zhivex-ai-sdk 0.5.0

Agent-first Python SDK for building orchestrated AI systems across multiple providers

Zhivex AI SDK for Python

Zhivex AI SDK for Python is an async-first, agent-first SDK for building orchestrated AI systems across multiple providers.

Production maturity plan: see MATURITY_PLAN.md.

It brings the same design goals as the TypeScript Zhivex AI SDK into Python:

• one agent runtime with executable handoffs, shared sessions, memory summaries, approval policies, tool registries, and traces
• one normalized foundation layer for text generation, streaming, tools, structured output, embeddings, audio, grounded text, and routing
• thin provider adapters instead of provider-specific app logic everywhere
• portable application code across the portable tier, with explicit native escape hatches for provider-specific features

Why Zhivex AI SDK

Modern AI apps usually start simple and then drift into provider lock-in:

• OpenAI requests look one way
• Anthropic uses a different message format
• Gemini and Vertex differ again
• local and routed setups add yet another layer

Zhivex AI SDK gives you a common agent runtime and model contract so your application code can stay stable while providers change underneath.

Stability And Support

Zhivex AI SDK is now published as a beta package with a documented stable surface, explicit stability levels, and versioning rules for downstream integrators.

Production integrations should import supported APIs from zhivex_ai, prefer the documented stable surface and tier-1 providers, and isolate beta or experimental areas behind an application-owned service layer.

See STABILITY.md, VERSIONING.md, SUPPORT.md, and CHANGELOG.md for the contract that governs public API expectations, support scope, and release communication.

Highlights

• Agent runtime with executable handoffs, input/output guardrails, registry-based orchestration, transcript + summary memory, permission-aware tool execution, and traces
• AgentRuntime, AgentRegistry, and ToolRegistry as the primary orchestration layer
• Unified generate_text() and stream_text() foundation primitives
• Structured output with generate_object() and stream_object()
• Grounded text for providers with web search support
• Audio transcription and speech generation where the provider supports it
• Experimental realtime/live voice sessions plus stream_live_agent() for voice-first agents
• Embeddings support where the provider supports it
• Provider factories for hosted and local models
• Gateway routing with fallback support
• HTTP/UI helpers for SSE, plain text streams, and UI message transport
• Middleware for telemetry, caching, and circuit breaking
• Model catalog helpers for cost and recommendation metadata

Supported Providers

Provider factories now return a ProviderBundle with two explicit namespaces:

• provider("model-id") or provider.portable.language_model("model-id") for the strict portable contract
• provider.native.language_model("model-id") for provider-specific options, hosted tools, and escape hatches

Portable construction fails fast for providers that do not satisfy the portable contract. Those providers remain available through provider.native.

For production API work, the current tier-1 provider story for the stable surface is OpenAI, Anthropic, Azure OpenAI, Gemini, and Vertex. Other providers remain available, but their supported feature set should be evaluated against the matrix below and the stability definitions in STABILITY.md.

This matrix is generated from runtime support metadata via scripts/generate_support_matrix.py.

Tier-1 Providers

These providers back the stable surface for production API work in this SDK today:

• openai
• anthropic
• azure-openai
• gemini
• vertex

Portable Support

Provider	Tier	Portable Badge	Text	Streaming	Structured Output	Tools	Embeddings	Grounding	Retrieval	Transcription	Speech
anthropic	portable	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes	No	No
azure-openai	portable	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
bedrock	native-only	No	Yes	No	No	No	No	No	Yes	No	No
gemini	portable	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
kimi	compatibility	No	Yes	Yes	Yes	Yes	Yes	No	Yes	No	No
ollama	compatibility	No	Yes	Yes	Yes	Yes	Yes	No	Yes	No	No
openai	portable	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
openrouter	native-only	No	Yes	Yes	Yes	Yes	Yes	No	Yes	No	Yes
qwen	compatibility	No	Yes	Yes	Yes	No	Yes	No	Yes	No	Yes
vertex	portable	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Native Extras

Provider	Files	File Search	Images	Uploads	Moderations	Batches	Containers	Skills	Realtime	Responses	Conversations
anthropic	Yes	No	No	No	No	No	No	No	No	No	No
azure-openai	No	No	No	No	No	No	No	No	Yes	No	No
bedrock	No	No	No	No	No	No	No	No	Yes	No	No
gemini	Yes	Yes	No	No	No	No	No	No	Yes	No	No
kimi	No	No	No	No	No	No	No	No	No	No	No
ollama	No	No	No	No	No	No	No	No	No	No	No
openai	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
openrouter	No	No	No	No	No	No	No	No	No	No	No
qwen	No	No	No	No	No	No	No	No	No	No	No
vertex	No	No	No	No	No	No	No	No	Yes	No	No

Tool Calling Notes

Tool support now follows the same rule everywhere:

• The portable layer accepts only the SDK-owned contract. It rejects provider_options and any provider-managed tool payloads.
• Provider-specific hosted tools, raw Responses settings, Gemini built-in tools, and similar knobs must go through provider.native.*.
• Tier-1 support means the provider participates in the stable surface story, production API examples, and contract-level support assertions in this repository.
• Anthropic is part of the tier-1 text-generation story in this SDK. Extended thinking still restricts tool_choice to auto or none, and embeddings, transcription, and speech remain unavailable on the Anthropic provider path here.
• OpenAI, Anthropic, and Azure OpenAI currently cover the broadest production text-generation API paths in this SDK.
• Gemini and Vertex are portable for the core contract, but Gemini built-in tools such as google_search, google_maps, url_context, code_execution, file_search, and computer_use are native-only entrypoints.
• Bedrock and OpenRouter remain available, but only through provider.native until they satisfy the portable contract end to end.
• Qwen, Kimi, and Ollama are compatibility providers in this refactor. They remain usable through native adapters, but they do not receive the portable badge.
• Ollama defaults to base_url="http://localhost:11434/v1" and api_key="ollama" for local compatibility setups. Use provider.native.* for Ollama examples and override OLLAMA_API_KEY only when you front it with a proxy or remote gateway that requires auth.

Installation

pip install zhivex-ai-sdk

Optional extras:

pip install "zhivex-ai-sdk[postgres]"
pip install "zhivex-ai-sdk[mcp]"
pip install "zhivex-ai-sdk[api]"
pip install "zhivex-ai-sdk[otel]"

Quick Start

import asyncio

from zhivex_ai import Agent, create_in_memory_agent_memory_store, create_openai, run_agent


async def main() -> None:
    openai = create_openai()
    agent = Agent(
        name="assistant",
        instructions="Be concise and remember prior turns.",
        model=openai("gpt-5.4-mini"),
        memory=create_in_memory_agent_memory_store(),
    )

    first = await run_agent(agent=agent, prompt="Remember that project Apollo is important.")
    second = await run_agent(agent=agent, session=first.session, prompt="What project did I mention?")

    print(second.text)


asyncio.run(main())

Foundation APIs

Text generation

import asyncio

from zhivex_ai import create_openai, generate_text


async def main() -> None:
    openai = create_openai()

    result = await generate_text(
        model=openai("gpt-5.4-mini"),
        system="Be concise and technical.",
        prompt="What is a provider adapter?",
    )

    print(result.text)


asyncio.run(main())

Structured output

import asyncio

from pydantic import BaseModel

from zhivex_ai import create_openai, generate_object


class Recipe(BaseModel):
    title: str
    difficulty: str


async def main() -> None:
    openai = create_openai()

    result = await generate_object(
        model=openai("gpt-5.4-mini"),
        prompt="Return a compact JSON recipe summary.",
        schema=Recipe,
    )

    print(result.object.model_dump())


asyncio.run(main())

Streaming

import asyncio

from zhivex_ai import create_openai, stream_text


async def main() -> None:
    openai = create_openai()
    result = stream_text(
        model=openai("gpt-5.4-mini"),
        prompt="Reply in two short sentences.",
    )

    async for chunk in result.text_stream():
        print(chunk, end="")

    final = await result.collect()
    print("\n", final.finish_reason)


asyncio.run(main())

Structured output streaming

import asyncio

from pydantic import BaseModel

from zhivex_ai import create_openai, stream_object


class Recipe(BaseModel):
    title: str
    servings: int


async def main() -> None:
    openai = create_openai()
    result = stream_object(
        model=openai("gpt-5.4-mini"),
        prompt="Return a compact JSON recipe.",
        schema=Recipe,
    )

    async for partial in result.partial_object_stream():
        print(partial)

    final = await result.collect()
    print(final.object.model_dump())


asyncio.run(main())

Grounded text

import asyncio

from zhivex_ai import create_openai, generate_grounded_text


async def main() -> None:
    openai = create_openai()

    result = await generate_grounded_text(
        model=openai.grounded_language_model("gpt-5.4-mini"),
        prompt="Find one recent fact about AI infrastructure.",
    )

    print(result.text)
    for source in result.sources:
        print(source.title, source.url)


asyncio.run(main())

Anthropic grounding is also available on the portable tier:

import asyncio

from zhivex_ai import create_anthropic, generate_grounded_text


async def main() -> None:
    anthropic = create_anthropic()

    result = await generate_grounded_text(
        model=anthropic.grounded_language_model("claude-sonnet-4-20250514"),
        prompt="Find one recent fact about AI infrastructure.",
    )

    print(result.text)
    for source in result.sources:
        print(source.title, source.url)


asyncio.run(main())

Gemini and Vertex grounding are also explicit and opt-in:

import asyncio

from zhivex_ai import create_gemini, generate_grounded_text


async def main() -> None:
    gemini = create_gemini()

    result = await generate_grounded_text(
        model=gemini.grounded_language_model("gemini-2.5-flash"),
        prompt="Find one recent fact about AI infrastructure.",
    )

    print(result.text)
    for source in result.sources:
        print(source.title, source.url)


asyncio.run(main())

Files and multimodal input

FilePart is no longer PDF-only. Gemini accepts inline or uploaded files for documents, audio, images, and video. Vertex accepts inline files plus URI-based file references such as gs://....

Inline document input:

import asyncio

from zhivex_ai import FilePart, ModelMessage, TextPart, create_openai, generate_text


async def main() -> None:
    openai = create_openai()
    result = await generate_text(
        model=openai("gpt-5.4-mini"),
        messages=[
            ModelMessage(
                role="user",
                parts=[
                    FilePart(
                        data="JVBERi0xLjQK",
                        media_type="application/pdf",
                        filename="statement.pdf",
                    ),
                    TextPart(text="Summarize this PDF in three bullets."),
                ],
            )
        ],
    )

    print(result.text)


asyncio.run(main())

Reusing a previously uploaded Anthropic file through the native file flow:

import asyncio

from zhivex_ai import FilePart, ModelMessage, create_anthropic, generate_text


async def main() -> None:
    anthropic = create_anthropic()
    result = await generate_text(
        model=anthropic.native.language_model("claude-sonnet-4-20250514"),
        messages=[
            ModelMessage(
                role="user",
                parts=[FilePart(file_id="file_123"), FilePart(file_id="file_456")],
            )
        ],
    )

    print(result.text)


asyncio.run(main())

Using the Gemini Files API first, then passing the returned reference:

import asyncio

from zhivex_ai import FilePart, ModelMessage, TextPart, create_gemini, generate_text


async def main() -> None:
    gemini = create_gemini()
    uploaded = await gemini.files().upload(
        data=b"%PDF-1.4...",
        filename="statement.pdf",
    )

    result = await generate_text(
        model=gemini("gemini-2.5-flash"),
        messages=[
            ModelMessage(
                role="user",
                parts=[
                    FilePart(file_uri=uploaded.file_uri),
                    TextPart(text="Extract the key numbers from this statement."),
                ],
            )
        ],
    )

    print(result.text)


asyncio.run(main())

Counting tokens before sending a request:

import asyncio

from zhivex_ai import create_gemini


async def main() -> None:
    gemini = create_gemini()
    counts = await gemini.tokens().count(
        model_id="gemini-2.5-flash",
        prompt="Summarize this in one line.",
    )

    print(counts.total_tokens)


asyncio.run(main())

Managing Gemini File Search stores:

import asyncio

from zhivex_ai import create_gemini


async def main() -> None:
    gemini = create_gemini()
    store = await gemini.file_search_stores().create(display_name="Docs")
    operation = await gemini.file_search_stores().upload(
        file_search_store_name=store.name,
        data=b"%PDF-1.4...",
        filename="manual.pdf",
        media_type="application/pdf",
    )

    await gemini.file_search_stores().wait_operation(operation.name)


asyncio.run(main())

Managing OpenAI Vector Stores / File Search stores:

import asyncio

from zhivex_ai import create_openai


async def main() -> None:
    openai = create_openai()
    store = await openai.file_search_stores().create(display_name="Docs")
    operation = await openai.file_search_stores().upload(
        file_search_store_name=store.name,
        data=b"%PDF-1.4...",
        filename="manual.pdf",
        media_type="application/pdf",
        custom_metadata=[{"key": "lang", "value": "en"}],
    )

    await openai.file_search_stores().wait_operation(operation.name)


asyncio.run(main())

Using OpenAI uploads and images:

import asyncio

from zhivex_ai import create_openai


async def main() -> None:
    openai = create_openai()
    file = await openai.uploads().upload_bytes(
        data=b'{"messages":[{"role":"user","content":"hello"}]}\n',
        filename="batch.jsonl",
        mime_type="application/jsonl",
        purpose="batch",
    )
    image = await openai.images().generate(prompt="A paper sketch of a transit map", model="gpt-image-1")

    print(file.id)
    print(image.images[0].b64_json is not None)


asyncio.run(main())

Passing inline audio to Gemini text generation:

import asyncio

from zhivex_ai import FilePart, ModelMessage, TextPart, create_gemini, generate_text


async def main() -> None:
    gemini = create_gemini()
    result = await generate_text(
        model=gemini("gemini-2.5-flash"),
        messages=[
            ModelMessage(
                role="user",
                parts=[
                    FilePart(
                        data="SUQzBAAAAAAA...",
                        media_type="audio/mpeg",
                        filename="call.mp3",
                    ),
                    TextPart(text="Summarize the call in five bullets."),
                ],
            )
        ],
    )

    print(result.text)


asyncio.run(main())

Passing a Vertex-hosted file reference:

import asyncio

from zhivex_ai import FilePart, ModelMessage, TextPart, create_vertex, generate_text


async def main() -> None:
    vertex = create_vertex()
    result = await generate_text(
        model=vertex("gemini-2.5-flash"),
        messages=[
            ModelMessage(
                role="user",
                parts=[
                    FilePart(file_uri="gs://my-bucket/meeting.mp4", media_type="video/mp4"),
                    TextPart(text="Extract the main decisions from this meeting."),
                ],
            )
        ],
    )

    print(result.text)


asyncio.run(main())

Audio

import asyncio
from pathlib import Path

from zhivex_ai import AudioInput, create_openai, transcribe_audio


async def main() -> None:
    openai = create_openai()
    audio = AudioInput(
        data=Path("sample.wav").read_bytes(),
        media_type="audio/wav",
        filename="sample.wav",
    )

    result = await transcribe_audio(
        model=openai.transcription_model("gpt-4o-transcribe"),
        audio=audio,
    )

    print(result.text)


asyncio.run(main())

import asyncio
import wave
from pathlib import Path

from zhivex_ai import create_gemini, generate_speech


def save_wave(path: Path, pcm: bytes, *, channels: int = 1, rate: int = 24_000, sample_width: int = 2) -> None:
    with wave.open(str(path), "wb") as wav_file:
        wav_file.setnchannels(channels)
        wav_file.setsampwidth(sample_width)
        wav_file.setframerate(rate)
        wav_file.writeframes(pcm)


async def main() -> None:
    gemini = create_gemini()
    result = await generate_speech(
        model=gemini.speech_model("gemini-2.5-flash-preview-tts"),
        input="Zhivex AI SDK makes provider switching easier.",
        voice="Kore",
    )
    save_wave(Path("speech.wav"), result.audio)


asyncio.run(main())

Agent runtime

import asyncio
from pydantic import BaseModel, ConfigDict

from zhivex_ai import (
    Agent,
    create_openai,
    handoff_to,
    run_agent,
    tool,
)


class DelegateInput(BaseModel):
    model_config = ConfigDict(extra="forbid")
    task: str


async def main() -> None:
    openai = create_openai()
    researcher = Agent(
        name="researcher",
        instructions="Answer delegated research questions directly.",
        model=openai("gpt-5.4-mini"),
    )
    triage = Agent(
        name="triage",
        instructions="Delegate research work to the researcher agent.",
        model=openai("gpt-5.4-mini"),
        tools={
            "delegate": tool(
                name="delegate",
                schema=DelegateInput,
                execute=lambda input: handoff_to("researcher", input=input.task),
            )
        },
        subagents={"researcher": researcher},
    )

    result = await run_agent(agent=triage, prompt="Research the Apollo migration status.")

    print(result.text)
    print(result.orchestration_path)


asyncio.run(main())

If you want Gemini research with provider-native web search in an agent run, put the agent on a native Gemini model first:

from zhivex_ai import create_gemini

gemini = create_gemini()
triage.model = gemini.native.language_model("gemini-3-flash-preview")
result = await run_agent(
    agent=triage,
    prompt="Research the Apollo migration status.",
    provider_options={"google_search": True},
)

Built-in Gemini tools can also be configured directly:

result = await generate_text(
    model=gemini.native.language_model("gemini-3-flash-preview"),
    prompt="Research this page and show your work.",
    provider_options={
        "google_search": {"excludeDomains": ["example.com"]},
        "url_context": {},
        "code_execution": True,
    },
)

Gateway fallback routing

import asyncio

from zhivex_ai import (
    GatewayConfig,
    GatewayMessage,
    GatewayModelTarget,
    create_anthropic,
    create_gateway,
    create_openai,
)


async def main() -> None:
    gateway = create_gateway(
        GatewayConfig(
            adapters={
                "openai": create_openai(),
                "anthropic": create_anthropic(),
            }
        )
    )

    result = await gateway.generate(
        messages=[GatewayMessage(role="user", content="Say hello in one sentence.")],
        primary=GatewayModelTarget(provider="openai", model_id="gpt-5.4-mini"),
        fallbacks=[GatewayModelTarget(provider="anthropic", model_id="claude-sonnet-4-20250514")],
    )

    print(result.text)
    print(result.provider_used, result.model_used)


asyncio.run(main())

Provider Factories

The package currently exposes:

• create_openai()
• create_azure_openai()
• create_anthropic()
• create_gemini()
• create_vertex()
• create_bedrock()
• create_openrouter()
• create_qwen()
• create_kimi()
• create_ollama()

Every factory now returns a ProviderBundle.

Portable usage:

• provider("model-id")
• provider.language_model("model-id")
• provider.embedding_model(...)
• provider.transcription_model(...)
• provider.speech_model(...)
• provider.grounded_language_model(...)

Native usage:

• provider.native.language_model("model-id")
• provider.native.embedding_model(...)
• provider.native.transcription_model(...)
• provider.native.speech_model(...)
• provider.native.grounded_language_model(...)
• provider.native.realtime_model(...)

Portable model construction fails fast when the provider does not hold the portable badge. That is intentional: the default path is the portability promise, and provider.native is the explicit escape hatch.

OpenAI-compatible providers such as OpenRouter, Qwen, Kimi, and Ollama still reuse the same normalized adapter model internally, but they no longer imply portable parity.

Local Ollama usage follows the same native escape hatch:

import asyncio

from zhivex_ai import create_ollama, generate_text


async def main() -> None:
    provider = create_ollama(base_url="http://localhost:11434/v1")
    result = await generate_text(
        model=provider.native.language_model("llama3.2"),
        prompt="Explain Zhivex AI SDK in one sentence.",
    )
    print(result.text)


asyncio.run(main())

For local Ollama installs, api_key="ollama" is the default compatibility token and is usually sufficient. Set OLLAMA_API_KEY only when your Ollama endpoint is behind a proxy or hosted gateway that expects authentication.

Adapters may also expose optional factories such as:

• provider.native.realtime_model("gpt-realtime")
• provider.files()
• provider.images()
• provider.uploads()
• provider.tokens()
• provider.file_search_stores()
• provider.responses()
• provider.conversations()

OpenAI providers may additionally expose low-level lifecycle clients:

• provider.responses() for raw Responses API operations such as create, create_background, retrieve, wait, cancel, compact, and list_input_items
• provider.conversations() for raw Conversations API operations such as create, retrieve, update, create_item, and list_items
• provider.file_search_stores() for Vector Store / File Search lifecycle operations such as create, update, search, upload, list_documents, get_operation, and wait_operation
• provider.images() for standalone image generation and edit flows
• provider.uploads() for multi-part uploads that complete into reusable OpenAI Files
• provider.moderations() for raw Moderations API requests
• provider.batches() for raw Batch API lifecycle operations such as create, retrieve, list, and cancel
• provider.containers() for container lifecycle and container-file management
• provider.skills() for skill lifecycle and skill-version management

OpenAI helper builders cover the modern hosted-tool surface, including file search filters, code-interpreter containers, shell environments, MCP servers, inline skills, skill references, custom tools, namespaces, and tool search.

Capability Matrix

The canonical matrix now lives in runtime metadata:

• provider.portable_support
• provider.native_support
• provider.tier

To regenerate the markdown tables used above:

uv run python scripts/generate_support_matrix.py

Notes:

• provider("model-id") is shorthand for the portable namespace.
• provider.native.* is the only place where provider-specific request shapes belong.
• Some providers support a capability only for specific model IDs even when the adapter exposes the factory.
• create_gemini().files() exposes the Gemini Files API. create_vertex() does not expose a hosted files client; on Vertex, pass FilePart(file_uri="gs://...") or inline media instead.
• create_anthropic().tokens() exposes Anthropic message token counting.
• anthropic_web_search_tool(...), anthropic_mcp_server(...), and anthropic_code_execution_tool(...) build the current hosted-tool payloads for Claude-native runs.
• create_gemini().tokens() and create_vertex().tokens() expose token counting clients.
• create_gemini().file_search_stores() exposes Gemini File Search store management.
• create_openai().file_search_stores() exposes OpenAI Vector Store / File Search management.
• create_vertex() now exports native grounding helpers such as vertex_google_search_tool(...), vertex_google_maps_tool(...), vertex_vertex_ai_search_tool(...), and vertex_external_search_tool(...).
• create_vertex().native.language_model(...) and create_vertex().native.grounded_language_model(...) also accept provider_options={"vertex_ai_search": {...}} and provider_options={"external_search": {...}}, which are normalized into the Vertex tool payloads automatically.
• create_openai().images() and create_openai().uploads() expose standalone OpenAI Images and Uploads APIs.
• create_openai().containers() and create_openai().skills() expose the raw OpenAI Containers and Skills APIs.
• create_vertex().realtime_model(...).create_browser_token() is intentionally unsupported. Vertex realtime sessions use server-side authentication instead of OpenAI/Gemini-style ephemeral browser tokens in this SDK.
• OpenAI and Azure OpenAI browser bootstrap now follows the official realtime/client_secrets flow, while Gemini browser tokens still come from v1alpha/authTokens.
• Realtime sessions emit realtime-response-complete when a model turn finishes and reserve realtime-end for actual session shutdown or transport closure.
• Gemini and Vertex speech generation return PCM audio in the current examples, so the demo writes a .wav container around the bytes.

Why not use provider SDKs directly?

Using provider SDKs directly is totally reasonable when:

• you only target one provider
• you are comfortable rewriting message, tool, and streaming logic per vendor
• you do not need fallback routing or a shared abstraction layer

Zhivex AI SDK is a better fit when:

• you want one contract across multiple model vendors
• you expect to switch providers over time
• you want tools, structured output, caching, telemetry, and routing to live above the provider layer
• you want application code that reads the same whether the model is OpenAI, Anthropic, Gemini, or local

Middleware

Zhivex AI SDK includes middleware helpers similar to the TypeScript SDK:

• wrap_language_model(...)
• create_telemetry_middleware(...)
• create_cached_generate_middleware(...)
• create_in_memory_generate_cache()
• create_file_generate_cache(...)
• create_circuit_breaker_middleware(...)

These let you keep cross-cutting concerns outside provider adapters and application prompts.

For production logging, request correlation, gateway attempt tracing, and OpenTelemetry guidance, see OBSERVABILITY.md.

UI And Transport

The Python SDK now includes helpers for UI and transport-oriented flows:

• to_ui_message(...), to_ui_messages(...)
• from_ui_message(...), from_ui_messages(...)
• serialize_ui_message(...), deserialize_ui_message(...)
• parse_ui_message_request(...)
• create_ui_message_json_response(...)
• create_ui_message_lines_response(...)
• to_sse_stream(...), to_sse_response(...)
• to_text_stream_response(...)
• to_ui_message_stream_response(...)

These are useful when wiring the SDK into web servers, SSE endpoints, or custom chat frontends.

For production-style FastAPI integration patterns, see PRODUCTION_APIS.md and the reference apps in examples/integrations/.

Agents

The Python SDK now exposes an agent-first runtime on top of the core model contract:

• Agent(...)
• AgentRuntime(...)
• AgentRegistry(...)
• ToolRegistry(...)
• AgentSession
• run_agent(...)
• resume_agent(...)
• stream_agent(...)
• create_in_memory_agent_memory_store()
• create_in_memory_checkpoint_store()
• create_sqlite_agent_memory_store(...)
• create_sqlite_checkpoint_store(...)
• create_postgres_agent_memory_store(...)
• create_postgres_checkpoint_store(...)
• create_otel_agent_observer()
• load_agent_session(...)
• ApprovalDecision, ToolApprovalRequest
• GuardrailResult, InputGuardrailRequest, OutputGuardrailRequest
• permission_allowlist_approval_policy(...)
• input and output guardrails on Agent(...)
• handoff_to(...)
• remote_tool(...)
• skill(...)
• load_skill(...)
• discover_skills(...)
• discover_mcp_tools(...)
• mcp_stdio_server(...)
• mcp_http_server(...)
• create_mcp_tool_registry(...)

This layer is intended for stateful, tool-using, multi-agent assistants where you want executable handoffs, shared sessions, transcript + summary memory, approval hooks, traces, durable Postgres-backed state, and MCP-backed tool registries without rewriting the lower-level loop yourself.

Agent skills are also available across the agent runtime. These are provider-agnostic workflow packs that inject task-specific instructions and optional tool dependencies before a run starts. They are distinct from the raw OpenAI Skills API:

• Agent(..., skills=...) activates provider-agnostic runtime skills
• skill(...), load_skill(...), and discover_skills(...) help define or load skills from SKILL.md
• set_agent_session_skills(...), get_agent_session_skills(...), and clear_agent_session_skills(...) manage sticky session skills explicitly
• provider.skills() remains the native OpenAI lifecycle client for hosted OpenAI skills
• activated skills stick to the agent session through session.metadata["sticky_skills"]
• the runtime emits AgentSkillActivatedEvent and AgentSkillSkippedEvent for observability

Runtime skills follow the Codex-style SKILL.md layout: frontmatter with name and description, instruction body, and optional agents/openai.yaml metadata for display text, implicit-invocation policy, and MCP tool dependencies.

The runtime also supports production-oriented policy metadata:

• priority to resolve competing implicit skills
• triggers and anti_triggers for deterministic activation rules
• allowed_providers and allowed_models to constrain where a skill can run
• persist_to_session to opt out of sticky reuse
• dependency_failure_mode set to "skip" or "fail"

import asyncio

from zhivex_ai import (
    Agent,
    clear_agent_session_skills,
    create_agent_session,
    create_openai,
    get_agent_session_skills,
    run_agent,
    set_agent_session_skills,
    skill,
)


async def main() -> None:
    openai = create_openai()
    release_notes = skill(
        name="release-notes",
        description="Use when a user asks for changelog summaries or release notes.",
        instructions="Write highlights, breaking changes, and migration notes when needed.",
    )
    agent = Agent(
        name="assistant",
        instructions="You are a careful SDK assistant.",
        model=openai("gpt-5.4-mini"),
        skills={"release-notes": release_notes},
    )
    session = create_agent_session()

    set_agent_session_skills(session, "release-notes")
    result = await run_agent(agent=agent, session=session, prompt="Summarize the latest SDK updates.")
    print(result.text)
    print(result.session.metadata["active_skills"])
    print(get_agent_session_skills(session))
    clear_agent_session_skills(session)


asyncio.run(main())

For new MCP integrations, prefer the higher-level helpers:

import asyncio

from zhivex_ai import Agent, create_mcp_tool_registry, create_openai, mcp_stdio_server, run_agent


async def main() -> None:
    async with await create_mcp_tool_registry(
        mcp_stdio_server(
            name="fs",
            command="npx",
            args=["-y", "@modelcontextprotocol/server-filesystem", "."],
        )
    ) as tools:
        openai = create_openai()
        agent = Agent(
            name="assistant",
            instructions="Use the filesystem MCP tools when needed.",
            model=openai("gpt-5.4-mini"),
            tools=tools,
        )

        result = await run_agent(agent=agent, prompt="List the Python files in the current directory.")
        print(result.text)


asyncio.run(main())

discover_mcp_tools(...) is still available when you want raw tool definitions or full control over prefixes and registry composition. ToolRegistry also supports async with so MCP-backed runtimes can be closed cleanly after use.

Examples

See examples/README.md for the full list. Highlights:

• Text: openai_text.py, stream_text.py, structured_output.py
• Local Ollama: ollama_text.py
• Agents: agent_basic.py, stream_agent.py, mcp_tools.py
• Agent skills: skills.py
• Realtime: openai_realtime.py, gemini_realtime.py, live_agent_realtime.py
• Audio: transcribe_audio.py, generate_speech.py
• Integrations: ui_messages.py, http_responses.py, gateway_fallback.py

For real provider validation, the repo also includes a live smoke runner:

export ZHIVEX_SMOKE_OPENAI_MODEL=your-openai-model
export ZHIVEX_SMOKE_GEMINI_MODEL=your-gemini-model
export ZHIVEX_SMOKE_ANTHROPIC_MODEL=your-anthropic-model
export ZHIVEX_SMOKE_VERTEX_MODEL=your-vertex-model
export ZHIVEX_SMOKE_OLLAMA_MODEL=your-local-ollama-model
make smoke

It only runs providers that have the required credentials and model IDs configured, and you can scope it with ZHIVEX_SMOKE_PROVIDERS=openai,gemini,ollama. Ollama smoke runs default to http://localhost:11434/v1 and can be redirected with ZHIVEX_SMOKE_OLLAMA_BASE_URL.

If realtime examples fail on macOS with ssl.SSLCertVerificationError: CERTIFICATE_VERIFY_FAILED, the issue is usually the local Python certificate bundle rather than the SDK. Two practical fixes are:

SSL_CERT_FILE="$(".venv/bin/python" -c 'import certifi; print(certifi.where())')" \
GOOGLE_API_KEY=... \
.venv/bin/python examples/realtime/gemini_realtime.py

or, for a permanent fix with the official python.org installer:

"/Applications/Python 3.14/Install Certificates.command"

License

MIT. See LICENSE.