draft a demo code for agent memory.

[lkk12014402]

Description

integrate llama stack implementations of the agent memory, refer https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/impls/meta_reference/agents/agents.py#L27

[codecov]

Codecov Report

Attention: Patch coverage is 0% with 9 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
comps/cores/proto/agents/agents.py	0.00%	8 Missing ⚠️
comps/cores/proto/agents/__init__.py	0.00%	1 Missing ⚠️

Files with missing lines	Coverage Δ
comps/cores/proto/agents/__init__.py	0.00% <0.00%> (ø)
comps/cores/proto/agents/agents.py	0.00% <0.00%> (ø)

[minmin-intel]

A few thoughts from my side:

1. Langgraph has "checkpoint" and "store" for short-term and long-term memory, both have in-memory and also SQL db based implementations. We can utilize those langgraph APIs. https://langchain-ai.github.io/langgraph/concepts/persistence/, https://langchain-ai.github.io/langgraph/reference/checkpoints/, https://langchain-ai.github.io/langgraph/reference/store/
2. What and how to save/retrieve/use memories: depend on different agent designs, but we should provide common functions, for example, save agent_id, thread_id, messages - these will be part of our assistants APIs.
3. Ultimately (may be not in v1.1 release), the memories will be microservices, and agents send requests to memory DBs. Your diagram captured that, which I agree. Right now, langgraph implemented postgres sql db based checkpointers and stores. Langgraph also has an example showing vector db as memory store. We need to decide which DBs to support and the interface.
4. We should expose a unified interface to send requests to memory microservices and process the returns from memory microservices, so that from agent developer point of view, they just need to use this unified interface with the memory endpoint url, agent_id, thread_id, and content.