kestrel-sovereign-sdk

Lightweight SDK providing base interfaces, protocols, and utilities for Kestrel Sovereign feature package development. Feature packages depend on this SDK instead of the full framework, keeping dependencies minimal and development fast.

Installation

uv pip install git+https://github.com/KestrelSovereignAI/kestrel-sovereign-sdk.git

With encryption helpers:

uv pip install "kestrel-sovereign-sdk[crypto] @ git+https://github.com/KestrelSovereignAI/kestrel-sovereign-sdk.git"

Dependencies

• pydantic>=2.0
• Optional: cryptography>=42.0 (via [crypto] extra)

Usage

from kestrel_sdk.features.base import Feature, Tool

class MyFeature(Feature):
    name = "my-feature"

    def get_tools(self):
        return [Tool(name="my-tool", description="Does something", handler=self.handle)]

Host features (host/fleet scope)

Feature is a subagent — each instance is bound to one agent (self.agent), mounts its router under that agent's prefix, and can be called as a tool with its own LLM context. HostFeature is the host/fleet-scoped sibling: it runs once per host, has no agent binding, mounts its router at the host root, and lives across host start/stop rather than agent enable/disable. It is what kestrel-sovereign discovers and mounts, and what fleet-observability host features implement.

from kestrel_sdk import HostFeature, HostContext, UIContributions

class FleetObservability(HostFeature):
    name = "fleet-observability"       # stable slug for discovery / mounting
    capability = "fleet.observe"       # optional capability gate

    def get_router(self):
        # Mounted at the HOST ROOT — no agent prefix, no get_agent dependency.
        from fastapi import APIRouter
        router = APIRouter()
        # ... host-scoped routes ...
        return router

    async def on_host_start(self, ctx: HostContext):
        # Host-scoped store handle built on the SDK's OWN storage layer.
        # The feature layer (entities + a fleet TenantContext) is layered on
        # top of this handle — the SDK stays dependency-free.
        target = self.resolve_host_engine_target(ctx.config["host_db_url"])
        self.db = ctx.db
        await ctx.backplane.subscribe("fleet.events", self._on_event)

    async def _on_event(self, event):
        # Handle a live fleet event (persist, fan out to console, etc.).
        ...

    async def on_host_stop(self, ctx: HostContext):
        await ctx.backplane.close()

    def get_ui_contributions(self):
        return UIContributions(
            static_dir="/pkg/fleet/static",
            modules=["fleet-panel.js"],
            capability=self.capability,
        )

HostContext is a minimal, runtime_checkable Protocol exposing the host db backend, a pub/sub backplane handle, and host config. UIContributions is a pure-data dataclass (static_dir / modules / css / capability) shared by agent and host features, so feature packages never need to import Sovereign or carry a fallback copy just to describe their console assets.

Application extensions

Application packages can customize agent prompt context through the SDK-owned AppExtension contract without importing the Sovereign runtime:

from kestrel_sdk import AppExtension

class CompanionExtension(AppExtension):
    def get_system_prompt_prefix(self) -> str:
        return "You are this application's companion persona."

Sovereign consumes this contract and keeps a compatibility re-export at its historic import path.

Database surface (entity feature packages)

Feature packages that need raw SQL or ORM access (e.g. kestrel-feature-entities) develop against kestrel_sdk.storage.database:

from kestrel_sdk.storage.database import (
    DatabaseBackend,           # async ABC: execute / fetch_* / transaction
    PrivacyMode,               # 6-mode enum
    EngineTarget,              # frozen dataclass: url, persistent, description
    resolve_engine_target,     # PrivacyMode + fallback_url -> EngineTarget
)

target = resolve_engine_target(PrivacyMode.NORMAL, "postgresql+asyncpg://...")
# target.url is the SQLAlchemy URL the feature should bind its ORM engine to.
# Volatile modes (EPHEMERAL/ISOLATED) ignore fallback_url and return
# in-memory or tempfile sqlite URLs with persistent=False.

To get the active DatabaseBackend instance at runtime, features access it through the agent context they already receive in their Feature.__init__:

class MyEntityFeature(Feature):
    def __init__(self, agent):
        super().__init__(agent)
        self.db: DatabaseBackend = agent.db   # provided by sovereign

The SDK declares the DatabaseBackend ABC; sovereign provides the concrete SQLiteBackend / PostgresBackend instance via agent.db. Feature packages should never instantiate their own backend — that creates a parallel connection pool and bypasses the agent's privacy enforcement.

Channels, Delivery, And Output Contracts

Channel and delivery packages use SDK contracts rather than importing from the full framework:

from kestrel_sdk.channels import ChannelAdapter, ChannelMessage
from kestrel_sdk.delivery import DeliveryProvider, DeliveryTask, DeliveryResult
from kestrel_sdk.outputs import OutputEvent, OutputKind

Feature packages register concrete channel adapters through:

[project.entry-points."kestrel_sovereign.channel_adapters"]
telegram = "kestrel_channel_telegram:TelegramAdapter"

Delivery providers register through:

[project.entry-points."kestrel_sovereign.delivery_providers"]
sendgrid = "kestrel_delivery_sendgrid:SendGridDeliveryProvider"

The SDK owns only the public contracts. The framework owns runtime privacy checks, signal dispatch, durable queues, and server composition.

Timeline Protocols

Timeline implementations (e.g., story archive, health timelines) use SDK protocols for cross-package interoperability. The SDK provides three core protocols: TimelineProtocol defines the minimal shape any timeline must conform to, TimelineSharingProtocol enables pluggable serialization formats (JSON, FHIR, IPFS), and VectorSearchBackend abstracts semantic search across different vector stores (pgvector, pure-Python cosine).

Implementing TimelineProtocol

Any class with the required attributes can serve as a timeline:

from datetime import datetime

class StoryTimeline:
    def __init__(self):
        self.id = "timeline-123"
        self.agent_did = "did:key:abc"
        self.subject_name = "Jane Doe"
        self.title = "Jane's Life Story"
        self.coherence_score = 0.95
        self.created_at = datetime.now()

Sharing and Serialization

Use JSONTimelineSerializer for default JSON output, or implement TimelineSharingProtocol for custom formats:

from kestrel_sdk.timeline import JSONTimelineSerializer, TimelineSharingProtocol
import json

# Default JSON sharing
serializer = JSONTimelineSerializer()
data = serializer.serialize(timeline, events, people)

# Custom FHIR serializer
class FHIRTimelineSerializer:
    content_type = "application/fhir+json"

    def serialize(self, timeline, events, people) -> bytes:
        # Convert to FHIR Bundle format
        bundle = {"resourceType": "Bundle", "entry": [...]}
        return json.dumps(bundle).encode("utf-8")

Vector Search

Implement VectorSearchBackend for semantic timeline search. The SDK ships two reference implementations in kestrel-feature-story-archive: PgVectorBackend (PostgreSQL with pgvector extension) and PurePythonBackend (SQLite with cosine similarity).

from kestrel_sdk.timeline import VectorSearchBackend

class MyVectorBackend:
    async def knn(self, query_embedding: bytes, k: int, filter: dict | None = None):
        # Return k-nearest neighbors by cosine similarity
        return [("event-5", 0.95), ("event-12", 0.89)]

    @property
    def supports_filters(self) -> bool:
        return True  # Can filter by timeline_id at query time

For a full timeline implementation with persistence, embeddings, and IPFS export, see kestrel-feature-story-archive.

Configuration

No environment variables required. This is a development-time dependency only.

Development

uv pip install kestrel-sovereign-sdk && uv pip install -e .
uv run pytest