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Merge branch 'dev' into ntindle/google-issues-fix

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This commit is contained in:
Nicholas Tindle
2026-02-12 16:12:20 -06:00
committed by GitHub
parent 2b8134a711 d95aef7665
commit 201ec5aa3a
394 changed files with 15086 additions and 10934 deletions
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2
.github/workflows/claude-ci-failure-auto-fix.yml vendored
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@@ -22,7 +22,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
ref: ${{ github.event.workflow_run.head_branch }}
fetch-depth: 0

2
.github/workflows/claude-dependabot.yml vendored
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@@ -30,7 +30,7 @@ jobs:
actions: read # Required for CI access
steps:
- name: Checkout code
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
fetch-depth: 1

2
.github/workflows/claude.yml vendored
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@@ -40,7 +40,7 @@ jobs:
actions: read # Required for CI access
steps:
- name: Checkout code
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
fetch-depth: 1

2
.github/workflows/codeql.yml vendored
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@@ -58,7 +58,7 @@ jobs:
# your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
steps:
- name: Checkout repository
uses: actions/checkout@v4
uses: actions/checkout@v6
# Initializes the CodeQL tools for scanning.
- name: Initialize CodeQL

2
.github/workflows/copilot-setup-steps.yml vendored
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@@ -27,7 +27,7 @@ jobs:
# If you do not check out your code, Copilot will do this for you.
steps:
- name: Checkout code
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
fetch-depth: 0
submodules: true

2
.github/workflows/docs-block-sync.yml vendored
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@@ -23,7 +23,7 @@ jobs:
steps:
- name: Checkout code
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
fetch-depth: 1

2
.github/workflows/docs-claude-review.yml vendored
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@@ -23,7 +23,7 @@ jobs:
steps:
- name: Checkout code
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
fetch-depth: 0

2
.github/workflows/docs-enhance.yml vendored
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@@ -28,7 +28,7 @@ jobs:
steps:
- name: Checkout code
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
fetch-depth: 1

4
.github/workflows/platform-autogpt-deploy-dev.yaml vendored
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@@ -25,7 +25,7 @@ jobs:
steps:
- name: Checkout code
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
ref: ${{ github.event.inputs.git_ref || github.ref_name }}
@@ -52,7 +52,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Trigger deploy workflow
uses: peter-evans/repository-dispatch@v3
uses: peter-evans/repository-dispatch@v4
with:
token: ${{ secrets.DEPLOY_TOKEN }}
repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

4
.github/workflows/platform-autogpt-deploy-prod.yml vendored
View File

@@ -17,7 +17,7 @@ jobs:
steps:
- name: Checkout code
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
ref: ${{ github.ref_name || 'master' }}
@@ -45,7 +45,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Trigger deploy workflow
uses: peter-evans/repository-dispatch@v3
uses: peter-evans/repository-dispatch@v4
with:
token: ${{ secrets.DEPLOY_TOKEN }}
repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

2
.github/workflows/platform-backend-ci.yml vendored
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@@ -68,7 +68,7 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
fetch-depth: 0
submodules: true

6
.github/workflows/platform-dev-deploy-event-dispatcher.yml vendored
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@@ -82,7 +82,7 @@ jobs:
- name: Dispatch Deploy Event
if: steps.check_status.outputs.should_deploy == 'true'
uses: peter-evans/repository-dispatch@v3
uses: peter-evans/repository-dispatch@v4
with:
token: ${{ secrets.DISPATCH_TOKEN }}
repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -110,7 +110,7 @@ jobs:
- name: Dispatch Undeploy Event (from comment)
if: steps.check_status.outputs.should_undeploy == 'true'
uses: peter-evans/repository-dispatch@v3
uses: peter-evans/repository-dispatch@v4
with:
token: ${{ secrets.DISPATCH_TOKEN }}
repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -168,7 +168,7 @@ jobs:
github.event_name == 'pull_request' &&
github.event.action == 'closed' &&
steps.check_pr_close.outputs.should_undeploy == 'true'
uses: peter-evans/repository-dispatch@v3
uses: peter-evans/repository-dispatch@v4
with:
token: ${{ secrets.DISPATCH_TOKEN }}
repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

10
.github/workflows/platform-frontend-ci.yml vendored
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@@ -31,7 +31,7 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@v4
uses: actions/checkout@v6
- name: Check for component changes
uses: dorny/paths-filter@v3
@@ -71,7 +71,7 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@v4
uses: actions/checkout@v6
- name: Set up Node.js
uses: actions/setup-node@v6
@@ -107,7 +107,7 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
fetch-depth: 0
@@ -148,7 +148,7 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
submodules: recursive
@@ -277,7 +277,7 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
submodules: recursive

8
.github/workflows/platform-fullstack-ci.yml vendored
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@@ -29,7 +29,7 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@v4
uses: actions/checkout@v6
- name: Set up Node.js
uses: actions/setup-node@v6
@@ -56,14 +56,14 @@ jobs:
run: pnpm install --frozen-lockfile
types:
runs-on: ubuntu-latest
runs-on: big-boi
needs: setup
strategy:
fail-fast: false
steps:
- name: Checkout repository
uses: actions/checkout@v4
uses: actions/checkout@v6
with:
submodules: recursive
@@ -85,7 +85,7 @@ jobs:
- name: Run docker compose
run: |
docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
docker compose -f ../docker-compose.yml --profile local up -d deps_backend
- name: Restore dependencies cache
uses: actions/cache@v5

2
.github/workflows/repo-workflow-checker.yml vendored
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@@ -11,7 +11,7 @@ jobs:
steps:
# - name: Wait some time for all actions to start
# run: sleep 30
- uses: actions/checkout@v4
- uses: actions/checkout@v6
# with:
# fetch-depth: 0
- name: Set up Python

220
autogpt_platform/autogpt_libs/poetry.lock generated
View File

@@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
[[package]]
name = "annotated-doc"
@@ -67,7 +67,7 @@ description = "Backport of asyncio.Runner, a context manager that controls event
optional = false
python-versions = "<3.11,>=3.8"
groups = ["dev"]
markers = "python_version == \"3.10\""
markers = "python_version < \"3.11\""
files = [
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{file = "backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162"},
@@ -326,100 +326,118 @@ files = [
[[package]]
name = "coverage"
version = "7.10.5"
version = "7.13.4"
description = "Code coverage measurement for Python"
optional = false
python-versions = ">=3.9"
python-versions = ">=3.10"
groups = ["dev"]
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{file = "coverage-7.13.4.tar.gz", hash = "sha256:e5c8f6ed1e61a8b2dcdf31eb0b9bbf0130750ca79c1c49eb898e2ad86f5ccc91"},
]
[package.dependencies]
@@ -523,7 +541,7 @@ description = "Backport of PEP 654 (exception groups)"
optional = false
python-versions = ">=3.7"
groups = ["main", "dev"]
markers = "python_version == \"3.10\""
markers = "python_version < \"3.11\""
files = [
{file = "exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10"},
{file = "exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88"},
@@ -2162,23 +2180,23 @@ testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
[[package]]
name = "pytest-cov"
version = "6.2.1"
version = "7.0.0"
description = "Pytest plugin for measuring coverage."
optional = false
python-versions = ">=3.9"
groups = ["dev"]
files = [
{file = "pytest_cov-6.2.1-py3-none-any.whl", hash = "sha256:f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5"},
{file = "pytest_cov-6.2.1.tar.gz", hash = "sha256:25cc6cc0a5358204b8108ecedc51a9b57b34cc6b8c967cc2c01a4e00d8a67da2"},
{file = "pytest_cov-7.0.0-py3-none-any.whl", hash = "sha256:3b8e9558b16cc1479da72058bdecf8073661c7f57f7d3c5f22a1c23507f2d861"},
{file = "pytest_cov-7.0.0.tar.gz", hash = "sha256:33c97eda2e049a0c5298e91f519302a1334c26ac65c1a483d6206fd458361af1"},
]
[package.dependencies]
coverage = {version = ">=7.5", extras = ["toml"]}
coverage = {version = ">=7.10.6", extras = ["toml"]}
pluggy = ">=1.2"
pytest = ">=6.2.5"
pytest = ">=7"
[package.extras]
testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
testing = ["process-tests", "pytest-xdist", "virtualenv"]
[[package]]
name = "pytest-mock"
@@ -2545,7 +2563,7 @@ description = "A lil' TOML parser"
optional = false
python-versions = ">=3.8"
groups = ["dev"]
markers = "python_version == \"3.10\""
markers = "python_version < \"3.11\""
files = [
{file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
{file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"},
@@ -2893,4 +2911,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.1"
python-versions = ">=3.10,<4.0"
content-hash = "b7ac335a86aa44c3d7d2802298818b389a6f1286e3e9b7b0edb2ff06377cecaf"
content-hash = "40eae94995dc0a388fa832ed4af9b6137f28d5b5ced3aaea70d5f91d4d9a179d"

2
autogpt_platform/autogpt_libs/pyproject.toml
View File

@@ -26,7 +26,7 @@ pyright = "^1.1.408"
pytest = "^8.4.1"
pytest-asyncio = "^1.3.0"
pytest-mock = "^3.15.1"
pytest-cov = "^6.2.1"
pytest-cov = "^7.0.0"
ruff = "^0.15.0"
[build-system]

6
autogpt_platform/backend/backend/api/external/v1/routes.py vendored
View File

@@ -10,7 +10,7 @@ from typing_extensions import TypedDict
import backend.api.features.store.cache as store_cache
import backend.api.features.store.model as store_model
import backend.data.block
import backend.blocks
from backend.api.external.middleware import require_permission
from backend.data import execution as execution_db
from backend.data import graph as graph_db
@@ -67,7 +67,7 @@ async def get_user_info(
dependencies=[Security(require_permission(APIKeyPermission.READ_BLOCK))],
)
async def get_graph_blocks() -> Sequence[dict[Any, Any]]:
blocks = [block() for block in backend.data.block.get_blocks().values()]
blocks = [block() for block in backend.blocks.get_blocks().values()]
return [b.to_dict() for b in blocks if not b.disabled]
@@ -83,7 +83,7 @@ async def execute_graph_block(
require_permission(APIKeyPermission.EXECUTE_BLOCK)
),
) -> CompletedBlockOutput:
obj = backend.data.block.get_block(block_id)
obj = backend.blocks.get_block(block_id)
if not obj:
raise HTTPException(status_code=404, detail=f"Block #{block_id} not found.")
if obj.disabled:

19
autogpt_platform/backend/backend/api/features/builder/db.py
View File

@@ -10,10 +10,15 @@ import backend.api.features.library.db as library_db
import backend.api.features.library.model as library_model
import backend.api.features.store.db as store_db
import backend.api.features.store.model as store_model
import backend.data.block
from backend.blocks import load_all_blocks
from backend.blocks._base import (
AnyBlockSchema,
BlockCategory,
BlockInfo,
BlockSchema,
BlockType,
)
from backend.blocks.llm import LlmModel
from backend.data.block import AnyBlockSchema, BlockCategory, BlockInfo, BlockSchema
from backend.data.db import query_raw_with_schema
from backend.integrations.providers import ProviderName
from backend.util.cache import cached
@@ -22,7 +27,7 @@ from backend.util.models import Pagination
from .model import (
BlockCategoryResponse,
BlockResponse,
BlockType,
BlockTypeFilter,
CountResponse,
FilterType,
Provider,
@@ -88,7 +93,7 @@ def get_block_categories(category_blocks: int = 3) -> list[BlockCategoryResponse
def get_blocks(
*,
category: str | None = None,
type: BlockType | None = None,
type: BlockTypeFilter | None = None,
provider: ProviderName | None = None,
page: int = 1,
page_size: int = 50,
@@ -669,9 +674,9 @@ async def get_suggested_blocks(count: int = 5) -> list[BlockInfo]:
for block_type in load_all_blocks().values():
block: AnyBlockSchema = block_type()
if block.disabled or block.block_type in (
backend.data.block.BlockType.INPUT,
backend.data.block.BlockType.OUTPUT,
backend.data.block.BlockType.AGENT,
BlockType.INPUT,
BlockType.OUTPUT,
BlockType.AGENT,
):
continue
# Find the execution count for this block

4
autogpt_platform/backend/backend/api/features/builder/model.py
View File

@@ -4,7 +4,7 @@ from pydantic import BaseModel
import backend.api.features.library.model as library_model
import backend.api.features.store.model as store_model
from backend.data.block import BlockInfo
from backend.blocks._base import BlockInfo
from backend.integrations.providers import ProviderName
from backend.util.models import Pagination
@@ -15,7 +15,7 @@ FilterType = Literal[
"my_agents",
]
BlockType = Literal["all", "input", "action", "output"]
BlockTypeFilter = Literal["all", "input", "action", "output"]
class SearchEntry(BaseModel):

2
autogpt_platform/backend/backend/api/features/builder/routes.py
View File

@@ -88,7 +88,7 @@ async def get_block_categories(
)
async def get_blocks(
category: Annotated[str | None, fastapi.Query()] = None,
type: Annotated[builder_model.BlockType | None, fastapi.Query()] = None,
type: Annotated[builder_model.BlockTypeFilter | None, fastapi.Query()] = None,
provider: Annotated[ProviderName | None, fastapi.Query()] = None,
page: Annotated[int, fastapi.Query()] = 1,
page_size: Annotated[int, fastapi.Query()] = 50,

6
autogpt_platform/backend/backend/api/features/chat/config.py
View File

@@ -93,6 +93,12 @@ class ChatConfig(BaseSettings):
description="Name of the prompt in Langfuse to fetch",
)
# Extended thinking configuration for Claude models
thinking_enabled: bool = Field(
default=True,
description="Enable adaptive thinking for Claude models via OpenRouter",
)
@field_validator("api_key", mode="before")
@classmethod
def get_api_key(cls, v):

65
autogpt_platform/backend/backend/api/features/chat/model.py
View File

@@ -2,7 +2,7 @@ import asyncio
import logging
import uuid
from datetime import UTC, datetime
from typing import Any
from typing import Any, cast
from weakref import WeakValueDictionary
from openai.types.chat import (
@@ -104,6 +104,26 @@ class ChatSession(BaseModel):
successful_agent_runs: dict[str, int] = {}
successful_agent_schedules: dict[str, int] = {}
def add_tool_call_to_current_turn(self, tool_call: dict) -> None:
"""Attach a tool_call to the current turn's assistant message.
Searches backwards for the most recent assistant message (stopping at
any user message boundary). If found, appends the tool_call to it.
Otherwise creates a new assistant message with the tool_call.
"""
for msg in reversed(self.messages):
if msg.role == "user":
break
if msg.role == "assistant":
if not msg.tool_calls:
msg.tool_calls = []
msg.tool_calls.append(tool_call)
return
self.messages.append(
ChatMessage(role="assistant", content="", tool_calls=[tool_call])
)
@staticmethod
def new(user_id: str) -> "ChatSession":
return ChatSession(
@@ -172,6 +192,47 @@ class ChatSession(BaseModel):
successful_agent_schedules=successful_agent_schedules,
)
@staticmethod
def _merge_consecutive_assistant_messages(
messages: list[ChatCompletionMessageParam],
) -> list[ChatCompletionMessageParam]:
"""Merge consecutive assistant messages into single messages.
Long-running tool flows can create split assistant messages: one with
text content and another with tool_calls. Anthropic's API requires
tool_result blocks to reference a tool_use in the immediately preceding
assistant message, so these splits cause 400 errors via OpenRouter.
"""
if len(messages) < 2:
return messages
result: list[ChatCompletionMessageParam] = [messages[0]]
for msg in messages[1:]:
prev = result[-1]
if prev.get("role") != "assistant" or msg.get("role") != "assistant":
result.append(msg)
continue
prev = cast(ChatCompletionAssistantMessageParam, prev)
curr = cast(ChatCompletionAssistantMessageParam, msg)
curr_content = curr.get("content") or ""
if curr_content:
prev_content = prev.get("content") or ""
prev["content"] = (
f"{prev_content}\n{curr_content}" if prev_content else curr_content
)
curr_tool_calls = curr.get("tool_calls")
if curr_tool_calls:
prev_tool_calls = prev.get("tool_calls")
prev["tool_calls"] = (
list(prev_tool_calls) + list(curr_tool_calls)
if prev_tool_calls
else list(curr_tool_calls)
)
return result
def to_openai_messages(self) -> list[ChatCompletionMessageParam]:
messages = []
for message in self.messages:
@@ -258,7 +319,7 @@ class ChatSession(BaseModel):
name=message.name or "",
)
)
return messages
return self._merge_consecutive_assistant_messages(messages)
async def _get_session_from_cache(session_id: str) -> ChatSession | None:

214
autogpt_platform/backend/backend/api/features/chat/model_test.py
View File

@@ -1,4 +1,16 @@
from typing import cast
import pytest
from openai.types.chat import (
ChatCompletionAssistantMessageParam,
ChatCompletionMessageParam,
ChatCompletionToolMessageParam,
ChatCompletionUserMessageParam,
)
from openai.types.chat.chat_completion_message_tool_call_param import (
ChatCompletionMessageToolCallParam,
Function,
)
from .model import (
ChatMessage,
@@ -117,3 +129,205 @@ async def test_chatsession_db_storage(setup_test_user, test_user_id):
loaded.tool_calls is not None
), f"Tool calls missing for {orig.role} message"
assert len(orig.tool_calls) == len(loaded.tool_calls)
# --------------------------------------------------------------------------- #
# _merge_consecutive_assistant_messages #
# --------------------------------------------------------------------------- #
_tc = ChatCompletionMessageToolCallParam(
id="tc1", type="function", function=Function(name="do_stuff", arguments="{}")
)
_tc2 = ChatCompletionMessageToolCallParam(
id="tc2", type="function", function=Function(name="other", arguments="{}")
)
def test_merge_noop_when_no_consecutive_assistants():
"""Messages without consecutive assistants are returned unchanged."""
msgs = [
ChatCompletionUserMessageParam(role="user", content="hi"),
ChatCompletionAssistantMessageParam(role="assistant", content="hello"),
ChatCompletionUserMessageParam(role="user", content="bye"),
]
merged = ChatSession._merge_consecutive_assistant_messages(msgs)
assert len(merged) == 3
assert [m["role"] for m in merged] == ["user", "assistant", "user"]
def test_merge_splits_text_and_tool_calls():
"""The exact bug scenario: text-only assistant followed by tool_calls-only assistant."""
msgs = [
ChatCompletionUserMessageParam(role="user", content="build agent"),
ChatCompletionAssistantMessageParam(
role="assistant", content="Let me build that"
),
ChatCompletionAssistantMessageParam(
role="assistant", content="", tool_calls=[_tc]
),
ChatCompletionToolMessageParam(role="tool", content="ok", tool_call_id="tc1"),
]
merged = ChatSession._merge_consecutive_assistant_messages(msgs)
assert len(merged) == 3
assert merged[0]["role"] == "user"
assert merged[2]["role"] == "tool"
a = cast(ChatCompletionAssistantMessageParam, merged[1])
assert a["role"] == "assistant"
assert a.get("content") == "Let me build that"
assert a.get("tool_calls") == [_tc]
def test_merge_combines_tool_calls_from_both():
"""Both consecutive assistants have tool_calls — they get merged."""
msgs: list[ChatCompletionAssistantMessageParam] = [
ChatCompletionAssistantMessageParam(
role="assistant", content="text", tool_calls=[_tc]
),
ChatCompletionAssistantMessageParam(
role="assistant", content="", tool_calls=[_tc2]
),
]
merged = ChatSession._merge_consecutive_assistant_messages(msgs) # type: ignore[arg-type]
assert len(merged) == 1
a = cast(ChatCompletionAssistantMessageParam, merged[0])
assert a.get("tool_calls") == [_tc, _tc2]
assert a.get("content") == "text"
def test_merge_three_consecutive_assistants():
"""Three consecutive assistants collapse into one."""
msgs: list[ChatCompletionAssistantMessageParam] = [
ChatCompletionAssistantMessageParam(role="assistant", content="a"),
ChatCompletionAssistantMessageParam(role="assistant", content="b"),
ChatCompletionAssistantMessageParam(
role="assistant", content="", tool_calls=[_tc]
),
]
merged = ChatSession._merge_consecutive_assistant_messages(msgs) # type: ignore[arg-type]
assert len(merged) == 1
a = cast(ChatCompletionAssistantMessageParam, merged[0])
assert a.get("content") == "a\nb"
assert a.get("tool_calls") == [_tc]
def test_merge_empty_and_single_message():
"""Edge cases: empty list and single message."""
assert ChatSession._merge_consecutive_assistant_messages([]) == []
single: list[ChatCompletionMessageParam] = [
ChatCompletionUserMessageParam(role="user", content="hi")
]
assert ChatSession._merge_consecutive_assistant_messages(single) == single
# --------------------------------------------------------------------------- #
# add_tool_call_to_current_turn #
# --------------------------------------------------------------------------- #
_raw_tc = {
"id": "tc1",
"type": "function",
"function": {"name": "f", "arguments": "{}"},
}
_raw_tc2 = {
"id": "tc2",
"type": "function",
"function": {"name": "g", "arguments": "{}"},
}
def test_add_tool_call_appends_to_existing_assistant():
"""When the last assistant is from the current turn, tool_call is added to it."""
session = ChatSession.new(user_id="u")
session.messages = [
ChatMessage(role="user", content="hi"),
ChatMessage(role="assistant", content="working on it"),
]
session.add_tool_call_to_current_turn(_raw_tc)
assert len(session.messages) == 2 # no new message created
assert session.messages[1].tool_calls == [_raw_tc]
def test_add_tool_call_creates_assistant_when_none_exists():
"""When there's no current-turn assistant, a new one is created."""
session = ChatSession.new(user_id="u")
session.messages = [
ChatMessage(role="user", content="hi"),
]
session.add_tool_call_to_current_turn(_raw_tc)
assert len(session.messages) == 2
assert session.messages[1].role == "assistant"
assert session.messages[1].tool_calls == [_raw_tc]
def test_add_tool_call_does_not_cross_user_boundary():
"""A user message acts as a boundary — previous assistant is not modified."""
session = ChatSession.new(user_id="u")
session.messages = [
ChatMessage(role="assistant", content="old turn"),
ChatMessage(role="user", content="new message"),
]
session.add_tool_call_to_current_turn(_raw_tc)
assert len(session.messages) == 3 # new assistant was created
assert session.messages[0].tool_calls is None # old assistant untouched
assert session.messages[2].role == "assistant"
assert session.messages[2].tool_calls == [_raw_tc]
def test_add_tool_call_multiple_times():
"""Multiple long-running tool calls accumulate on the same assistant."""
session = ChatSession.new(user_id="u")
session.messages = [
ChatMessage(role="user", content="hi"),
ChatMessage(role="assistant", content="doing stuff"),
]
session.add_tool_call_to_current_turn(_raw_tc)
# Simulate a pending tool result in between (like _yield_tool_call does)
session.messages.append(
ChatMessage(role="tool", content="pending", tool_call_id="tc1")
)
session.add_tool_call_to_current_turn(_raw_tc2)
assert len(session.messages) == 3 # user, assistant, tool — no extra assistant
assert session.messages[1].tool_calls == [_raw_tc, _raw_tc2]
def test_to_openai_messages_merges_split_assistants():
"""End-to-end: session with split assistants produces valid OpenAI messages."""
session = ChatSession.new(user_id="u")
session.messages = [
ChatMessage(role="user", content="build agent"),
ChatMessage(role="assistant", content="Let me build that"),
ChatMessage(
role="assistant",
content="",
tool_calls=[
{
"id": "tc1",
"type": "function",
"function": {"name": "create_agent", "arguments": "{}"},
}
],
),
ChatMessage(role="tool", content="done", tool_call_id="tc1"),
ChatMessage(role="assistant", content="Saved!"),
ChatMessage(role="user", content="show me an example run"),
]
openai_msgs = session.to_openai_messages()
# The two consecutive assistants at index 1,2 should be merged
roles = [m["role"] for m in openai_msgs]
assert roles == ["user", "assistant", "tool", "assistant", "user"]
# The merged assistant should have both content and tool_calls
merged = cast(ChatCompletionAssistantMessageParam, openai_msgs[1])
assert merged.get("content") == "Let me build that"
tc_list = merged.get("tool_calls")
assert tc_list is not None and len(list(tc_list)) == 1
assert list(tc_list)[0]["id"] == "tc1"

61
autogpt_platform/backend/backend/api/features/chat/response_model.py
View File

@@ -10,6 +10,8 @@ from typing import Any
from pydantic import BaseModel, Field
from backend.util.json import dumps as json_dumps
class ResponseType(str, Enum):
"""Types of streaming responses following AI SDK protocol."""
@@ -18,6 +20,10 @@ class ResponseType(str, Enum):
START = "start"
FINISH = "finish"
# Step lifecycle (one LLM API call within a message)
START_STEP = "start-step"
FINISH_STEP = "finish-step"
# Text streaming
TEXT_START = "text-start"
TEXT_DELTA = "text-delta"
@@ -57,6 +63,16 @@ class StreamStart(StreamBaseResponse):
description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
)
def to_sse(self) -> str:
"""Convert to SSE format, excluding non-protocol fields like taskId."""
import json
data: dict[str, Any] = {
"type": self.type.value,
"messageId": self.messageId,
}
return f"data: {json.dumps(data)}\n\n"
class StreamFinish(StreamBaseResponse):
"""End of message/stream."""
@@ -64,6 +80,26 @@ class StreamFinish(StreamBaseResponse):
type: ResponseType = ResponseType.FINISH
class StreamStartStep(StreamBaseResponse):
"""Start of a step (one LLM API call within a message).
The AI SDK uses this to add a step-start boundary to message.parts,
enabling visual separation between multiple LLM calls in a single message.
"""
type: ResponseType = ResponseType.START_STEP
class StreamFinishStep(StreamBaseResponse):
"""End of a step (one LLM API call within a message).
The AI SDK uses this to reset activeTextParts and activeReasoningParts,
so the next LLM call in a tool-call continuation starts with clean state.
"""
type: ResponseType = ResponseType.FINISH_STEP
# ========== Text Streaming ==========
@@ -117,7 +153,7 @@ class StreamToolOutputAvailable(StreamBaseResponse):
type: ResponseType = ResponseType.TOOL_OUTPUT_AVAILABLE
toolCallId: str = Field(..., description="Tool call ID this responds to")
output: str | dict[str, Any] = Field(..., description="Tool execution output")
# Additional fields for internal use (not part of AI SDK spec but useful)
# Keep these for internal backend use
toolName: str | None = Field(
default=None, description="Name of the tool that was executed"
)
@@ -125,6 +161,17 @@ class StreamToolOutputAvailable(StreamBaseResponse):
default=True, description="Whether the tool execution succeeded"
)
def to_sse(self) -> str:
"""Convert to SSE format, excluding non-spec fields."""
import json
data = {
"type": self.type.value,
"toolCallId": self.toolCallId,
"output": self.output,
}
return f"data: {json.dumps(data)}\n\n"
# ========== Other ==========
@@ -148,6 +195,18 @@ class StreamError(StreamBaseResponse):
default=None, description="Additional error details"
)
def to_sse(self) -> str:
"""Convert to SSE format, only emitting fields required by AI SDK protocol.
The AI SDK uses z.strictObject({type, errorText}) which rejects
any extra fields like `code` or `details`.
"""
data = {
"type": self.type.value,
"errorText": self.errorText,
}
return f"data: {json_dumps(data)}\n\n"
class StreamHeartbeat(StreamBaseResponse):
"""Heartbeat to keep SSE connection alive during long-running operations.

250
autogpt_platform/backend/backend/api/features/chat/routes.py
View File

@@ -6,7 +6,7 @@ from collections.abc import AsyncGenerator
from typing import Annotated
from autogpt_libs import auth
from fastapi import APIRouter, Depends, Header, HTTPException, Query, Security
from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response, Security
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
@@ -17,7 +17,29 @@ from . import stream_registry
from .completion_handler import process_operation_failure, process_operation_success
from .config import ChatConfig
from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
from .response_model import StreamFinish, StreamHeartbeat, StreamStart
from .response_model import StreamFinish, StreamHeartbeat
from .tools.models import (
AgentDetailsResponse,
AgentOutputResponse,
AgentPreviewResponse,
AgentSavedResponse,
AgentsFoundResponse,
BlockListResponse,
BlockOutputResponse,
ClarificationNeededResponse,
DocPageResponse,
DocSearchResultsResponse,
ErrorResponse,
ExecutionStartedResponse,
InputValidationErrorResponse,
NeedLoginResponse,
NoResultsResponse,
OperationInProgressResponse,
OperationPendingResponse,
OperationStartedResponse,
SetupRequirementsResponse,
UnderstandingUpdatedResponse,
)
config = ChatConfig()
@@ -269,8 +291,6 @@ async def stream_chat_post(
import time
stream_start_time = time.perf_counter()
# Base log metadata (task_id added after creation)
log_meta = {"component": "ChatStream", "session_id": session_id}
if user_id:
log_meta["user_id"] = user_id
@@ -328,24 +348,6 @@ async def stream_chat_post(
first_chunk_time, ttfc = None, None
chunk_count = 0
try:
# Emit a start event with task_id for reconnection
start_chunk = StreamStart(messageId=task_id, taskId=task_id)
await stream_registry.publish_chunk(task_id, start_chunk)
logger.info(
f"[TIMING] StreamStart published at {(time_module.perf_counter() - gen_start_time)*1000:.1f}ms",
extra={
"json_fields": {
**log_meta,
"elapsed_ms": (time_module.perf_counter() - gen_start_time)
* 1000,
}
},
)
logger.info(
"[TIMING] Calling stream_chat_completion",
extra={"json_fields": log_meta},
)
async for chunk in chat_service.stream_chat_completion(
session_id,
request.message,
@@ -353,6 +355,7 @@ async def stream_chat_post(
user_id=user_id,
session=session, # Pass pre-fetched session to avoid double-fetch
context=request.context,
_task_id=task_id, # Pass task_id so service emits start with taskId for reconnection
):
chunk_count += 1
if first_chunk_time is None:
@@ -388,7 +391,6 @@ async def stream_chat_post(
}
},
)
await stream_registry.mark_task_completed(task_id, "completed")
except Exception as e:
elapsed = time_module.perf_counter() - gen_start_time
@@ -428,34 +430,13 @@ async def stream_chat_post(
chunks_yielded = 0
try:
# Subscribe to the task stream (this replays existing messages + live updates)
subscribe_start = time_module.perf_counter()
logger.info(
"[TIMING] Calling subscribe_to_task",
extra={"json_fields": log_meta},
)
subscriber_queue = await stream_registry.subscribe_to_task(
task_id=task_id,
user_id=user_id,
last_message_id="0-0", # Get all messages from the beginning
)
subscribe_time = (time_module.perf_counter() - subscribe_start) * 1000
logger.info(
f"[TIMING] subscribe_to_task completed in {subscribe_time:.1f}ms, "
f"queue_ok={subscriber_queue is not None}",
extra={
"json_fields": {
**log_meta,
"duration_ms": subscribe_time,
"queue_obtained": subscriber_queue is not None,
}
},
)
if subscriber_queue is None:
logger.info(
"[TIMING] subscriber_queue is None, yielding finish",
extra={"json_fields": log_meta},
)
yield StreamFinish().to_sse()
yield "data: [DONE]\n\n"
return
@@ -467,11 +448,7 @@ async def stream_chat_post(
)
while True:
try:
queue_wait_start = time_module.perf_counter()
chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
queue_wait_time = (
time_module.perf_counter() - queue_wait_start
) * 1000
chunks_yielded += 1
if not first_chunk_yielded:
@@ -479,26 +456,12 @@ async def stream_chat_post(
elapsed = time_module.perf_counter() - event_gen_start
logger.info(
f"[TIMING] FIRST CHUNK from queue at {elapsed:.2f}s, "
f"type={type(chunk).__name__}, "
f"wait={queue_wait_time:.1f}ms",
f"type={type(chunk).__name__}",
extra={
"json_fields": {
**log_meta,
"chunk_type": type(chunk).__name__,
"elapsed_ms": elapsed * 1000,
"queue_wait_ms": queue_wait_time,
}
},
)
elif chunks_yielded % 50 == 0:
logger.info(
f"[TIMING] Chunk #{chunks_yielded}, "
f"type={type(chunk).__name__}",
extra={
"json_fields": {
**log_meta,
"chunk_number": chunks_yielded,
"chunk_type": type(chunk).__name__,
}
},
)
@@ -521,13 +484,6 @@ async def stream_chat_post(
)
break
except asyncio.TimeoutError:
# Send heartbeat to keep connection alive
logger.info(
f"[TIMING] Heartbeat timeout, chunks_so_far={chunks_yielded}",
extra={
"json_fields": {**log_meta, "chunks_so_far": chunks_yielded}
},
)
yield StreamHeartbeat().to_sse()
except GeneratorExit:
@@ -592,63 +548,90 @@ async def stream_chat_post(
@router.get(
"/sessions/{session_id}/stream",
)
async def stream_chat_get(
async def resume_session_stream(
session_id: str,
message: Annotated[str, Query(min_length=1, max_length=10000)],
user_id: str | None = Depends(auth.get_user_id),
is_user_message: bool = Query(default=True),
):
"""
Stream chat responses for a session (GET - legacy endpoint).
Resume an active stream for a session.
Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
- Text fragments as they are generated
- Tool call UI elements (if invoked)
- Tool execution results
Called by the AI SDK's ``useChat(resume: true)`` on page load.
Checks for an active (in-progress) task on the session and either replays
the full SSE stream or returns 204 No Content if nothing is running.
Args:
session_id: The chat session identifier to associate with the streamed messages.
message: The user's new message to process.
session_id: The chat session identifier.
user_id: Optional authenticated user ID.
is_user_message: Whether the message is a user message.
Returns:
StreamingResponse: SSE-formatted response chunks.
Returns:
StreamingResponse (SSE) when an active stream exists,
or 204 No Content when there is nothing to resume.
"""
session = await _validate_and_get_session(session_id, user_id)
import asyncio
active_task, _last_id = await stream_registry.get_active_task_for_session(
session_id, user_id
)
if not active_task:
return Response(status_code=204)
subscriber_queue = await stream_registry.subscribe_to_task(
task_id=active_task.task_id,
user_id=user_id,
last_message_id="0-0", # Full replay so useChat rebuilds the message
)
if subscriber_queue is None:
return Response(status_code=204)
async def event_generator() -> AsyncGenerator[str, None]:
chunk_count = 0
first_chunk_type: str | None = None
async for chunk in chat_service.stream_chat_completion(
session_id,
message,
is_user_message=is_user_message,
user_id=user_id,
session=session, # Pass pre-fetched session to avoid double-fetch
):
if chunk_count < 3:
logger.info(
"Chat stream chunk",
extra={
"session_id": session_id,
"chunk_type": str(chunk.type),
},
try:
while True:
try:
chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
if chunk_count < 3:
logger.info(
"Resume stream chunk",
extra={
"session_id": session_id,
"chunk_type": str(chunk.type),
},
)
if not first_chunk_type:
first_chunk_type = str(chunk.type)
chunk_count += 1
yield chunk.to_sse()
if isinstance(chunk, StreamFinish):
break
except asyncio.TimeoutError:
yield StreamHeartbeat().to_sse()
except GeneratorExit:
pass
except Exception as e:
logger.error(f"Error in resume stream for session {session_id}: {e}")
finally:
try:
await stream_registry.unsubscribe_from_task(
active_task.task_id, subscriber_queue
)
if not first_chunk_type:
first_chunk_type = str(chunk.type)
chunk_count += 1
yield chunk.to_sse()
logger.info(
"Chat stream completed",
extra={
"session_id": session_id,
"n_chunks": chunk_count,
"first_chunk_type": first_chunk_type,
},
)
# AI SDK protocol termination
yield "data: [DONE]\n\n"
except Exception as unsub_err:
logger.error(
f"Error unsubscribing from task {active_task.task_id}: {unsub_err}",
exc_info=True,
)
logger.info(
"Resume stream completed",
extra={
"session_id": session_id,
"n_chunks": chunk_count,
"first_chunk_type": first_chunk_type,
},
)
yield "data: [DONE]\n\n"
return StreamingResponse(
event_generator(),
@@ -656,8 +639,8 @@ async def stream_chat_get(
headers={
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no", # Disable nginx buffering
"x-vercel-ai-ui-message-stream": "v1", # AI SDK protocol header
"X-Accel-Buffering": "no",
"x-vercel-ai-ui-message-stream": "v1",
},
)
@@ -969,3 +952,42 @@ async def health_check() -> dict:
"service": "chat",
"version": "0.1.0",
}
# ========== Schema Export (for OpenAPI / Orval codegen) ==========
ToolResponseUnion = (
AgentsFoundResponse
| NoResultsResponse
| AgentDetailsResponse
| SetupRequirementsResponse
| ExecutionStartedResponse
| NeedLoginResponse
| ErrorResponse
| InputValidationErrorResponse
| AgentOutputResponse
| UnderstandingUpdatedResponse
| AgentPreviewResponse
| AgentSavedResponse
| ClarificationNeededResponse
| BlockListResponse
| BlockOutputResponse
| DocSearchResultsResponse
| DocPageResponse
| OperationStartedResponse
| OperationPendingResponse
| OperationInProgressResponse
)
@router.get(
"/schema/tool-responses",
response_model=ToolResponseUnion,
include_in_schema=True,
summary="[Dummy] Tool response type export for codegen",
description="This endpoint is not meant to be called. It exists solely to "
"expose tool response models in the OpenAPI schema for frontend codegen.",
)
async def _tool_response_schema() -> ToolResponseUnion: # type: ignore[return]
"""Never called at runtime. Exists only so Orval generates TS types."""
raise HTTPException(status_code=501, detail="Schema-only endpoint")

66
autogpt_platform/backend/backend/api/features/chat/service.py
View File

@@ -52,8 +52,10 @@ from .response_model import (
StreamBaseResponse,
StreamError,
StreamFinish,
StreamFinishStep,
StreamHeartbeat,
StreamStart,
StreamStartStep,
StreamTextDelta,
StreamTextEnd,
StreamTextStart,
@@ -351,6 +353,10 @@ async def stream_chat_completion(
retry_count: int = 0,
session: ChatSession | None = None,
context: dict[str, str] | None = None, # {url: str, content: str}
_continuation_message_id: (
str | None
) = None, # Internal: reuse message ID for tool call continuations
_task_id: str | None = None, # Internal: task ID for SSE reconnection support
) -> AsyncGenerator[StreamBaseResponse, None]:
"""Main entry point for streaming chat completions with database handling.
@@ -517,16 +523,21 @@ async def stream_chat_completion(
# Generate unique IDs for AI SDK protocol
import uuid as uuid_module
message_id = str(uuid_module.uuid4())
is_continuation = _continuation_message_id is not None
message_id = _continuation_message_id or str(uuid_module.uuid4())
text_block_id = str(uuid_module.uuid4())
# Yield message start
# Only yield message start for the initial call, not for continuations.
setup_time = (time.monotonic() - completion_start) * 1000
logger.info(
f"[TIMING] Setup complete, yielding StreamStart at {setup_time:.1f}ms",
extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
)
yield StreamStart(messageId=message_id)
if not is_continuation:
yield StreamStart(messageId=message_id, taskId=_task_id)
# Emit start-step before each LLM call (AI SDK uses this to add step boundaries)
yield StreamStartStep()
try:
logger.info(
@@ -632,6 +643,10 @@ async def stream_chat_completion(
)
yield chunk
elif isinstance(chunk, StreamFinish):
if has_done_tool_call:
# Tool calls happened — close the step but don't send message-level finish.
# The continuation will open a new step, and finish will come at the end.
yield StreamFinishStep()
if not has_done_tool_call:
# Emit text-end before finish if we received text but haven't closed it
if has_received_text and not text_streaming_ended:
@@ -663,6 +678,8 @@ async def stream_chat_completion(
has_saved_assistant_message = True
has_yielded_end = True
# Emit finish-step before finish (resets AI SDK text/reasoning state)
yield StreamFinishStep()
yield chunk
elif isinstance(chunk, StreamError):
has_yielded_error = True
@@ -712,6 +729,10 @@ async def stream_chat_completion(
logger.info(
f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
)
# Close the current step before retrying so the recursive call's
# StreamStartStep doesn't produce unbalanced step events.
if not has_yielded_end:
yield StreamFinishStep()
should_retry = True
else:
# Non-retryable error or max retries exceeded
@@ -747,6 +768,7 @@ async def stream_chat_completion(
error_response = StreamError(errorText=error_message)
yield error_response
if not has_yielded_end:
yield StreamFinishStep()
yield StreamFinish()
return
@@ -761,6 +783,8 @@ async def stream_chat_completion(
retry_count=retry_count + 1,
session=session,
context=context,
_continuation_message_id=message_id, # Reuse message ID since start was already sent
_task_id=_task_id,
):
yield chunk
return # Exit after retry to avoid double-saving in finally block
@@ -776,9 +800,13 @@ async def stream_chat_completion(
# Build the messages list in the correct order
messages_to_save: list[ChatMessage] = []
# Add assistant message with tool_calls if any
# Add assistant message with tool_calls if any.
# Use extend (not assign) to preserve tool_calls already added by
# _yield_tool_call for long-running tools.
if accumulated_tool_calls:
assistant_response.tool_calls = accumulated_tool_calls
if not assistant_response.tool_calls:
assistant_response.tool_calls = []
assistant_response.tool_calls.extend(accumulated_tool_calls)
logger.info(
f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
)
@@ -830,6 +858,8 @@ async def stream_chat_completion(
session=session, # Pass session object to avoid Redis refetch
context=context,
tool_call_response=str(tool_response_messages),
_continuation_message_id=message_id, # Reuse message ID to avoid duplicates
_task_id=_task_id,
):
yield chunk
@@ -1040,6 +1070,10 @@ async def _stream_chat_chunks(
:128
] # OpenRouter limit
# Enable adaptive thinking for Anthropic models via OpenRouter
if config.thinking_enabled and "anthropic" in model.lower():
extra_body["reasoning"] = {"enabled": True}
api_call_start = time_module.perf_counter()
stream = await client.chat.completions.create(
model=model,
@@ -1374,13 +1408,9 @@ async def _yield_tool_call(
operation_id=operation_id,
)
# Save assistant message with tool_call FIRST (required by LLM)
assistant_message = ChatMessage(
role="assistant",
content="",
tool_calls=[tool_calls[yield_idx]],
)
session.messages.append(assistant_message)
# Attach the tool_call to the current turn's assistant message
# (or create one if this is a tool-only response with no text).
session.add_tool_call_to_current_turn(tool_calls[yield_idx])
# Then save pending tool result
pending_message = ChatMessage(
@@ -1686,6 +1716,7 @@ async def _execute_long_running_tool_with_streaming(
task_id,
StreamError(errorText=str(e)),
)
await stream_registry.publish_chunk(task_id, StreamFinishStep())
await stream_registry.publish_chunk(task_id, StreamFinish())
await _update_pending_operation(
@@ -1802,6 +1833,10 @@ async def _generate_llm_continuation(
if session_id:
extra_body["session_id"] = session_id[:128]
# Enable adaptive thinking for Anthropic models via OpenRouter
if config.thinking_enabled and "anthropic" in config.model.lower():
extra_body["reasoning"] = {"enabled": True}
retry_count = 0
last_error: Exception | None = None
response = None
@@ -1932,6 +1967,10 @@ async def _generate_llm_continuation_with_streaming(
if session_id:
extra_body["session_id"] = session_id[:128]
# Enable adaptive thinking for Anthropic models via OpenRouter
if config.thinking_enabled and "anthropic" in config.model.lower():
extra_body["reasoning"] = {"enabled": True}
# Make streaming LLM call (no tools - just text response)
from typing import cast
@@ -1943,6 +1982,7 @@ async def _generate_llm_continuation_with_streaming(
# Publish start event
await stream_registry.publish_chunk(task_id, StreamStart(messageId=message_id))
await stream_registry.publish_chunk(task_id, StreamStartStep())
await stream_registry.publish_chunk(task_id, StreamTextStart(id=text_block_id))
# Stream the response
@@ -1966,6 +2006,7 @@ async def _generate_llm_continuation_with_streaming(
# Publish end events
await stream_registry.publish_chunk(task_id, StreamTextEnd(id=text_block_id))
await stream_registry.publish_chunk(task_id, StreamFinishStep())
if assistant_content:
# Reload session from DB to avoid race condition with user messages
@@ -2007,4 +2048,5 @@ async def _generate_llm_continuation_with_streaming(
task_id,
StreamError(errorText=f"Failed to generate response: {e}"),
)
await stream_registry.publish_chunk(task_id, StreamFinishStep())
await stream_registry.publish_chunk(task_id, StreamFinish())

4
autogpt_platform/backend/backend/api/features/chat/stream_registry.py
View File

@@ -857,8 +857,10 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
ResponseType,
StreamError,
StreamFinish,
StreamFinishStep,
StreamHeartbeat,
StreamStart,
StreamStartStep,
StreamTextDelta,
StreamTextEnd,
StreamTextStart,
@@ -872,6 +874,8 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
type_to_class: dict[str, type[StreamBaseResponse]] = {
ResponseType.START.value: StreamStart,
ResponseType.FINISH.value: StreamFinish,
ResponseType.START_STEP.value: StreamStartStep,
ResponseType.FINISH_STEP.value: StreamFinishStep,
ResponseType.TEXT_START.value: StreamTextStart,
ResponseType.TEXT_DELTA.value: StreamTextDelta,
ResponseType.TEXT_END.value: StreamTextEnd,

154
autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/dummy.py Normal file
View File

@@ -0,0 +1,154 @@
"""Dummy Agent Generator for testing.
Returns mock responses matching the format expected from the external service.
Enable via AGENTGENERATOR_USE_DUMMY=true in settings.
WARNING: This is for testing only. Do not use in production.
"""
import asyncio
import logging
import uuid
from typing import Any
logger = logging.getLogger(__name__)
# Dummy decomposition result (instructions type)
DUMMY_DECOMPOSITION_RESULT: dict[str, Any] = {
"type": "instructions",
"steps": [
{
"description": "Get input from user",
"action": "input",
"block_name": "AgentInputBlock",
},
{
"description": "Process the input",
"action": "process",
"block_name": "TextFormatterBlock",
},
{
"description": "Return output to user",
"action": "output",
"block_name": "AgentOutputBlock",
},
],
}
# Block IDs from backend/blocks/io.py
AGENT_INPUT_BLOCK_ID = "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b"
AGENT_OUTPUT_BLOCK_ID = "363ae599-353e-4804-937e-b2ee3cef3da4"
def _generate_dummy_agent_json() -> dict[str, Any]:
"""Generate a minimal valid agent JSON for testing."""
input_node_id = str(uuid.uuid4())
output_node_id = str(uuid.uuid4())
return {
"id": str(uuid.uuid4()),
"version": 1,
"is_active": True,
"name": "Dummy Test Agent",
"description": "A dummy agent generated for testing purposes",
"nodes": [
{
"id": input_node_id,
"block_id": AGENT_INPUT_BLOCK_ID,
"input_default": {
"name": "input",
"title": "Input",
"description": "Enter your input",
"placeholder_values": [],
},
"metadata": {"position": {"x": 0, "y": 0}},
},
{
"id": output_node_id,
"block_id": AGENT_OUTPUT_BLOCK_ID,
"input_default": {
"name": "output",
"title": "Output",
"description": "Agent output",
"format": "{output}",
},
"metadata": {"position": {"x": 400, "y": 0}},
},
],
"links": [
{
"id": str(uuid.uuid4()),
"source_id": input_node_id,
"sink_id": output_node_id,
"source_name": "result",
"sink_name": "value",
"is_static": False,
},
],
}
async def decompose_goal_dummy(
description: str,
context: str = "",
library_agents: list[dict[str, Any]] | None = None,
) -> dict[str, Any]:
"""Return dummy decomposition result."""
logger.info("Using dummy agent generator for decompose_goal")
return DUMMY_DECOMPOSITION_RESULT.copy()
async def generate_agent_dummy(
instructions: dict[str, Any],
library_agents: list[dict[str, Any]] | None = None,
operation_id: str | None = None,
task_id: str | None = None,
) -> dict[str, Any]:
"""Return dummy agent JSON after a simulated delay."""
logger.info("Using dummy agent generator for generate_agent (30s delay)")
await asyncio.sleep(30)
return _generate_dummy_agent_json()
async def generate_agent_patch_dummy(
update_request: str,
current_agent: dict[str, Any],
library_agents: list[dict[str, Any]] | None = None,
operation_id: str | None = None,
task_id: str | None = None,
) -> dict[str, Any]:
"""Return dummy patched agent (returns the current agent with updated description)."""
logger.info("Using dummy agent generator for generate_agent_patch")
patched = current_agent.copy()
patched["description"] = (
f"{current_agent.get('description', '')} (updated: {update_request})"
)
return patched
async def customize_template_dummy(
template_agent: dict[str, Any],
modification_request: str,
context: str = "",
) -> dict[str, Any]:
"""Return dummy customized template (returns template with updated description)."""
logger.info("Using dummy agent generator for customize_template")
customized = template_agent.copy()
customized["description"] = (
f"{template_agent.get('description', '')} (customized: {modification_request})"
)
return customized
async def get_blocks_dummy() -> list[dict[str, Any]]:
"""Return dummy blocks list."""
logger.info("Using dummy agent generator for get_blocks")
return [
{"id": AGENT_INPUT_BLOCK_ID, "name": "AgentInputBlock"},
{"id": AGENT_OUTPUT_BLOCK_ID, "name": "AgentOutputBlock"},
]
async def health_check_dummy() -> bool:
"""Always returns healthy for dummy service."""
return True

57
autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py
View File

@@ -12,8 +12,19 @@ import httpx
from backend.util.settings import Settings
from .dummy import (
customize_template_dummy,
decompose_goal_dummy,
generate_agent_dummy,
generate_agent_patch_dummy,
get_blocks_dummy,
health_check_dummy,
)
logger = logging.getLogger(__name__)
_dummy_mode_warned = False
def _create_error_response(
error_message: str,
@@ -90,10 +101,26 @@ def _get_settings() -> Settings:
return _settings
def is_external_service_configured() -> bool:
"""Check if external Agent Generator service is configured."""
def _is_dummy_mode() -> bool:
"""Check if dummy mode is enabled for testing."""
global _dummy_mode_warned
settings = _get_settings()
return bool(settings.config.agentgenerator_host)
is_dummy = bool(settings.config.agentgenerator_use_dummy)
if is_dummy and not _dummy_mode_warned:
logger.warning(
"Agent Generator running in DUMMY MODE - returning mock responses. "
"Do not use in production!"
)
_dummy_mode_warned = True
return is_dummy
def is_external_service_configured() -> bool:
"""Check if external Agent Generator service is configured (or dummy mode)."""
settings = _get_settings()
return bool(settings.config.agentgenerator_host) or bool(
settings.config.agentgenerator_use_dummy
)
def _get_base_url() -> str:
@@ -137,6 +164,9 @@ async def decompose_goal_external(
- {"type": "error", "error": "...", "error_type": "..."} on error
Or None on unexpected error
"""
if _is_dummy_mode():
return await decompose_goal_dummy(description, context, library_agents)
client = _get_client()
if context:
@@ -226,6 +256,11 @@ async def generate_agent_external(
Returns:
Agent JSON dict, {"status": "accepted"} for async, or error dict {"type": "error", ...} on error
"""
if _is_dummy_mode():
return await generate_agent_dummy(
instructions, library_agents, operation_id, task_id
)
client = _get_client()
# Build request payload
@@ -297,6 +332,11 @@ async def generate_agent_patch_external(
Returns:
Updated agent JSON, clarifying questions dict, {"status": "accepted"} for async, or error dict on error
"""
if _is_dummy_mode():
return await generate_agent_patch_dummy(
update_request, current_agent, library_agents, operation_id, task_id
)
client = _get_client()
# Build request payload
@@ -383,6 +423,11 @@ async def customize_template_external(
Returns:
Customized agent JSON, clarifying questions dict, or error dict on error
"""
if _is_dummy_mode():
return await customize_template_dummy(
template_agent, modification_request, context
)
client = _get_client()
request = modification_request
@@ -445,6 +490,9 @@ async def get_blocks_external() -> list[dict[str, Any]] | None:
Returns:
List of block info dicts or None on error
"""
if _is_dummy_mode():
return await get_blocks_dummy()
client = _get_client()
try:
@@ -478,6 +526,9 @@ async def health_check() -> bool:
if not is_external_service_configured():
return False
if _is_dummy_mode():
return await health_check_dummy()
client = _get_client()
try:

3
autogpt_platform/backend/backend/api/features/chat/tools/find_block.py
View File

@@ -13,7 +13,8 @@ from backend.api.features.chat.tools.models import (
NoResultsResponse,
)
from backend.api.features.store.hybrid_search import unified_hybrid_search
from backend.data.block import BlockType, get_block
from backend.blocks import get_block
from backend.blocks._base import BlockType
logger = logging.getLogger(__name__)

2
autogpt_platform/backend/backend/api/features/chat/tools/find_block_test.py
View File

@@ -10,7 +10,7 @@ from backend.api.features.chat.tools.find_block import (
FindBlockTool,
)
from backend.api.features.chat.tools.models import BlockListResponse
from backend.data.block import BlockType
from backend.blocks._base import BlockType
from ._test_data import make_session

3
autogpt_platform/backend/backend/api/features/chat/tools/run_block.py
View File

@@ -12,7 +12,8 @@ from backend.api.features.chat.tools.find_block import (
COPILOT_EXCLUDED_BLOCK_IDS,
COPILOT_EXCLUDED_BLOCK_TYPES,
)
from backend.data.block import AnyBlockSchema, get_block
from backend.blocks import get_block
from backend.blocks._base import AnyBlockSchema
from backend.data.execution import ExecutionContext
from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
from backend.data.workspace import get_or_create_workspace

2
autogpt_platform/backend/backend/api/features/chat/tools/run_block_test.py
View File

@@ -6,7 +6,7 @@ import pytest
from backend.api.features.chat.tools.models import ErrorResponse
from backend.api.features.chat.tools.run_block import RunBlockTool
from backend.data.block import BlockType
from backend.blocks._base import BlockType
from ._test_data import make_session

5
autogpt_platform/backend/backend/api/features/library/db.py
View File

@@ -12,12 +12,11 @@ import backend.api.features.store.image_gen as store_image_gen
import backend.api.features.store.media as store_media
import backend.data.graph as graph_db
import backend.data.integrations as integrations_db
from backend.data.block import BlockInput
from backend.data.db import transaction
from backend.data.execution import get_graph_execution
from backend.data.graph import GraphSettings
from backend.data.includes import AGENT_PRESET_INCLUDE, library_agent_include
from backend.data.model import CredentialsMetaInput
from backend.data.model import CredentialsMetaInput, GraphInput
from backend.integrations.creds_manager import IntegrationCredentialsManager
from backend.integrations.webhooks.graph_lifecycle_hooks import (
on_graph_activate,
@@ -1130,7 +1129,7 @@ async def create_preset_from_graph_execution(
async def update_preset(
user_id: str,
preset_id: str,
inputs: Optional[BlockInput] = None,
inputs: Optional[GraphInput] = None,
credentials: Optional[dict[str, CredentialsMetaInput]] = None,
name: Optional[str] = None,
description: Optional[str] = None,

13
autogpt_platform/backend/backend/api/features/library/model.py
View File

@@ -6,9 +6,12 @@ import prisma.enums
import prisma.models
import pydantic
from backend.data.block import BlockInput
from backend.data.graph import GraphModel, GraphSettings, GraphTriggerInfo
from backend.data.model import CredentialsMetaInput, is_credentials_field_name
from backend.data.model import (
CredentialsMetaInput,
GraphInput,
is_credentials_field_name,
)
from backend.util.json import loads as json_loads
from backend.util.models import Pagination
@@ -323,7 +326,7 @@ class LibraryAgentPresetCreatable(pydantic.BaseModel):
graph_id: str
graph_version: int
inputs: BlockInput
inputs: GraphInput
credentials: dict[str, CredentialsMetaInput]
name: str
@@ -352,7 +355,7 @@ class LibraryAgentPresetUpdatable(pydantic.BaseModel):
Request model used when updating a preset for a library agent.
"""
inputs: Optional[BlockInput] = None
inputs: Optional[GraphInput] = None
credentials: Optional[dict[str, CredentialsMetaInput]] = None
name: Optional[str] = None
@@ -395,7 +398,7 @@ class LibraryAgentPreset(LibraryAgentPresetCreatable):
"Webhook must be included in AgentPreset query when webhookId is set"
)
input_data: BlockInput = {}
input_data: GraphInput = {}
input_credentials: dict[str, CredentialsMetaInput] = {}
for preset_input in preset.InputPresets:

2
autogpt_platform/backend/backend/api/features/otto/service.py
View File

@@ -5,8 +5,8 @@ from typing import Optional
import aiohttp
from fastapi import HTTPException
from backend.blocks import get_block
from backend.data import graph as graph_db
from backend.data.block import get_block
from backend.util.settings import Settings
from .models import ApiResponse, ChatRequest, GraphData

4
autogpt_platform/backend/backend/api/features/store/content_handlers.py
View File

@@ -152,7 +152,7 @@ class BlockHandler(ContentHandler):
async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
"""Fetch blocks without embeddings."""
from backend.data.block import get_blocks
from backend.blocks import get_blocks
# Get all available blocks
all_blocks = get_blocks()
@@ -249,7 +249,7 @@ class BlockHandler(ContentHandler):
async def get_stats(self) -> dict[str, int]:
"""Get statistics about block embedding coverage."""
from backend.data.block import get_blocks
from backend.blocks import get_blocks
all_blocks = get_blocks()

8
autogpt_platform/backend/backend/api/features/store/content_handlers_test.py
View File

@@ -93,7 +93,7 @@ async def test_block_handler_get_missing_items(mocker):
mock_existing = []
with patch(
"backend.data.block.get_blocks",
"backend.blocks.get_blocks",
return_value=mock_blocks,
):
with patch(
@@ -135,7 +135,7 @@ async def test_block_handler_get_stats(mocker):
mock_embedded = [{"count": 2}]
with patch(
"backend.data.block.get_blocks",
"backend.blocks.get_blocks",
return_value=mock_blocks,
):
with patch(
@@ -327,7 +327,7 @@ async def test_block_handler_handles_missing_attributes():
mock_blocks = {"block-minimal": mock_block_class}
with patch(
"backend.data.block.get_blocks",
"backend.blocks.get_blocks",
return_value=mock_blocks,
):
with patch(
@@ -360,7 +360,7 @@ async def test_block_handler_skips_failed_blocks():
mock_blocks = {"good-block": good_block, "bad-block": bad_block}
with patch(
"backend.data.block.get_blocks",
"backend.blocks.get_blocks",
return_value=mock_blocks,
):
with patch(

2
autogpt_platform/backend/backend/api/features/store/embeddings.py
View File

@@ -662,7 +662,7 @@ async def cleanup_orphaned_embeddings() -> dict[str, Any]:
)
current_ids = {row["id"] for row in valid_agents}
elif content_type == ContentType.BLOCK:
from backend.data.block import get_blocks
from backend.blocks import get_blocks
current_ids = set(get_blocks().keys())
elif content_type == ContentType.DOCUMENTATION:

94
autogpt_platform/backend/backend/api/features/store/hybrid_search.py
View File

@@ -8,6 +8,7 @@ Includes BM25 reranking for improved lexical relevance.
import logging
import re
import time
from dataclasses import dataclass
from typing import Any, Literal
@@ -362,7 +363,11 @@ async def unified_hybrid_search(
LIMIT {limit_param} OFFSET {offset_param}
"""
results = await query_raw_with_schema(sql_query, *params)
try:
results = await query_raw_with_schema(sql_query, *params)
except Exception as e:
await _log_vector_error_diagnostics(e)
raise
total = results[0]["total_count"] if results else 0
# Apply BM25 reranking
@@ -686,7 +691,11 @@ async def hybrid_search(
LIMIT {limit_param} OFFSET {offset_param}
"""
results = await query_raw_with_schema(sql_query, *params)
try:
results = await query_raw_with_schema(sql_query, *params)
except Exception as e:
await _log_vector_error_diagnostics(e)
raise
total = results[0]["total_count"] if results else 0
@@ -718,6 +727,87 @@ async def hybrid_search_simple(
return await hybrid_search(query=query, page=page, page_size=page_size)
# ============================================================================
# Diagnostics
# ============================================================================
# Rate limit: only log vector error diagnostics once per this interval
_VECTOR_DIAG_INTERVAL_SECONDS = 60
_last_vector_diag_time: float = 0
async def _log_vector_error_diagnostics(error: Exception) -> None:
"""Log diagnostic info when 'type vector does not exist' error occurs.
Note: Diagnostic queries use query_raw_with_schema which may run on a different
pooled connection than the one that failed. Session-level search_path can differ,
so these diagnostics show cluster-wide state, not necessarily the failed session.
Includes rate limiting to avoid log spam - only logs once per minute.
Caller should re-raise the error after calling this function.
"""
global _last_vector_diag_time
# Check if this is the vector type error
error_str = str(error).lower()
if not (
"type" in error_str and "vector" in error_str and "does not exist" in error_str
):
return
# Rate limit: only log once per interval
now = time.time()
if now - _last_vector_diag_time < _VECTOR_DIAG_INTERVAL_SECONDS:
return
_last_vector_diag_time = now
try:
diagnostics: dict[str, object] = {}
try:
search_path_result = await query_raw_with_schema("SHOW search_path")
diagnostics["search_path"] = search_path_result
except Exception as e:
diagnostics["search_path"] = f"Error: {e}"
try:
schema_result = await query_raw_with_schema("SELECT current_schema()")
diagnostics["current_schema"] = schema_result
except Exception as e:
diagnostics["current_schema"] = f"Error: {e}"
try:
user_result = await query_raw_with_schema(
"SELECT current_user, session_user, current_database()"
)
diagnostics["user_info"] = user_result
except Exception as e:
diagnostics["user_info"] = f"Error: {e}"
try:
# Check pgvector extension installation (cluster-wide, stable info)
ext_result = await query_raw_with_schema(
"SELECT extname, extversion, nspname as schema "
"FROM pg_extension e "
"JOIN pg_namespace n ON e.extnamespace = n.oid "
"WHERE extname = 'vector'"
)
diagnostics["pgvector_extension"] = ext_result
except Exception as e:
diagnostics["pgvector_extension"] = f"Error: {e}"
logger.error(
f"Vector type error diagnostics:\n"
f" Error: {error}\n"
f" search_path: {diagnostics.get('search_path')}\n"
f" current_schema: {diagnostics.get('current_schema')}\n"
f" user_info: {diagnostics.get('user_info')}\n"
f" pgvector_extension: {diagnostics.get('pgvector_extension')}"
)
except Exception as diag_error:
logger.error(f"Failed to collect vector error diagnostics: {diag_error}")
# Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
# for existing code that expects the popularity parameter
HybridSearchWeights = StoreAgentSearchWeights

19
autogpt_platform/backend/backend/api/features/store/image_gen.py
View File

@@ -7,15 +7,6 @@ from replicate.client import Client as ReplicateClient
from replicate.exceptions import ReplicateError
from replicate.helpers import FileOutput
from backend.blocks.ideogram import (
AspectRatio,
ColorPalettePreset,
IdeogramModelBlock,
IdeogramModelName,
MagicPromptOption,
StyleType,
UpscaleOption,
)
from backend.data.graph import GraphBaseMeta
from backend.data.model import CredentialsMetaInput, ProviderName
from backend.integrations.credentials_store import ideogram_credentials
@@ -50,6 +41,16 @@ async def generate_agent_image_v2(graph: GraphBaseMeta | AgentGraph) -> io.Bytes
if not ideogram_credentials.api_key:
raise ValueError("Missing Ideogram API key")
from backend.blocks.ideogram import (
AspectRatio,
ColorPalettePreset,
IdeogramModelBlock,
IdeogramModelName,
MagicPromptOption,
StyleType,
UpscaleOption,
)
name = graph.name
description = f"{name} ({graph.description})" if graph.description else name

3
autogpt_platform/backend/backend/api/features/v1.py
View File

@@ -40,10 +40,11 @@ from backend.api.model import (
UpdateTimezoneRequest,
UploadFileResponse,
)
from backend.blocks import get_block, get_blocks
from backend.data import execution as execution_db
from backend.data import graph as graph_db
from backend.data.auth import api_key as api_key_db
from backend.data.block import BlockInput, CompletedBlockOutput, get_block, get_blocks
from backend.data.block import BlockInput, CompletedBlockOutput
from backend.data.credit import (
AutoTopUpConfig,
RefundRequest,

66
autogpt_platform/backend/backend/blocks/__init__.py
View File

@@ -3,22 +3,19 @@ import logging
import os
import re
from pathlib import Path
from typing import TYPE_CHECKING, TypeVar
from typing import Sequence, Type, TypeVar
from backend.blocks._base import AnyBlockSchema, BlockType
from backend.util.cache import cached
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from backend.data.block import Block
T = TypeVar("T")
@cached(ttl_seconds=3600)
def load_all_blocks() -> dict[str, type["Block"]]:
from backend.data.block import Block
def load_all_blocks() -> dict[str, type["AnyBlockSchema"]]:
from backend.blocks._base import Block
from backend.util.settings import Config
# Check if example blocks should be loaded from settings
@@ -50,8 +47,8 @@ def load_all_blocks() -> dict[str, type["Block"]]:
importlib.import_module(f".{module}", package=__name__)
# Load all Block instances from the available modules
available_blocks: dict[str, type["Block"]] = {}
for block_cls in all_subclasses(Block):
available_blocks: dict[str, type["AnyBlockSchema"]] = {}
for block_cls in _all_subclasses(Block):
class_name = block_cls.__name__
if class_name.endswith("Base"):
@@ -64,7 +61,7 @@ def load_all_blocks() -> dict[str, type["Block"]]:
"please name the class with 'Base' at the end"
)
block = block_cls.create()
block = block_cls() # pyright: ignore[reportAbstractUsage]
if not isinstance(block.id, str) or len(block.id) != 36:
raise ValueError(
@@ -105,7 +102,7 @@ def load_all_blocks() -> dict[str, type["Block"]]:
available_blocks[block.id] = block_cls
# Filter out blocks with incomplete auth configs, e.g. missing OAuth server secrets
from backend.data.block import is_block_auth_configured
from ._utils import is_block_auth_configured
filtered_blocks = {}
for block_id, block_cls in available_blocks.items():
@@ -115,11 +112,48 @@ def load_all_blocks() -> dict[str, type["Block"]]:
return filtered_blocks
__all__ = ["load_all_blocks"]
def all_subclasses(cls: type[T]) -> list[type[T]]:
def _all_subclasses(cls: type[T]) -> list[type[T]]:
subclasses = cls.__subclasses__()
for subclass in subclasses:
subclasses += all_subclasses(subclass)
subclasses += _all_subclasses(subclass)
return subclasses
# ============== Block access helper functions ============== #
def get_blocks() -> dict[str, Type["AnyBlockSchema"]]:
return load_all_blocks()
# Note on the return type annotation: https://github.com/microsoft/pyright/issues/10281
def get_block(block_id: str) -> "AnyBlockSchema | None":
cls = get_blocks().get(block_id)
return cls() if cls else None
@cached(ttl_seconds=3600)
def get_webhook_block_ids() -> Sequence[str]:
return [
id
for id, B in get_blocks().items()
if B().block_type in (BlockType.WEBHOOK, BlockType.WEBHOOK_MANUAL)
]
@cached(ttl_seconds=3600)
def get_io_block_ids() -> Sequence[str]:
return [
id
for id, B in get_blocks().items()
if B().block_type in (BlockType.INPUT, BlockType.OUTPUT)
]
@cached(ttl_seconds=3600)
def get_human_in_the_loop_block_ids() -> Sequence[str]:
return [
id
for id, B in get_blocks().items()
if B().block_type == BlockType.HUMAN_IN_THE_LOOP
]

739
autogpt_platform/backend/backend/blocks/_base.py Normal file
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@@ -0,0 +1,739 @@
import inspect
import logging
from abc import ABC, abstractmethod
from enum import Enum
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Generic,
Optional,
Type,
TypeAlias,
TypeVar,
cast,
get_origin,
)
import jsonref
import jsonschema
from pydantic import BaseModel
from backend.data.block import BlockInput, BlockOutput, BlockOutputEntry
from backend.data.model import (
Credentials,
CredentialsFieldInfo,
CredentialsMetaInput,
SchemaField,
is_credentials_field_name,
)
from backend.integrations.providers import ProviderName
from backend.util import json
from backend.util.exceptions import (
BlockError,
BlockExecutionError,
BlockInputError,
BlockOutputError,
BlockUnknownError,
)
from backend.util.settings import Config
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from backend.data.execution import ExecutionContext
from backend.data.model import ContributorDetails, NodeExecutionStats
from ..data.graph import Link
app_config = Config()
BlockTestOutput = BlockOutputEntry | tuple[str, Callable[[Any], bool]]
class BlockType(Enum):
STANDARD = "Standard"
INPUT = "Input"
OUTPUT = "Output"
NOTE = "Note"
WEBHOOK = "Webhook"
WEBHOOK_MANUAL = "Webhook (manual)"
AGENT = "Agent"
AI = "AI"
AYRSHARE = "Ayrshare"
HUMAN_IN_THE_LOOP = "Human In The Loop"
class BlockCategory(Enum):
AI = "Block that leverages AI to perform a task."
SOCIAL = "Block that interacts with social media platforms."
TEXT = "Block that processes text data."
SEARCH = "Block that searches or extracts information from the internet."
BASIC = "Block that performs basic operations."
INPUT = "Block that interacts with input of the graph."
OUTPUT = "Block that interacts with output of the graph."
LOGIC = "Programming logic to control the flow of your agent"
COMMUNICATION = "Block that interacts with communication platforms."
DEVELOPER_TOOLS = "Developer tools such as GitHub blocks."
DATA = "Block that interacts with structured data."
HARDWARE = "Block that interacts with hardware."
AGENT = "Block that interacts with other agents."
CRM = "Block that interacts with CRM services."
SAFETY = (
"Block that provides AI safety mechanisms such as detecting harmful content"
)
PRODUCTIVITY = "Block that helps with productivity"
ISSUE_TRACKING = "Block that helps with issue tracking"
MULTIMEDIA = "Block that interacts with multimedia content"
MARKETING = "Block that helps with marketing"
def dict(self) -> dict[str, str]:
return {"category": self.name, "description": self.value}
class BlockCostType(str, Enum):
RUN = "run" # cost X credits per run
BYTE = "byte" # cost X credits per byte
SECOND = "second" # cost X credits per second
class BlockCost(BaseModel):
cost_amount: int
cost_filter: BlockInput
cost_type: BlockCostType
def __init__(
self,
cost_amount: int,
cost_type: BlockCostType = BlockCostType.RUN,
cost_filter: Optional[BlockInput] = None,
**data: Any,
) -> None:
super().__init__(
cost_amount=cost_amount,
cost_filter=cost_filter or {},
cost_type=cost_type,
**data,
)
class BlockInfo(BaseModel):
id: str
name: str
inputSchema: dict[str, Any]
outputSchema: dict[str, Any]
costs: list[BlockCost]
description: str
categories: list[dict[str, str]]
contributors: list[dict[str, Any]]
staticOutput: bool
uiType: str
class BlockSchema(BaseModel):
cached_jsonschema: ClassVar[dict[str, Any]]
@classmethod
def jsonschema(cls) -> dict[str, Any]:
if cls.cached_jsonschema:
return cls.cached_jsonschema
model = jsonref.replace_refs(cls.model_json_schema(), merge_props=True)
def ref_to_dict(obj):
if isinstance(obj, dict):
# OpenAPI <3.1 does not support sibling fields that has a $ref key
# So sometimes, the schema has an "allOf"/"anyOf"/"oneOf" with 1 item.
keys = {"allOf", "anyOf", "oneOf"}
one_key = next((k for k in keys if k in obj and len(obj[k]) == 1), None)
if one_key:
obj.update(obj[one_key][0])
return {
key: ref_to_dict(value)
for key, value in obj.items()
if not key.startswith("$") and key != one_key
}
elif isinstance(obj, list):
return [ref_to_dict(item) for item in obj]
return obj
cls.cached_jsonschema = cast(dict[str, Any], ref_to_dict(model))
return cls.cached_jsonschema
@classmethod
def validate_data(cls, data: BlockInput) -> str | None:
return json.validate_with_jsonschema(
schema=cls.jsonschema(),
data={k: v for k, v in data.items() if v is not None},
)
@classmethod
def get_mismatch_error(cls, data: BlockInput) -> str | None:
return cls.validate_data(data)
@classmethod
def get_field_schema(cls, field_name: str) -> dict[str, Any]:
model_schema = cls.jsonschema().get("properties", {})
if not model_schema:
raise ValueError(f"Invalid model schema {cls}")
property_schema = model_schema.get(field_name)
if not property_schema:
raise ValueError(f"Invalid property name {field_name}")
return property_schema
@classmethod
def validate_field(cls, field_name: str, data: BlockInput) -> str | None:
"""
Validate the data against a specific property (one of the input/output name).
Returns the validation error message if the data does not match the schema.
"""
try:
property_schema = cls.get_field_schema(field_name)
jsonschema.validate(json.to_dict(data), property_schema)
return None
except jsonschema.ValidationError as e:
return str(e)
@classmethod
def get_fields(cls) -> set[str]:
return set(cls.model_fields.keys())
@classmethod
def get_required_fields(cls) -> set[str]:
return {
field
for field, field_info in cls.model_fields.items()
if field_info.is_required()
}
@classmethod
def __pydantic_init_subclass__(cls, **kwargs):
"""Validates the schema definition. Rules:
- Fields with annotation `CredentialsMetaInput` MUST be
named `credentials` or `*_credentials`
- Fields named `credentials` or `*_credentials` MUST be
of type `CredentialsMetaInput`
"""
super().__pydantic_init_subclass__(**kwargs)
# Reset cached JSON schema to prevent inheriting it from parent class
cls.cached_jsonschema = {}
credentials_fields = cls.get_credentials_fields()
for field_name in cls.get_fields():
if is_credentials_field_name(field_name):
if field_name not in credentials_fields:
raise TypeError(
f"Credentials field '{field_name}' on {cls.__qualname__} "
f"is not of type {CredentialsMetaInput.__name__}"
)
CredentialsMetaInput.validate_credentials_field_schema(
cls.get_field_schema(field_name), field_name
)
elif field_name in credentials_fields:
raise KeyError(
f"Credentials field '{field_name}' on {cls.__qualname__} "
"has invalid name: must be 'credentials' or *_credentials"
)
@classmethod
def get_credentials_fields(cls) -> dict[str, type[CredentialsMetaInput]]:
return {
field_name: info.annotation
for field_name, info in cls.model_fields.items()
if (
inspect.isclass(info.annotation)
and issubclass(
get_origin(info.annotation) or info.annotation,
CredentialsMetaInput,
)
)
}
@classmethod
def get_auto_credentials_fields(cls) -> dict[str, dict[str, Any]]:
"""
Get fields that have auto_credentials metadata (e.g., GoogleDriveFileInput).
Returns a dict mapping kwarg_name -> {field_name, auto_credentials_config}
Raises:
ValueError: If multiple fields have the same kwarg_name, as this would
cause silent overwriting and only the last field would be processed.
"""
result: dict[str, dict[str, Any]] = {}
schema = cls.jsonschema()
properties = schema.get("properties", {})
for field_name, field_schema in properties.items():
auto_creds = field_schema.get("auto_credentials")
if auto_creds:
kwarg_name = auto_creds.get("kwarg_name", "credentials")
if kwarg_name in result:
raise ValueError(
f"Duplicate auto_credentials kwarg_name '{kwarg_name}' "
f"in fields '{result[kwarg_name]['field_name']}' and "
f"'{field_name}' on {cls.__qualname__}"
)
result[kwarg_name] = {
"field_name": field_name,
"config": auto_creds,
}
return result
@classmethod
def get_credentials_fields_info(cls) -> dict[str, CredentialsFieldInfo]:
result = {}
# Regular credentials fields
for field_name in cls.get_credentials_fields().keys():
result[field_name] = CredentialsFieldInfo.model_validate(
cls.get_field_schema(field_name), by_alias=True
)
# Auto-generated credentials fields (from GoogleDriveFileInput etc.)
for kwarg_name, info in cls.get_auto_credentials_fields().items():
config = info["config"]
# Build a schema-like dict that CredentialsFieldInfo can parse
auto_schema = {
"credentials_provider": [config.get("provider", "google")],
"credentials_types": [config.get("type", "oauth2")],
"credentials_scopes": config.get("scopes"),
}
result[kwarg_name] = CredentialsFieldInfo.model_validate(
auto_schema, by_alias=True
)
return result
@classmethod
def get_input_defaults(cls, data: BlockInput) -> BlockInput:
return data # Return as is, by default.
@classmethod
def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
input_fields_from_nodes = {link.sink_name for link in links}
return input_fields_from_nodes - set(data)
@classmethod
def get_missing_input(cls, data: BlockInput) -> set[str]:
return cls.get_required_fields() - set(data)
class BlockSchemaInput(BlockSchema):
"""
Base schema class for block inputs.
All block input schemas should extend this class for consistency.
"""
pass
class BlockSchemaOutput(BlockSchema):
"""
Base schema class for block outputs that includes a standard error field.
All block output schemas should extend this class to ensure consistent error handling.
"""
error: str = SchemaField(
description="Error message if the operation failed", default=""
)
BlockSchemaInputType = TypeVar("BlockSchemaInputType", bound=BlockSchemaInput)
BlockSchemaOutputType = TypeVar("BlockSchemaOutputType", bound=BlockSchemaOutput)
class EmptyInputSchema(BlockSchemaInput):
pass
class EmptyOutputSchema(BlockSchemaOutput):
pass
# For backward compatibility - will be deprecated
EmptySchema = EmptyOutputSchema
# --8<-- [start:BlockWebhookConfig]
class BlockManualWebhookConfig(BaseModel):
"""
Configuration model for webhook-triggered blocks on which
the user has to manually set up the webhook at the provider.
"""
provider: ProviderName
"""The service provider that the webhook connects to"""
webhook_type: str
"""
Identifier for the webhook type. E.g. GitHub has repo and organization level hooks.
Only for use in the corresponding `WebhooksManager`.
"""
event_filter_input: str = ""
"""
Name of the block's event filter input.
Leave empty if the corresponding webhook doesn't have distinct event/payload types.
"""
event_format: str = "{event}"
"""
Template string for the event(s) that a block instance subscribes to.
Applied individually to each event selected in the event filter input.
Example: `"pull_request.{event}"` -> `"pull_request.opened"`
"""
class BlockWebhookConfig(BlockManualWebhookConfig):
"""
Configuration model for webhook-triggered blocks for which
the webhook can be automatically set up through the provider's API.
"""
resource_format: str
"""
Template string for the resource that a block instance subscribes to.
Fields will be filled from the block's inputs (except `payload`).
Example: `f"{repo}/pull_requests"` (note: not how it's actually implemented)
Only for use in the corresponding `WebhooksManager`.
"""
# --8<-- [end:BlockWebhookConfig]
class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
def __init__(
self,
id: str = "",
description: str = "",
contributors: list["ContributorDetails"] = [],
categories: set[BlockCategory] | None = None,
input_schema: Type[BlockSchemaInputType] = EmptyInputSchema,
output_schema: Type[BlockSchemaOutputType] = EmptyOutputSchema,
test_input: BlockInput | list[BlockInput] | None = None,
test_output: BlockTestOutput | list[BlockTestOutput] | None = None,
test_mock: dict[str, Any] | None = None,
test_credentials: Optional[Credentials | dict[str, Credentials]] = None,
disabled: bool = False,
static_output: bool = False,
block_type: BlockType = BlockType.STANDARD,
webhook_config: Optional[BlockWebhookConfig | BlockManualWebhookConfig] = None,
is_sensitive_action: bool = False,
):
"""
Initialize the block with the given schema.
Args:
id: The unique identifier for the block, this value will be persisted in the
DB. So it should be a unique and constant across the application run.
Use the UUID format for the ID.
description: The description of the block, explaining what the block does.
contributors: The list of contributors who contributed to the block.
input_schema: The schema, defined as a Pydantic model, for the input data.
output_schema: The schema, defined as a Pydantic model, for the output data.
test_input: The list or single sample input data for the block, for testing.
test_output: The list or single expected output if the test_input is run.
test_mock: function names on the block implementation to mock on test run.
disabled: If the block is disabled, it will not be available for execution.
static_output: Whether the output links of the block are static by default.
"""
from backend.data.model import NodeExecutionStats
self.id = id
self.input_schema = input_schema
self.output_schema = output_schema
self.test_input = test_input
self.test_output = test_output
self.test_mock = test_mock
self.test_credentials = test_credentials
self.description = description
self.categories = categories or set()
self.contributors = contributors or set()
self.disabled = disabled
self.static_output = static_output
self.block_type = block_type
self.webhook_config = webhook_config
self.is_sensitive_action = is_sensitive_action
self.execution_stats: "NodeExecutionStats" = NodeExecutionStats()
if self.webhook_config:
if isinstance(self.webhook_config, BlockWebhookConfig):
# Enforce presence of credentials field on auto-setup webhook blocks
if not (cred_fields := self.input_schema.get_credentials_fields()):
raise TypeError(
"credentials field is required on auto-setup webhook blocks"
)
# Disallow multiple credentials inputs on webhook blocks
elif len(cred_fields) > 1:
raise ValueError(
"Multiple credentials inputs not supported on webhook blocks"
)
self.block_type = BlockType.WEBHOOK
else:
self.block_type = BlockType.WEBHOOK_MANUAL
# Enforce shape of webhook event filter, if present
if self.webhook_config.event_filter_input:
event_filter_field = self.input_schema.model_fields[
self.webhook_config.event_filter_input
]
if not (
isinstance(event_filter_field.annotation, type)
and issubclass(event_filter_field.annotation, BaseModel)
and all(
field.annotation is bool
for field in event_filter_field.annotation.model_fields.values()
)
):
raise NotImplementedError(
f"{self.name} has an invalid webhook event selector: "
"field must be a BaseModel and all its fields must be boolean"
)
# Enforce presence of 'payload' input
if "payload" not in self.input_schema.model_fields:
raise TypeError(
f"{self.name} is webhook-triggered but has no 'payload' input"
)
# Disable webhook-triggered block if webhook functionality not available
if not app_config.platform_base_url:
self.disabled = True
@abstractmethod
async def run(self, input_data: BlockSchemaInputType, **kwargs) -> BlockOutput:
"""
Run the block with the given input data.
Args:
input_data: The input data with the structure of input_schema.
Kwargs: Currently 14/02/2025 these include
graph_id: The ID of the graph.
node_id: The ID of the node.
graph_exec_id: The ID of the graph execution.
node_exec_id: The ID of the node execution.
user_id: The ID of the user.
Returns:
A Generator that yields (output_name, output_data).
output_name: One of the output name defined in Block's output_schema.
output_data: The data for the output_name, matching the defined schema.
"""
# --- satisfy the type checker, never executed -------------
if False: # noqa: SIM115
yield "name", "value" # pyright: ignore[reportMissingYield]
raise NotImplementedError(f"{self.name} does not implement the run method.")
async def run_once(
self, input_data: BlockSchemaInputType, output: str, **kwargs
) -> Any:
async for item in self.run(input_data, **kwargs):
name, data = item
if name == output:
return data
raise ValueError(f"{self.name} did not produce any output for {output}")
def merge_stats(self, stats: "NodeExecutionStats") -> "NodeExecutionStats":
self.execution_stats += stats
return self.execution_stats
@property
def name(self):
return self.__class__.__name__
def to_dict(self):
return {
"id": self.id,
"name": self.name,
"inputSchema": self.input_schema.jsonschema(),
"outputSchema": self.output_schema.jsonschema(),
"description": self.description,
"categories": [category.dict() for category in self.categories],
"contributors": [
contributor.model_dump() for contributor in self.contributors
],
"staticOutput": self.static_output,
"uiType": self.block_type.value,
}
def get_info(self) -> BlockInfo:
from backend.data.credit import get_block_cost
return BlockInfo(
id=self.id,
name=self.name,
inputSchema=self.input_schema.jsonschema(),
outputSchema=self.output_schema.jsonschema(),
costs=get_block_cost(self),
description=self.description,
categories=[category.dict() for category in self.categories],
contributors=[
contributor.model_dump() for contributor in self.contributors
],
staticOutput=self.static_output,
uiType=self.block_type.value,
)
async def execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
try:
async for output_name, output_data in self._execute(input_data, **kwargs):
yield output_name, output_data
except Exception as ex:
if isinstance(ex, BlockError):
raise ex
else:
raise (
BlockExecutionError
if isinstance(ex, ValueError)
else BlockUnknownError
)(
message=str(ex),
block_name=self.name,
block_id=self.id,
) from ex
async def is_block_exec_need_review(
self,
input_data: BlockInput,
*,
user_id: str,
node_id: str,
node_exec_id: str,
graph_exec_id: str,
graph_id: str,
graph_version: int,
execution_context: "ExecutionContext",
**kwargs,
) -> tuple[bool, BlockInput]:
"""
Check if this block execution needs human review and handle the review process.
Returns:
Tuple of (should_pause, input_data_to_use)
- should_pause: True if execution should be paused for review
- input_data_to_use: The input data to use (may be modified by reviewer)
"""
if not (
self.is_sensitive_action and execution_context.sensitive_action_safe_mode
):
return False, input_data
from backend.blocks.helpers.review import HITLReviewHelper
# Handle the review request and get decision
decision = await HITLReviewHelper.handle_review_decision(
input_data=input_data,
user_id=user_id,
node_id=node_id,
node_exec_id=node_exec_id,
graph_exec_id=graph_exec_id,
graph_id=graph_id,
graph_version=graph_version,
block_name=self.name,
editable=True,
)
if decision is None:
# We're awaiting review - pause execution
return True, input_data
if not decision.should_proceed:
# Review was rejected, raise an error to stop execution
raise BlockExecutionError(
message=f"Block execution rejected by reviewer: {decision.message}",
block_name=self.name,
block_id=self.id,
)
# Review was approved - use the potentially modified data
# ReviewResult.data must be a dict for block inputs
reviewed_data = decision.review_result.data
if not isinstance(reviewed_data, dict):
raise BlockExecutionError(
message=f"Review data must be a dict for block input, got {type(reviewed_data).__name__}",
block_name=self.name,
block_id=self.id,
)
return False, reviewed_data
async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
# Check for review requirement only if running within a graph execution context
# Direct block execution (e.g., from chat) skips the review process
has_graph_context = all(
key in kwargs
for key in (
"node_exec_id",
"graph_exec_id",
"graph_id",
"execution_context",
)
)
if has_graph_context:
should_pause, input_data = await self.is_block_exec_need_review(
input_data, **kwargs
)
if should_pause:
return
# Validate the input data (original or reviewer-modified) once
if error := self.input_schema.validate_data(input_data):
raise BlockInputError(
message=f"Unable to execute block with invalid input data: {error}",
block_name=self.name,
block_id=self.id,
)
# Use the validated input data
async for output_name, output_data in self.run(
self.input_schema(**{k: v for k, v in input_data.items() if v is not None}),
**kwargs,
):
if output_name == "error":
raise BlockExecutionError(
message=output_data, block_name=self.name, block_id=self.id
)
if self.block_type == BlockType.STANDARD and (
error := self.output_schema.validate_field(output_name, output_data)
):
raise BlockOutputError(
message=f"Block produced an invalid output data: {error}",
block_name=self.name,
block_id=self.id,
)
yield output_name, output_data
def is_triggered_by_event_type(
self, trigger_config: dict[str, Any], event_type: str
) -> bool:
if not self.webhook_config:
raise TypeError("This method can't be used on non-trigger blocks")
if not self.webhook_config.event_filter_input:
return True
event_filter = trigger_config.get(self.webhook_config.event_filter_input)
if not event_filter:
raise ValueError("Event filter is not configured on trigger")
return event_type in [
self.webhook_config.event_format.format(event=k)
for k in event_filter
if event_filter[k] is True
]
# Type alias for any block with standard input/output schemas
AnyBlockSchema: TypeAlias = Block[BlockSchemaInput, BlockSchemaOutput]

122
autogpt_platform/backend/backend/blocks/_utils.py Normal file
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import logging
import os
from backend.integrations.providers import ProviderName
from ._base import AnyBlockSchema
logger = logging.getLogger(__name__)
def is_block_auth_configured(
block_cls: type[AnyBlockSchema],
) -> bool:
"""
Check if a block has a valid authentication method configured at runtime.
For example if a block is an OAuth-only block and there env vars are not set,
do not show it in the UI.
"""
from backend.sdk.registry import AutoRegistry
# Create an instance to access input_schema
try:
block = block_cls()
except Exception as e:
# If we can't create a block instance, assume it's not OAuth-only
logger.error(f"Error creating block instance for {block_cls.__name__}: {e}")
return True
logger.debug(
f"Checking if block {block_cls.__name__} has a valid provider configured"
)
# Get all credential inputs from input schema
credential_inputs = block.input_schema.get_credentials_fields_info()
required_inputs = block.input_schema.get_required_fields()
if not credential_inputs:
logger.debug(
f"Block {block_cls.__name__} has no credential inputs - Treating as valid"
)
return True
# Check credential inputs
if len(required_inputs.intersection(credential_inputs.keys())) == 0:
logger.debug(
f"Block {block_cls.__name__} has only optional credential inputs"
" - will work without credentials configured"
)
# Check if the credential inputs for this block are correctly configured
for field_name, field_info in credential_inputs.items():
provider_names = field_info.provider
if not provider_names:
logger.warning(
f"Block {block_cls.__name__} "
f"has credential input '{field_name}' with no provider options"
" - Disabling"
)
return False
# If a field has multiple possible providers, each one needs to be usable to
# prevent breaking the UX
for _provider_name in provider_names:
provider_name = _provider_name.value
if provider_name in ProviderName.__members__.values():
logger.debug(
f"Block {block_cls.__name__} credential input '{field_name}' "
f"provider '{provider_name}' is part of the legacy provider system"
" - Treating as valid"
)
break
provider = AutoRegistry.get_provider(provider_name)
if not provider:
logger.warning(
f"Block {block_cls.__name__} credential input '{field_name}' "
f"refers to unknown provider '{provider_name}' - Disabling"
)
return False
# Check the provider's supported auth types
if field_info.supported_types != provider.supported_auth_types:
logger.warning(
f"Block {block_cls.__name__} credential input '{field_name}' "
f"has mismatched supported auth types (field <> Provider): "
f"{field_info.supported_types} != {provider.supported_auth_types}"
)
if not (supported_auth_types := provider.supported_auth_types):
# No auth methods are been configured for this provider
logger.warning(
f"Block {block_cls.__name__} credential input '{field_name}' "
f"provider '{provider_name}' "
"has no authentication methods configured - Disabling"
)
return False
# Check if provider supports OAuth
if "oauth2" in supported_auth_types:
# Check if OAuth environment variables are set
if (oauth_config := provider.oauth_config) and bool(
os.getenv(oauth_config.client_id_env_var)
and os.getenv(oauth_config.client_secret_env_var)
):
logger.debug(
f"Block {block_cls.__name__} credential input '{field_name}' "
f"provider '{provider_name}' is configured for OAuth"
)
else:
logger.error(
f"Block {block_cls.__name__} credential input '{field_name}' "
f"provider '{provider_name}' "
"is missing OAuth client ID or secret - Disabling"
)
return False
logger.debug(
f"Block {block_cls.__name__} credential input '{field_name}' is valid; "
f"supported credential types: {', '.join(field_info.supported_types)}"
)
return True

13
autogpt_platform/backend/backend/blocks/agent.py
View File

@@ -1,7 +1,7 @@
import logging
from typing import Any, Optional
from typing import TYPE_CHECKING, Any, Optional
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockInput,
@@ -9,13 +9,15 @@ from backend.data.block import (
BlockSchema,
BlockSchemaInput,
BlockType,
get_block,
)
from backend.data.execution import ExecutionContext, ExecutionStatus, NodesInputMasks
from backend.data.model import NodeExecutionStats, SchemaField
from backend.util.json import validate_with_jsonschema
from backend.util.retry import func_retry
if TYPE_CHECKING:
from backend.executor.utils import LogMetadata
_logger = logging.getLogger(__name__)
@@ -124,9 +126,10 @@ class AgentExecutorBlock(Block):
graph_version: int,
graph_exec_id: str,
user_id: str,
logger,
logger: "LogMetadata",
) -> BlockOutput:
from backend.blocks import get_block
from backend.data.execution import ExecutionEventType
from backend.executor import utils as execution_utils
@@ -198,7 +201,7 @@ class AgentExecutorBlock(Block):
self,
graph_exec_id: str,
user_id: str,
logger,
logger: "LogMetadata",
) -> None:
from backend.executor import utils as execution_utils

12
autogpt_platform/backend/backend/blocks/ai_condition.py
View File

@@ -1,5 +1,11 @@
from typing import Any
from backend.blocks._base import (
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.llm import (
DEFAULT_LLM_MODEL,
TEST_CREDENTIALS,
@@ -11,12 +17,6 @@ from backend.blocks.llm import (
LLMResponse,
llm_call,
)
from backend.data.block import (
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.data.model import APIKeyCredentials, NodeExecutionStats, SchemaField

2
autogpt_platform/backend/backend/blocks/ai_image_customizer.py
View File

@@ -6,7 +6,7 @@ from pydantic import SecretStr
from replicate.client import Client as ReplicateClient
from replicate.helpers import FileOutput
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

7
autogpt_platform/backend/backend/blocks/ai_image_generator_block.py
View File

@@ -5,7 +5,12 @@ from pydantic import SecretStr
from replicate.client import Client as ReplicateClient
from replicate.helpers import FileOutput
from backend.data.block import Block, BlockCategory, BlockSchemaInput, BlockSchemaOutput
from backend.blocks._base import (
Block,
BlockCategory,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.data.execution import ExecutionContext
from backend.data.model import (
APIKeyCredentials,

2
autogpt_platform/backend/backend/blocks/ai_music_generator.py
View File

@@ -6,7 +6,7 @@ from typing import Literal
from pydantic import SecretStr
from replicate.client import Client as ReplicateClient
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py
View File

@@ -6,7 +6,7 @@ from typing import Literal
from pydantic import SecretStr
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

14
autogpt_platform/backend/backend/blocks/apollo/organization.py
View File

@@ -1,3 +1,10 @@
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.apollo._api import ApolloClient
from backend.blocks.apollo._auth import (
TEST_CREDENTIALS,
@@ -10,13 +17,6 @@ from backend.blocks.apollo.models import (
PrimaryPhone,
SearchOrganizationsRequest,
)
from backend.data.block import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.data.model import CredentialsField, SchemaField

14
autogpt_platform/backend/backend/blocks/apollo/people.py
View File

@@ -1,5 +1,12 @@
import asyncio
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.apollo._api import ApolloClient
from backend.blocks.apollo._auth import (
TEST_CREDENTIALS,
@@ -14,13 +21,6 @@ from backend.blocks.apollo.models import (
SearchPeopleRequest,
SenorityLevels,
)
from backend.data.block import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.data.model import CredentialsField, SchemaField

14
autogpt_platform/backend/backend/blocks/apollo/person.py
View File

@@ -1,3 +1,10 @@
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.apollo._api import ApolloClient
from backend.blocks.apollo._auth import (
TEST_CREDENTIALS,
@@ -6,13 +13,6 @@ from backend.blocks.apollo._auth import (
ApolloCredentialsInput,
)
from backend.blocks.apollo.models import Contact, EnrichPersonRequest
from backend.data.block import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.data.model import CredentialsField, SchemaField

2
autogpt_platform/backend/backend/blocks/ayrshare/_util.py
View File

@@ -3,7 +3,7 @@ from typing import Optional
from pydantic import BaseModel, Field
from backend.data.block import BlockSchemaInput
from backend.blocks._base import BlockSchemaInput
from backend.data.model import SchemaField, UserIntegrations
from backend.integrations.ayrshare import AyrshareClient
from backend.util.clients import get_database_manager_async_client

2
autogpt_platform/backend/backend/blocks/basic.py
View File

@@ -1,7 +1,7 @@
import enum
from typing import Any
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/block.py
View File

@@ -2,7 +2,7 @@ import os
import re
from typing import Type
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/branching.py
View File

@@ -1,7 +1,7 @@
from enum import Enum
from typing import Any
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

181
autogpt_platform/backend/backend/blocks/claude_code.py
View File

@@ -1,12 +1,12 @@
import json
import shlex
import uuid
from typing import Literal, Optional
from typing import TYPE_CHECKING, Literal, Optional
from e2b import AsyncSandbox as BaseAsyncSandbox
from pydantic import BaseModel, SecretStr
from pydantic import SecretStr
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
@@ -20,6 +20,13 @@ from backend.data.model import (
SchemaField,
)
from backend.integrations.providers import ProviderName
from backend.util.sandbox_files import (
SandboxFileOutput,
extract_and_store_sandbox_files,
)
if TYPE_CHECKING:
from backend.executor.utils import ExecutionContext
class ClaudeCodeExecutionError(Exception):
@@ -174,22 +181,15 @@ class ClaudeCodeBlock(Block):
advanced=True,
)
class FileOutput(BaseModel):
"""A file extracted from the sandbox."""
path: str
relative_path: str # Path relative to working directory (for GitHub, etc.)
name: str
content: str
class Output(BlockSchemaOutput):
response: str = SchemaField(
description="The output/response from Claude Code execution"
)
files: list["ClaudeCodeBlock.FileOutput"] = SchemaField(
files: list[SandboxFileOutput] = SchemaField(
description=(
"List of text files created/modified by Claude Code during this execution. "
"Each file has 'path', 'relative_path', 'name', and 'content' fields."
"Each file has 'path', 'relative_path', 'name', 'content', and 'workspace_ref' fields. "
"workspace_ref contains a workspace:// URI if the file was stored to workspace."
)
)
conversation_history: str = SchemaField(
@@ -252,6 +252,7 @@ class ClaudeCodeBlock(Block):
"relative_path": "index.html",
"name": "index.html",
"content": "<html>Hello World</html>",
"workspace_ref": None,
}
],
),
@@ -267,11 +268,12 @@ class ClaudeCodeBlock(Block):
"execute_claude_code": lambda *args, **kwargs: (
"Created index.html with hello world content", # response
[
ClaudeCodeBlock.FileOutput(
SandboxFileOutput(
path="/home/user/index.html",
relative_path="index.html",
name="index.html",
content="<html>Hello World</html>",
workspace_ref=None,
)
], # files
"User: Create a hello world HTML file\n"
@@ -294,7 +296,8 @@ class ClaudeCodeBlock(Block):
existing_sandbox_id: str,
conversation_history: str,
dispose_sandbox: bool,
) -> tuple[str, list["ClaudeCodeBlock.FileOutput"], str, str, str]:
execution_context: "ExecutionContext",
) -> tuple[str, list[SandboxFileOutput], str, str, str]:
"""
Execute Claude Code in an E2B sandbox.
@@ -449,14 +452,18 @@ class ClaudeCodeBlock(Block):
else:
new_conversation_history = turn_entry
# Extract files created/modified during this run
files = await self._extract_files(
sandbox, working_directory, start_timestamp
# Extract files created/modified during this run and store to workspace
sandbox_files = await extract_and_store_sandbox_files(
sandbox=sandbox,
working_directory=working_directory,
execution_context=execution_context,
since_timestamp=start_timestamp,
text_only=True,
)
return (
response,
files,
sandbox_files, # Already SandboxFileOutput objects
new_conversation_history,
current_session_id,
sandbox_id,
@@ -471,140 +478,6 @@ class ClaudeCodeBlock(Block):
if dispose_sandbox and sandbox:
await sandbox.kill()
async def _extract_files(
self,
sandbox: BaseAsyncSandbox,
working_directory: str,
since_timestamp: str | None = None,
) -> list["ClaudeCodeBlock.FileOutput"]:
"""
Extract text files created/modified during this Claude Code execution.
Args:
sandbox: The E2B sandbox instance
working_directory: Directory to search for files
since_timestamp: ISO timestamp - only return files modified after this time
Returns:
List of FileOutput objects with path, relative_path, name, and content
"""
files: list[ClaudeCodeBlock.FileOutput] = []
# Text file extensions we can safely read as text
text_extensions = {
".txt",
".md",
".html",
".htm",
".css",
".js",
".ts",
".jsx",
".tsx",
".json",
".xml",
".yaml",
".yml",
".toml",
".ini",
".cfg",
".conf",
".py",
".rb",
".php",
".java",
".c",
".cpp",
".h",
".hpp",
".cs",
".go",
".rs",
".swift",
".kt",
".scala",
".sh",
".bash",
".zsh",
".sql",
".graphql",
".env",
".gitignore",
".dockerfile",
"Dockerfile",
".vue",
".svelte",
".astro",
".mdx",
".rst",
".tex",
".csv",
".log",
}
try:
# List files recursively using find command
# Exclude node_modules and .git directories, but allow hidden files
# like .env and .gitignore (they're filtered by text_extensions later)
# Filter by timestamp to only get files created/modified during this run
safe_working_dir = shlex.quote(working_directory)
timestamp_filter = ""
if since_timestamp:
timestamp_filter = f"-newermt {shlex.quote(since_timestamp)} "
find_result = await sandbox.commands.run(
f"find {safe_working_dir} -type f "
f"{timestamp_filter}"
f"-not -path '*/node_modules/*' "
f"-not -path '*/.git/*' "
f"2>/dev/null"
)
if find_result.stdout:
for file_path in find_result.stdout.strip().split("\n"):
if not file_path:
continue
# Check if it's a text file we can read
is_text = any(
file_path.endswith(ext) for ext in text_extensions
) or file_path.endswith("Dockerfile")
if is_text:
try:
content = await sandbox.files.read(file_path)
# Handle bytes or string
if isinstance(content, bytes):
content = content.decode("utf-8", errors="replace")
# Extract filename from path
file_name = file_path.split("/")[-1]
# Calculate relative path by stripping working directory
relative_path = file_path
if file_path.startswith(working_directory):
relative_path = file_path[len(working_directory) :]
# Remove leading slash if present
if relative_path.startswith("/"):
relative_path = relative_path[1:]
files.append(
ClaudeCodeBlock.FileOutput(
path=file_path,
relative_path=relative_path,
name=file_name,
content=content,
)
)
except Exception:
# Skip files that can't be read
pass
except Exception:
# If file extraction fails, return empty results
pass
return files
def _escape_prompt(self, prompt: str) -> str:
"""Escape the prompt for safe shell execution."""
# Use single quotes and escape any single quotes in the prompt
@@ -617,6 +490,7 @@ class ClaudeCodeBlock(Block):
*,
e2b_credentials: APIKeyCredentials,
anthropic_credentials: APIKeyCredentials,
execution_context: "ExecutionContext",
**kwargs,
) -> BlockOutput:
try:
@@ -637,6 +511,7 @@ class ClaudeCodeBlock(Block):
existing_sandbox_id=input_data.sandbox_id,
conversation_history=input_data.conversation_history,
dispose_sandbox=input_data.dispose_sandbox,
execution_context=execution_context,
)
yield "response", response

75
autogpt_platform/backend/backend/blocks/code_executor.py
View File

@@ -1,12 +1,12 @@
from enum import Enum
from typing import Any, Literal, Optional
from typing import TYPE_CHECKING, Any, Literal, Optional
from e2b_code_interpreter import AsyncSandbox
from e2b_code_interpreter import Result as E2BExecutionResult
from e2b_code_interpreter.charts import Chart as E2BExecutionResultChart
from pydantic import BaseModel, Field, JsonValue, SecretStr
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
@@ -20,6 +20,13 @@ from backend.data.model import (
SchemaField,
)
from backend.integrations.providers import ProviderName
from backend.util.sandbox_files import (
SandboxFileOutput,
extract_and_store_sandbox_files,
)
if TYPE_CHECKING:
from backend.executor.utils import ExecutionContext
TEST_CREDENTIALS = APIKeyCredentials(
id="01234567-89ab-cdef-0123-456789abcdef",
@@ -85,6 +92,9 @@ class CodeExecutionResult(MainCodeExecutionResult):
class BaseE2BExecutorMixin:
"""Shared implementation methods for E2B executor blocks."""
# Default working directory in E2B sandboxes
WORKING_DIR = "/home/user"
async def execute_code(
self,
api_key: str,
@@ -95,14 +105,21 @@ class BaseE2BExecutorMixin:
timeout: Optional[int] = None,
sandbox_id: Optional[str] = None,
dispose_sandbox: bool = False,
execution_context: Optional["ExecutionContext"] = None,
extract_files: bool = False,
):
"""
Unified code execution method that handles all three use cases:
1. Create new sandbox and execute (ExecuteCodeBlock)
2. Create new sandbox, execute, and return sandbox_id (InstantiateCodeSandboxBlock)
3. Connect to existing sandbox and execute (ExecuteCodeStepBlock)
Args:
extract_files: If True and execution_context provided, extract files
created/modified during execution and store to workspace.
""" # noqa
sandbox = None
files: list[SandboxFileOutput] = []
try:
if sandbox_id:
# Connect to existing sandbox (ExecuteCodeStepBlock case)
@@ -118,6 +135,12 @@ class BaseE2BExecutorMixin:
for cmd in setup_commands:
await sandbox.commands.run(cmd)
# Capture timestamp before execution to scope file extraction
start_timestamp = None
if extract_files:
ts_result = await sandbox.commands.run("date -u +%Y-%m-%dT%H:%M:%S")
start_timestamp = ts_result.stdout.strip() if ts_result.stdout else None
# Execute the code
execution = await sandbox.run_code(
code,
@@ -133,7 +156,24 @@ class BaseE2BExecutorMixin:
stdout_logs = "".join(execution.logs.stdout)
stderr_logs = "".join(execution.logs.stderr)
return results, text_output, stdout_logs, stderr_logs, sandbox.sandbox_id
# Extract files created/modified during this execution
if extract_files and execution_context:
files = await extract_and_store_sandbox_files(
sandbox=sandbox,
working_directory=self.WORKING_DIR,
execution_context=execution_context,
since_timestamp=start_timestamp,
text_only=False, # Include binary files too
)
return (
results,
text_output,
stdout_logs,
stderr_logs,
sandbox.sandbox_id,
files,
)
finally:
# Dispose of sandbox if requested to reduce usage costs
if dispose_sandbox and sandbox:
@@ -238,6 +278,12 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
description="Standard output logs from execution"
)
stderr_logs: str = SchemaField(description="Standard error logs from execution")
files: list[SandboxFileOutput] = SchemaField(
description=(
"Files created or modified during execution. "
"Each file has path, name, content, and workspace_ref (if stored)."
),
)
def __init__(self):
super().__init__(
@@ -259,23 +305,30 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
("results", []),
("response", "Hello World"),
("stdout_logs", "Hello World\n"),
("files", []),
],
test_mock={
"execute_code": lambda api_key, code, language, template_id, setup_commands, timeout, dispose_sandbox: ( # noqa
"execute_code": lambda api_key, code, language, template_id, setup_commands, timeout, dispose_sandbox, execution_context, extract_files: ( # noqa
[], # results
"Hello World", # text_output
"Hello World\n", # stdout_logs
"", # stderr_logs
"sandbox_id", # sandbox_id
[], # files
),
},
)
async def run(
self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
self,
input_data: Input,
*,
credentials: APIKeyCredentials,
execution_context: "ExecutionContext",
**kwargs,
) -> BlockOutput:
try:
results, text_output, stdout, stderr, _ = await self.execute_code(
results, text_output, stdout, stderr, _, files = await self.execute_code(
api_key=credentials.api_key.get_secret_value(),
code=input_data.code,
language=input_data.language,
@@ -283,6 +336,8 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
setup_commands=input_data.setup_commands,
timeout=input_data.timeout,
dispose_sandbox=input_data.dispose_sandbox,
execution_context=execution_context,
extract_files=True,
)
# Determine result object shape & filter out empty formats
@@ -296,6 +351,8 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
yield "stdout_logs", stdout
if stderr:
yield "stderr_logs", stderr
# Always yield files (empty list if none)
yield "files", [f.model_dump() for f in files]
except Exception as e:
yield "error", str(e)
@@ -393,6 +450,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
"Hello World\n", # stdout_logs
"", # stderr_logs
"sandbox_id", # sandbox_id
[], # files
),
},
)
@@ -401,7 +459,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
) -> BlockOutput:
try:
_, text_output, stdout, stderr, sandbox_id = await self.execute_code(
_, text_output, stdout, stderr, sandbox_id, _ = await self.execute_code(
api_key=credentials.api_key.get_secret_value(),
code=input_data.setup_code,
language=input_data.language,
@@ -500,6 +558,7 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
"Hello World\n", # stdout_logs
"", # stderr_logs
sandbox_id, # sandbox_id
[], # files
),
},
)
@@ -508,7 +567,7 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
) -> BlockOutput:
try:
results, text_output, stdout, stderr, _ = await self.execute_code(
results, text_output, stdout, stderr, _, _ = await self.execute_code(
api_key=credentials.api_key.get_secret_value(),
code=input_data.step_code,
language=input_data.language,

2
autogpt_platform/backend/backend/blocks/code_extraction_block.py
View File

@@ -1,6 +1,6 @@
import re
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/codex.py
View File

@@ -6,7 +6,7 @@ from openai import AsyncOpenAI
from openai.types.responses import Response as OpenAIResponse
from pydantic import SecretStr
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/compass/triggers.py
View File

@@ -1,6 +1,6 @@
from pydantic import BaseModel
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockManualWebhookConfig,

2
autogpt_platform/backend/backend/blocks/count_words_and_char_block.py
View File

@@ -1,4 +1,4 @@
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/data_manipulation.py
View File

@@ -1,6 +1,6 @@
from typing import Any, List
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/decoder_block.py
View File

@@ -1,6 +1,6 @@
import codecs
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/discord/bot_blocks.py
View File

@@ -8,7 +8,7 @@ from typing import Any, Literal, cast
import discord
from pydantic import SecretStr
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/discord/oauth_blocks.py
View File

@@ -2,7 +2,7 @@
Discord OAuth-based blocks.
"""
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/email_block.py
View File

@@ -7,7 +7,7 @@ from typing import Literal
from pydantic import BaseModel, ConfigDict, SecretStr
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/encoder_block.py
View File

@@ -2,7 +2,7 @@
import codecs
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/enrichlayer/linkedin.py
View File

@@ -8,7 +8,7 @@ which provides access to LinkedIn profile data and related information.
import logging
from typing import Optional
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

14
autogpt_platform/backend/backend/blocks/fal/ai_video_generator.py
View File

@@ -3,6 +3,13 @@ import logging
from enum import Enum
from typing import Any
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.fal._auth import (
TEST_CREDENTIALS,
TEST_CREDENTIALS_INPUT,
@@ -10,13 +17,6 @@ from backend.blocks.fal._auth import (
FalCredentialsField,
FalCredentialsInput,
)
from backend.data.block import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.data.execution import ExecutionContext
from backend.data.model import SchemaField
from backend.util.file import store_media_file

2
autogpt_platform/backend/backend/blocks/flux_kontext.py
View File

@@ -5,7 +5,7 @@ from pydantic import SecretStr
from replicate.client import Client as ReplicateClient
from replicate.helpers import FileOutput
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/github/checks.py
View File

@@ -3,7 +3,7 @@ from typing import Optional
from pydantic import BaseModel
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/github/ci.py
View File

@@ -5,7 +5,7 @@ from typing import Optional
from typing_extensions import TypedDict
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/github/issues.py
View File

@@ -3,7 +3,7 @@ from urllib.parse import urlparse
from typing_extensions import TypedDict
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/github/pull_requests.py
View File

@@ -2,7 +2,7 @@ import re
from typing_extensions import TypedDict
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/github/repo.py
View File

@@ -2,7 +2,7 @@ import base64
from typing_extensions import TypedDict
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/github/reviews.py
View File

@@ -4,7 +4,7 @@ from typing import Any, List, Optional
from typing_extensions import TypedDict
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/github/statuses.py
View File

@@ -3,7 +3,7 @@ from typing import Optional
from pydantic import BaseModel
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/github/triggers.py
View File

@@ -4,7 +4,7 @@ from pathlib import Path
from pydantic import BaseModel
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

2
autogpt_platform/backend/backend/blocks/google/calendar.py
View File

@@ -8,7 +8,7 @@ from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build
from pydantic import BaseModel
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

4
autogpt_platform/backend/backend/blocks/google/docs.py
View File

@@ -7,14 +7,14 @@ from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build
from gravitas_md2gdocs import to_requests
from backend.blocks.google._drive import GoogleDriveFile, GoogleDriveFileField
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.google._drive import GoogleDriveFile, GoogleDriveFileField
from backend.data.model import SchemaField
from backend.util.settings import Settings

2
autogpt_platform/backend/backend/blocks/google/gmail.py
View File

@@ -14,7 +14,7 @@ from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build
from pydantic import BaseModel, Field
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

4
autogpt_platform/backend/backend/blocks/google/sheets.py
View File

@@ -7,14 +7,14 @@ from enum import Enum
from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build
from backend.blocks.google._drive import GoogleDriveFile, GoogleDriveFileField
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.google._drive import GoogleDriveFile, GoogleDriveFileField
from backend.data.model import SchemaField
from backend.util.settings import Settings

2
autogpt_platform/backend/backend/blocks/google_maps.py
View File

@@ -3,7 +3,7 @@ from typing import Literal
import googlemaps
from pydantic import BaseModel, SecretStr
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

7
autogpt_platform/backend/backend/blocks/helpers/review.py
View File

@@ -9,9 +9,7 @@ from typing import Any, Optional
from prisma.enums import ReviewStatus
from pydantic import BaseModel
from backend.data.execution import ExecutionStatus
from backend.data.human_review import ReviewResult
from backend.executor.manager import async_update_node_execution_status
from backend.util.clients import get_database_manager_async_client
logger = logging.getLogger(__name__)
@@ -43,6 +41,8 @@ class HITLReviewHelper:
@staticmethod
async def update_node_execution_status(**kwargs) -> None:
"""Update the execution status of a node."""
from backend.executor.manager import async_update_node_execution_status
await async_update_node_execution_status(
db_client=get_database_manager_async_client(), **kwargs
)
@@ -88,12 +88,13 @@ class HITLReviewHelper:
Raises:
Exception: If review creation or status update fails
"""
from backend.data.execution import ExecutionStatus
# Note: Safe mode checks (human_in_the_loop_safe_mode, sensitive_action_safe_mode)
# are handled by the caller:
# - HITL blocks check human_in_the_loop_safe_mode in their run() method
# - Sensitive action blocks check sensitive_action_safe_mode in is_block_exec_need_review()
# This function only handles checking for existing approvals.
# Check if this node has already been approved (normal or auto-approval)
if approval_result := await HITLReviewHelper.check_approval(
node_exec_id=node_exec_id,

2
autogpt_platform/backend/backend/blocks/http.py
View File

@@ -8,7 +8,7 @@ from typing import Literal
import aiofiles
from pydantic import SecretStr
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

12
autogpt_platform/backend/backend/blocks/hubspot/company.py
View File

@@ -1,15 +1,15 @@
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.model import SchemaField
from backend.util.request import Requests

12
autogpt_platform/backend/backend/blocks/hubspot/contact.py
View File

@@ -1,15 +1,15 @@
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.model import SchemaField
from backend.util.request import Requests

12
autogpt_platform/backend/backend/blocks/hubspot/engagement.py
View File

@@ -1,17 +1,17 @@
from datetime import datetime, timedelta
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.model import SchemaField
from backend.util.request import Requests

60
autogpt_platform/backend/backend/blocks/human_in_the_loop.py
View File

@@ -3,8 +3,7 @@ from typing import Any
from prisma.enums import ReviewStatus
from backend.blocks.helpers.review import HITLReviewHelper
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
@@ -12,6 +11,7 @@ from backend.data.block import (
BlockSchemaOutput,
BlockType,
)
from backend.blocks.helpers.review import HITLReviewHelper
from backend.data.execution import ExecutionContext
from backend.data.human_review import ReviewResult
from backend.data.model import SchemaField
@@ -21,43 +21,71 @@ logger = logging.getLogger(__name__)
class HumanInTheLoopBlock(Block):
"""
This block pauses execution and waits for human approval or modification of the data.
Pauses execution and waits for human approval or rejection of the data.
When executed, it creates a pending review entry and sets the node execution status
to REVIEW. The execution will remain paused until a human user either:
- Approves the data (with or without modifications)
- Rejects the data
When executed, this block creates a pending review entry and sets the node execution
status to REVIEW. The execution remains paused until a human user either approves
or rejects the data.
This is useful for workflows that require human validation or intervention before
proceeding to the next steps.
**How it works:**
- The input data is presented to a human reviewer
- The reviewer can approve or reject (and optionally modify the data if editable)
- On approval: the data flows out through the `approved_data` output pin
- On rejection: the data flows out through the `rejected_data` output pin
**Important:** The output pins yield the actual data itself, NOT status strings.
The approval/rejection decision determines WHICH output pin fires, not the value.
You do NOT need to compare the output to "APPROVED" or "REJECTED" - simply connect
downstream blocks to the appropriate output pin for each case.
**Example usage:**
- Connect `approved_data` → next step in your workflow (data was approved)
- Connect `rejected_data` → error handling or notification (data was rejected)
"""
class Input(BlockSchemaInput):
data: Any = SchemaField(description="The data to be reviewed by a human user")
data: Any = SchemaField(
description="The data to be reviewed by a human user. "
"This exact data will be passed through to either approved_data or "
"rejected_data output based on the reviewer's decision."
)
name: str = SchemaField(
description="A descriptive name for what this data represents",
description="A descriptive name for what this data represents. "
"This helps the reviewer understand what they are reviewing.",
)
editable: bool = SchemaField(
description="Whether the human reviewer can edit the data",
description="Whether the human reviewer can edit the data before "
"approving or rejecting it",
default=True,
advanced=True,
)
class Output(BlockSchemaOutput):
approved_data: Any = SchemaField(
description="The data when approved (may be modified by reviewer)"
description="Outputs the input data when the reviewer APPROVES it. "
"The value is the actual data itself (not a status string like 'APPROVED'). "
"If the reviewer edited the data, this contains the modified version. "
"Connect downstream blocks here for the 'approved' workflow path."
)
rejected_data: Any = SchemaField(
description="The data when rejected (may be modified by reviewer)"
description="Outputs the input data when the reviewer REJECTS it. "
"The value is the actual data itself (not a status string like 'REJECTED'). "
"If the reviewer edited the data, this contains the modified version. "
"Connect downstream blocks here for the 'rejected' workflow path."
)
review_message: str = SchemaField(
description="Any message provided by the reviewer", default=""
description="Optional message provided by the reviewer explaining their "
"decision. Only outputs when the reviewer provides a message; "
"this pin does not fire if no message was given.",
default="",
)
def __init__(self):
super().__init__(
id="8b2a7b3c-6e9d-4a5f-8c1b-2e3f4a5b6c7d",
description="Pause execution and wait for human approval or modification of data",
description="Pause execution for human review. Data flows through "
"approved_data or rejected_data output based on the reviewer's decision. "
"Outputs contain the actual data, not status strings.",
categories={BlockCategory.BASIC},
input_schema=HumanInTheLoopBlock.Input,
output_schema=HumanInTheLoopBlock.Output,

2
autogpt_platform/backend/backend/blocks/ideogram.py
View File

@@ -3,7 +3,7 @@ from typing import Any, Dict, Literal, Optional
from pydantic import SecretStr
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

7
autogpt_platform/backend/backend/blocks/io.py
View File

@@ -2,9 +2,7 @@ import copy
from datetime import date, time
from typing import Any, Optional
# Import for Google Drive file input block
from backend.blocks.google._drive import AttachmentView, GoogleDriveFile
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
@@ -12,6 +10,9 @@ from backend.data.block import (
BlockSchemaInput,
BlockType,
)
# Import for Google Drive file input block
from backend.blocks.google._drive import AttachmentView, GoogleDriveFile
from backend.data.execution import ExecutionContext
from backend.data.model import SchemaField
from backend.util.file import store_media_file

2
autogpt_platform/backend/backend/blocks/iteration.py
View File

@@ -1,6 +1,6 @@
from typing import Any
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,

12
autogpt_platform/backend/backend/blocks/jina/chunking.py
View File

@@ -1,15 +1,15 @@
from backend.blocks.jina._auth import (
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.jina._auth import (
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.data.model import SchemaField
from backend.util.request import Requests

12
autogpt_platform/backend/backend/blocks/jina/embeddings.py
View File

@@ -1,15 +1,15 @@
from backend.blocks.jina._auth import (
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.jina._auth import (
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.data.model import SchemaField
from backend.util.request import Requests

12
autogpt_platform/backend/backend/blocks/jina/fact_checker.py
View File

@@ -3,18 +3,18 @@ from urllib.parse import quote
from typing_extensions import TypedDict
from backend.blocks.jina._auth import (
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.data.block import (
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.jina._auth import (
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.data.model import SchemaField
from backend.util.request import Requests

14
autogpt_platform/backend/backend/blocks/jina/search.py
View File

@@ -1,5 +1,12 @@
from urllib.parse import quote
from backend.blocks._base import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.blocks.jina._auth import (
TEST_CREDENTIALS,
TEST_CREDENTIALS_INPUT,
@@ -8,13 +15,6 @@ from backend.blocks.jina._auth import (
JinaCredentialsInput,
)
from backend.blocks.search import GetRequest
from backend.data.block import (
Block,
BlockCategory,
BlockOutput,
BlockSchemaInput,
BlockSchemaOutput,
)
from backend.data.model import SchemaField
from backend.util.exceptions import BlockExecutionError

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