smooai-smooth-operator-core 1.7.2

The agent brain you can point at production — right in your Python process.

Most agent frameworks hand the model a pile of tools and hope. This one gives you the loop and the brakes: draw hard lines the model can never cross, then let it run.

smooai-smooth-operator-core is the agent engine itself, in-process — an observe→think→act loop over any OpenAI-compatible client, with typed tools, streaming, checkpointing, cost budgets, and a permission gate you control. Not a client to a remote server: the agent is your process.

It's the native Python port of the Rust reference engine — one of five siblings (Rust, TypeScript, Python, Go, C#/.NET) that share one wire spec and one eval suite. The same agent brain, the same guarantees, wherever your stack already lives. Every surface is covered by fast, offline tests on a deterministic MockLlmProvider, so the loop is verified — not vibe-coded.

Install

pip install smooai-smooth-operator-core

Import as smooth_operator_core.

Quickstart

A complete agent — no credentials needed — using the deterministic mock provider the engine's own tests run on:

A complete agent with one tool — the mock is scripted to call the tool, then answer:

import asyncio
import json
from smooth_operator_core import SmoothAgent, AgentOptions, FunctionTool, MockLlmProvider

async def get_weather(args):
    return f"Weather in {args['city']}: 72F, sunny"

async def main():
    weather = FunctionTool(
        name="get_weather",
        description="Get the current weather for a city",
        parameters={"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]},
        func=get_weather,
    )

    provider = MockLlmProvider()
    provider.push_tool_call("call_1", "get_weather", json.dumps({"city": "Tokyo"}))
    provider.push_text("It's 72F and sunny in Tokyo.")

    agent = SmoothAgent(provider, AgentOptions(instructions="You are a helpful assistant", tools=[weather]))
    result = await agent.run("what's the weather in Tokyo?")
    print(result.text)

asyncio.run(main())

SmoothAgent(chat_client, options) takes the provider (the MockLlmProvider — swap in any OpenAI-compatible client) and an AgentOptions dataclass (all fields default, so AgentOptions() is valid). FunctionTool wraps an async function as a tool. await agent.run(...) returns an AgentRunResponse; result.text is the final answer.

Features

The full parity surface — every engine in the polyglot set ships it:

• Agentic tool-calling loop — observe→think→act, looping until the model answers.
• Typed tools — register tools the model can call, with parallel dispatch.
• Knowledge / RAG + vectors — ground the turn in retrieved documents.
• Memory — long-term entries recalled into context each turn.
• Compaction — a sliding-window token budget keeps the prompt under a ceiling.
• Cost / budget — per-model pricing, token + USD accounting, early stop on budget.
• Checkpointing — persist/resume a conversation via a checkpoint store.
• Rerank — rerank retrieved hits before injection (lexical reranker built in).
• Sub-agents / delegation — spawn child agents for sub-tasks.
• Cast + clearance — roles with per-role tool-access policy.
• Permissions + deny-policy — a tool-call gate (AutoMode: ask / accept-edits / deny-unmatched / bypass) with hard circuit-breakers (rm -rf /, credential paths, pipe-to-shell, dangerous domains), a persisted allow-list, and a consumer DenyPolicy — declarative TOML rules plus semantic predicates for what strings can't express.
• Human-in-the-loop gate — require approval before designated tool calls run.
• Conversation thread — SmoothAgentThread carries a conversation across multiple run calls.
• LlmProvider seam + MockLlmProvider — inject any OpenAI-compatible client; the record/replay mock drives the offline tests.
• Deferred tools + tool_search — hide rarely-used tool schemas behind a meta-tool the model calls to promote the ones it needs.
• Typed workflow graph — a node/edge workflow engine alongside the agent loop.
• Parallel tool calls — dispatch ≥2 tool calls concurrently (transcript order preserved).
• Retry / backoff — retry transient model-call failures with exponential backoff.
• Streaming — stream incremental text, tool calls, and tool results as the turn runs.

Permissions & deny-policy — lines the agent can't cross

This is what makes an agent safe to point at real infrastructure: you decide what it can never do, and no prompt or model mistake talks it out of that. Every tool call passes through a gate. AutoMode sets the posture — read-only calls allow, mutating calls ask, dangerous calls deny — and hard circuit-breakers (rm -rf /, credential paths, pipe-to-shell, dangerous domains) fire in every mode, BYPASS included. Attach a DenyPolicy on top: declarative TOML rules for the lines you can name, semantic predicates for the ones you can't. A match is a hard deny no stored grant and no mode can waive.

from smooth_operator_core import (
    SmoothAgent, AgentOptions, AutoMode, DenyPolicy, DenyPredicate, DenyReason,
)

# Declarative rules (TOML): never the prod AWS profile, never a prod host.
policy = DenyPolicy.from_toml(
    """
    schema_version = 1
    [bash]
    deny_patterns = ["aws * --profile prod"]
    [network]
    deny_hosts = ["*.prod.internal"]
    """
)

# Predicate for what strings can't express — return a DenyReason to deny, None to allow.
class DenyDbWriter(DenyPredicate):
    def evaluate(self, call):
        if call.name == "db_query" and "writer" in str(call.arguments):
            return DenyReason.new("DB writer endpoint is off-limits — reads go to the replica")
        return None

agent = SmoothAgent(
    provider,
    AgentOptions(
        instructions="You are a careful assistant",
        tools=[weather],
        permission_mode=AutoMode.ASK,  # read allow · mutate ask · dangerous deny
        deny_policy=policy.with_predicate(DenyDbWriter()),
    ),
)

Streaming

run_stream is the async streaming variant of run: it yields incremental events — text deltas as the model produces them, each tool call before dispatch, each tool result after it finishes, and a terminal done event carrying the same response run would have returned.

async for event in agent.run_stream("what is the answer?"):
    if event.type == "text":
        print(event.text, end="")
    elif event.type == "done":
        print(f"\n{event.response.text}")

Part of Smoo AI

smooth-operator-core is built and open-sourced by Smoo AI — the AI-powered business platform with AI built into every product: CRM, customer support, campaigns, field service, observability, and developer tools.

• 🚀 Smooth on the platform — smoo.ai/th
• 🧰 More open source from Smoo AI — smoo.ai/open-source
• 🧩 Run it hosted — lom.smoo.ai

Links

• lom.smoo.ai — run it hosted
• smooth-operator-core — the polyglot engine repo
• Polyglot Engines — install + hello-agent in all five languages
• smoo.ai — the product · smoo.ai/open-source — more open source

License

MIT — see LICENSE.

Built by Smoo AI — AI built into every product.