priest-core 2.8.0

Pure core library for AI orchestration — transport-agnostic, host-agnostic, async-first

priest

Pure core library for AI orchestration. Transport-agnostic, host-agnostic, async-first.

priest handles single run execution and session continuation. It is designed to be embedded into other applications — CLI apps, web apps, bots, games, etc. The separate priests repo provides the CLI and service layer built on top.

What it does

• Executes a single AI request against a configured provider
• Loads behavior profiles from disk (identity, rules, custom context, memories)
• Persists and continues conversation sessions (SQLite-backed)
• Transports images, function tools, and safe provider reasoning state
• Returns structured responses with usage, latency, and error info

What it does not do

• No CLI, no HTTP server, no config files required
• No multi-step orchestration or workflow chaining (that belongs in priests)
• No hardcoded paths or model preferences
• No response parsing — response.text is always the raw string from the model

Install

Requires Python 3.11+ and uv.

uv sync

This creates a .venv and installs all dependencies in isolation. Dependencies: pydantic>=2, httpx, aiosqlite, openai>=1.0, anyio[trio].

Quick start

import asyncio
from pathlib import Path
from priest import PriestConfig, PriestEngine, PriestRequest
from priest.profile.loader import FilesystemProfileLoader
from priest.providers.ollama_provider import OllamaProvider

async def main():
    engine = PriestEngine(
        profile_loader=FilesystemProfileLoader(Path("profiles/")),
        adapters={"ollama": OllamaProvider()},
    )
    response = await engine.run(PriestRequest(
        config=PriestConfig(provider="ollama", model="qwen3.5:9b"),
        profile="default",
        prompt="Hello.",
    ))
    print(response.text)  # always a raw string — parse it yourself if needed

asyncio.run(main())

Profiles

Profiles live in a directory and define behavior context — identity, rules, custom overrides, and memories. They are model-agnostic.

profiles/
  default/
    PROFILE.md    # identity and behavior
    RULES.md      # strict constraints
    CUSTOM.md     # user customization layer
    memories/     # optional memory files (.md or .txt)
    profile.toml  # optional machine-readable metadata

A built-in default profile is included. Host apps can override it by providing their own default/ folder.

Sessions

Sessions persist conversation turns to SQLite. Pass a SessionRef with your chosen ID to start or continue a conversation. The ID you provide is canonical — the session is created with it if it does not exist yet.

from priest import SessionRef
from priest.session.sqlite_store import SqliteSessionStore

async with SqliteSessionStore(db_path=Path("sessions.db")) as store:
    engine = PriestEngine(..., session_store=store)

    # First turn — session created with ID "my-session"
    r1 = await engine.run(PriestRequest(
        ...,
        prompt="Remember this number: 7.",
        session=SessionRef(id="my-session", create_if_missing=True),
    ))

    # Second turn — session continued by the same ID
    r2 = await engine.run(PriestRequest(
        ...,
        prompt="What number did I ask you to remember?",
        session=SessionRef(id="my-session"),
    ))

The SQLite schema and timestamp format are shared with the TypeScript, .NET, Rust, and Swift SDKs. Protocol v2.8 does not change either format or require a database migration.

Output format hints

priest never parses the response. response.text is always the raw string returned by the model — format handling is the app layer's responsibility.

Three independent mechanisms are available to hint the model's output format:

from priest.schema.request import OutputSpec

# Activate provider-native JSON mode (e.g. Ollama's format field, OpenAI's json_object mode)
output=OutputSpec(provider_format="json")

# Inject a natural-language instruction into the system prompt (works with any provider)
output=OutputSpec(prompt_format="json")   # also: "xml", "code"

# JSON Schema structured output — preferred for strict schema compliance
output=OutputSpec(
    json_schema={
        "type": "object",
        "properties": {"name": {"type": "string"}, "age": {"type": "integer"}},
        "required": ["name", "age"],
    },
    json_schema_name="person",    # optional, defaults to "response"
    json_schema_strict=False,     # True requires additionalProperties:false on all objects
)

json_schema wires to provider-native structured output when available (OpenAI json_schema mode, Ollama format field). For Anthropic, the schema is injected into the system message. json_schema takes precedence over provider_format when both are set. Either, both, or none of the three mechanisms can be set independently.

System context

App-layer policy can be injected at the top of the system prompt — above profile rules — via context:

PriestRequest(
    ...,
    context=["Today is 2026-04-01.", "Running inside priests CLI."],
)

Images

Attach local, remote, or already encoded images with ImageInput. Images are included in the current user turn and are never persisted to the session database.

from priest import ImageInput

PriestRequest(
    ...,
    prompt="Classify this image.",
    images=[ImageInput(path="sample.png", media_type="image/png")],
)

Providers

Provider	Class	Notes
Ollama	OllamaProvider	Local models. Default base URL: http://localhost:11434
OpenAI-compatible	OpenAICompatProvider	OpenAI, Gemini, Bailian, MiniMax, DeepSeek, Kimi, Groq, OpenRouter, and any custom /v1/chat/completions endpoint
OpenAI Responses	OpenAIResponsesProvider	First-class /v1/responses transport with semantic SSE, tools, images, structured output, and reasoning
Anthropic	AnthropicProvider	Anthropic Claude. Uses the native /v1/messages API

OpenAI-compatible adapter

from priest.providers.openai_compat_provider import OpenAICompatProvider

adapter = OpenAICompatProvider(
    name="bailian",
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
    api_key="sk-...",
    proxy="http://127.0.0.1:7890",  # optional
)

The adapter runs via the sync openai SDK in a worker thread to avoid Python 3.14+ compatibility issues with httpcore's anyio async TLS backend.

OpenAI Responses adapter

from priest.providers.openai_responses_provider import OpenAIResponsesProvider

adapter = OpenAIResponsesProvider(
    api_key="sk-...",
    url="https://api.openai.com/v1/responses",  # optional exact endpoint
    proxy="http://127.0.0.1:7890",              # optional
)

This is separate from the OpenAI-compatible Chat Completions adapter and does not change that adapter's wire behavior.

Anthropic adapter

from priest.providers.anthropic_provider import AnthropicProvider

adapter = AnthropicProvider(
    api_key="sk-ant-...",
    proxy="http://127.0.0.1:7890",  # optional
)

Provider options

Pass provider-specific options via PriestConfig.provider_options. These are forwarded as extra fields in the request body (OpenAI: extra_body; Ollama/Anthropic: merged into the payload).

# Enable/disable thinking mode on Qwen3 models (Bailian / Ollama)
PriestConfig(provider="bailian", model="qwen3-32b", provider_options={"think": True})

Not all providers accept the same options — pass only what the target provider supports.

Reasoning

PriestConfig.reasoning requests provider-neutral reasoning behavior:

from priest import ReasoningConfig

config = PriestConfig(
    provider="responses",
    model="gpt-5",
    reasoning=ReasoningConfig(enabled=True, effort="high", summary="auto"),
)

OpenAI Responses, Anthropic, and Ollama translate these options to their native request fields. PriestResponse.reasoning contains only safe, provider-supplied summaries and request-local opaque continuation state; UsageInfo.reasoning_tokens reports provider-supplied usage. Raw chain-of-thought is never exposed. Continuation is replayed only inside the current tool loop and is never written to sessions or compaction metadata.

PriestEngine.spec_version is "2.8.0".

Testing

# Unit tests (no Ollama required)
uv run pytest tests/ -m "not integration" -v

# Integration tests (requires running Ollama)
uv run pytest tests/ -v

# Single prompt against Ollama
uv run python scripts/try_run.py --model qwen3.5:9b --prompt "hello"

# Interactive chat
uv run python scripts/try_run.py --model qwen3.5:9b --chat

# Full smoke test
uv run python scripts/try_run.py --model qwen3.5:9b

Package structure

priest/
├── errors.py              # error codes and exception hierarchy
├── engine.py              # PriestEngine — single run orchestration
├── schema/
│   ├── request.py         # PriestRequest, PriestConfig, SessionRef, OutputSpec
│   └── response.py        # PriestResponse, ExecutionInfo, UsageInfo, PriestError
├── profile/
│   ├── default_profile.py # built-in fallback default profile
│   ├── loader.py          # FilesystemProfileLoader, ProfileLoader protocol
│   ├── model.py           # Profile dataclass
│   └── context_builder.py # message assembly
├── session/
│   ├── store.py           # SessionStore ABC
│   ├── sqlite_store.py    # SqliteSessionStore (default)
│   ├── memory_store.py    # InMemorySessionStore (tests/ephemeral)
│   └── model.py           # Session, Turn dataclasses
└── providers/
    ├── base.py                    # ProviderAdapter ABC, AdapterResult
    ├── ollama_provider.py         # OllamaProvider
    ├── openai_compat_provider.py  # OpenAICompatProvider (OpenAI SDK, sync-in-thread)
    ├── openai_responses_provider.py # OpenAIResponsesProvider (Responses API)
    └── anthropic_provider.py      # AnthropicProvider (httpx async)