Debugged local agent execution and environment issues

• Day: 2025-05-03
• Time: 03:55 to 04:30
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Debugging, Python, Agent Execution, Environment Variables, API, LLM

Description

Session Goal

The session aimed to debug local agent execution issues, focusing on empty output and environment configuration problems.

Key Activities

• Successfully executed a local agent but identified an empty output issue.
• Discussed best practices for Python environment isolation and module deployment.
• Debugged module resolution issues for the Cerebrum SDK.
• Investigated silent errors in LLM calls and API key issues in ConfigManager.
• Explored methods to load environment variables in Python using [[python]]-dotenv.

Achievements

• Identified the root causes of empty output in local agent execution.
• Clarified best practices for Python environment management.
• Resolved API key issues by configuring environment variables and using a config.yaml file.
• Improved understanding of loading .env files in Python scripts.

Pending Tasks

• Further refine the agent’s run method to handle empty outputs more effectively.
• Implement additional debug prints and assertions for LLM calls.
• Ensure consistent environment variable loading across all agent scripts.

Evidence

• source_file=2025-05-03.sessions.jsonl, line_number=2, event_count=0, session_id=66dcdecc378ef7ac2fc6ab5b704f7c5b9cfc203d394cd76af4432f2c58f7d952
• event_ids: []