GitHub - polarsen-io/padwan-ai: Minimal, provider-agnostic Python client for large language models, built on niquests.

Lightweight async client for OpenAI, Gemini, Mistral, Grok, Anthropic, TypeSafe (JEV, experimental), and any OpenAI-compatible API.
One HTTP dependency (niquests, plus its qh3/urllib3-future stack), TypedDict everywhere, HTTP/2 and HTTP/3 out of the box.

Documentation · PyPI · Changelog

Install

Extras: [realtime] for voice sessions, [otel] for OpenTelemetry, [langfuse] for the Langfuse adapter.

Quickstart

from padwan_ai import LLMClient

async with LLMClient(model="gpt-5.5") as client:
    response, usage = await client.complete_chat(
        [{"role": "user", "content": "Hello!"}]
    )
    print(response["content"])

The provider is picked from the model name; only the matching *_API_KEY env var is needed.

Streaming

from padwan_ai import ConversationState, LLMClient

state = ConversationState(system="You are a concise assistant.")
state.add_user_message("What's Python?")

async with LLMClient(model="gemini-3.5-flash") as client:
    chunks: list[str] = []
    async for text in client.stream_chat(state.messages):
        print(text, end="", flush=True)
        chunks.append(text)
    state.add_assistant_message("".join(chunks))

Agent with tools

AgentSession runs the tool loop: call the model, dispatch tool calls, feed results back, repeat until a final answer. Tools come from typed Python functions or from MCP servers.

from padwan_ai import AgentSession, LLMClient, McpStdio
from padwan_ai.tools import tool


@tool
async def add(a: int, b: int) -> int:
    """Add two integers."""
    return a + b


async with AgentSession(
    client=LLMClient(model="claude-sonnet-5"),
    mcp_tools=[add, McpStdio(command="uvx", args=["my-mcp-server"])],
    system="Use tools when helpful.",
) as session:
    print(await session.send("What is 2 + 3, and what's the weather in Paris?"))

@tool needs msgspec or pydantic installed to build the parameter schema.

Typed final answers (AgentOutput), approval hooks, parallel tool execution and snapshot persistence are covered in the agents guide.

One-shot from the shell

uvx padwan-ai "Hello!" -m gpt-5.4-mini

For an interactive chat TUI use padwan-cli.

Providers

Provider	Chat + streaming	Batch	Realtime voice	Transcription	TTS	Embeddings
OpenAI	✅	✅	✅	❌	❌	✅
Gemini	✅	✅	✅	➖	✅	✅
Anthropic	✅	❌	➖	➖	➖	➖
Mistral	✅	❌	❌	✅	❌	✅
Grok	✅	✅	✅	➖	❌	➖
Voyage AI	➖	➖	➖	➖	➖	✅
OpenAI-compatible (`base_url=`)	✅	depends on the server	depends on the server	depends on the server	❌	depends on the server

❌ = the provider offers it, not implemented yet.
➖ = the provider has no such API.

Thinking tokens stream separately through an on_thought callback on every client that exposes them. Per-provider details, multimodal input (images, audio, files) and embeddings: docs/clients, docs/multimodal.md.

TypeSafe (JEV, experimental) structured evaluations (Noul, Choice, Score questions) use the standalone TypeSafeClient.

MCP: streamable-HTTP and stdio transports, usable standalone or inside an agent. docs/mcp.md
Realtime voice: RealtimeClient speech-to-speech over WebSocket for OpenAI, Gemini Live and Grok Voice. docs/clients/openai.md
Text-to-speech: GeminiClient.generate_speech for Gemini TTS models, single voice or scripted two-speaker dialogue. docs/clients/gemini.md
Observability: opt-in OpenTelemetry GenAI spans and metrics with otel.instrument(), or a one-call Langfuse adapter. Ships a Grafana dashboard. docs/observability.md
Gateway mode: route every model through one OpenAI-compatible endpoint with PADWAN_BASE_URL and PADWAN_API_KEY. docs/clients/openai-compatible.md
Testing agents: padwan_ai.testing.ScriptedClient replays scripted responses, no API key needed. docs/agents.md

Development

uv sync --all-extras --all-groups
just ci          # ruff + pyright + pytest
just e2e         # live provider tests, keys from .env (see env.template)
just e2e --full  # same, plus a local vLLM server (Docker + NVIDIA GPU)
just docs        # serve the docs site locally