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feat: add Tuner Integration to Dograh (#311)
* Add tuner integration * bump pipecat version * chore: update pipecat submodule to match upstream and use tuner-pipecat-sdk 0.2.0 Update pipecat submodule from 0.0.109.dev23 to 13e98d0d9 (the exact commit upstream dograh-hq/dograh uses after v1.30.1). This installs pipecat-ai as 1.1.0.post277 via setuptools_scm, satisfying tuner-pipecat-sdk 0.2.0's pipecat-ai>=1.0.0 requirement. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * wire tuner * feat: refactor integrations into self contained packages * chore: simplify ensure_public_access_token * fix: remove NodeSpec and make DTOs the source of truth * feat: send relevant signal to mcp using to_mcp_dict * fix: fix tests * cleanup: remove nango integrations * feat: add agents.md for integrations --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: Abhishek Kumar <abhishek@a6k.me>
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Mohamed-Mamdouh
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93 changed files with 3388 additions and 3414 deletions
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@ -14,7 +14,12 @@ import re
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import pytest
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from api.services.workflow.dto import NodeType, ReactFlowDTO
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from api.services.workflow.dto import (
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ReactFlowDTO,
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all_node_type_names,
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get_node_data_model,
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)
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from api.services.workflow.node_data import BaseNodeData
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from api.services.workflow.node_specs import (
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NodeSpec,
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PropertySpec,
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@ -118,9 +123,9 @@ def test_fixed_collection_has_sub_properties(spec: NodeSpec):
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@pytest.mark.parametrize("spec", all_specs(), ids=lambda s: s.name)
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def test_spec_name_matches_dto_discriminator(spec: NodeSpec):
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valid_names = {t.value for t in NodeType}
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valid_names = all_node_type_names()
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assert spec.name in valid_names, (
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f"NodeSpec {spec.name!r} doesn't match any NodeType discriminator. "
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f"NodeSpec {spec.name!r} doesn't match any registered node type. "
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f"Valid: {sorted(valid_names)}"
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)
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@ -187,10 +192,226 @@ def test_examples_validate_against_dto(spec: NodeSpec):
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def test_all_dto_types_have_specs():
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"""Every NodeType discriminator value must have a registered NodeSpec —
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catches the case where someone adds a new node type to dto.py but
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forgets to author a spec."""
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"""Every registered node type must have a registered NodeSpec."""
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spec_names = {s.name for s in all_specs()}
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type_values = {t.value for t in NodeType}
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type_values = all_node_type_names()
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missing = type_values - spec_names
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assert not missing, f"NodeType discriminators without specs: {sorted(missing)}"
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assert not missing, f"Registered node types without specs: {sorted(missing)}"
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def test_all_registered_node_models_inherit_base_node_data():
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for type_name in sorted(all_node_type_names()):
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data_model = get_node_data_model(type_name)
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assert data_model is not None, f"{type_name}: missing node data model"
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assert issubclass(data_model, BaseNodeData), (
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f"{type_name}: node data model must inherit BaseNodeData"
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)
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@pytest.mark.parametrize(
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("spec_name", "expected_order"),
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[
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(
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"startCall",
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[
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"name",
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"greeting_type",
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"greeting",
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"greeting_recording_id",
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"prompt",
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"allow_interrupt",
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"add_global_prompt",
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"delayed_start",
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"delayed_start_duration",
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"extraction_enabled",
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"extraction_prompt",
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"extraction_variables",
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"tool_uuids",
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"document_uuids",
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"pre_call_fetch_enabled",
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"pre_call_fetch_url",
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"pre_call_fetch_credential_uuid",
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],
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),
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(
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"agentNode",
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[
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"name",
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"prompt",
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"allow_interrupt",
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"add_global_prompt",
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"extraction_enabled",
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"extraction_prompt",
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"extraction_variables",
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"tool_uuids",
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"document_uuids",
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],
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),
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(
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"endCall",
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[
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"name",
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"prompt",
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"add_global_prompt",
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"extraction_enabled",
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"extraction_prompt",
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"extraction_variables",
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],
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),
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("globalNode", ["name", "prompt"]),
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("trigger", ["name", "enabled", "trigger_path"]),
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(
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"webhook",
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[
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"name",
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"enabled",
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"http_method",
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"endpoint_url",
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"credential_uuid",
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"custom_headers",
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"payload_template",
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],
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),
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(
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"qa",
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[
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"name",
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"qa_enabled",
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"qa_system_prompt",
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"qa_min_call_duration",
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"qa_voicemail_calls",
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"qa_sample_rate",
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"qa_use_workflow_llm",
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"qa_provider",
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"qa_model",
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"qa_api_key",
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"qa_endpoint",
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],
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),
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(
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"tuner",
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[
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"name",
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"tuner_enabled",
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"tuner_agent_id",
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"tuner_workspace_id",
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"tuner_api_key",
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],
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),
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],
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)
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def test_node_spec_property_order_stable(spec_name: str, expected_order: list[str]):
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spec = next(spec for spec in all_specs() if spec.name == spec_name)
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assert [prop.name for prop in spec.properties] == expected_order
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# ─────────────────────────────────────────────────────────────────────────
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# `to_mcp_dict` projection — the lean view served by the `get_node_type`
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# MCP tool. UI-only metadata is dropped so it doesn't poison LLM context;
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# the full spec stays available to the frontend and SDK via other paths.
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# ─────────────────────────────────────────────────────────────────────────
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# Keys that are UI-rendering concerns and must never reach the LLM view, at
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# either the node or property level.
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_UI_ONLY_KEYS = frozenset(
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{
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"display_name",
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"icon",
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"category",
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"version",
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"placeholder",
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"display_options",
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"editor",
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"extra",
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"label", # PropertyOption display string
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}
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)
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def _walk_dicts(node):
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"""Yield every dict nested anywhere inside a projected structure."""
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if isinstance(node, dict):
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yield node
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for value in node.values():
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yield from _walk_dicts(value)
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elif isinstance(node, list):
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for item in node:
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yield from _walk_dicts(item)
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@pytest.mark.parametrize("spec", all_specs(), ids=lambda s: s.name)
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def test_to_mcp_dict_drops_ui_only_keys(spec: NodeSpec):
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projected = spec.to_mcp_dict()
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for d in _walk_dicts(projected):
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leaked = _UI_ONLY_KEYS & d.keys()
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assert not leaked, f"{spec.name}: UI-only keys leaked into LLM view: {leaked}"
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@pytest.mark.parametrize("spec", all_specs(), ids=lambda s: s.name)
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def test_to_mcp_dict_omits_null_and_empty(spec: NodeSpec):
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"""The lean view never emits null values — absent means unset/optional,
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which is what halves the noise versus the full model dump."""
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for d in _walk_dicts(spec.to_mcp_dict()):
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for key, value in d.items():
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assert value is not None, f"{spec.name}: {key!r} emitted as null"
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@pytest.mark.parametrize("spec", all_specs(), ids=lambda s: s.name)
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def test_to_mcp_dict_keeps_property_essentials(spec: NodeSpec):
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"""Every property in the LLM view carries the minimum an LLM needs to
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author a value: machine name, type, and a description."""
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def _check(props: list[dict]):
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for prop in props:
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assert prop.get("name"), f"{spec.name}: property missing name"
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assert prop.get("type"), f"{spec.name}.{prop.get('name')}: missing type"
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assert prop.get("description"), (
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f"{spec.name}.{prop.get('name')}: missing description"
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)
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if prop.get("properties"):
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_check(prop["properties"])
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_check(spec.to_mcp_dict()["properties"])
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def test_to_mcp_dict_retains_authoring_signal_startcall():
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"""startCall is the richest core node — lock in that the projection
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keeps the fields an LLM actually authors against while shedding the rest."""
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spec = next(s for s in all_specs() if s.name == "startCall")
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projected = spec.to_mcp_dict()
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assert set(projected) == {
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"name",
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"description",
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"llm_hint",
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"properties",
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"examples",
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"graph_constraints",
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}
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props = {p["name"]: p for p in projected["properties"]}
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# Required field keeps `required`; optional fields omit it.
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assert props["prompt"]["required"] is True
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assert "required" not in props["greeting"]
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# Enum options project to bare values, dropping the UI label.
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assert props["greeting_type"]["options"] == [{"value": "text"}, {"value": "audio"}]
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# Validation bounds survive (they constrain valid authored values).
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assert props["delayed_start_duration"]["min_value"] == 0.1
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assert props["delayed_start_duration"]["max_value"] == 10.0
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# llm_hint survives where present (catalog-tool references).
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assert "list_recordings" in props["greeting_recording_id"]["llm_hint"]
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# fixed_collection rows recurse through the same projection.
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var_rows = {p["name"]: p for p in props["extraction_variables"]["properties"]}
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assert var_rows["type"]["options"] == [
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{"value": "string"},
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{"value": "number"},
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{"value": "boolean"},
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]
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# graph_constraints drops its null sub-fields.
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assert projected["graph_constraints"] == {"min_incoming": 0, "max_incoming": 0}
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