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OPENAI_API_KEY=
OPENAI_MODEL_ID_MAPPING=gpt-5:gpt-5,gpt-5-mini:gpt-5-mini,gpt-5-nano:gpt-5-nano,gpt-5.1-codex-max:gpt-5.1-codex-max,gpt-5.4:gpt-5.4,gpt-5.4-mini:gpt-5.4-mini,whisper:whisper-1,text-embedding-3-small:text-embedding-3-small
DB_URL=postgresql+psycopg://postgres:admin@localhost/tero
# To generate a new secret key, run: devbox run generate-secret-key
SECRET_ENCRYPTION_KEY="Lbb-6S2PzX-QZ2Fusu1nagZtrZBqKEaypGTb_kGrNg4="
OPENID_URL=http://localhost:8080/realms/tero
OPENID_CLIENT_ID=tero
OPENID_SCOPE=openid profile
# You can uncomment and set this in case you need to use a different openid url for frontend (this might be necessary for production deployments)
# FRONTEND_OPENID_URL=
# This configuration allows you to only allow to login to the given list of users.
# Specify the users in a comma separated list of usernames, eg: test@test.com,test2@test.com.
# This is particularly handy when you use SSO to authenticate users but you want only to give access to some of them (for example in a dev environment).
ALLOWED_USERS=
# Disable global team members (non-owners) from publishing agents to global team. Set to true to restrict publishing to global owners only.
DISABLE_PUBLISH_GLOBAL=false
# You can uncomment this in case you want to build frontend and try hosting frontend in backend server while running dev environment
# FRONTEND_PATH=../frontend/dist/
FRONTEND_URL=http://localhost:5173
# Contact email displayed in manifest and used as sender address for system emails
CONTACT_EMAIL=support@tero-env.com
# List of Azure endpoints (separated by comma) for each of the OpenAI resources to be used. Eg: https://XXX.openai.azure.com/
AZURE_ENDPOINTS=
# List of API keys (separated by comma) for the provided Azure OpenAI resources
AZURE_API_KEYS=
AZURE_API_VERSION=2025-03-01-preview
# Azure Document Intelligence for advanced PDF processing in Docs tool. If not configured, basic PDF processing will be used.
# Eg: https://XXX.cognitiveservices.azure.com/
AZURE_DOC_INTELLIGENCE_ENDPOINT=
AZURE_DOC_INTELLIGENCE_KEY=
# https://azure.microsoft.com/en-us/pricing/details/ai-document-intelligence/
AZURE_DOC_INTELLIGENCE_COST_PER_1K_PAGES_USD=10.0
# List of llm models with associated Azure OpenAI deployment name and deployment resource list index.
# Format: modelId:deploymentName@resourceIndex,...
# Indexes start at 0, and refer to the list index of the deployment resource in AZURE_ENDPOINTS. When index is not specified 0 is used.
# Only configure the models you actually deployed on azure.
AZURE_MODEL_DEPLOYMENTS=gpt-5-nano:gpt-5-nano@1,gpt-5-mini:gpt-5-mini@1,gpt-5:gpt-5@1,gpt-5.1-codex-max:gpt-5.1-codex-max@1,gpt-5.4:gpt-5.4@1,gpt-5.4-mini:gpt-5.4-mini@1,whisper:whisper,text-embedding-3-small:text-embedding-3-small
TEMPERATURES=PRECISE:0,NEUTRAL:0.7,CREATIVE:1
# Model for internal generation tasks (e.g., auto-generating agent fields, chat names)
INTERNAL_GENERATOR_MODEL=gpt-5-nano
INTERNAL_GENERATOR_TEMPERATURE=0.7
# Reasoning effort for internal generator reasoning models (low, medium, high).
INTERNAL_GENERATOR_REASONING_EFFORT=medium
# Model for internal evaluator tasks (default agent/test-case evaluator when none is configured) and minutes-saved estimation. If unset, INTERNAL_GENERATOR_MODEL is used.
INTERNAL_EVALUATOR_MODEL=gpt-5.4-mini
MONTHLY_USD_LIMIT_DEFAULT=10
# Default model for new agents. If not set, will use INTERNAL_GENERATOR_MODEL
AGENT_DEFAULT_MODEL=gpt-5.4-mini
DEFAULT_AGENT_NAME=GPT-5 Nano
# List of models shown by default in the agent editor model selector
AGENT_BASIC_MODELS=gpt-5,gpt-5-nano,gpt-5.4-mini
EMBEDDING_MODEL=text-embedding-3-small
#This is the maxmimum context length in tokens that the embedding model can handle.
EMBEDDING_CONTEXT_LIMIT=8191
EMBEDDING_COST_PER_1K_TOKENS=0.000022
# you can set here a model of one of the providers that supports transcriptions.
# To use Amazon Transcribe service you can use "amazon-transcribe" here (and properly configure the associated AWS_* settings)
TRANSCRIPTION_MODEL=whisper
# AWS_ACCESS_KEY_ID=
# AWS_SECRET_ACCESS_KEY=
AWS_REGION=us-east-1
AWS_MODEL_ID_MAPPING=claude-sonnet-4:anthropic.claude-sonnet-4-20250514-v1:0,claude-sonnet-4-6:anthropic.claude-sonnet-4-6,claude-opus-4-6:anthropic.claude-opus-4-6-v1,amazon-nova-lite:amazon.nova-2-lite-v1:0,titan-embed-text-v2:amazon.titan-embed-text-v2:0,claude-haiku-4-5:anthropic.claude-haiku-4-5-20251001-v1:0
# This is an S3 bucket used to temporaly upload audio files and pdf files for async processing.
# This is only required if you want to use amazon-transcribe or amazon textract for enhanced pdf processing.
# AWS_S3_BUCKET=tero
# This setting is only required if you want to use amazon textract for enhanced pdf processing.
# To enable the usage of textract you need all the AWS_* settings and not configure azure doc intelligence.
# https://aws.amazon.com/textract/pricing/
# AWS_TEXTRACT_COST_PER_1K_PAGES_USD=15.0
GOOGLE_API_KEY=
GOOGLE_MODEL_ID_MAPPING=gemini-2.5-pro:gemini-2.5-pro,gemini-2.5-flash:gemini-2.5-flash
# Uncomment these lines for local vLLM (after starting with `devbox run vllm`, 16GB+ RAM advised to run this) with the pre configured models.
# To properly use the embedding and transcripts model you will also need to change EMBEDDING_*, DOCS_TOOL_CHUNK_*, and TRANSCRIPTION_MODEL properties.
# VLLM_URLS=http://localhost:8001/v1,http://localhost:8002/v1
# VLLM_API_KEYS=test-token,test-token
# VLLM_MODEL_ID_MAPPING=qwen-2.5-1.5b:Qwen/Qwen2.5-1.5B-Instruct,nomic-embed-text-v1:nomic-ai/nomic-embed-text-v1
# Chunk size for splitting documents in the docs tool for search and retrieval.
# These values should be smaller than EMBEDDING_CONTEXT_LIMIT, and DOCS_TOOL_RETRIEVE_TOP x DOCS_TOOL_CHUNK_SIZE should be smaller than contenxt limit of llm models.
DOCS_TOOL_CHUNK_SIZE=4000
DOCS_TOOL_CHUNK_OVERLAP=200
# Number of document chunks to retrieve when searching
DOCS_TOOL_RETRIEVE_TOP=5
# Chunk size and overlap used to generate file descriptions. Descriptions help agents understand when to use files based on their content, without needing to specify it in the system prompt
DOCS_TOOL_DESCRIPTION_CHUNK_SIZE=120000
DOCS_TOOL_DESCRIPTION_CHUNK_OVERLAP=100
# OAuth configuration (used in Jira and MCP tools)
# If a tool oauth token (and refresh token) is not updated for more than this time (43200=30 days), then it is removed from database to avoid potential exploits
TOOL_OAUTH_TOKEN_TTL_MINUTES=43200
# If a tool oauth does not complete wihtin the given amount of minutes, then associated entries are removed to avoid potential exploits
TOOL_OAUTH_STATE_TTL_MINUTES=10
# If mcp oauth client registration is not updated after the given time (259200=6 months aprox), then it is removed from the database to avoid potential exploits
MCP_TOOL_OAUTH_CLIENT_REGISTRATION_TTL_MINUTES=259200
# To configure web tools: set WEB_TOOL_TAVILY_API_KEY or both WEB_TOOL_GOOGLE_CUSTOM_SEARCH_ENGINE_ID and WEB_TOOL_GOOGLE_API_KEY
# If neither service is configured, web tools will not be available
WEB_TOOL_TAVILY_API_KEY=
WEB_TOOL_TAVILY_COST_PER_1K_CREDITS_USD=8.0
WEB_TOOL_GOOGLE_CUSTOM_SEARCH_ENGINE_ID=
WEB_TOOL_GOOGLE_API_KEY=
WEB_TOOL_GOOGLE_COST_PER_1K_SEARCHES_USD=5.0
BROWSER_TOOL_PLAYWRIGHT_MCP_URL=http://localhost:8931/mcp
BROWSER_TOOL_PLAYWRIGHT_OUTPUT_DIR=var/playwright-output
# Encryption key (passphrase) used to encrypt/decrypt database backups.
BACKUP_ENCRYPTION_KEY=