Profiles

Package per-provider and per-model defaults that Deep Agents applies when a model is selected

Harness and provider profiles are public beta APIs and may be updated in future releases.

Harness profiles let you package configuration that Deep Agents applies whenever a given provider or specific model is selected: system-prompt tweaks, tool description overrides, excluded tools or middleware, extra middleware, and general-purpose subagent edits. They are the main way to tune how the harness behaves for a particular model without changing your create_deep_agent call site. Use HarnessProfile when building profiles in Python; use HarnessProfileConfig when loading or saving YAML/JSON files. Deep Agents ships built-in harness profiles for OpenAI and Anthropic (Claude) models.

Provider profiles are a narrower companion API for model-construction kwargs, which don’t affect the harness. Most callers don’t need them; reach for one when you want init_chat_model defaults, credential checks, or runtime-derived kwargs as defaults with your provider choice (for example, when packaging a provider integration).

Harness profiles

A HarnessProfile describes prompt-assembly, tool-visibility, middleware, and default-subagent adjustments that create_deep_agent applies after the chat model has been constructed:

• base_system_prompt — replace the base Deep Agents system prompt
• system_prompt_suffix — append text to the base system prompt
• tool_description_overrides — override individual tool descriptions
• excluded_tools — remove specific harness-level tools from the tool set
• excluded_middleware — strip specific middleware classes from the stack
• extra_middleware — append middleware to every stack this profile applies to
• general_purpose_subagent — disable, rename, or re-prompt the general-purpose subagent

from deepagents import (
    GeneralPurposeSubagentProfile,
    HarnessProfile,
    register_harness_profile,
)

register_harness_profile(
    "openai:gpt-5.4",
    HarnessProfile(
        system_prompt_suffix="Respond in under 100 words.",
        excluded_tools={"execute"},
        excluded_middleware={"SummarizationMiddleware"},
        general_purpose_subagent=GeneralPurposeSubagentProfile(enabled=False),
    ),
)

excluded_middleware cannot remove scaffolding Deep Agents relies on. Listing FilesystemMiddleware, SubAgentMiddleware, or the internal permission middleware raises a ValueError.

Entries in excluded_middleware accept two forms:

• A middleware class (matched by exact type), or a plain string that matches AgentMiddleware.name. Use plain strings for built-ins and public aliases such as "SummarizationMiddleware".
• An module:Class import ref (for example, "my_pkg.middleware:TelemetryMiddleware") to target an exact middleware class from a config file. Import refs resolve lazily, so use them only for trusted local configuration — loading one imports Python code.

Lookup order for preconfigured model instances

When you pass a preconfigured chat model instance instead of a provider:model string, the harness synthesizes the canonical provider:identifier key from the instance and looks it up in this order:

1. Exact provider:identifier match
2. Identifier-only (only when the identifier already contains :)
3. Provider-only fallback

Registration keys

Both profile types use the same key format:

• Provider-level — a bare provider name like "openai" applies to every model from that provider.
• Model-level — a fully qualified provider:model key like "openai:gpt-5.4" applies only to that specific model.

When both a provider-level and a model-level profile exist, they are merged at resolution time. Unset model-level fields inherit from the provider-level profile; explicit model-level values override them.

Re-registering under an existing key merges the new profile on top of the prior one — it does not replace it. See Merge semantics for the per-field rules.

Merge semantics

Field	Merge behavior
base_system_prompt, system_prompt_suffix	New value wins when set; otherwise inherits
tool_description_overrides	Mappings merge per key; new value wins on a shared key
excluded_tools, excluded_middleware	Sets union
extra_middleware	Merged by concrete class: new instance replaces existing at its position, novel classes append
general_purpose_subagent	Merged field-wise (unset fields inherit)
init_kwargs (provider)	Dicts merge key-wise; new value wins on a shared key
pre_init (provider)	Callables chain: existing runs first, then the new one
init_kwargs_factory (provider)	Factories chain with their outputs merged every resolve_model call

Provider profiles

A ProviderProfile declares how Deep Agents should construct a chat model for a given provider or specific model spec. It applies only when you provide a provider:model string when creating the deep agent, not when you pass a preconfigured model with init_chat_model:

• init_kwargs — static initialization arguments forwarded to init_chat_model
• pre_init — side effects to run before construction (for example, credential validation)
• init_kwargs_factory — kwargs derived from runtime state (for example, headers pulled from environment variables)

from deepagents import ProviderProfile, register_provider_profile

register_provider_profile(
    "openai",
    ProviderProfile(init_kwargs={"temperature": 0}),
)

Load profiles from config files

For YAML/JSON-backed workflows, use HarnessProfileConfig. It mirrors the declarative subset of HarnessProfile (prompt text, tool-description overrides, excluded tools and middleware, general-purpose subagent edits) and owns to_dict / from_dict. Runtime-only state — middleware instances, factories, and class-form excluded_middleware entries — stays on HarnessProfile.

register_harness_profile accepts either type, so config-backed callers don’t need a manual conversion step:

# openai.yaml
base_system_prompt: You are helpful.
system_prompt_suffix: Respond briefly.
excluded_tools:
  - execute
  - grep
excluded_middleware:
  - SummarizationMiddleware
  - my_pkg.middleware:TelemetryMiddleware
general_purpose_subagent:
  enabled: false

import yaml
from deepagents import HarnessProfileConfig, register_harness_profile

with open("openai.yaml") as f:
    register_harness_profile(
        "openai",
        HarnessProfileConfig.from_dict(yaml.safe_load(f)),
    )

To go the other direction, HarnessProfileConfig.from_harness_profile(...) exports a runtime profile back to the declarative shape when it only uses serializable features:

• Class-form excluded_middleware entries serialize as a public alias (when the class exposes one via serialized_name: ClassVar[str]) or as a module:Class import ref.
• Non-empty extra_middleware and middleware classes declared in __main__ or inside a function scope cannot be serialized — export raises ValueError.

Ship a profile as a plugin

Distributable profiles can register themselves via importlib.metadata entry points instead of requiring callers to run register_*_profile by hand. Load order is built-ins first, then entry-point plugins, then any direct register_*_profile calls in user code; all three paths funnel through the same additive registration, so later registrations layer on top of earlier ones under the same key.

Declare an entry point in the distribution’s own pyproject.toml under the appropriate group:

[project.entry-points."deepagents.harness_profiles"]
my_provider = "my_pkg.profiles:register_harness"

[project.entry-points."deepagents.provider_profiles"]
my_provider = "my_pkg.profiles:register_provider"

Each target resolves to a zero-arg callable that performs the registrations when deepagents.profiles is imported:

from deepagents import (
    HarnessProfile,
    ProviderProfile,
    register_harness_profile,
    register_provider_profile,
)


def register_harness() -> None:
    register_harness_profile(
        "my_provider",
        HarnessProfile(system_prompt_suffix="Batch independent tool calls in parallel."),
    )


def register_provider() -> None:
    register_provider_profile(
        "my_provider",
        ProviderProfile(init_kwargs={"temperature": 0}),
    )

Related

• Harness — overview of harness capabilities
• Models — configure model providers and parameters
• Customization — full create_deep_agent configuration surface

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