.env.example

# Application Settings
DEBUG=True
APP_ENV=development

# docker build args
NEXT_PUBLIC_API_URL="http://localhost:8000/api"
NEXT_PUBLIC_DEBUG_STREAM=false

AGENT_RECURSION_LIMIT=30

# CORS settings
# Comma-separated list of allowed origins for CORS requests
# Example: ALLOWED_ORIGINS=http://localhost:3000,http://example.com
ALLOWED_ORIGINS=http://localhost:3000

# Enable or disable MCP server configuration, the default is false.
# Please enable this feature before securing your front-end and back-end in a managed environment.
# Otherwise, you system could be compromised.
ENABLE_MCP_SERVER_CONFIGURATION=false

# Enable or disable PYTHON_REPL configuration, the default is false.
# Please enable this feature before securing your in a managed environment.
# Otherwise, you system could be compromised.
ENABLE_PYTHON_REPL=false

# Search Engine, Supported values: tavily (recommended), duckduckgo, brave_search, arxiv, searx
SEARCH_API=tavily
TAVILY_API_KEY=tvly-xxx
# SEARX_HOST=xxx # Required only if SEARCH_API is searx.(compatible with both Searx and SearxNG)
# BRAVE_SEARCH_API_KEY=xxx # Required only if SEARCH_API is brave_search
# JINA_API_KEY=jina_xxx # Optional, default is None

# Optional, RAG provider
# RAG_PROVIDER=vikingdb_knowledge_base
# VIKINGDB_KNOWLEDGE_BASE_API_URL="api-knowledgebase.mlp.cn-beijing.volces.com"
# VIKINGDB_KNOWLEDGE_BASE_API_AK="AKxxx"
# VIKINGDB_KNOWLEDGE_BASE_API_SK=""
# VIKINGDB_KNOWLEDGE_BASE_RETRIEVAL_SIZE=15

# RAG_PROVIDER=ragflow
# RAGFLOW_API_URL="http://localhost:9388"
# RAGFLOW_API_KEY="ragflow-xxx"
# RAGFLOW_RETRIEVAL_SIZE=10
# RAGFLOW_CROSS_LANGUAGES=English,Chinese,Spanish,French,German,Japanese,Korean # Optional. To use RAGFlow's cross-language search, please separate each language with a single comma

# RAG_PROVIDER=dify
# DIFY_API_URL="https://api.dify.ai/v1"
# DIFY_API_KEY="dataset-xxx"

# MOI is a hybrid database that mainly serves enterprise users (https://www.matrixorigin.io/matrixone-intelligence)
# RAG_PROVIDER=moi
# MOI_API_URL="https://cluster.matrixonecloud.cn"
# MOI_API_KEY="xxx-xxx-xxx-xxx"
# MOI_RETRIEVAL_SIZE=10
# MOI_LIST_LIMIT=10


# RAG_PROVIDER: milvus  (using free milvus instance on zilliz cloud: https://docs.zilliz.com/docs/quick-start )
# RAG_PROVIDER=milvus
# MILVUS_URI=<endpoint_of_self_hosted_milvus_or_zilliz_cloud>
# MILVUS_USER=<username_of_self_hosted_milvus_or_zilliz_cloud>
# MILVUS_PASSWORD=<password_of_self_hosted_milvus_or_zilliz_cloud>
# MILVUS_COLLECTION=documents
# MILVUS_EMBEDDING_PROVIDER=openai # support openai,dashscope
# MILVUS_EMBEDDING_BASE_URL=
# MILVUS_EMBEDDING_MODEL=
# MILVUS_EMBEDDING_API_KEY=
# MILVUS_AUTO_LOAD_EXAMPLES=true

# RAG_PROVIDER: milvus  (using milvus lite on Mac or Linux)
# RAG_PROVIDER=milvus
# MILVUS_URI=./milvus_demo.db
# MILVUS_COLLECTION=documents
# MILVUS_EMBEDDING_PROVIDER=openai # support openai,dashscope
# MILVUS_EMBEDDING_BASE_URL=
# MILVUS_EMBEDDING_MODEL=
# MILVUS_EMBEDDING_API_KEY=
# MILVUS_AUTO_LOAD_EXAMPLES=true

# Optional, volcengine TTS for generating podcast
VOLCENGINE_TTS_APPID=xxx
VOLCENGINE_TTS_ACCESS_TOKEN=xxx
# VOLCENGINE_TTS_CLUSTER=volcano_tts # Optional, default is volcano_tts
# VOLCENGINE_TTS_VOICE_TYPE=BV700_V2_streaming # Optional, default is BV700_V2_streaming

# Option, for langsmith tracing and monitoring
# LANGSMITH_TRACING=true
# LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
# LANGSMITH_API_KEY="xxx"
# LANGSMITH_PROJECT="xxx"

# [!NOTE]
# For model settings and other configurations, please refer to `docs/configuration_guide.md`

# Option, for langgraph mongodb checkpointer
# Enable LangGraph checkpoint saver, supports MongoDB, Postgres
#LANGGRAPH_CHECKPOINT_SAVER=true
# Set the database URL for saving checkpoints
#LANGGRAPH_CHECKPOINT_DB_URL=mongodb://localhost:27017/
#LANGGRAPH_CHECKPOINT_DB_URL=postgresql://localhost:5432/postgres

# When using the local dockerized Postgres service (`checkpoint-db`)
#POSTGRES_DB=checkpointing_db
#POSTGRES_USER=postgres
#POSTGRES_PASSWORD=postgres
#POSTGRES_HOST_PORT=5433
# Backend running on host (uv run server.py)
#LANGGRAPH_CHECKPOINT_DB_URL=postgresql://postgres:postgres@localhost:${POSTGRES_HOST_PORT:-5433}/checkpointing_db
# Backend running inside docker compose backend service
#LANGGRAPH_CHECKPOINT_DB_URL=postgresql://postgres:postgres@checkpoint-db:5432/checkpointing_db


# Langfuse configuration
# LANGFUSE_PUBLIC_KEY = your_langfuse_public_key
# LANGFUSE_SECRET_KEY = your_langfuse_secret_key
# LANGFUSE_HOST = https://cloud.langfuse.com

# Working directory for Deep Agent
DEEP_AGENT_WORKDIR =  

# -------------------------------------------------------------------------
## DEEPAGENT CONFIGURATION ##
# -------------------------------------------------------------------------
# OpenAI
OPENAI_API_KEY="sk-xxx"
DEEP_AGENT_PROVIDER="openai"      
# or DEEP_AGENT_LLM_TYPE=deepagent_openai
DEEPAGENT_MODEL__model_name= "gpt-4.1-nano"  
DEEPAGENT_MODEL__temperature=0.2


# # CLAUDE
# ANTHROPIC_API_KEY= "sk-xxx"
# DEEP_AGENT_PROVIDER=anthropic      # or DEEP_AGENT_LLM_TYPE=deepagent
# DEEPAGENT_MODEL__model_name= "clauude-xx" 
# DEEPAGENT_MODEL__temperature=0.2


# DEEPSEEK
# DEEPSEEK_API_KEY = "sk-xxx"
# DEEP_AGENT_PROVIDER=deepseek     
#  # or DEEP_AGENT_LLM_TYPE=deepagent_deepseek
# DEEPAGENT_MODEL__model_name= "deepseek-chat" 
# DEEPAGENT_MODEL__base_url=https://api.deepseek.com
# DEEPAGENT_MODEL__temperature=0.2


# -------------------------------------------------------------------------
# TOKEN LIMIT SETTINGS
# -------------------------------------------------------------------------
# DEEPAGENT_MODEL__token_limit = 150000 #The total context window you want the orchestration stack to assume for that model.
# We pass this value into the ContextManager and summarisation middleware to decide how aggressively to compress history, how many recent messages to keep, etc

# DEEPAGENT_MODEL__max_tokens = 32000 # The completion length (tokens the model may generate per response). 
# We forward this directly to the LLM client (ChatOpenAI/ChatDeepSeek/etc.) as max_tokens so the model doesn’t over-generate on a single call.