root', {}).get('result', {})

• Extract text from artifact.parts[].text and stop — skip multi-path heuristics or “longest-chunk” logic for now.

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🔹 4. Modularize Message Building

• Factor your message creation into a helper:

  def build_message_params(text, task_id=None, context_id=None) -> MessageSendParams:
      ...

• Keeps your node cleaner and matches the test_client.py structure.

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🔹 5. Drop Unneeded Fallbacks

• The non-streaming fallback inside call_a2a_server() isn’t needed for your current setup.
• Keep it in a separate function or remove it unless you’re testing fault tolerance.

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✅ In short:

Area	Current	Align With
A2A logic	Inlined in graph node	Reusable adapter (get_a2a_answer)
Response handling	Manual JSON parsing	Typed SDK models
Message payloads	Built inline	Helper function
Graph role	Does everything	Just orchestrates nodes
Fallbacks	Inline	Optional helper

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Outcome: Your code becomes 40–50% smaller, easier to maintain, and fully aligned with how most of the A2A user community structures LangGraph clients today.