lovia 0.3.2

A lightweight and elegant Agent framework

lovia

A lightweight, provider-neutral agent framework for Python.

简体中文

import asyncio
from lovia import Agent, Runner

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
    model="openai:gpt-4o-mini",
)
result = asyncio.run(Runner.run(agent, "What is the capital of France?"))
print(result.output)  # Paris

Two hard dependencies (httpx, pydantic). No DSL, no graph, no global state. Every advanced feature — tools, sessions, handoffs, structured output, MCP, streaming — is opt-in.

---

Install

pip install lovia

Optional extras:

pip install "lovia[mcp]"    # Model Context Protocol client
pip install "lovia[tools]"  # web_search with DuckDuckGo backend
pip install "lovia[web]"    # FastAPI + SSE chat server

---

Tools

Any typed Python function becomes a tool with @tool. Sync and async both work.

from lovia import Agent, Runner, tool

@tool
def calculate(expression: str) -> float:
    """Evaluate a simple math expression."""
    return eval(expression, {"__builtins__": {}})

agent = Agent(
    name="Calc",
    instructions="Use calculate() for arithmetic.",
    model="openai:gpt-4o-mini",
    tools=[calculate],
)
result = asyncio.run(Runner.run(agent, "What is 1337 * 42?"))

Use Annotated to add per-parameter descriptions to the JSON schema:

from typing import Annotated

@tool
def search(
    query: Annotated[str, "Keywords to search for."],
    limit: Annotated[int, "Max results, 1-20."] = 5,
) -> list[str]: ...

Simple execution policies stay as decorator kwargs:

@tool(timeout=5, retries=2, needs_approval=True)
async def send_email(to: str, body: str) -> str: ...

For advanced cases, pass composable policies; simple kwargs still work.

from lovia import RunContext

async def redact(next_tool, args, ctx):
    result = await next_tool(args, ctx)
    return str(result).replace(ctx.context.api_key, "[redacted]")

@tool(policies=[redact])
async def call_api(ctx: RunContext, path: str) -> str: ...

---

Structured output

Pass any Pydantic model as output_type and the result is validated automatically. output_repair=True lets the model self-correct if the first parse fails.

from pydantic import BaseModel
from lovia import Agent, Runner

class Review(BaseModel):
    rating: int       # 1-5
    summary: str
    pros: list[str]
    cons: list[str]

agent = Agent(
    name="Reviewer",
    instructions="Extract a structured review from the user text.",
    model="openai:gpt-4o-mini",
    output_type=Review,
    output_repair=True,
)
result = asyncio.run(Runner.run(agent, "The battery lasts all day but the screen is dim."))
print(result.output.rating)   # -> int

Override output_type for a single call without touching the agent:

result = await Runner.run(agent, "Summarize in plain text.", output_type=str)

---

Streaming

async for event in Runner.stream(agent, "Tell me a joke"):
    print(event)

Or directly from the agent instance:

async for event in agent.stream("Tell me a joke"):
    print(event)

---

Dynamic instructions

Inject context-aware content at runtime with @agent.system_prompt. Multiple fragments compose with the base instructions.

agent = Agent(name="Support", instructions="You are a support bot.", model="openai:gpt-4o-mini")

@agent.system_prompt
async def inject_user(ctx) -> str:
    user = await db.get_user(ctx.context.user_id)
    return f"The user's name is {user.name}. Their plan is {user.plan}."

# Append one-off context at call time:
result = await Runner.run(agent, "I need help.", append_instructions="Reply in Spanish.")

Prefer functional configuration when cloning reusable agents:

agent = agent.with_system_prompt(inject_user)

---

Handoffs

An agent can delegate to another agent mid-conversation. The Runner follows the chain automatically.

billing = Agent(name="Billing", instructions="Handle billing questions.", model="openai:gpt-4o-mini")
support = Agent(name="Support", instructions="Answer support questions. Hand off billing questions.", model="openai:gpt-4o-mini", handoffs=[billing])

result = await Runner.run(support, "Can I get a refund?")

---

Sessions

Persist conversation history across calls with a session= argument. The default in-memory store is a good starting point; swap in Redis or SQL as needed.

from lovia.stores import InMemorySessionStore

session_store = InMemorySessionStore()

result1 = await Runner.run(agent, "My name is Alice.", session=session_store.session("u42"))
result2 = await Runner.run(agent, "What is my name?", session=session_store.session("u42"))
# → "Your name is Alice."

---

Approval (human in the loop)

Mark sensitive tools with needs_approval=True to require human sign-off.

from lovia import ApprovalChannel

channel = ApprovalChannel()

@tool(needs_approval=True)
def send_email(to: str, body: str) -> str:
    ...

# In your UI, call channel.approve(request_id) or channel.deny(request_id, reason)
result = await Runner.run(agent, "Send a welcome email to [e-mail]", approval_channel=channel)

---

Sync helpers

Runner.run_sync and agent.run_sync are convenience wrappers around asyncio.run. Use them in scripts or wherever you can't await.

result = Runner.run_sync(agent, "What is 2+2?")
print(result.output)

---

Built-in tools

lovia.builtins ships practical, framework-agnostic tools you can drop straight into any agent. Nothing is imported automatically — grab only what you need.

from lovia.builtins.http import http_fetch
from lovia.builtins.fs import FileSystem
from lovia.builtins.shell import Shell, allowlist
from lovia.builtins.search import duckduckgo_search_tool
from lovia.builtins.todo import TodoList, todo_tools
from lovia.builtins.human import HumanChannel, ask_human
from lovia.builtins.think import think
from lovia.builtins.time import now
from lovia.builtins.code import PythonRunner

fs = FileSystem(root="/tmp/sandbox", writable=True)
sh = Shell(cwd="/tmp", needs_approval=allowlist(["ls", "cat"]))
todos = TodoList()
channel = HumanChannel()

agent = Agent(
    name="Worker",
    instructions="Plan, reason, act.",
    model="openai:gpt-4o-mini",
    tools=[
        http_fetch, now, think,
        *fs.tools(),
        sh.tool(),
        duckduckgo_search_tool(),  # requires lovia[tools]
        *todo_tools(todos),
        ask_human(channel),
        PythonRunner(needs_approval=False).tool(),
    ],
)

Shell and PythonRunner default to needs_approval=True for safety. The allowlist(commands) helper builds an approval predicate that auto-approves whitelisted commands and blocks everything else.

Builtin convention: stateless helpers export ready-to-use Tool instances, pluggable backends use factories, stateful single-tool helpers expose .tool(), and stateful multi-tool helpers expose .tools().

Runnable demos for each tool live in examples/builtins/.

---

Skills

Skills are Markdown-driven instruction packs stored in a directory tree. They let you compose domain knowledge without bloating the system prompt.

skills/
  translation/
    SKILL.md          # name, description, usage instructions
    references/       # reference files the agent can read

from lovia.skills import SkillCatalog

catalog = SkillCatalog.from_dir("./skills")   # lazy by default
agent = Agent(
    name="Expert",
    instructions=catalog.render_catalog(),
    model="openai:gpt-4o-mini",
    tools=catalog.tools(),
)

In lazy mode the catalog renders as a compact index; the model calls load_skill to pull in a full skill body on demand. Switch to mode="eager" to inline all bodies up front.

---

Multiple providers

The model= field accepts any "provider:model" string or a Provider instance.

# OpenAI
agent = Agent(model="openai:gpt-4o-mini", ...)
# Anthropic
agent = Agent(model="anthropic:claude-3-5-haiku-20241022", ...)
# Any OpenAI-compatible endpoint
from lovia import OpenAIChatProvider
provider = OpenAIChatProvider(model="deepseek-chat", base_url="https://api.deepseek.com/v1", api_key="...")
agent = Agent(model=provider, ...)

---

Examples

examples/
  01_hello.py                  Minimal agent
  02_tools.py                  Tool calling
  03_streaming.py              Streaming tokens
  04_structured_output.py      Pydantic output
  05_handoff.py                Agent-to-agent delegation
  06_agent_as_tool.py          Sub-agent as a tool
  07_session.py                Persistent sessions
  08_skills.py                 SkillCatalog
  09_compat_provider.py        Custom OpenAI-compatible provider
  10_hooks.py                  Lifecycle hooks / tracing
  11_approval.py               Human-in-the-loop approval
  12_multimodal.py             Image input
  13_budget_and_cancel.py      Token budget & cancellation
  14_guardrails.py             Input/output guards
  15_resume.py                 Resume interrupted runs
  16_web_serve.py              FastAPI + SSE server
  17_responses_reasoning.py    OpenAI Responses API + reasoning
  18_context_policy.py         Auto-summarize long history
  19_dynamic_instructions.py   Dynamic system prompt
  20_builtins.py               Several builtins together
  21_dx.py                     Annotated schemas, run_sync
  builtins/                    One focused demo per builtin
  workflows/                   Multi-agent workflow patterns

---

Development

git clone https://github.com/cymoo/lovia
pip install -e ".[dev]"
pytest          # run tests
ruff check .    # lint
mypy lovia      # type-check

See AGENTS.md for architecture notes, design philosophy, and commit conventions.

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MIT License