env.example

# Copy to .env: `cp env.example .env`

# Required - the model the executor runs.
AGENT_MODEL=gemini-3.1-flash-lite-preview

# Optional separate model for the planner and replanner.
# Falls back to AGENT_MODEL when unset.
AGENT_PLANNER_MODEL=gemini-3.1-flash-lite-preview

# Optional separate model for the final answer synthesizer.
# Falls back to AGENT_MODEL when unset. It can make sense to put a larger
# model here when the executor is a small tool-calling model.
# AGENT_SYNTHESIZER_MODEL=gemini-3.1-flash-lite-preview

# Per-stage provider override (advanced). Use these when the planner or
# synthesizer should run on a DIFFERENT provider / endpoint / API key than
# the executor. Each block is fully optional - omit a variable to inherit
# the top-level AGENT_* default.
#
# Cross-provider override caveat: setting AGENT_PLANNER_PROVIDER_TYPE to a
# different vendor than AGENT_PROVIDER_TYPE requires AGENT_PLANNER_API_KEY
# (the agent refuses to inherit a key across vendors).
#
# AGENT_PLANNER_PROVIDER_TYPE=anthropic
# AGENT_PLANNER_BASE_URL=
# AGENT_PLANNER_API_KEY=sk-ant-...
#
# AGENT_SYNTHESIZER_PROVIDER_TYPE=openai
# AGENT_SYNTHESIZER_BASE_URL=
# AGENT_SYNTHESIZER_API_KEY=sk-...

# Provider type. Each provider's SDK is an OPTIONAL peerDependency: install
# only the one(s) you actually use. Available:
#   openai             @ai-sdk/openai
#   anthropic          @ai-sdk/anthropic
#   google             @ai-sdk/google         (Gemini native - PREFER for Gemini)
#   openai-compatible  @ai-sdk/openai-compatible (vLLM / Ollama / any OpenAI-spec server)
#   xai                @ai-sdk/xai            (Grok)
#   azure              @ai-sdk/azure
#   amazon-bedrock     @ai-sdk/amazon-bedrock
#   google-vertex      @ai-sdk/google-vertex
#   deepseek           @ai-sdk/deepseek
#   gateway            (ships inside `ai` - no extra package)
#   cloudflare         workers-ai-provider
#
# For Gemini USE google (the native API): the OpenAI-compat endpoint is broken
# for structured outputs (responseFormat is ignored) and for streamed
# tool_calls (the `index` field is missing). The google provider hits the
# native API directly and behaves correctly.
AGENT_PROVIDER_TYPE=google
# Optional for openai/anthropic/google/xai/deepseek/gateway/amazon-bedrock/
# google-vertex/cloudflare (the SDK default endpoint is used); REQUIRED for
# openai-compatible and azure (point at your deployment URL).
# AGENT_BASE_URL=
AGENT_API_KEY=replace-me

# Provider-specific extras forwarded to the SDK factory. Set as a JSON
# object - whatever you put in here is spread into the create*() call after
# baseURL / apiKey, so anything documented for the underlying SDK works:
#   azure          -> { "apiVersion": "2024-08-01-preview", "resourceName": "..." }
#   amazon-bedrock -> { "region": "us-east-1", "accessKeyId": "...", "secretAccessKey": "..." }
#   google-vertex  -> { "project": "my-proj", "location": "us-central1" }
#   cloudflare     -> { "accountId": "..." }
# Per-stage equivalents: AGENT_PLANNER_PROVIDER_OPTIONS, AGENT_SYNTHESIZER_PROVIDER_OPTIONS.
# AGENT_PROVIDER_OPTIONS={"region":"us-east-1"}
#
# Plain env-var fallbacks for the most common cases (used only when the
# corresponding key is absent from AGENT_PROVIDER_OPTIONS):
#
# cloudflare: account ID. CLOUDFLARE_ACCOUNT_ID is the standard fallback.
# CLOUDFLARE_ACCOUNT_ID=
#
# amazon-bedrock: AGENT_API_KEY is forwarded as a Bearer token. Without it
# the SDK falls back to AWS_REGION + AWS_ACCESS_KEY_ID + AWS_SECRET_ACCESS_KEY.
# AWS_REGION=us-east-1
#
# google-vertex: AGENT_API_KEY is IGNORED (Vertex uses Google ADC, not an
# API key string). Auth is picked up from the environment:
# GOOGLE_VERTEX_PROJECT=
# GOOGLE_VERTEX_LOCATION=
# GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json

# Client name (forwarded as User-Agent on MCP/provider requests).
AGENT_CLIENT_NAME=vercel-mcp-test

# none | error | warn | info | debug
AGENT_LOG_LEVEL=info

# Cap on plan/execute/replan iterations. Guards against runaway loops.
AGENT_MAX_ITERATIONS=10

# Cap on LLM steps inside a single executor call (multi-step tool calling).
# A `tool-call -> tool-result -> assistant-message` cycle counts as 2 steps.
# Sensible minimum is 2 (one tool + final answer); recommended 6-10.
AGENT_MAX_STEPS_PER_TASK=8

# Per-LLM-call timeout (planner / executor / replanner / synthesizer), in ms.
# 0 or empty disables the timeout. 60000-120000 is recommended in production.
# AGENT_LLM_TIMEOUT_MS=90000

# How many times to retry an LLM call on 5xx / 429 / network errors. Default 2.
# AGENT_LLM_MAX_RETRIES=2

# Cap on the number of "revise" decisions the replanner can make per run.
# Prevents the LLM from looping in "revise -> execute -> revise". Default 2.
# AGENT_MAX_REVISIONS=2

# Hard cap on total tokens per run (input + output). When crossed the agent
# exits the execution loop and proceeds to synthesize the final answer.
# AGENT_MAX_TOTAL_TOKENS=200000

# Tool selection strategy for the executor:
# - all (default)         every step sees the full filtered ToolSet.
#                         Works well with <=50 tools.
# - plan-narrowed         executor gets only the tools listed in
#                         step.suggestedTools. The planner MUST populate
#                         suggestedTools for every step that needs tools.
#                         Cuts context tokens significantly when the catalog
#                         has 100+ tools.
# AGENT_TOOL_SELECTION_STRATEGY=all

# Fine-grained MCP tool control. Names use the `serverName__toolName` format.
# availableTools wins over excludedTools.
# AGENT_AVAILABLE_TOOLS=demo__list_companies,demo__get_company
# AGENT_EXCLUDED_TOOLS=

# Remote MCP servers. JSON: Record<name, { url, headers? }>.
# headers.Authorization: 'Bearer <token>' for pre-baked authorization.
MCP_SERVERS={"demo":{"url":"https://mcp.miniaccountant.app/v1beta"}}