sagaflow 0.10.19

Temporal-backed workflow runtime for Claude Code skills

sagaflow

Durable execution for long-running agent workflows, on top of Temporal.

You write a Python workflow that calls models, runs tools, and writes artifacts. sagaflow runs each step as a Temporal activity, so when the worker dies — or a 40-minute fan-out crashes halfway — the next launch resumes from the last completed step instead of starting over. Results land in ~/.sagaflow/INBOX.md whether or not you're still attached to the session that started them.

Quick start

pip install sagaflow
temporal server start-dev &
export ANTHROPIC_API_KEY=sk-ant-...

sagaflow launch hello-world --name alice --await
# → hello, alice

Kill the terminal mid-run and re-launch the same workflow ID: it picks up from where the worker died.

What you get

• Resumes after crashes. Activity-level checkpointing via Temporal — workers, sessions, and laptops can all die without losing in-flight work.
• Decoupled from the caller. Fire-and-forget submissions land in an append-only inbox; a session-start hook surfaces unread results next time you open Claude Code.
• Provider-agnostic transport. Anthropic SDK by default; point ANTHROPIC_BASE_URL at Bedrock, a model gateway, or any compatible proxy.
• Auto-managed worker. First sagaflow launch spawns a worker daemon; sagaflow doctor reports health.

Install

pip install sagaflow

Requirements:

• Python 3.11+
• Temporal CLI running locally: brew install temporal && temporal server start-dev
• An Anthropic API key (or a compatible proxy via ANTHROPIC_BASE_URL)

Authoring a skill

A skill is a directory under ~/.claude/skills/<skill-name>/ containing three things:

1. workflow.py — a Temporal workflow class (the durable orchestration)
2. __init__.py — a register() function that wires the workflow into sagaflow
3. prompts/*.md — the system/user prompts the workflow's activities load

Here is the complete hello-world skill (the one sagaflow launch hello-world --name alice runs).

~/.claude/skills/hello-world/workflow.py — the durable workflow:

from dataclasses import dataclass
from datetime import timedelta

from temporalio import workflow

with workflow.unsafe.imports_passed_through():
    from sagaflow.durable.activities import (
        EmitFindingInput, SpawnSubagentInput, WriteArtifactInput,
    )
    from sagaflow.durable.retry_policies import HAIKU_POLICY


@dataclass(frozen=True)
class HelloWorldInput:
    run_id: str
    name: str
    inbox_path: str
    run_dir: str
    greeter_system_prompt: str
    greeter_user_prompt: str


@workflow.defn(name="HelloWorldWorkflow")
class HelloWorldWorkflow:
    @workflow.run
    async def run(self, inp: HelloWorldInput) -> str:
        prompt_path = f"{inp.run_dir}/prompt.txt"
        await workflow.execute_activity(
            "write_artifact",
            WriteArtifactInput(path=prompt_path, content=inp.greeter_user_prompt),
            start_to_close_timeout=timedelta(seconds=10),
            retry_policy=HAIKU_POLICY,
        )
        parsed = await workflow.execute_activity(
            "spawn_subagent",
            SpawnSubagentInput(
                role="greeter", tier_name="HAIKU",
                system_prompt=inp.greeter_system_prompt,
                user_prompt_path=prompt_path,
                max_tokens=64, tools_needed=False,
            ),
            start_to_close_timeout=timedelta(seconds=600),
            heartbeat_timeout=timedelta(seconds=120),
            retry_policy=HAIKU_POLICY,
        )
        greeting = parsed.get("GREETING", "hello")
        await workflow.execute_activity(
            "emit_finding",
            EmitFindingInput(
                inbox_path=inp.inbox_path, run_id=inp.run_id,
                skill="hello-world", status="DONE", summary=greeting,
                timestamp_iso=workflow.now().isoformat(timespec="seconds"),
            ),
            start_to_close_timeout=timedelta(seconds=10),
            retry_policy=HAIKU_POLICY,
        )
        return greeting

Each execute_activity call is a checkpoint. If the worker dies between them, replay resumes from the last completed one.

~/.claude/skills/hello-world/__init__.py — registration:

from typing import Any

from sagaflow.durable.activities import emit_finding, spawn_subagent, write_artifact
from sagaflow.prompts import load_prompt
from sagaflow.registry import SkillRegistry, SkillSpec

from skills.hello_world.workflow import HelloWorldInput, HelloWorldWorkflow


def _build_input(*, run_id, run_dir, inbox_path, cli_args: dict[str, Any]) -> HelloWorldInput:
    name = str(cli_args.get("name", "world"))
    return HelloWorldInput(
        run_id=run_id, name=name,
        inbox_path=inbox_path, run_dir=run_dir,
        greeter_system_prompt=load_prompt(__file__, "greeter.system"),
        greeter_user_prompt=load_prompt(__file__, "greeter.user", substitutions={"name": name}),
    )


def register(registry: SkillRegistry) -> None:
    registry.register(SkillSpec(
        name="hello-world",
        workflow_cls=HelloWorldWorkflow,
        activities=[write_artifact, emit_finding, spawn_subagent],
        build_input=_build_input,
    ))

register() is what the worker calls at startup to discover the skill. _build_input translates CLI args (--name alice) into the workflow's input dataclass and loads prompts from disk.

~/.claude/skills/hello-world/prompts/greeter.system.md:

You are a greeter. Output a greeting using the format
STRUCTURED_OUTPUT_START / GREETING|<text> / STRUCTURED_OUTPUT_END.
Do not include any other text.

~/.claude/skills/hello-world/prompts/greeter.user.md:

Greet $name

That's the whole skill. sagaflow launch hello-world --name alice finds the registration, builds the input, hands the workflow to Temporal, and the worker runs it durably.

CLI

sagaflow launch <name> --arg key=value [--await]   # submit a workflow
sagaflow inbox                                     # list unread results
sagaflow dismiss <run-id>                          # mark as read
sagaflow doctor                                    # diagnose temporal/worker/hook

How it works

sagaflow launch
   │
   ▼
preflight (install hook, spawn worker if missing)
   │
   ▼
Temporal (localhost:7233) ── workflow ID ── worker daemon
                                              │
                                              ▼
                                         activities:
                                          • model calls
                                          • file I/O
                                          • inbox emit
                                              │
                                              ▼
                              ~/.sagaflow/INBOX.md  +  desktop notify
                                              │
                                              ▼
                                  next session: SessionStart
                                  hook surfaces unread runs

If the worker crashes mid-run, the next sagaflow launch (or the next worker poll) resumes from the last completed activity. Activities that already succeeded don't re-execute.

Development

git clone https://github.com/npow/sagaflow
cd sagaflow
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

ruff check sagaflow tests
mypy sagaflow
pytest

# Opt-in end-to-end tests (require live Temporal + real Anthropic access)
SAGAFLOW_E2E=1 pytest

License

MIT