mantyx-sdk 0.1.1

Official Python SDK for the MANTYX agent runtime. Define ephemeral agents, mix server-side MANTYX tools with locally-executed tools, run them remotely.

mantyx-sdk

The official Python SDK for the MANTYX agent runtime. Define ephemeral agents that mix server-side MANTYX tools with locally-executed tools, run them remotely, and stream events back into your process.

• LLM loop runs on MANTYX (BYOK or platform-hosted models).
• Server-side tools (mantyx, mantyx_plugin) execute inside MANTYX.
• Local tools execute inside your Python process; the SDK shuttles inputs and outputs over an SSE stream + a tool-result POST.
• Sync and async clients (MantyxClient, AsyncMantyxClient), both backed by httpx.
• One-shot runs and multi-turn sessions, both with persisted observability.
• Authenticated with a single workspace API key.

For background, see the agent-runs protocol spec (a copy ships with the package).

Install

pip install mantyx-sdk
# or: uv add mantyx-sdk
# or: poetry add mantyx-sdk

Requires Python 3.9+ and runs on macOS, Linux, and Windows. The runtime dependencies are httpx and pydantic v2.

Quickstart

import os
from pathlib import Path

from pydantic import BaseModel
from mantyx import MantyxClient, define_local_tool, mantyx_tool


class ReadFileArgs(BaseModel):
    path: str


client = MantyxClient(
    api_key=os.environ["MANTYX_API_KEY"],
    workspace_slug=os.environ["MANTYX_WORKSPACE_SLUG"],
    # base_url="https://api.mantyx.com",  # override for self-hosted
)

result = client.run_agent(
    system_prompt="You are a helpful assistant.",
    prompt="Read /etc/hostname and summarise what it says.",
    tools=[
        # Local tool — defined and executed in this process.
        define_local_tool(
            name="read_file",
            description="Read a file from the local filesystem.",
            parameters=ReadFileArgs,
            execute=lambda args: Path(args.path).read_text(),
        ),
        # Reference to an existing MANTYX workspace tool.
        mantyx_tool("tool_cm6abc123"),
    ],
)

print(result.text)

The SDK opens an SSE stream to MANTYX, listens for local_tool_call events, runs the matching local handler, and POSTs the result back. The server keeps running the agent loop until it produces a final reply.

Async client

import asyncio

from mantyx import AsyncMantyxClient


async def main() -> None:
    async with AsyncMantyxClient(
        api_key="...",
        workspace_slug="acme-corp",
    ) as client:
        result = await client.run_agent(
            system_prompt="You are a helpful assistant.",
            prompt="Hi!",
        )
        print(result.text)


asyncio.run(main())

AsyncMantyxClient exposes the same API as MantyxClient with async/await semantics. Local tool handlers may be sync or async — the SDK awaits them as needed.

Triggering a persisted MANTYX agent

Pass agent_id to run an agent that already exists in your workspace. The server hydrates the agent's system prompt, model, and server-side tools (memory, skills, plugin tools, …) from the Agent row at run time. Anything you pass in tools is merged on top — typically local tools you want the agent to be able to call back into for this specific run.

from pathlib import Path

from pydantic import BaseModel
from mantyx import MantyxClient, define_local_tool


class ReadFileArgs(BaseModel):
    path: str


client = MantyxClient(api_key="...", workspace_slug="acme")

result = client.run_agent(
    agent_id="agent_cm6abc123",  # workspace agent id
    prompt="Pull the latest deploy logs and summarise them.",
    tools=[
        define_local_tool(
            name="read_local_file",
            parameters=ReadFileArgs,
            execute=lambda args: Path(args.path).read_text(),
        ),
    ],
)
print(result.text)

Notes:

• system_prompt becomes optional when agent_id is set; if both are sent, the agent's stored prompt wins.
• model_id is also optional: omit it to use the agent's configured LLM provider, or pass it to override the model for this run.
• The API key must be authorized for the agent (an empty agentIds allowlist on the key counts as "all agents in the workspace"). Otherwise the call returns 403.

The same agent_id field works on client.create_session(...) for multi-turn conversations against a persisted agent.

Picking a model

catalog = client.list_models()
print("\n".join(f"{m.id}\t{m.label}" for m in catalog.models))

client.run_agent(
    system_prompt="...",
    prompt="Hi!",
    model_id="platform:cm6abc123",  # or "provider:<id>", or "<vendorModelId>"
)

model_id accepts:

• platform:<offeringId> — a platform-hosted model offering.
• provider:<llmProviderId> — your own BYOK provider's default model.
• provider:<llmProviderId>:<vendorModelId> — your provider, override model.
• <vendorModelId> — bare vendor id; only resolves when one workspace provider can run it.
• omitted — workspace default.

Streaming tokens

# Iterator
for event in client.stream_agent(system_prompt="...", prompt="Tell me a story."):
    if event.type == "assistant_delta":
        print(event.text, end="", flush=True)
    elif event.type == "result":
        print()

# Or use the on_assistant_delta callback on run_agent:
client.run_agent(
    system_prompt="...",
    prompt="...",
    on_assistant_delta=lambda d: print(d, end="", flush=True),
)

# Async equivalents
async for event in async_client.stream_agent(system_prompt="...", prompt="..."):
    ...

Multi-turn sessions

Sessions own the agent spec (system prompt, model, tool defs) and the full message history. Each send is a run scoped to the session.

from datetime import date

from pydantic import BaseModel
from mantyx import MantyxClient, define_local_tool


class TodayArgs(BaseModel):
    pass


client = MantyxClient(api_key="...", workspace_slug="acme")

session = client.create_session(
    system_prompt="You are a friendly REPL.",
    tools=[
        define_local_tool(
            name="today",
            description="Get today's date as ISO 8601.",
            parameters=TodayArgs,
            execute=lambda _args: date.today().isoformat(),
        ),
    ],
)

r1 = session.send("What day is it?")
print(r1.text)

r2 = session.send("And what about tomorrow?")
print(r2.text)

session.end()

Resuming a session from a different process re-binds your local tool handlers; pass them in via resume_session:

session = client.resume_session(
    session_id,
    tools=[
        define_local_tool(name="today", parameters=TodayArgs, execute=...),
    ],
)

Tagging runs and sessions with metadata

Attach a flat dict[str, str] to runs and sessions so your team can filter the dashboard by it:

client.run_agent(
    system_prompt="...",
    prompt="...",
    metadata={"customer": "acme", "env": "prod", "workflow": "support_triage"},
)

session = client.create_session(
    system_prompt="...",
    metadata={"customer": "acme", "env": "prod"},
)

# Per-message override — merged on top of the session's metadata
# (run-level keys win)
session.send("trace this turn", metadata={"trace_id": "trace_abc"})

Limits enforced server-side: max 16 entries; keys match [A-Za-z0-9._-]{1,64}; values are strings ≤ 256 chars; serialised JSON ≤ 4 KB. Bigger payloads return 400 invalid_request.

API reference

MantyxClient(...) / AsyncMantyxClient(...)

class MantyxClient:
    def __init__(
        self,
        *,
        api_key: str,
        workspace_slug: str,
        base_url: str = "https://api.mantyx.com",
        timeout: float = 60.0,
        http_client: httpx.Client | None = None,
    ): ...

AsyncMantyxClient accepts an httpx.AsyncClient instead.

Methods

Method	Returns (sync)
list_models()	ModelCatalog
run_agent(...)	RunResult
stream_agent(...)	Iterator[RunEvent]
create_session(...)	AgentSession
resume_session(session_id, *, tools=None)	AgentSession
end_session(session_id)	None
cancel_run(run_id)	None

The async client returns awaitable / async-iterator equivalents (e.g. await async_client.run_agent(...), async for ev in async_client.stream_agent(...)).

Tools

Helper	Use case
define_local_tool(...)	Define a local tool with a Pydantic parameter schema and handler.
mantyx_tool(id)	Reference an existing MANTYX workspace tool by id.
mantyx_plugin_tool(name)	Reference an installed platform plugin tool by @plugin/tool name.

Errors

All raised errors extend MantyxError. Common subclasses:

• MantyxAuthError — 401/403 from the server (bad API key, wrong workspace).
• MantyxNetworkError — transport-layer failures.
• MantyxRunError — the agent loop terminated with an error.
• MantyxToolError — a local tool handler raised or timed out.

Examples

Self-contained example projects live under examples/:

• examples/oneshot-local-tool — minimal one-shot run with a local tool.
• examples/session-chat — interactive REPL on top of a session.
• examples/mixed-tools — combines local, MANTYX, and plugin tools.
• examples/streaming — token streaming to stdout.
• examples/list-models — model catalog + pick-and-run.
• examples/agent-by-id — trigger a persisted MANTYX agent by id.

Each example is its own project (pyproject.toml, README.md, main.py) so you can copy any one of them out of the repo and run it standalone.

Wire protocol

This SDK is a thin client over a stable HTTP/SSE protocol. The full specification ships with the package at docs/agent-runs-protocol.md. Anyone can implement a compatible client in another language.

Development

python -m venv .venv
. .venv/bin/activate
python -m pip install -e ".[dev]"

pytest -q          # unit + mock-server tests
ruff check .       # lint
ruff format .      # format
mypy src           # strict type-check

The SDK has no internal workspace:*-style dependencies. python -m build produces a self-contained dist/ ready for python -m twine upload (or PyPI Trusted Publishing — see CONTRIBUTING.md).

See CONTRIBUTING.md for the contribution flow and EXTRACT.md for the (very small) steps to lift this folder into its own public repository.

License

Apache-2.0