sample-deep-research-bedrock-agentcore/.env.example at main · aws-samples/sample-deep-research-bedrock-agentcore · GitHub

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# ================================================================
# Dimensional Research Agent - Environment Configuration
# ================================================================
#
# This file contains all configuration needed to run the agent.
# Copy this to .env and fill in the values.
#
# IMPORTANT: Most values are auto-populated by Terraform after
# running ./deploy.sh. You only need to manually add API keys.
# ================================================================

# ----------------------------------------------------------------
# AWS Configuration (Auto-populated by Terraform)
# ----------------------------------------------------------------
AWS_REGION=

# ----------------------------------------------------------------
# AgentCore Memory (Auto-populated by Terraform)
# ----------------------------------------------------------------
# Long-term semantic memory for research sessions (6-month retention)
# This is created during backend deployment and ID is auto-filled
AGENTCORE_MEMORY_ID=

# ----------------------------------------------------------------
# DynamoDB Tables (Auto-populated by Terraform)
# ----------------------------------------------------------------
# Real-time status tracking for research progress
DYNAMODB_STATUS_TABLE=

# User preferences and settings
DYNAMODB_USER_PREFERENCES_TABLE=

# ----------------------------------------------------------------
# S3 Storage (Auto-populated by Terraform)
# ----------------------------------------------------------------
# Storage for research outputs (markdown, DOCX, PDF, charts)
S3_OUTPUTS_BUCKET=

# ----------------------------------------------------------------
# AgentCore Gateway (Auto-populated by Terraform)
# ----------------------------------------------------------------
# Gateway ID and URL for Lambda-based tools
AGENTCORE_GATEWAY_ID=
AGENTCORE_GATEWAY_URL=

# ----------------------------------------------------------------
# OPTIONAL: Search API Keys
# ----------------------------------------------------------------
# You should add these manually for better research quality

# Tavily AI Search API Key (RECOMMENDED)
# - High-quality AI-powered search engine
# - Best for comprehensive research
# - Sign up: https://tavily.com/
# - Free tier: 1000 searches/month
TAVILY_API_KEY=

# Google Custom Search API (OPTIONAL)
# - Requires both API key and Search Engine ID
# - Sign up: https://developers.google.com/custom-search
# - Create search engine: https://programmablesearchengine.google.com/
# - Free tier: 100 searches/day
GOOGLE_API_KEY=
GOOGLE_SEARCH_ENGINE_ID=

# ----------------------------------------------------------------
# OPTIONAL: LangSmith Tracing
# ----------------------------------------------------------------
# For debugging and monitoring LangGraph workflows
# Sign up: https://smith.langchain.com/

# Your LangSmith API key
LANGCHAIN_API_KEY=

# Enable tracing (set to "true" to enable)
LANGCHAIN_TRACING_V2=false

# Project name for organizing traces
LANGCHAIN_PROJECT=sample-deep-research-bedrock-agentcore

# ----------------------------------------------------------------
# Chat Agent Configuration (Auto-populated by Terraform)
# ----------------------------------------------------------------
# Strands-based chat agent settings
CHAT_AGENT_MEMORY_ID=

# ----------------------------------------------------------------
# DEPLOYMENT NOTES
# ----------------------------------------------------------------
#
# 1. Initial Setup:
#    cp .env.example .env
#    # Add API keys above (at minimum TAVILY_API_KEY recommended)
#
# 2. Deploy Backend:
#    ./deploy.sh (choose option 1 for Backend)
#    # Terraform will auto-update .env with resource IDs
#
# 3. Manual Update (if needed):
#    python scripts/update_env.py
#    # Re-reads Terraform outputs and updates .env
#
# 4. Required API Keys:
#    - NONE required, but TAVILY_API_KEY highly recommended
#    - Without API keys, only DuckDuckGo and Wikipedia available
#    - With TAVILY_API_KEY: Full research capabilities
#
# 5. Model Configuration:
#    - See shared/model_registry.json for available models
#    - See MODEL_CONFIGURATION.md for customization
#    - Default: Amazon Nova Pro (good balance of speed/cost/quality)
#
# ----------------------------------------------------------------