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feat(backend): Add AI-generated activity status for agent executions (#10487)

Browse Source 
## Summary
- Adds AI-generated activity status summaries for agent execution
results
- Provides users with conversational, non-technical summaries of what
their agents accomplished
- Includes comprehensive execution data analysis with honest failure
reporting

## Changes Made
- **Backend**: Added `ActivityStatusGenerator` module with async LLM
integration
- **Database**: Extended `GraphExecutionStats` and `Stats` models with
`activity_status` field
- **Frontend**: Added "Smart Agent Execution Summary" display with
disclaimer tooltip
- **Settings**: Added `execution_enable_ai_activity_status` toggle
(disabled by default)
- **Testing**: Comprehensive test suite with 12 test cases covering all
scenarios

## Key Features
- Collects execution data including graph structure, node relations,
errors, and I/O samples
- Generates user-friendly summaries from first-person perspective
- Honest reporting of failures and invalid inputs (no sugar-coating)
- Payload optimization for LLM context limits
- Full async implementation with proper error handling

## Test Plan
- [x] All existing tests pass
- [x] New comprehensive test suite covers success/failure scenarios
- [x] Feature toggle testing (enabled/disabled states)
- [x] Frontend integration displays correctly
- [x] Error handling and edge cases covered

🤖 Generated with [Claude Code](https://claude.ai/code)

---------

Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Zamil Majdy
2025-08-01 19:37:49 +08:00
committed by GitHub
parent 878f61aaf4
commit e632549175
9 changed files with 1288 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
autogpt_platform/autogpt_libs/autogpt_libs/feature_flag/client.py
View File

@@ -17,9 +17,13 @@ logger = logging.getLogger(__name__)
P = ParamSpec("P")
T = TypeVar("T")
_is_initialized = False
def get_client() -> LDClient:
"""Get the LaunchDarkly client singleton."""
if not _is_initialized:
initialize_launchdarkly()
return ldclient.get()
@@ -37,6 +41,8 @@ def initialize_launchdarkly() -> None:
ldclient.set_config(config)
if ldclient.get().is_initialized():
global _is_initialized
_is_initialized = True
logger.info("LaunchDarkly client initialized successfully")
else:
logger.error("LaunchDarkly client failed to initialize")
@@ -60,6 +66,30 @@ def create_context(
return builder.build()
def is_feature_enabled(flag_key: str, user_id: str, default: bool = False) -> bool:
"""
Simple helper to check if a feature flag is enabled for a user.
Args:
flag_key: The LaunchDarkly feature flag key
user_id: The user ID to evaluate the flag for
default: Default value if LaunchDarkly is unavailable or flag evaluation fails
Returns:
True if feature is enabled, False otherwise
"""
try:
client = get_client()
context = create_context(str(user_id))
return client.variation(flag_key, context, default)
except Exception as e:
logger.debug(
f"LaunchDarkly flag evaluation failed for {flag_key}: {e}, using default={default}"
)
return default
def feature_flag(
flag_key: str,
default: bool = False,

41
autogpt_platform/autogpt_libs/autogpt_libs/feature_flag/client_test.py
View File

@@ -1,7 +1,11 @@
import pytest
from ldclient import LDClient
from autogpt_libs.feature_flag.client import feature_flag, mock_flag_variation
from autogpt_libs.feature_flag.client import (
feature_flag,
is_feature_enabled,
mock_flag_variation,
)
@pytest.fixture
@@ -43,3 +47,38 @@ def test_mock_flag_variation(ld_client):
with mock_flag_variation("test-flag", False):
assert ld_client.variation("test-flag", None, False)
def test_is_feature_enabled(ld_client):
"""Test the is_feature_enabled helper function."""
ld_client.is_initialized.return_value = True
ld_client.variation.return_value = True
result = is_feature_enabled("test-flag", "user123", default=False)
assert result is True
ld_client.variation.assert_called_once()
call_args = ld_client.variation.call_args
assert call_args[0][0] == "test-flag" # flag_key
assert call_args[0][2] is False # default value
def test_is_feature_enabled_not_initialized(ld_client):
"""Test is_feature_enabled when LaunchDarkly is not initialized."""
ld_client.is_initialized.return_value = False
result = is_feature_enabled("test-flag", "user123", default=True)
assert result is True # Should return default
ld_client.variation.assert_not_called()
def test_is_feature_enabled_exception(mocker):
"""Test is_feature_enabled when get_client() raises an exception."""
mocker.patch(
"autogpt_libs.feature_flag.client.get_client",
side_effect=Exception("Client error"),
)
result = is_feature_enabled("test-flag", "user123", default=True)
assert result is True # Should return default

6
autogpt_platform/backend/backend/data/execution.py
View File

@@ -134,6 +134,10 @@ class GraphExecutionMeta(BaseDbModel):
default=None,
description="Error message if any",
)
activity_status: str | None = Field(
default=None,
description="AI-generated summary of what the agent did",
)
def to_db(self) -> GraphExecutionStats:
return GraphExecutionStats(
@@ -145,6 +149,7 @@ class GraphExecutionMeta(BaseDbModel):
node_count=self.node_exec_count,
node_error_count=self.node_error_count,
error=self.error,
activity_status=self.activity_status,
)
stats: Stats | None
@@ -189,6 +194,7 @@ class GraphExecutionMeta(BaseDbModel):
if isinstance(stats.error, Exception)
else stats.error
),
activity_status=stats.activity_status,
)
if stats
else None

3
autogpt_platform/backend/backend/data/model.py
View File

@@ -706,3 +706,6 @@ class GraphExecutionStats(BaseModel):
default=0, description="Total number of errors generated"
)
cost: int = Field(default=0, description="Total execution cost (cents)")
activity_status: Optional[str] = Field(
default=None, description="AI-generated summary of what the agent did"
)

435
autogpt_platform/backend/backend/executor/activity_status_generator.py Normal file
View File

@@ -0,0 +1,435 @@
"""
Module for generating AI-based activity status for graph executions.
"""
import json
import logging
from typing import TYPE_CHECKING, Any, NotRequired, TypedDict
from autogpt_libs.feature_flag.client import is_feature_enabled
from pydantic import SecretStr
from backend.blocks.llm import LlmModel, llm_call
from backend.data.block import get_block
from backend.data.execution import ExecutionStatus, NodeExecutionResult
from backend.data.model import APIKeyCredentials, GraphExecutionStats
from backend.util.settings import Settings
from backend.util.truncate import truncate
# LaunchDarkly feature flag key for AI activity status generation
AI_ACTIVITY_STATUS_FLAG_KEY = "ai-agent-execution-summary"
if TYPE_CHECKING:
from backend.executor import DatabaseManagerAsyncClient
logger = logging.getLogger(__name__)
class ErrorInfo(TypedDict):
"""Type definition for error information."""
error: str
execution_id: str
timestamp: str
class InputOutputInfo(TypedDict):
"""Type definition for input/output information."""
execution_id: str
output_data: dict[str, Any] # Used for both input and output data
timestamp: str
class NodeInfo(TypedDict):
"""Type definition for node information."""
node_id: str
block_id: str
block_name: str
block_description: str
execution_count: int
error_count: int
recent_errors: list[ErrorInfo]
recent_outputs: list[InputOutputInfo]
recent_inputs: list[InputOutputInfo]
class NodeRelation(TypedDict):
"""Type definition for node relation information."""
source_node_id: str
sink_node_id: str
source_name: str
sink_name: str
is_static: bool
source_block_name: NotRequired[str] # Optional, only set if block exists
sink_block_name: NotRequired[str] # Optional, only set if block exists
def _truncate_uuid(uuid_str: str) -> str:
"""Truncate UUID to first segment to reduce payload size."""
if not uuid_str:
return uuid_str
return uuid_str.split("-")[0] if "-" in uuid_str else uuid_str[:8]
async def generate_activity_status_for_execution(
graph_exec_id: str,
graph_id: str,
graph_version: int,
execution_stats: GraphExecutionStats,
db_client: "DatabaseManagerAsyncClient",
user_id: str,
execution_status: ExecutionStatus | None = None,
) -> str | None:
"""
Generate an AI-based activity status summary for a graph execution.
This function handles all the data collection and AI generation logic,
keeping the manager integration simple.
Args:
graph_exec_id: The graph execution ID
graph_id: The graph ID
graph_version: The graph version
execution_stats: Execution statistics
db_client: Database client for fetching data
user_id: User ID for LaunchDarkly feature flag evaluation
execution_status: The overall execution status (COMPLETED, FAILED, TERMINATED)
Returns:
AI-generated activity status string, or None if feature is disabled
"""
# Check LaunchDarkly feature flag for AI activity status generation
if not is_feature_enabled(AI_ACTIVITY_STATUS_FLAG_KEY, user_id, default=False):
logger.debug("AI activity status generation is disabled via LaunchDarkly")
return None
# Check if we have OpenAI API key
try:
settings = Settings()
if not settings.secrets.openai_api_key:
logger.debug(
"OpenAI API key not configured, skipping activity status generation"
)
return None
# Get all node executions for this graph execution
node_executions = await db_client.get_node_executions(
graph_exec_id, include_exec_data=True
)
# Get graph metadata and full graph structure for name, description, and links
graph_metadata = await db_client.get_graph_metadata(graph_id, graph_version)
graph = await db_client.get_graph(graph_id, graph_version)
graph_name = graph_metadata.name if graph_metadata else f"Graph {graph_id}"
graph_description = graph_metadata.description if graph_metadata else ""
graph_links = graph.links if graph else []
# Build execution data summary
execution_data = _build_execution_summary(
node_executions,
execution_stats,
graph_name,
graph_description,
graph_links,
execution_status,
)
# Prepare prompt for AI
prompt = [
{
"role": "system",
"content": (
"You are an AI assistant summarizing what you just did for a user in simple, friendly language. "
"Write from the user's perspective about what they accomplished, NOT about technical execution details. "
"Focus on the ACTUAL TASK the user wanted done, not the internal workflow steps. "
"Avoid technical terms like 'workflow', 'execution', 'components', 'nodes', 'processing', etc. "
"Keep it to 3 sentences maximum. Be conversational and human-friendly.\n\n"
"IMPORTANT: Be HONEST about what actually happened:\n"
"- If the input was invalid/nonsensical, say so directly\n"
"- If the task failed, explain what went wrong in simple terms\n"
"- If errors occurred, focus on what the user needs to know\n"
"- Only claim success if the task was genuinely completed\n"
"- Don't sugar-coat failures or present them as helpful feedback\n\n"
"Understanding Errors:\n"
"- Node errors: Individual steps may fail but the overall task might still complete (e.g., one data source fails but others work)\n"
"- Graph error (in overall_status.graph_error): This means the entire execution failed and nothing was accomplished\n"
"- Even if execution shows 'completed', check if critical nodes failed that would prevent the desired outcome\n"
"- Focus on the end result the user wanted, not whether technical steps completed"
),
},
{
"role": "user",
"content": (
f"A user ran '{graph_name}' to accomplish something. Based on this execution data, "
f"write what they achieved in simple, user-friendly terms:\n\n"
f"{json.dumps(execution_data, indent=2)}\n\n"
"CRITICAL: Check overall_status.graph_error FIRST - if present, the entire execution failed.\n"
"Then check individual node errors to understand partial failures.\n\n"
"Write 1-3 sentences about what the user accomplished, such as:\n"
"- 'I analyzed your resume and provided detailed feedback for the IT industry.'\n"
"- 'I couldn't analyze your resume because the input was just nonsensical text.'\n"
"- 'I failed to complete the task due to missing API access.'\n"
"- 'I extracted key information from your documents and organized it into a summary.'\n"
"- 'The task failed to run due to system configuration issues.'\n\n"
"Focus on what ACTUALLY happened, not what was attempted."
),
},
]
# Log the prompt for debugging purposes
logger.debug(
f"Sending prompt to LLM for graph execution {graph_exec_id}: {json.dumps(prompt, indent=2)}"
)
# Create credentials for LLM call
credentials = APIKeyCredentials(
id="openai",
provider="openai",
api_key=SecretStr(settings.secrets.openai_api_key),
title="System OpenAI",
)
# Make LLM call using current event loop
activity_status = await _call_llm_direct(credentials, prompt)
logger.debug(
f"Generated activity status for {graph_exec_id}: {activity_status}"
)
return activity_status
except Exception as e:
logger.error(
f"Failed to generate activity status for execution {graph_exec_id}: {str(e)}"
)
return None
def _build_execution_summary(
node_executions: list[NodeExecutionResult],
execution_stats: GraphExecutionStats,
graph_name: str,
graph_description: str,
graph_links: list[Any],
execution_status: ExecutionStatus | None = None,
) -> dict[str, Any]:
"""Build a structured summary of execution data for AI analysis."""
nodes: list[NodeInfo] = []
node_execution_counts: dict[str, int] = {}
node_error_counts: dict[str, int] = {}
node_errors: dict[str, list[ErrorInfo]] = {}
node_outputs: dict[str, list[InputOutputInfo]] = {}
node_inputs: dict[str, list[InputOutputInfo]] = {}
input_output_data: dict[str, Any] = {}
node_map: dict[str, NodeInfo] = {}
# Process node executions
for node_exec in node_executions:
block = get_block(node_exec.block_id)
if not block:
logger.warning(
f"Block {node_exec.block_id} not found for node {node_exec.node_id}"
)
continue
# Track execution counts per node
if node_exec.node_id not in node_execution_counts:
node_execution_counts[node_exec.node_id] = 0
node_execution_counts[node_exec.node_id] += 1
# Track errors per node and group them
if node_exec.status == ExecutionStatus.FAILED:
if node_exec.node_id not in node_error_counts:
node_error_counts[node_exec.node_id] = 0
node_error_counts[node_exec.node_id] += 1
# Extract actual error message from output_data
error_message = "Unknown error"
if node_exec.output_data and isinstance(node_exec.output_data, dict):
# Check if error is in output_data
if "error" in node_exec.output_data:
error_output = node_exec.output_data["error"]
if isinstance(error_output, list) and error_output:
error_message = str(error_output[0])
else:
error_message = str(error_output)
# Group errors by node_id
if node_exec.node_id not in node_errors:
node_errors[node_exec.node_id] = []
node_errors[node_exec.node_id].append(
{
"error": error_message,
"execution_id": _truncate_uuid(node_exec.node_exec_id),
"timestamp": node_exec.add_time.isoformat(),
}
)
# Collect output samples for each node (latest executions)
if node_exec.output_data:
if node_exec.node_id not in node_outputs:
node_outputs[node_exec.node_id] = []
# Truncate output data to 100 chars to save space
truncated_output = truncate(node_exec.output_data, 100)
node_outputs[node_exec.node_id].append(
{
"execution_id": _truncate_uuid(node_exec.node_exec_id),
"output_data": truncated_output,
"timestamp": node_exec.add_time.isoformat(),
}
)
# Collect input samples for each node (latest executions)
if node_exec.input_data:
if node_exec.node_id not in node_inputs:
node_inputs[node_exec.node_id] = []
# Truncate input data to 100 chars to save space
truncated_input = truncate(node_exec.input_data, 100)
node_inputs[node_exec.node_id].append(
{
"execution_id": _truncate_uuid(node_exec.node_exec_id),
"output_data": truncated_input, # Reuse field name for consistency
"timestamp": node_exec.add_time.isoformat(),
}
)
# Build node data (only add unique nodes)
if node_exec.node_id not in node_map:
node_data: NodeInfo = {
"node_id": _truncate_uuid(node_exec.node_id),
"block_id": _truncate_uuid(node_exec.block_id),
"block_name": block.name,
"block_description": block.description or "",
"execution_count": 0, # Will be set later
"error_count": 0, # Will be set later
"recent_errors": [], # Will be set later
"recent_outputs": [], # Will be set later
"recent_inputs": [], # Will be set later
}
nodes.append(node_data)
node_map[node_exec.node_id] = node_data
# Store input/output data for special blocks (input/output blocks)
if block.name in ["AgentInputBlock", "AgentOutputBlock", "UserInputBlock"]:
if node_exec.input_data:
input_output_data[f"{node_exec.node_id}_inputs"] = dict(
node_exec.input_data
)
if node_exec.output_data:
input_output_data[f"{node_exec.node_id}_outputs"] = dict(
node_exec.output_data
)
# Add execution and error counts to node data, plus limited errors and output samples
for node in nodes:
# Use original node_id for lookups (before truncation)
original_node_id = None
for orig_id, node_data in node_map.items():
if node_data == node:
original_node_id = orig_id
break
if original_node_id:
node["execution_count"] = node_execution_counts.get(original_node_id, 0)
node["error_count"] = node_error_counts.get(original_node_id, 0)
# Add limited errors for this node (latest 10 or first 5 + last 5)
if original_node_id in node_errors:
node_error_list = node_errors[original_node_id]
if len(node_error_list) <= 10:
node["recent_errors"] = node_error_list
else:
# First 5 + last 5 if more than 10 errors
node["recent_errors"] = node_error_list[:5] + node_error_list[-5:]
# Add latest output samples (latest 3)
if original_node_id in node_outputs:
node_output_list = node_outputs[original_node_id]
# Sort by timestamp if available, otherwise take last 3
if node_output_list and node_output_list[0].get("timestamp"):
node_output_list.sort(
key=lambda x: x.get("timestamp", ""), reverse=True
)
node["recent_outputs"] = node_output_list[:3]
# Add latest input samples (latest 3)
if original_node_id in node_inputs:
node_input_list = node_inputs[original_node_id]
# Sort by timestamp if available, otherwise take last 3
if node_input_list and node_input_list[0].get("timestamp"):
node_input_list.sort(
key=lambda x: x.get("timestamp", ""), reverse=True
)
node["recent_inputs"] = node_input_list[:3]
# Build node relations from graph links
node_relations: list[NodeRelation] = []
for link in graph_links:
# Include link details with source and sink information (truncated UUIDs)
relation: NodeRelation = {
"source_node_id": _truncate_uuid(link.source_id),
"sink_node_id": _truncate_uuid(link.sink_id),
"source_name": link.source_name,
"sink_name": link.sink_name,
"is_static": link.is_static if hasattr(link, "is_static") else False,
}
# Add block names if nodes exist in our map
if link.source_id in node_map:
relation["source_block_name"] = node_map[link.source_id]["block_name"]
if link.sink_id in node_map:
relation["sink_block_name"] = node_map[link.sink_id]["block_name"]
node_relations.append(relation)
# Build overall summary
return {
"graph_info": {"name": graph_name, "description": graph_description},
"nodes": nodes,
"node_relations": node_relations,
"input_output_data": input_output_data,
"overall_status": {
"total_nodes_in_graph": len(nodes),
"total_executions": execution_stats.node_count,
"total_errors": execution_stats.node_error_count,
"execution_time_seconds": execution_stats.walltime,
"has_errors": bool(
execution_stats.error or execution_stats.node_error_count > 0
),
"graph_error": (
str(execution_stats.error) if execution_stats.error else None
),
"graph_execution_status": (
execution_status.value if execution_status else None
),
},
}
async def _call_llm_direct(
credentials: APIKeyCredentials, prompt: list[dict[str, str]]
) -> str:
"""Make direct LLM call."""
response = await llm_call(
credentials=credentials,
llm_model=LlmModel.GPT4O_MINI,
prompt=prompt,
json_format=False,
max_tokens=150,
compress_prompt_to_fit=True,
)
if response and response.response:
return response.response.strip()
else:
return "Unable to generate activity summary"

702
autogpt_platform/backend/backend/executor/activity_status_generator_test.py Normal file
View File

@@ -0,0 +1,702 @@
"""
Tests for activity status generator functionality.
"""
from datetime import datetime, timezone
from unittest.mock import AsyncMock, MagicMock, patch
import pytest
from backend.blocks.llm import LLMResponse
from backend.data.execution import ExecutionStatus, NodeExecutionResult
from backend.data.model import GraphExecutionStats
from backend.executor.activity_status_generator import (
_build_execution_summary,
_call_llm_direct,
generate_activity_status_for_execution,
)
@pytest.fixture
def mock_node_executions():
"""Create mock node executions for testing."""
return [
NodeExecutionResult(
user_id="test_user",
graph_id="test_graph",
graph_version=1,
graph_exec_id="test_exec",
node_exec_id="123e4567-e89b-12d3-a456-426614174001",
node_id="456e7890-e89b-12d3-a456-426614174002",
block_id="789e1234-e89b-12d3-a456-426614174003",
status=ExecutionStatus.COMPLETED,
input_data={"user_input": "Hello, world!"},
output_data={"processed_input": ["Hello, world!"]},
add_time=datetime.now(timezone.utc),
queue_time=None,
start_time=None,
end_time=None,
),
NodeExecutionResult(
user_id="test_user",
graph_id="test_graph",
graph_version=1,
graph_exec_id="test_exec",
node_exec_id="234e5678-e89b-12d3-a456-426614174004",
node_id="567e8901-e89b-12d3-a456-426614174005",
block_id="890e2345-e89b-12d3-a456-426614174006",
status=ExecutionStatus.COMPLETED,
input_data={"data": "Hello, world!"},
output_data={"result": ["Processed data"]},
add_time=datetime.now(timezone.utc),
queue_time=None,
start_time=None,
end_time=None,
),
NodeExecutionResult(
user_id="test_user",
graph_id="test_graph",
graph_version=1,
graph_exec_id="test_exec",
node_exec_id="345e6789-e89b-12d3-a456-426614174007",
node_id="678e9012-e89b-12d3-a456-426614174008",
block_id="901e3456-e89b-12d3-a456-426614174009",
status=ExecutionStatus.FAILED,
input_data={"final_data": "Processed data"},
output_data={
"error": ["Connection timeout: Unable to reach external service"]
},
add_time=datetime.now(timezone.utc),
queue_time=None,
start_time=None,
end_time=None,
),
]
@pytest.fixture
def mock_execution_stats():
"""Create mock execution stats for testing."""
return GraphExecutionStats(
walltime=2.5,
cputime=1.8,
nodes_walltime=2.0,
nodes_cputime=1.5,
node_count=3,
node_error_count=1,
cost=10,
error=None,
)
@pytest.fixture
def mock_execution_stats_with_graph_error():
"""Create mock execution stats with graph-level error."""
return GraphExecutionStats(
walltime=2.5,
cputime=1.8,
nodes_walltime=2.0,
nodes_cputime=1.5,
node_count=3,
node_error_count=1,
cost=10,
error="Graph execution failed: Invalid API credentials",
)
@pytest.fixture
def mock_blocks():
"""Create mock blocks for testing."""
input_block = MagicMock()
input_block.name = "AgentInputBlock"
input_block.description = "Handles user input"
process_block = MagicMock()
process_block.name = "ProcessingBlock"
process_block.description = "Processes data"
output_block = MagicMock()
output_block.name = "AgentOutputBlock"
output_block.description = "Provides output to user"
return {
"789e1234-e89b-12d3-a456-426614174003": input_block,
"890e2345-e89b-12d3-a456-426614174006": process_block,
"901e3456-e89b-12d3-a456-426614174009": output_block,
"process_block_id": process_block, # Keep old key for different error format test
}
class TestBuildExecutionSummary:
"""Tests for _build_execution_summary function."""
def test_build_summary_with_successful_execution(
self, mock_node_executions, mock_execution_stats, mock_blocks
):
"""Test building summary for successful execution."""
# Create mock links with realistic UUIDs
mock_links = [
MagicMock(
source_id="456e7890-e89b-12d3-a456-426614174002",
sink_id="567e8901-e89b-12d3-a456-426614174005",
source_name="output",
sink_name="input",
is_static=False,
)
]
with patch(
"backend.executor.activity_status_generator.get_block"
) as mock_get_block:
mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
summary = _build_execution_summary(
mock_node_executions[:2],
mock_execution_stats,
"Test Graph",
"A test graph for processing",
mock_links,
ExecutionStatus.COMPLETED,
)
# Check graph info
assert summary["graph_info"]["name"] == "Test Graph"
assert summary["graph_info"]["description"] == "A test graph for processing"
# Check nodes with per-node counts
assert len(summary["nodes"]) == 2
assert summary["nodes"][0]["block_name"] == "AgentInputBlock"
assert summary["nodes"][0]["execution_count"] == 1
assert summary["nodes"][0]["error_count"] == 0
assert summary["nodes"][1]["block_name"] == "ProcessingBlock"
assert summary["nodes"][1]["execution_count"] == 1
assert summary["nodes"][1]["error_count"] == 0
# Check node relations (UUIDs are truncated to first segment)
assert len(summary["node_relations"]) == 1
assert (
summary["node_relations"][0]["source_node_id"] == "456e7890"
) # Truncated
assert (
summary["node_relations"][0]["sink_node_id"] == "567e8901"
) # Truncated
assert (
summary["node_relations"][0]["source_block_name"] == "AgentInputBlock"
)
assert summary["node_relations"][0]["sink_block_name"] == "ProcessingBlock"
# Check overall status
assert summary["overall_status"]["total_nodes_in_graph"] == 2
assert summary["overall_status"]["total_executions"] == 3
assert summary["overall_status"]["total_errors"] == 1
assert summary["overall_status"]["execution_time_seconds"] == 2.5
assert summary["overall_status"]["graph_execution_status"] == "COMPLETED"
# Check input/output data (using actual node UUIDs)
assert (
"456e7890-e89b-12d3-a456-426614174002_inputs"
in summary["input_output_data"]
)
assert (
"456e7890-e89b-12d3-a456-426614174002_outputs"
in summary["input_output_data"]
)
def test_build_summary_with_failed_execution(
self, mock_node_executions, mock_execution_stats, mock_blocks
):
"""Test building summary for execution with failures."""
mock_links = [] # No links for this test
with patch(
"backend.executor.activity_status_generator.get_block"
) as mock_get_block:
mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
summary = _build_execution_summary(
mock_node_executions,
mock_execution_stats,
"Failed Graph",
"Test with failures",
mock_links,
ExecutionStatus.FAILED,
)
# Check that errors are now in node's recent_errors field
# Find the output node (with truncated UUID)
output_node = next(
n for n in summary["nodes"] if n["node_id"] == "678e9012" # Truncated
)
assert output_node["error_count"] == 1
assert output_node["execution_count"] == 1
# Check recent_errors field
assert "recent_errors" in output_node
assert len(output_node["recent_errors"]) == 1
assert (
output_node["recent_errors"][0]["error"]
== "Connection timeout: Unable to reach external service"
)
assert (
"execution_id" in output_node["recent_errors"][0]
) # Should include execution ID
def test_build_summary_with_missing_blocks(
self, mock_node_executions, mock_execution_stats
):
"""Test building summary when blocks are missing."""
with patch(
"backend.executor.activity_status_generator.get_block"
) as mock_get_block:
mock_get_block.return_value = None
summary = _build_execution_summary(
mock_node_executions,
mock_execution_stats,
"Missing Blocks Graph",
"Test with missing blocks",
[],
ExecutionStatus.COMPLETED,
)
# Should handle missing blocks gracefully
assert len(summary["nodes"]) == 0
# No top-level errors field anymore, errors are in nodes' recent_errors
assert summary["graph_info"]["name"] == "Missing Blocks Graph"
def test_build_summary_with_graph_error(
self, mock_node_executions, mock_execution_stats_with_graph_error, mock_blocks
):
"""Test building summary with graph-level error."""
mock_links = []
with patch(
"backend.executor.activity_status_generator.get_block"
) as mock_get_block:
mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
summary = _build_execution_summary(
mock_node_executions,
mock_execution_stats_with_graph_error,
"Graph with Error",
"Test with graph error",
mock_links,
ExecutionStatus.FAILED,
)
# Check that graph error is included in overall status
assert summary["overall_status"]["has_errors"] is True
assert (
summary["overall_status"]["graph_error"]
== "Graph execution failed: Invalid API credentials"
)
assert summary["overall_status"]["total_errors"] == 1
assert summary["overall_status"]["graph_execution_status"] == "FAILED"
def test_build_summary_with_different_error_formats(
self, mock_execution_stats, mock_blocks
):
"""Test building summary with different error formats."""
# Create node executions with different error formats and realistic UUIDs
mock_executions = [
NodeExecutionResult(
user_id="test_user",
graph_id="test_graph",
graph_version=1,
graph_exec_id="test_exec",
node_exec_id="111e2222-e89b-12d3-a456-426614174010",
node_id="333e4444-e89b-12d3-a456-426614174011",
block_id="process_block_id",
status=ExecutionStatus.FAILED,
input_data={},
output_data={"error": ["Simple string error message"]},
add_time=datetime.now(timezone.utc),
queue_time=None,
start_time=None,
end_time=None,
),
NodeExecutionResult(
user_id="test_user",
graph_id="test_graph",
graph_version=1,
graph_exec_id="test_exec",
node_exec_id="555e6666-e89b-12d3-a456-426614174012",
node_id="777e8888-e89b-12d3-a456-426614174013",
block_id="process_block_id",
status=ExecutionStatus.FAILED,
input_data={},
output_data={}, # No error in output
add_time=datetime.now(timezone.utc),
queue_time=None,
start_time=None,
end_time=None,
),
]
with patch(
"backend.executor.activity_status_generator.get_block"
) as mock_get_block:
mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
summary = _build_execution_summary(
mock_executions,
mock_execution_stats,
"Error Test Graph",
"Testing error formats",
[],
ExecutionStatus.FAILED,
)
# Check different error formats - errors are now in nodes' recent_errors
error_nodes = [n for n in summary["nodes"] if n.get("recent_errors")]
assert len(error_nodes) == 2
# String error format - find node with truncated ID
string_error_node = next(
n for n in summary["nodes"] if n["node_id"] == "333e4444" # Truncated
)
assert len(string_error_node["recent_errors"]) == 1
assert (
string_error_node["recent_errors"][0]["error"]
== "Simple string error message"
)
# No error output format - find node with truncated ID
no_error_node = next(
n for n in summary["nodes"] if n["node_id"] == "777e8888" # Truncated
)
assert len(no_error_node["recent_errors"]) == 1
assert no_error_node["recent_errors"][0]["error"] == "Unknown error"
class TestLLMCall:
"""Tests for LLM calling functionality."""
@pytest.mark.asyncio
async def test_call_llm_direct_success(self):
"""Test successful LLM call."""
from pydantic import SecretStr
from backend.data.model import APIKeyCredentials
mock_response = LLMResponse(
raw_response={},
prompt=[],
response="Agent successfully processed user input and generated response.",
tool_calls=None,
prompt_tokens=50,
completion_tokens=20,
)
with patch(
"backend.executor.activity_status_generator.llm_call"
) as mock_llm_call:
mock_llm_call.return_value = mock_response
credentials = APIKeyCredentials(
id="test",
provider="openai",
api_key=SecretStr("test_key"),
title="Test",
)
prompt = [{"role": "user", "content": "Test prompt"}]
result = await _call_llm_direct(credentials, prompt)
assert (
result
== "Agent successfully processed user input and generated response."
)
mock_llm_call.assert_called_once()
@pytest.mark.asyncio
async def test_call_llm_direct_no_response(self):
"""Test LLM call with no response."""
from pydantic import SecretStr
from backend.data.model import APIKeyCredentials
with patch(
"backend.executor.activity_status_generator.llm_call"
) as mock_llm_call:
mock_llm_call.return_value = None
credentials = APIKeyCredentials(
id="test",
provider="openai",
api_key=SecretStr("test_key"),
title="Test",
)
prompt = [{"role": "user", "content": "Test prompt"}]
result = await _call_llm_direct(credentials, prompt)
assert result == "Unable to generate activity summary"
class TestGenerateActivityStatusForExecution:
"""Tests for the main generate_activity_status_for_execution function."""
@pytest.mark.asyncio
async def test_generate_status_success(
self, mock_node_executions, mock_execution_stats, mock_blocks
):
"""Test successful activity status generation."""
mock_db_client = AsyncMock()
mock_db_client.get_node_executions.return_value = mock_node_executions
mock_graph_metadata = MagicMock()
mock_graph_metadata.name = "Test Agent"
mock_graph_metadata.description = "A test agent"
mock_db_client.get_graph_metadata.return_value = mock_graph_metadata
mock_graph = MagicMock()
mock_graph.links = []
mock_db_client.get_graph.return_value = mock_graph
with patch(
"backend.executor.activity_status_generator.get_block"
) as mock_get_block, patch(
"backend.executor.activity_status_generator.Settings"
) as mock_settings, patch(
"backend.executor.activity_status_generator._call_llm_direct"
) as mock_llm, patch(
"backend.executor.activity_status_generator.is_feature_enabled",
return_value=True,
):
mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
mock_settings.return_value.secrets.openai_api_key = "test_key"
mock_llm.return_value = (
"I analyzed your data and provided the requested insights."
)
result = await generate_activity_status_for_execution(
graph_exec_id="test_exec",
graph_id="test_graph",
graph_version=1,
execution_stats=mock_execution_stats,
db_client=mock_db_client,
user_id="test_user",
)
assert result == "I analyzed your data and provided the requested insights."
mock_db_client.get_node_executions.assert_called_once()
mock_db_client.get_graph_metadata.assert_called_once()
mock_db_client.get_graph.assert_called_once()
mock_llm.assert_called_once()
@pytest.mark.asyncio
async def test_generate_status_feature_disabled(self, mock_execution_stats):
"""Test activity status generation when feature is disabled."""
mock_db_client = AsyncMock()
with patch(
"backend.executor.activity_status_generator.is_feature_enabled",
return_value=False,
):
result = await generate_activity_status_for_execution(
graph_exec_id="test_exec",
graph_id="test_graph",
graph_version=1,
execution_stats=mock_execution_stats,
db_client=mock_db_client,
user_id="test_user",
)
assert result is None
mock_db_client.get_node_executions.assert_not_called()
@pytest.mark.asyncio
async def test_generate_status_no_api_key(self, mock_execution_stats):
"""Test activity status generation with no API key."""
mock_db_client = AsyncMock()
with patch(
"backend.executor.activity_status_generator.Settings"
) as mock_settings, patch(
"backend.executor.activity_status_generator.is_feature_enabled",
return_value=True,
):
mock_settings.return_value.secrets.openai_api_key = ""
result = await generate_activity_status_for_execution(
graph_exec_id="test_exec",
graph_id="test_graph",
graph_version=1,
execution_stats=mock_execution_stats,
db_client=mock_db_client,
user_id="test_user",
)
assert result is None
mock_db_client.get_node_executions.assert_not_called()
@pytest.mark.asyncio
async def test_generate_status_exception_handling(self, mock_execution_stats):
"""Test activity status generation with exception."""
mock_db_client = AsyncMock()
mock_db_client.get_node_executions.side_effect = Exception("Database error")
with patch(
"backend.executor.activity_status_generator.Settings"
) as mock_settings, patch(
"backend.executor.activity_status_generator.is_feature_enabled",
return_value=True,
):
mock_settings.return_value.secrets.openai_api_key = "test_key"
result = await generate_activity_status_for_execution(
graph_exec_id="test_exec",
graph_id="test_graph",
graph_version=1,
execution_stats=mock_execution_stats,
db_client=mock_db_client,
user_id="test_user",
)
assert result is None
@pytest.mark.asyncio
async def test_generate_status_with_graph_name_fallback(
self, mock_node_executions, mock_execution_stats, mock_blocks
):
"""Test activity status generation with graph name fallback."""
mock_db_client = AsyncMock()
mock_db_client.get_node_executions.return_value = mock_node_executions
mock_db_client.get_graph_metadata.return_value = None # No metadata
mock_db_client.get_graph.return_value = None # No graph
with patch(
"backend.executor.activity_status_generator.get_block"
) as mock_get_block, patch(
"backend.executor.activity_status_generator.Settings"
) as mock_settings, patch(
"backend.executor.activity_status_generator._call_llm_direct"
) as mock_llm, patch(
"backend.executor.activity_status_generator.is_feature_enabled",
return_value=True,
):
mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
mock_settings.return_value.secrets.openai_api_key = "test_key"
mock_llm.return_value = "Agent completed execution."
result = await generate_activity_status_for_execution(
graph_exec_id="test_exec",
graph_id="test_graph",
graph_version=1,
execution_stats=mock_execution_stats,
db_client=mock_db_client,
user_id="test_user",
)
assert result == "Agent completed execution."
# Should use fallback graph name in prompt
call_args = mock_llm.call_args[0][1] # prompt argument
assert "Graph test_graph" in call_args[1]["content"]
class TestIntegration:
"""Integration tests to verify the complete flow."""
@pytest.mark.asyncio
async def test_full_integration_flow(
self, mock_node_executions, mock_execution_stats, mock_blocks
):
"""Test the complete integration flow."""
mock_db_client = AsyncMock()
mock_db_client.get_node_executions.return_value = mock_node_executions
mock_graph_metadata = MagicMock()
mock_graph_metadata.name = "Test Integration Agent"
mock_graph_metadata.description = "Integration test agent"
mock_db_client.get_graph_metadata.return_value = mock_graph_metadata
mock_graph = MagicMock()
mock_graph.links = []
mock_db_client.get_graph.return_value = mock_graph
expected_activity = "I processed user input but failed during final output generation due to system error."
with patch(
"backend.executor.activity_status_generator.get_block"
) as mock_get_block, patch(
"backend.executor.activity_status_generator.Settings"
) as mock_settings, patch(
"backend.executor.activity_status_generator.llm_call"
) as mock_llm_call, patch(
"backend.executor.activity_status_generator.is_feature_enabled",
return_value=True,
):
mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
mock_settings.return_value.secrets.openai_api_key = "test_key"
mock_response = LLMResponse(
raw_response={},
prompt=[],
response=expected_activity,
tool_calls=None,
prompt_tokens=100,
completion_tokens=30,
)
mock_llm_call.return_value = mock_response
result = await generate_activity_status_for_execution(
graph_exec_id="test_exec",
graph_id="test_graph",
graph_version=1,
execution_stats=mock_execution_stats,
db_client=mock_db_client,
user_id="test_user",
)
assert result == expected_activity
# Verify the correct data was passed to LLM
llm_call_args = mock_llm_call.call_args
prompt = llm_call_args[1]["prompt"]
# Check system prompt
assert prompt[0]["role"] == "system"
assert "user's perspective" in prompt[0]["content"]
# Check user prompt contains expected data
user_content = prompt[1]["content"]
assert "Test Integration Agent" in user_content
assert "user-friendly terms" in user_content.lower()
# Verify that execution data is present in the prompt
assert "{" in user_content # Should contain JSON data
assert "overall_status" in user_content
@pytest.mark.asyncio
async def test_manager_integration_with_disabled_feature(
self, mock_execution_stats
):
"""Test that when feature returns None, manager doesn't set activity_status."""
mock_db_client = AsyncMock()
with patch(
"backend.executor.activity_status_generator.is_feature_enabled",
return_value=False,
):
result = await generate_activity_status_for_execution(
graph_exec_id="test_exec",
graph_id="test_graph",
graph_version=1,
execution_stats=mock_execution_stats,
db_client=mock_db_client,
user_id="test_user",
)
# Should return None when disabled
assert result is None
# Verify no database calls were made
mock_db_client.get_node_executions.assert_not_called()
mock_db_client.get_graph_metadata.assert_not_called()
mock_db_client.get_graph.assert_not_called()

29
autogpt_platform/backend/backend/executor/manager.py
View File

@@ -24,6 +24,9 @@ from backend.data.notifications import (
NotificationType,
)
from backend.data.rabbitmq import SyncRabbitMQ
from backend.executor.activity_status_generator import (
generate_activity_status_for_execution,
)
from backend.executor.utils import LogMetadata, create_execution_queue_config
from backend.notifications.notifications import queue_notification
from backend.util.exceptions import InsufficientBalanceError
@@ -575,6 +578,32 @@ class Executor:
f"Graph Execution #{graph_exec.graph_exec_id} ended with unexpected status {status}"
)
# Generate AI activity status before updating stats
try:
activity_status = asyncio.run_coroutine_threadsafe(
generate_activity_status_for_execution(
graph_exec_id=graph_exec.graph_exec_id,
graph_id=graph_exec.graph_id,
graph_version=graph_exec.graph_version,
execution_stats=exec_stats,
db_client=get_db_async_client(),
user_id=graph_exec.user_id,
execution_status=status,
),
cls.node_execution_loop,
).result(timeout=60.0)
if activity_status is not None:
exec_stats.activity_status = activity_status
log_metadata.info(f"Generated activity status: {activity_status}")
else:
log_metadata.debug(
"Activity status generation disabled, not setting field"
)
except Exception as e:
log_metadata.error(f"Failed to generate activity status: {str(e)}")
# Don't set activity_status on exception - let it remain None/unset
if graph_exec_result := db_client.update_graph_execution_stats(
graph_exec_id=graph_exec.graph_exec_id,
status=status,

43
autogpt_platform/frontend/src/components/agents/agent-run-details-view.tsx
View File

@@ -15,9 +15,19 @@ import { useBackendAPI } from "@/lib/autogpt-server-api/context";
import ActionButtonGroup from "@/components/agptui/action-button-group";
import type { ButtonAction } from "@/components/agptui/types";
import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
import { IconRefresh, IconSquare } from "@/components/ui/icons";
import {
IconRefresh,
IconSquare,
IconCircleAlert,
} from "@/components/ui/icons";
import { Input } from "@/components/ui/input";
import LoadingBox from "@/components/ui/loading";
import {
Tooltip,
TooltipContent,
TooltipProvider,
TooltipTrigger,
} from "@/components/ui/tooltip";
import { useToastOnFail } from "@/components/molecules/Toast/use-toast";
import {
@@ -228,6 +238,37 @@ export default function AgentRunDetailsView({
</CardContent>
</Card>
{/* Smart Agent Execution Summary */}
{run.stats?.activity_status && (
<Card className="agpt-box">
<CardHeader>
<CardTitle className="flex items-center gap-2 font-poppins text-lg">
Smart Agent Execution Summary
<TooltipProvider>
<Tooltip>
<TooltipTrigger asChild>
<IconCircleAlert className="size-4 cursor-help text-neutral-500 hover:text-neutral-700" />
</TooltipTrigger>
<TooltipContent>
<p className="max-w-xs">
This is an AI-generated summary and may not be
completely accurate. It provides a conversational
overview of what the agent accomplished during
execution.
</p>
</TooltipContent>
</Tooltip>
</TooltipProvider>
</CardTitle>
</CardHeader>
<CardContent>
<p className="text-sm leading-relaxed text-neutral-700">
{run.stats.activity_status}
</p>
</CardContent>
</Card>
)}
{agentRunOutputs !== null && (
<Card className="agpt-box">
<CardHeader>

1
autogpt_platform/frontend/src/lib/autogpt-server-api/types.ts
View File

@@ -268,6 +268,7 @@ export type GraphExecutionMeta = {
node_exec_time: number;
node_exec_time_cpu_only: number;
node_exec_count: number;
activity_status?: string;
};
};