Configuration
ReMe uses YAML or JSON to describe its Service, Jobs, and Components. The built-in default is reme/config/default.yaml. Select another configuration at startup and apply command-line overrides when needed.
Precedence
Configuration is merged in this order, with later values winning:
application_defaultsfrom enabled plugins.- The selected file;
defaultis used when none is specified. - CLI dot-notation overrides.
Choose one startup command in one terminal and leave the service running:
reme start
reme start config=demo
reme start config=cookbook
reme start config=/absolute/path/to/app.yaml
reme start service.port=8181 workspace_dir=/data/remeconfig accepts a built-in name or a .yaml, .yml, or .json file. Overrides are deep-merged, so changing service.port preserves sibling service settings. The optional cookbook variant extends default and composes the separately installed Auto Fin, Daily Paper, and DingTalk plugins. It requires the three DingTalk application credential environment variables before configuration loading. It also enables text-embedding-v4 vector retrieval, uses AgentScope with ${LLM_MODEL_NAME:-qwen3.8-max} by default, and runs the DingTalk bridge through Claude Code with the same LLM_MODEL_NAME and LLM_API_KEY.
From another terminal, call reme <job-name>, such as reme help; the CLI discovers the running service configuration.
CLI values
Arguments use key=value; leading - or -- is accepted:
reme start --service.port=8181 --service.web_enabled=falseValues support null, booleans, numbers, JSON arrays and objects, quoted JSON strings, and plain strings. Numeric-looking values with leading zeroes, such as 007, remain strings, as do nan, inf, and Infinity. Quote values such as "true" in JSON when they must remain strings.
Environment variables
Configuration recursively expands:
api_key: ${LLM_API_KEY}
base_url: ${LLM_BASE_URL:-https://example.com/v1}${VAR} fails when undefined; ${VAR:-default} uses its fallback. ReMe also searches for .env from the command's working directory through at most five parents.
Keep secrets in .env or the process environment, never in committed configuration.
Application fields
| Field | Default | Purpose |
|---|---|---|
app_name | ReMe | Display name |
workspace_dir | .reme | User-owned workspace root, normalized to an absolute path |
metadata_dir | metadata | Rebuildable indexes, graphs, and catalogs |
session_dir | session | Agent sessions; standard transcripts use session/dialog |
mem_session_dir | mem_session | Agent-wrapper sessions and configuration |
resource_dir | resource | External resources |
daily_dir | daily | Daily memory |
digest_dir | digest | Consolidated long-term memory |
timezone | Asia/Shanghai | IANA timezone used for dates and cron jobs |
language | empty | Default language for LLM interactions |
plugins | [] | Installed plugins enabled for this Application |
service | HTTP | Service configuration |
jobs | default Jobs | Job configurations by name |
components | defaults | Components grouped by type and name |
session_dir must remain workspace-relative.
Enabling jobs
jobs.<name>.enabled defaults to true for all Job types. Disabled jobs retain their configuration but do not start, expose service interfaces, or accept calls through Application.run_job() / run_stream_job(). For example, disable ReMe's Dream cron when a host plugin owns the schedule:
reme start jobs.dream_cron.enabled=falseRestart the service to apply the override. The separate auto_dream API remains available, and other jobs continue running. enable_serve independently controls service exposure: enabled=true, enable_serve=false keeps a Job available for local calls. Background and cron jobs are never service-exposed.
LLM
The default LLM uses an OpenAI-compatible interface:
components:
as_llm:
default:
backend: openai
model: qwen3.7-plus
context_size: 200000
credential:
api_key: ${LLM_API_KEY:-}
base_url: ${LLM_BASE_URL:-}Built-in registrations include openai, anthropic, dashscope, deepseek, gemini, moonshot, ollama, and xai. Their detailed model fields follow the corresponding AgentScope wrappers.
File operations, BM25 search, wikilink traversal, and proactive_read do not require an LLM. Evolution workflows such as auto_memory, auto_resource, auto_dream, and proactive refresh do.
Decision Model Configuration
as_decision_model is a provider-independent interface for probabilistic decisions. The current agentscope adapter calls the TypeSafe API through AgentScope 2.0.10. It evaluates named binary, choice, or score questions against shared text or a JSON object.
Install the optional dependencies:
pip install "reme-ai[core,decision]"
# For a source checkout:
pip install -e ".[core,decision]"core excludes the decision model SDK; full includes this extra. Environment variables alone do not enable decision models, and the default decision model configuration remains commented out. Startup does not import optional decision model modules or create a client. The first decision request checks dependencies and credentials; shutdown waits for active requests before closing the shared client.
| Environment variable | Default | Purpose |
|---|---|---|
DECISION_MODEL_BACKEND | agentscope | Decision model backend |
DECISION_MODEL_NAME | null (adapter default) | Model name or alias |
DECISION_MODEL_API_KEY | Empty | TypeSafe API key, required for decision requests |
DECISION_MODEL_BASE_URL | null | Optional API URL; empty values use the SDK default |
To enable a component:
components:
as_decision_model:
default:
backend: ${DECISION_MODEL_BACKEND:-agentscope}
model: ${DECISION_MODEL_NAME:-null}
credential:
api_key: ${DECISION_MODEL_API_KEY:-}
base_url: ${DECISION_MODEL_BASE_URL:-null}
timeout: 30
max_retries: 2
retry_delay: 0.5timeout is the per-request timeout in seconds; max_retries counts retries after the initial request; retry_delay is the initial backoff in seconds. Configure these fields through YAML or CLI overrides. The SDK performs retries.
The decision_demo configuration uses extends: default to inherit the HTTP/MCP service, components, and Jobs, then adds a decision model and a service-exposed decide Job. The default background tasks also start. First start the service in one terminal:
export DECISION_MODEL_API_KEY=your-typesafe-api-key
reme start config=decision_demo \
components.as_decision_model.default.timeout=15Then call it from another terminal; the client discovers the running service configuration:
reme decide state="My account was charged twice. Please help urgently."Calls send the state and questions to TypeSafe. Omitting questions evaluates urgency, customer-support routing, and priority together. You can replace the demo questions completely and pass a JSON object as state:
reme decide \
state='{"request":"My account was charged twice","channel":"email"}' \
questions='{"urgent":{"type":"binary","instructions":"Does this need urgent attention?"}}'Question types are binary, choice, and score. Binary questions accept optional criteria.true / criteria.false descriptions. Choice questions require a non-empty criteria object mapping category names to descriptions or null. Score questions require a non-empty ordered list of descriptions, starting at score zero. Each question can include instructions. Explicit empty questions fail validation instead of selecting the demo defaults.
reme decide displays the standard Response JSON, with the decision result under answer, including model, content, usage, response identity, and timing information. Answers preserve probabilities and confidence and are keyed by question name; integer score-distribution keys become strings in JSON. The Step also writes context.data["decision_result"] for subsequent Steps in that request, without automatically writing memory files. SDK errors follow BaseJob's success=false contract.
Use decision_demo_step in your own Job and select a named component with the Step setting as_decision_model: <name>. Implement BaseAsDecisionModel and register a backend to add another provider. Jobs select a component by name; they do not select a provider directly. The current adapter uses AgentScope classifier question and answer schemas. See reme/components/as_decision_model/ for the interface. A null model uses the adapter default.
For tool-history compaction, start reme start config=decision_compaction and call reme compact_context from another terminal with messages and any protection or budget options. It uses the same decision model interface and returns a proposed snapshot; the caller owns archiving and applying it. Request token budgets are estimates with overhead headroom, independent of the host model tokenizer.
See Context compaction with decision models for the strategy, SVG walkthrough, and live integration cases.
Embeddings
Vector retrieval is disabled by default. Credentials alone do not enable it: configure as_embedding, embedding_store, and connect the store to file_store.
components:
as_embedding:
default:
backend: openai
model: text-embedding-v4
dimensions: 1024
credential:
api_key: ${EMBEDDING_API_KEY}
base_url: ${EMBEDDING_BASE_URL:-https://dashscope.aliyuncs.com/compatible-mode/v1}
embedding_store:
default:
backend: local
as_embedding: default
file_store:
default:
backend: local
embedding_store: default
keyword_index: default
file_graph: defaultRebuild the embedding index after changing the model or dimensions.
Service and Jobs
Minimal HTTP configuration:
service:
backend: http
host: 127.0.0.1
port: 2333
web_enabled: true
mcp_enabled: true
mcp_path: /mcpA Job declares a backend, parameter schema, and ordered Steps:
jobs:
example:
backend: base
description: Example job
parameters:
type: object
properties:
text: { type: string }
required: [text]
steps:
- backend: example_stepSet enable_serve: false to keep a Job internal. Background and cron Jobs are never service-exposed.
Inspect the effective configuration
reme app_configThe result is the merged, validated configuration with secrets redacted. Use it when diagnosing plugin or override precedence. The authoritative contracts remain reme/schema/application_config.py and reme/config/default.yaml.