fsr-playbooks 0.4.7

FortiSOAR playbook authoring/compiler framework: YAML->FSR-JSON compiler, LLM co-authoring, and MCP server tools.

fsr_playbooks

A FortiSOAR playbook authoring & compiler framework:

• Compiler — turn readable YAML into FortiSOAR playbook JSON (parser → resolver → validator → emitter), round-trip lossless with structured diagnostics.
• Agent — LLM co-authoring helpers for building and triaging playbooks.
• MCP server — tools that expose the compiler, connector/Jinja reference, and playbook authoring to MCP-compatible clients.

Install

pip install fsr_playbooks            # base: compiler + agent
pip install "fsr_playbooks[llm]"     # + OpenAI / Anthropic providers
pip install "fsr_playbooks[mcp]"     # + MCP server (implies [llm])

Requires Python 3.9+ (the base compiler). The [mcp] extra requires 3.10+.

End-to-end: author → compile → deploy → run

1. Author a playbook in YAML

The compiler input is a readable collection of playbooks. A minimal one (start → set_variable → connector):

# hello_connector.yaml
collection: Compiler Demo
description: Smallest possible end-to-end — start, set a variable, call a connector.
visible: true

playbooks:
  - name: Hello Connector
    description: Demonstrates start -> set_variable -> connector flow.
    steps:
      - name: Start
        type: start
        next: Prepare inputs

      - name: Prepare inputs
        type: set_variable
        next: Get organization
        vars:
          target_org: "Fortinet"

      - name: Get organization
        type: connector
        arguments:
          connector: fortinet-fortisiem
          operation: get_org_name_by_org_id
          config: ""
          params:
            domain_id: "{{ vars.target_org }}"

2. Compile it to FortiSOAR playbook JSON

from pathlib import Path
from fsr_playbooks import compile_yaml

# The reference DB resolves connectors, operations, params, step types, and
# Jinja. It ships with the full framework repo (store/fsr_reference.db), NOT
# the PyPI wheel — point this at your copy.
REFERENCE_DB = Path("store/fsr_reference.db")

text = Path("hello_connector.yaml").read_text()
result = compile_yaml(text, REFERENCE_DB)

if not result.ok:
    for err in result.errors:                       # structured, never raises
        print(f"[{err.severity}] {err.code}: {err.message}  ({err.path})")
    raise SystemExit("compile failed")

collection = result.fsr_json["data"][0]             # the FortiSOAR collection entity

compile_yaml(text, db_path, lax_codes=None) -> CompileResult returns a result object with .ok, .fsr_json ({"data": [collection]}), .errors, .warnings, and .ir (the parsed tree) — it reports problems as structured CompileErrors rather than raising.

3. Deploy the collection to a FortiSOAR instance

Pushing and triggering talk to a live FortiSOAR REST API, so they use the pyfsr client (pip install pyfsr) — this package does the compiling, pyfsr does the transport.

from pyfsr import FortiSOAR

client = FortiSOAR(
    base_url="https://your-fortisoar-host",
    auth="<api-key>",                  # or ("username", "password")
    verify_ssl=True,
)

# Push the compiled collection. The compiler-assigned uuid is preserved, so
# re-deploys can target it with client.workflow_collections.update(uuid, ...)
# / .delete(uuid).
client.workflow_collections.create(
    name=collection["name"],
    description=collection.get("description", ""),
    visible=collection.get("visible", True),
    uuid=collection["uuid"],
    workflows=collection["workflows"],     # full workflow objects (steps + routes)
    record_tags=collection.get("recordTags"),
)

4. Trigger the playbook and wait for it

import time

wf_uuid = collection["workflows"][0]["uuid"]

run = client.playbooks.trigger(playbook="Hello Connector", inputs={})
task_id = run["task_id"]

# Poll the run records (shaped dicts: task_id, name, status, error_message, ...)
# until this run reaches a terminal state.
while True:
    match = next(
        (r for r in client.playbooks.runs(playbook_uuid=wf_uuid, limit=5)
         if r["task_id"] == task_id),
        None,
    )
    status = match["status"] if match else "pending"
    if status in ("finished", "failed", "terminated"):
        print("execution status:", status)
        break
    time.sleep(2)

What needs what: step 2 (compile) is pure fsr_playbooks + the reference DB. Steps 3–4 (deploy/run) additionally need pyfsr and a reachable FortiSOAR instance. The fsr_playbooks package never imports pyfsr itself.

Extras

Extra	Adds	Use for
(base)	pyyaml, ruamel.yaml, jinja2	YAML → FSR JSON compilation
[llm]	openai, anthropic	LLM-assisted authoring / triage
[mcp]	mcp (+ [llm])	running the MCP server tools

License

MIT — see LICENSE.

Links

• Source: https://github.com/ftnt-dspille/fsr-playbook-framework

Note: the MCP server's live-execution / recipe tools depend on the reference store and probe helpers that ship with the full framework repo, not this package alone. The compiler, agent, and the rest of the MCP surface work standalone.