config

Imported from JuneY-Y/optimization-agent (backend/packages/harness/deerflow/config/AGENTS.md). Install upstream with npx skills add JuneY-Y/optimization-agent --skill config. Copyright stays with the author.

Configuration System

Main Configuration (config.yaml):

Setup: Copy config.example.yaml to config.yaml in the project root directory.

Config Versioning: config.example.yaml has a config_version field. On startup, AppConfig.from_file() compares user version vs example version and emits a warning if outdated. Missing config_version = version 0. Run make config-upgrade to auto-merge missing fields. When changing the config schema, bump config_version in config.example.yaml.

Config Caching: get_app_config() caches the parsed config, but automatically reloads it when the resolved config path or file content signature changes. The signature includes file metadata and a content digest, so Gateway and LangGraph reads stay aligned with config.yaml edits even on object-store or network mounts where mtime can remain stale.

Config Hot-Reload Boundary: Gateway dependencies route through get_app_config() on every request, so per-run fields like models[*].max_tokens, summarization.*, title.*, memory.*, subagents.*, verification.*, tools[*], and the agent system prompt pick up config.yaml edits on the next message. AppConfig is intentionally not cached on app.state — lifespan() keeps a local startup_config variable for one-shot bootstrap work and passes it to langgraph_runtime(app, startup_config).

Infrastructure fields are restart-required. The authoritative list lives in packages/harness/deerflow/config/reload_boundary.py::STARTUP_ONLY_FIELDS and is mirrored by the standardised "startup-only:" prefix on the corresponding Field(description=...) in AppConfig, so IDE hover on those fields surfaces the reason inline (no need to context-switch into this table). Currently registered: plugins, database, checkpointer, run_events, stream_bridge, sandbox, log_level, logging, channels, channel_connections, scheduler, mcp_tasks, subagent_runtime, subagent_batches, run_ownership. Adding a new restart-required field requires updating the registry; drift is pinned by tests/test_reload_boundary.py. scheduler.recursion_limit is the exception inside that section: it is read from get_app_config() at each scheduled dispatch, so a YAML edit applies to the next run without restarting the poller.

Persistence backend resolution: the unified database section selects the Gateway's LangGraph checkpointer, LangGraph Store, and DeerFlow SQL repositories. The deprecated checkpointer section remains backward compatible and, when present, overrides database for the LangGraph checkpointer and Store only; application repositories continue to use database.

Configuration priority:

1. Explicit config_path argument
2. DEER_FLOW_CONFIG_PATH environment variable
3. config.yaml in current directory (backend/)
4. config.yaml in parent directory (project root - recommended location)

Config values starting with $ are resolved as environment variables (e.g., $OPENAI_API_KEY). ModelConfig also declares use_responses_api and output_version so OpenAI /v1/responses can be enabled explicitly while still using langchain_openai:ChatOpenAI.

Extensions Configuration (extensions_config.json):

MCP servers and skills are configured together in extensions_config.json in project root:

Docker development mounts the project directory at /app/project and points DEER_FLOW_CONFIG_PATH / DEER_FLOW_EXTENSIONS_CONFIG_PATH into that directory. Keep mutable config files behind a directory bind mount: single-file bind mounts can become stale or inaccessible when a host editor replaces a file on save.

Configuration priority:

1. Explicit config_path argument
2. DEER_FLOW_EXTENSIONS_CONFIG_PATH environment variable
3. extensions_config.json in current directory (backend/)
4. extensions_config.json in parent directory (project root - recommended location)

Extensions are optional only in the fallback search mode (priority 3-4 above): ExtensionsConfig.resolve_config_path() returns None when neither an explicit config_path nor DEER_FLOW_EXTENSIONS_CONFIG_PATH is given and the search locations find nothing. An explicit config_path argument or a set DEER_FLOW_EXTENSIONS_CONFIG_PATH (priority 1-2) is an operator assertion that one particular file must be used, so a missing file in either of those modes raises FileNotFoundError instead — including when the file existed earlier and has since been deleted. The MCP tools cache's staleness check (deerflow.mcp.cache._resolve_config_path) is a narrow, deliberate exception to that rule: it catches that FileNotFoundError locally and treats it as "unconfigured" so a previously-valid config disappearing mid-run degrades the cache to serving its last-known-good tools instead of raising out of a per-request hot path (see the MCP System section below).

Config Schema

config.yaml key sections:

• models[] - LLM configs with use class path, supports_thinking, supports_vision, provider-specific fields
• logging.enhance - Optional request trace correlation (enabled, format) for Gateway X-Trace-Id, log trace_id, and Langfuse deerflow_trace_id
• vLLM reasoning models should use deerflow.models.vllm_provider:VllmChatModel; for Qwen-style parsers prefer when_thinking_enabled.extra_body.chat_template_kwargs.enable_thinking, and DeerFlow will also normalize the older thinking alias
• tools[] - Tool configs with use variable path and group
• tool_groups[] - Logical groupings for tools
• sandbox.use - Sandbox provider class path
• skills.path / skills.container_path - Host and container paths to skills directory
• skills.deferred_discovery - When true, replaces the full-metadata <available_skills> prompt block with a compact <skill_index> (names only) and registers the describe_skill tool so the agent fetches metadata on demand. Defaults to false (legacy full-metadata injection)
• title - Auto-title generation (enabled, max_words, max_chars, model_name; null model_name uses fast local fallback, explicit model_name uses the prompt_template LLM path)
• summarization - Context summarization (enabled, trigger conditions, keep policy)
• subagents.enabled - Master switch for subagent delegation
• subagent_runtime - Startup-only shared process admission (max_running, bounded async wait queue, queue/reject policy, and queue timeout) for ordinary and durable-batch native subagents
• subagent_batches - Startup-only explicit durable batch scheduler limits (disabled by default), including separate total, live, and running dimensions plus leases/retries/result bounds
• memory - Memory system (enabled, storage_path, debounce_seconds, shutdown_flush_timeout_seconds, model_name, max_facts, fact_confidence_threshold, injection_enabled, max_injection_tokens, staleness_review_enabled, staleness_age_days, staleness_min_candidates, staleness_max_removals_per_cycle, staleness_protected_categories, staleness_max_lifetime_multiplier, staleness_max_extension_days)

extensions_config.json:

• mcpServers - Map of server name → config (enabled, type, command, args, env, url, headers, oauth, description, routing, tools, tool_call_timeout, session_init_timeout). routing.mode="prefer" emits <mcp_routing_hints> prompt guidance; if tool_search defers the hinted tool, McpRoutingMiddleware can also auto-promote matching deferred schemas before the model call. It does not hard-disable other tools. session_init_timeout (default DEFAULT_MCP_SESSION_INIT_TIMEOUT = 60s, null to disable) bounds server bring-up: tool discovery and persistent stdio session initialization, so a hung server cannot block agent construction indefinitely; durable HTTP/SSE task calls use it for their ephemeral session initialization too. tool_call_timeout bounds individual stdio calls and durable-task calls on every transport; other HTTP/SSE tools use transport-level timeouts.
• tool_search.auto_promote_top_k - Global MCP routing auto-promote breadth. Default 3, clamped to 1..5; applies only when tool_search.enabled=true and only to deferred MCP tools with routing.mode="prefer" and non-empty keywords. For lead agents the deferred catalog is built from the full configured MCP set; auto-promotion never grants authority because an active skill's runtime policy still filters model-visible schemas, tool_search results, and execution.
• skills - Map of skill name → state (enabled)
• middlewares - Zero-argument AgentMiddleware class paths for lead and subagent runtime extension. config.yaml -> extensions can override these fields after validation; overrides are replace-per-field, not list concatenation.

Gateway API endpoints and DeerFlowClient methods can modify MCP servers and skill state at runtime; their extensions_config.json writes use the shared atomic replacement helper, while middlewares remains an operator-controlled config-file extension point.