aws-agentcore
by riolaf05
Overview
An AI Assistant suite designed for personal and professional organization, featuring multi-agent orchestration for task management, daily briefings, web research, contact management, event planning, and candidate-job matching, integrated with AWS Bedrock AgentCore and various external APIs.
Installation
python mcp-server/server.pyEnvironment Variables
- AWS_REGION
- AWS_ACCOUNT_ID
- TELEGRAM_BOT_TOKEN
- TELEGRAM_WEBHOOK_URL
- TELEGRAM_CHAT_ID
- LAMBDA_TASK_POST_ARN
- LAMBDA_TASK_GET_ARN
- LAMBDA_ORCHESTRATOR_ARN
- MCP_SERVER_URL
- MCP_API_KEY
- MCP_SECRET_NAME
- ORCHESTRATOR_AGENT_ARN
- TASK_MANAGER_AGENT_ARN
- DAILY_BRIEFING_AGENT_ARN
- RESEARCHER_AGENT_ARN
- CALCULATOR_AGENT_ARN
- PROJECT_GOAL_WRITER_READER_AGENT_ARN
- CONTACT_WRITER_READER_AGENT_ARN
- EVENT_PLACE_WRITER_READER_AGENT_ARN
- NEEDS_READER_AGENT_ARN
- CANDIDATE_MATCHER_AGENT_ARN
- GATEWAY_MCP_URL
- GATEWAY_CLIENT_ID
- GATEWAY_CLIENT_SECRET
- GATEWAY_TOKEN_ENDPOINT
- GATEWAY_SCOPE
- MEMORY_ID
- ACTOR_ID
- OUTLOOK_CLIENT_ID
- OUTLOOK_CLIENT_SECRET
- OUTLOOK_TENANT_ID
- PORT
- LOG_LEVEL
Security Notes
Critical security risks identified: Multiple agent files (`agents/*/agent.py`) contain hardcoded OAuth2 client IDs and secrets (`CLIENT_ID`, `CLIENT_SECRET`, `TOKEN_URL`, `GATEWAY_URL`). These credentials are used to authenticate with an AWS AgentCore Gateway, granting potentially wide access if compromised. This directly contradicts best practices for sensitive data handling, which are mentioned in the main README (`GATEWAY_CLIENT_SECRET` via .env) but not implemented in the individual agent's source code. While input sanitization (`sanitize_input` in `shared/utils/helpers.py`) exists, its consistent application across all data interactions (especially for MongoDB search in `Needs API`) needs further verification. The main `mcp-server/server.py` correctly loads secrets from environment variables or AWS Secrets Manager.
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