assada/agent_template

AI Summary: Implement guardrails for the LangGraph API HTTP template to prevent the LLM from generating unsafe or undesirable outputs. This involves integrating either the LLMGuard library or leveraging LiteLLM's guardrail functionality. The choice depends on preference and existing project dependencies.

AI Summary: The task requires creating an abstraction layer within the LangGraph API HTTP template to decouple the project's observability from specific tools like LangFuse, LangSmith, or Grafana Alloy. This will allow the template to easily integrate with various observability solutions without requiring significant code changes.

AI Summary: The task requires creating an abstraction layer within the existing FastAPI-based LangGraph agent template to support multiple AI frameworks, including LlamaIndex and Google ADK, in addition to the current LangGraph implementation. This involves designing interfaces and potentially adapters to allow the agent's core logic to remain framework-agnostic.

AI Summary: Implement a robust API for managing threads, including creation, retrieval, updating, deletion, and searching. The API should handle storing thread data (including metadata, status, and messages) in a database, likely using ULIDs for IDs. The provided Python code and JSON schema serve as a starting point for the implementation. The task also involves updating the existing `Thread` model to reflect the database storage requirements and potentially integrating with an existing LangGraph setup.