lovia 0.8.8

A lightweight and elegant Agent framework

lovia

中文文档 · Documentation · Examples

lovia is an elegant, restrained Python framework for developers who want to own the agent loop without rebuilding every supporting primitive from scratch. It covers the recurring hard parts of agent applications — tools, sessions, events, context compaction, serving — without turning into a platform.

pip install lovia

from lovia import Agent, tool


@tool
def get_order(order_id: str) -> str:
    """Look up an order's status by id."""
    return f"Order {order_id}: shipped 2 days ago, arriving Thursday."


# Set OPENAI_API_KEY in your environment first; for OpenAI-compatible
# services (DeepSeek, Ollama, vLLM, ...) also set OPENAI_BASE_URL.
agent = Agent(
    name="support",
    instructions="You are a customer-support agent. Look the order up before "
    "answering, and reply in one or two concrete sentences.",
    model="deepseek-v4-pro",
    tools=[get_order],
)

# run_sync() suits scripts and notebooks; from async code,
# use `await Runner.run(agent, ...)` instead.
result = agent.run_sync("Where is my order A-1042?")
print(result.output)

Or serve a full chat UI — memory, skills, scheduling, and a workspace on the current directory included — in one line:

pip install "lovia[web]" && python -m lovia.web

Anthropic is built in too — set ANTHROPIC_API_KEY and use model="anthropic:claude-4-8-opus"; everything else about models lives in Providers & models.

Documentation

This README is the tour. The documentation goes deep, one feature per page — start with the quickstart and core concepts — and the examples are a numbered, runnable learning path.

Why lovia

Composable primitives, ordinary Python — no new universe of abstractions:

• Minimum dependencies. The core depends only on httpx and pydantic; everything else is an extra, which keeps installs small, version conflicts rarer, and the supply-chain attack surface narrower.
• Few abstractions. An Agent is immutable configuration, a Runner executes one run, a @tool is a typed function; handoff and agent-as-tool compose agents; plugins package the rest.
• Readable. The critical path is concentrated and explicit: model calls, tool execution, retries, and persistence happen in a clear order. When something surprises you, there is one chain to follow.
• Lightweight model integration. OpenAI and Anthropic are built in, and OpenAI-compatible endpoints work directly. There is no adapter stack to fight; a new provider is just a small Protocol.
• Cache-friendly context management. Compaction only changes what the model sees on the next call, keeping prompt prefixes stable while the complete record stays intact.
• Production seams, not a production costume. Approvals, budgets, cancellation, mid-run steering, retries, checkpoint/resume — explicit knobs you wire into your own app.
• One extension axis. Skills, MCP, Todo, and Memory are all plugins on the same seam you get for your own capabilities.

The design pressure throughout is restraint: if a feature can be a short user-side recipe, it stays out of the framework.

The tour

Every stop below has a full guide; each snippet runs as written.

Agents

An Agent is declarative configuration — no conversation state, safe to share, cheap to clone(). Prompt fragments can be dynamic per run:

from lovia import Agent

agent = Agent(
    name="writer",
    instructions="Write concrete, concise answers.",
    model="deepseek-v4-pro",
    workspace=Workspace.local(".")
)

→ Agents

Running and streaming

One run, three consumption styles — and the stream handle is both async-iterable and awaitable. Iteration never raises: every stream ends with exactly one terminal event.

from lovia import Runner, events

handle = Runner.stream(agent, "Explain context windows in one paragraph.")

async for ev in handle:
    if isinstance(ev, events.TextDelta):
        print(ev.delta, end="", flush=True)

result = await handle.result()

→ Running · Streaming

Tools

Typed Python functions; the schema comes from signatures, docstrings, Annotated, and Pydantic Fields. One turn's calls execute concurrently by default — tools with non-reentrant side effects opt out and become execution barriers:

from typing import Annotated
from pydantic import Field
from lovia import tool


@tool(strict=True)
def search_docs(
    query: Annotated[str, "Search terms"],
    limit: Annotated[int, Field(ge=1, le=10)] = 5,
) -> list[str]:
    """Search internal documentation."""
    return []


@tool(parallel=False)
async def apply_migration(name: str) -> str:
    """Apply a database migration (never concurrently with other tools)."""
    return "applied"

→ Tools · Built-in tools

Structured output

Pass a Pydantic model, dataclass, TypedDict, or plain type; the final answer is validated — and repaired once, by default, when it doesn't parse:

from pydantic import BaseModel
from lovia import Agent, Runner


class Brief(BaseModel):
    title: str
    bullets: list[str]


agent = Agent(name="summarizer", model="deepseek-v4-pro", output_type=Brief)
result = await Runner.run(agent, "Summarize Transformer for a Python developer.")
print(result.output.title)

→ Structured output

Providers

Model strings, provider instances, or a fallback chain; OpenAI-compatible endpoints ride OPENAI_BASE_URL, prompt caching and reasoning models are handled per host, and a custom provider is a small Protocol:

from lovia import Agent, ModelSettings

agent = Agent(
    name="assistant",
    model=["anthropic:claude-4-8-opus", "deepseek-v4-pro"],  # fallback chain
    settings=ModelSettings(temperature=0.2, max_tokens=800),
)

→ Providers & models

Multi-agent

Two primitives, both ordinary tools underneath. Handoff transfers the conversation — the specialist continues with the full history and answers the user. Agent-as-tool delegates a bounded subtask — the child sees only the prompt and its answer comes back as a tool result:

from lovia import Agent, Runner

billing = Agent(name="billing", instructions="Handle billing issues.", model="deepseek-v4-pro")
support = Agent(name="support", instructions="Handle technical issues.", model="glm-5.2")

triage = Agent(
    name="triage",
    instructions="Route the user to the right specialist.",
    model="deepseek-v4-flash",
    handoffs=[billing, support],       # handoff: the specialist takes over
)
result = await Runner.run(triage, "I was charged twice.")

summarizer = Agent(name="summarizer", instructions="Summarize text in five bullets.",
                   model="deepseek-v4-pro")

manager = Agent(
    name="manager",
    instructions="Delegate summarization when useful.",
    model="deepseek-v4-flash",
    tools=[summarizer.as_tool(description="Summarize a passage.")],  # delegate a subtask
)

→ Multi-agent

Human in the loop

Gate sensitive tools; resolve from your UI, a server-side policy, or an out-of-band channel — unresolved approvals deny, so runs never hang:

from lovia import Runner, events, tool


@tool(needs_approval=True)
async def refund(order_id: str, amount_cents: int) -> str:
    """Issue a refund."""
    return "refunded"


async for ev in Runner.stream(agent, "Refund order A123."):
    if isinstance(ev, events.ApprovalRequired):
        ev.approve()          # or ev.reject()

→ Human in the loop

Sessions and checkpoints

Sessions persist conversations across runs; checkpoints make single runs crash-safe and idempotent — re-issuing a completed run_id replays its result without calling the model:

from lovia import CheckpointOptions, Runner, SQLiteCheckpointer, SQLiteSession

session = SQLiteSession("chat.db")
await Runner.run(agent, "My project is called Atlas.", session=session, session_id="u1")

cp = SQLiteCheckpointer("runs.db")
result = await Runner.run(
    agent,
    "Migrate the report format.",
    checkpoint=CheckpointOptions(cp, "report-migration-42"),
)

→ Sessions & checkpoints

Context compaction

Long conversations survive the window without rewriting history: compaction is view-only, keeps the prompt prefix stable for provider caches:

from lovia import Agent, Compaction

agent = Agent(
    name="companion",
    model="deepseek-v4-pro",
    context_policy=Compaction(context_window=200_000, compact_at=0.75, compact_to=0.50),
)

→ Context management

Guardrails and reliability

Guardrails veto runs at the input/output boundary. Reliability follows one placement rule — retry posture on the agent, per-run limits on the run:

from lovia import Agent, RetryPolicy, RunBudget, Runner
from lovia.exceptions import GuardrailTripped


async def must_cite(output, ctx):
    if "source:" not in str(output).lower():
        return "Missing source citation."


agent = Agent(
    name="researcher",
    model="deepseek-v4-pro",
    output_guardrails=[must_cite],
    retry=RetryPolicy(max_attempts=2)
)

result = await Runner.run(
    agent,
    "Analyze these logs.",
    budget=RunBudget(max_tool_calls=20, max_seconds=60)
)

→ Guardrails · Reliability

Hooks and steering

Hooks observe every run event (fail-open, same types as streaming); the mailbox is the inbound dual of cancellation — push a message into a live run and the model sees it next turn. A run can even steer itself:

from lovia import Mailbox, RunContext, Runner, events
from lovia.hooks import AgentHooks

hooks = AgentHooks()


@hooks.on(events.TurnStarted)
def deadline(ev, ctx: RunContext):
    if ev.turn == 9:
        ctx.mailbox.push("Last turn: answer with what you have.")


mailbox = Mailbox()
handle = Runner.stream(agent.clone(hooks=hooks), "Analyze these logs.", mailbox=mailbox)
mailbox.push("Focus on the 5xx spike around 14:00.")  # seen next turn

→ Observability · Reliability

Workspace

File and shell tools scoped to a root, governed by one allow/ask/deny policy over paths and commands — ask decisions ride the normal approval channel:

from lovia import Agent
from lovia.workspace import CommandRule, Workspace

agent = Agent(
    name="coder",
    instructions="Make small, targeted code changes.",
    model="deepseek-v4-pro",
    workspace=Workspace.local(
        ".",
        mode="coding",
        denied_paths=(".env*",),
        command_rules=(CommandRule("pytest", "allow"), CommandRule("rm -rf", "deny")),
    ),
)

→ Workspace

Plugins

One extension axis: a plugin contributes tools, prompt text, per-turn view injectors, hooks, and guardrails — never control flow:

from lovia import Agent, Skills, Todo
from lovia.plugins.mcp import MCP, MCPServerStdio

agent = Agent(
    name="builder",
    model="deepseek-v4-pro",
    plugins=[
        Todo(),
        Skills("./skills"),
        MCP(MCPServerStdio(name="web", command="uvx", args=["mcp-server-fetch"])),
    ],
)

• Todo() — gives the model a checklist for multi-step work; the current list is re-shown every turn without growing the transcript.
• Skills(dir) — reusable instruction bundles (SKILL.md + files): a one-line index stays in the prompt, full content loads on demand.
• MCP(server) — tools from Model Context Protocol servers, stdio or HTTP, with per-server name prefixes and approval gates.

Writing your own is a name plus one async setup() returning the contributions.

→ Plugins · Skills · MCP

Memory

Long-term memory across conversations — always-in-prompt Notes plus a searchable Archive, zero-config, upgradeable one argument at a time:

from lovia import Agent, Memory
from lovia.plugins import OpenAIEmbedder

agent = Agent(name="assistant", model="deepseek-v4-pro",
              plugins=[Memory("./.lovia/memory")])

Memory("./memory")                             # stdlib keyword search (FTS5 bm25)
Memory("./memory", embedder=OpenAIEmbedder())  # + semantic arm -> hybrid recall
Memory("./memory", index=None)                 # notes only, no archive

→ Memory

Web UI

A lightweight FastAPI app — SSE streaming, sessions with titles, approvals, schedules, a memory editor — whose runs survive browser disconnects:

from lovia.web import serve

serve(agent, host="127.0.0.1", port=8000, db_path="lovia.db")

python -m lovia.web --port 9000 --model deepseek-v4-pro   # or zero-config

The bundled page is optional: everything is exposed as a JSON + SSE REST API (browse it at /api/docs), so create_app(agent, ui=False) — or mounting the router into your own FastAPI app — lets you build a custom front-end on the same endpoints.

→ Web UI & server · HTTP API

Evals

Behavioral test suites: a Case pairs input with checks, any function is a check, the LLM judge is just another check, and reports diff against a baseline in CI:

from lovia.eval import Case, contains, evaluate, llm_judge, tool_called

report = await evaluate(agent, [
    Case("What is the capital of France?", checks=[contains("Paris")]),
    Case("What's 23.4 * 91?", checks=[tool_called("calculator")]),
    Case("Write a haiku about spring",
         checks=[llm_judge("A 5-7-5 haiku that evokes spring")],
         samples=4, pass_threshold=0.75),
])
print(report)
assert report.passed

→ Evals

Testing

Everything runs offline against a scripted provider — real tools, real loop, canned model:

from lovia.testing import ScriptedProvider, call, text

provider = ScriptedProvider([
    call("add", {"a": 2, "b": 3}, call_id="c1"),
    text("The answer is 5."),
])

→ Testing

Examples

The examples/ directory is a numbered learning path of thirty self-contained, runnable scripts — from 01_hello.py to a terminal support bot — plus one script per built-in tool family (tools/) and the classic agentic patterns in plain Python (workflows/). Setup and the full index: examples/README.md.

Install extras

Need	Install
Core framework	pip install lovia
DuckDuckGo search	pip install "lovia[ddg]"
MCP integration	pip install "lovia[mcp]"
Web UI	pip install "lovia[web]"
Runnable examples	pip install "lovia[examples,web]"
Development	pip install -e ".[dev]"

Development

pip install -e ".[dev]"
.venv/bin/python -m pytest
.venv/bin/python -m ruff check .
.venv/bin/python -m ruff format .
.venv/bin/python -m mypy lovia

Live provider tests are marked live_provider and stay skipped unless enabled explicitly. Contributor-facing internals are documented in docs/architecture.md.