lovia 0.5.1

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)

---

Tools

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

from lovia.tools.http import http_fetch
from lovia.tools.search import duckduckgo_search_tool
from lovia.tools.todo import TodoList, todo_tools
from lovia.tools.human import HumanChannel, ask_human
from lovia.tools.think import think
from lovia.tools.time import now

todos = TodoList()
channel = HumanChannel()

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

Tool 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().

Filesystem and shell tools use the same lovia.tools namespace, either through Agent(sandbox=...) or direct factories like coding_tools(root=".").

Runnable demos live in examples/tools/.

---

Sandbox tools

lovia.sandbox is the concise filesystem + process layer for coding agents. Give the agent a sandbox once and Lovia injects the standard file and shell tools for that boundary.

from lovia import Agent, Runner
from lovia.sandbox import Sandbox

agent = Agent(
    name="coder",
    instructions="You are a focused coding agent.",
    model="openai:gpt-4o-mini",
    sandbox=Sandbox.local("."),
)

await Runner.run(agent, "Create app.py and run it.")

If you are already running inside Docker or another controlled environment, use the same tool implementations directly:

from lovia.tools import coding_tools

agent = Agent(
    name="coder",
    model="openai:gpt-4o-mini",
    tools=coding_tools(root="."),
)

What you get for free:

• Path traversal guard — symlink-aware, blocks .., /etc/..., etc. File tools accept sandbox-relative paths only.
• Simple atomic tools — read_file, write_file, edit_file, glob, list_dir, and shell are implemented once and can be used through either Agent(sandbox=...) or coding_tools(root=...).
• Exact edits — edit_file replaces exact old_text with new_text; if the text is missing or ambiguous, it fails without writing so the model can re-read and retry.
• Structured command results — shell returns exit_code, stdout, stderr, timed_out, and truncated.
• Approval-aware shell — mode="coding" allows file edits but routes shell commands through Lovia's existing approval flow. Use mode="trusted" for fully automated local runs.
• Hidden-file filtering — glob and list_dir skip dotfiles by default. Pass include_hidden=True to look.

Sandbox.local(".") is not a security boundary. It confines Lovia's file APIs to a root and gates shell by policy, but approved commands run as the host user and writes modify real files. Future Docker / remote implementations can plug in by implementing the SandboxBackend Protocol.

---

Web UI

lovia.web ships a small FastAPI app + bundled vanilla-JS chat UI. It's the same wiring you'd build yourself, but pre-assembled so you can ship a demo in three lines:

from lovia import Agent
from lovia.web import serve

agent = Agent(name="assistant", instructions="…", model="openai:gpt-4o-mini")
serve(agent, db_path="lovia.db")   # http://127.0.0.1:8000

What you get out of the box:

• Sidebar of chats — every session lives in SQLite (db_path), so it survives restarts, can be renamed, deleted, switched.
• Auto-generated titles — after the first turn a tiny background call asks the same model for a 3-6 word headline.
• Streaming transcript with tool-call cards and approval prompts.
• Tool-call cards and approval prompts — UI primitives for real agent work, without coupling the core framework to a particular sandbox backend.

---

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 built-in tools together
  21_dx.py                     Annotated schemas, run_sync
  22_sandbox.py                Local Sandbox + code tools
  23_sandbox_agent.py          Coding agent with Sandbox.local
  tools/                       One focused demo per tool
  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