fix: wrap generate_agent call in try/except for consistency

Add exception handler for AgentGeneratorNotConfiguredError in generate_agent
call for defensive consistency, even though decompose_goal would typically
catch it first.

Addresses CodeRabbit review suggestion.

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/__init__.py

@@ -1,29 +1,28 @@
 """Agent generator package - Creates agents from natural language."""
 
 from .core import (
-    apply_agent_patch,
+    AgentGeneratorNotConfiguredError,
     decompose_goal,
     generate_agent,
     generate_agent_patch,
     get_agent_as_json,
+    json_to_graph,
     save_agent_to_library,
 )
-from .fixer import apply_all_fixes
-from .utils import get_blocks_info
-from .validator import validate_agent
+from .service import health_check as check_external_service_health
+from .service import is_external_service_configured
 
 __all__ = [
     # Core functions
     "decompose_goal",
     "generate_agent",
     "generate_agent_patch",
-    "apply_agent_patch",
     "save_agent_to_library",
     "get_agent_as_json",
-    # Fixer
-    "apply_all_fixes",
-    # Validator
-    "validate_agent",
-    # Utils
-    "get_blocks_info",
+    "json_to_graph",
+    # Exceptions
+    "AgentGeneratorNotConfiguredError",
+    # Service
+    "is_external_service_configured",
+    "check_external_service_health",
 ]

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/client.py

@@ -1,25 +0,0 @@
-"""OpenRouter client configuration for agent generation."""
-
-import os
-
-from openai import AsyncOpenAI
-
-# Configuration - use OPEN_ROUTER_API_KEY for consistency with chat/config.py
-OPENROUTER_API_KEY = os.getenv("OPEN_ROUTER_API_KEY")
-AGENT_GENERATOR_MODEL = os.getenv("AGENT_GENERATOR_MODEL", "anthropic/claude-opus-4.5")
-
-# OpenRouter client (OpenAI-compatible API)
-_client: AsyncOpenAI | None = None
-
-
-def get_client() -> AsyncOpenAI:
-    """Get or create the OpenRouter client."""
-    global _client
-    if _client is None:
-        if not OPENROUTER_API_KEY:
-            raise ValueError("OPENROUTER_API_KEY environment variable is required")
-        _client = AsyncOpenAI(
-            base_url="https://openrouter.ai/api/v1",
-            api_key=OPENROUTER_API_KEY,
-        )
-    return _client

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py

@@ -1,7 +1,5 @@
 """Core agent generation functions."""
 
-import copy
-import json
 import logging
 import uuid
 from typing import Any
@@ -9,13 +7,35 @@ from typing import Any
 from backend.api.features.library import db as library_db
 from backend.data.graph import Graph, Link, Node, create_graph
 
-from .client import AGENT_GENERATOR_MODEL, get_client
-from .prompts import DECOMPOSITION_PROMPT, GENERATION_PROMPT, PATCH_PROMPT
-from .utils import get_block_summaries, parse_json_from_llm
+from .service import (
+    decompose_goal_external,
+    generate_agent_external,
+    generate_agent_patch_external,
+    is_external_service_configured,
+)
 
 logger = logging.getLogger(__name__)
 
 
+class AgentGeneratorNotConfiguredError(Exception):
+    """Raised when the external Agent Generator service is not configured."""
+
+    pass
+
+
+def _check_service_configured() -> None:
+    """Check if the external Agent Generator service is configured.
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the service is not configured.
+    """
+    if not is_external_service_configured():
+        raise AgentGeneratorNotConfiguredError(
+            "Agent Generator service is not configured. "
+            "Set AGENTGENERATOR_HOST environment variable to enable agent generation."
+        )
+
+
 async def decompose_goal(description: str, context: str = "") -> dict[str, Any] | None:
     """Break down a goal into steps or return clarifying questions.
 
@@ -28,40 +48,13 @@ async def decompose_goal(description: str, context: str = "") -> dict[str, Any]
         - {"type": "clarifying_questions", "questions": [...]}
         - {"type": "instructions", "steps": [...]}
         Or None on error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
-    client = get_client()
-    prompt = DECOMPOSITION_PROMPT.format(block_summaries=get_block_summaries())
-
-    full_description = description
-    if context:
-        full_description = f"{description}\n\nAdditional context:\n{context}"
-
-    try:
-        response = await client.chat.completions.create(
-            model=AGENT_GENERATOR_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": full_description},
-            ],
-            temperature=0,
-        )
-
-        content = response.choices[0].message.content
-        if content is None:
-            logger.error("LLM returned empty content for decomposition")
-            return None
-
-        result = parse_json_from_llm(content)
-
-        if result is None:
-            logger.error(f"Failed to parse decomposition response: {content[:200]}")
-            return None
-
-        return result
-
-    except Exception as e:
-        logger.error(f"Error decomposing goal: {e}")
-        return None
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for decompose_goal")
+    return await decompose_goal_external(description, context)
 
 
 async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
@@ -72,31 +65,14 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
 
     Returns:
         Agent JSON dict or None on error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
-    client = get_client()
-    prompt = GENERATION_PROMPT.format(block_summaries=get_block_summaries())
-
-    try:
-        response = await client.chat.completions.create(
-            model=AGENT_GENERATOR_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": json.dumps(instructions, indent=2)},
-            ],
-            temperature=0,
-        )
-
-        content = response.choices[0].message.content
-        if content is None:
-            logger.error("LLM returned empty content for agent generation")
-            return None
-
-        result = parse_json_from_llm(content)
-
-        if result is None:
-            logger.error(f"Failed to parse agent JSON: {content[:200]}")
-            return None
-
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for generate_agent")
+    result = await generate_agent_external(instructions)
+    if result:
         # Ensure required fields
         if "id" not in result:
             result["id"] = str(uuid.uuid4())
@@ -104,12 +80,7 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
             result["version"] = 1
         if "is_active" not in result:
             result["is_active"] = True
-
-        return result
-
-    except Exception as e:
-        logger.error(f"Error generating agent: {e}")
-        return None
+    return result
 
 
 def json_to_graph(agent_json: dict[str, Any]) -> Graph:
@@ -284,108 +255,23 @@ async def get_agent_as_json(
 async def generate_agent_patch(
     update_request: str, current_agent: dict[str, Any]
 ) -> dict[str, Any] | None:
-    """Generate a patch to update an existing agent.
+    """Update an existing agent using natural language.
+
+    The external Agent Generator service handles:
+    - Generating the patch
+    - Applying the patch
+    - Fixing and validating the result
 
     Args:
         update_request: Natural language description of changes
         current_agent: Current agent JSON
 
     Returns:
-        Patch dict or clarifying questions, or None on error
+        Updated agent JSON, clarifying questions dict, or None on error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
-    client = get_client()
-    prompt = PATCH_PROMPT.format(
-        current_agent=json.dumps(current_agent, indent=2),
-        block_summaries=get_block_summaries(),
-    )
-
-    try:
-        response = await client.chat.completions.create(
-            model=AGENT_GENERATOR_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": update_request},
-            ],
-            temperature=0,
-        )
-
-        content = response.choices[0].message.content
-        if content is None:
-            logger.error("LLM returned empty content for patch generation")
-            return None
-
-        return parse_json_from_llm(content)
-
-    except Exception as e:
-        logger.error(f"Error generating patch: {e}")
-        return None
-
-
-def apply_agent_patch(
-    current_agent: dict[str, Any], patch: dict[str, Any]
-) -> dict[str, Any]:
-    """Apply a patch to an existing agent.
-
-    Args:
-        current_agent: Current agent JSON
-        patch: Patch dict with operations
-
-    Returns:
-        Updated agent JSON
-    """
-    agent = copy.deepcopy(current_agent)
-    patches = patch.get("patches", [])
-
-    for p in patches:
-        patch_type = p.get("type")
-
-        if patch_type == "modify":
-            node_id = p.get("node_id")
-            changes = p.get("changes", {})
-
-            for node in agent.get("nodes", []):
-                if node["id"] == node_id:
-                    _deep_update(node, changes)
-                    logger.debug(f"Modified node {node_id}")
-                    break
-
-        elif patch_type == "add":
-            new_nodes = p.get("new_nodes", [])
-            new_links = p.get("new_links", [])
-
-            agent["nodes"] = agent.get("nodes", []) + new_nodes
-            agent["links"] = agent.get("links", []) + new_links
-            logger.debug(f"Added {len(new_nodes)} nodes, {len(new_links)} links")
-
-        elif patch_type == "remove":
-            node_ids_to_remove = set(p.get("node_ids", []))
-            link_ids_to_remove = set(p.get("link_ids", []))
-
-            # Remove nodes
-            agent["nodes"] = [
-                n for n in agent.get("nodes", []) if n["id"] not in node_ids_to_remove
-            ]
-
-            # Remove links (both explicit and those referencing removed nodes)
-            agent["links"] = [
-                link
-                for link in agent.get("links", [])
-                if link["id"] not in link_ids_to_remove
-                and link["source_id"] not in node_ids_to_remove
-                and link["sink_id"] not in node_ids_to_remove
-            ]
-
-            logger.debug(
-                f"Removed {len(node_ids_to_remove)} nodes, {len(link_ids_to_remove)} links"
-            )
-
-    return agent
-
-
-def _deep_update(target: dict, source: dict) -> None:
-    """Recursively update a dict with another dict."""
-    for key, value in source.items():
-        if key in target and isinstance(target[key], dict) and isinstance(value, dict):
-            _deep_update(target[key], value)
-        else:
-            target[key] = value
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for generate_agent_patch")
+    return await generate_agent_patch_external(update_request, current_agent)

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/fixer.py

@@ -1,606 +0,0 @@
-"""Agent fixer - Fixes common LLM generation errors."""
-
-import logging
-import re
-import uuid
-from typing import Any
-
-from .utils import (
-    ADDTODICTIONARY_BLOCK_ID,
-    ADDTOLIST_BLOCK_ID,
-    CODE_EXECUTION_BLOCK_ID,
-    CONDITION_BLOCK_ID,
-    CREATEDICT_BLOCK_ID,
-    CREATELIST_BLOCK_ID,
-    DATA_SAMPLING_BLOCK_ID,
-    DOUBLE_CURLY_BRACES_BLOCK_IDS,
-    GET_CURRENT_DATE_BLOCK_ID,
-    STORE_VALUE_BLOCK_ID,
-    UNIVERSAL_TYPE_CONVERTER_BLOCK_ID,
-    get_blocks_info,
-    is_valid_uuid,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def fix_agent_ids(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix invalid UUIDs in agent and link IDs."""
-    # Fix agent ID
-    if not is_valid_uuid(agent.get("id", "")):
-        agent["id"] = str(uuid.uuid4())
-        logger.debug(f"Fixed agent ID: {agent['id']}")
-
-    # Fix node IDs
-    id_mapping = {}  # Old ID -> New ID
-    for node in agent.get("nodes", []):
-        if not is_valid_uuid(node.get("id", "")):
-            old_id = node.get("id", "")
-            new_id = str(uuid.uuid4())
-            id_mapping[old_id] = new_id
-            node["id"] = new_id
-            logger.debug(f"Fixed node ID: {old_id} -> {new_id}")
-
-    # Fix link IDs and update references
-    for link in agent.get("links", []):
-        if not is_valid_uuid(link.get("id", "")):
-            link["id"] = str(uuid.uuid4())
-            logger.debug(f"Fixed link ID: {link['id']}")
-
-        # Update source/sink IDs if they were remapped
-        if link.get("source_id") in id_mapping:
-            link["source_id"] = id_mapping[link["source_id"]]
-        if link.get("sink_id") in id_mapping:
-            link["sink_id"] = id_mapping[link["sink_id"]]
-
-    return agent
-
-
-def fix_double_curly_braces(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix single curly braces to double in template blocks."""
-    for node in agent.get("nodes", []):
-        if node.get("block_id") not in DOUBLE_CURLY_BRACES_BLOCK_IDS:
-            continue
-
-        input_data = node.get("input_default", {})
-        for key in ("prompt", "format"):
-            if key in input_data and isinstance(input_data[key], str):
-                original = input_data[key]
-                # Fix simple variable references: {var} -> {{var}}
-                fixed = re.sub(
-                    r"(?<!\{)\{([a-zA-Z_][a-zA-Z0-9_]*)\}(?!\})",
-                    r"{{\1}}",
-                    original,
-                )
-                if fixed != original:
-                    input_data[key] = fixed
-                    logger.debug(f"Fixed curly braces in {key}")
-
-    return agent
-
-
-def fix_storevalue_before_condition(agent: dict[str, Any]) -> dict[str, Any]:
-    """Add StoreValueBlock before ConditionBlock if needed for value2."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-
-    # Find all ConditionBlock nodes
-    condition_node_ids = {
-        node["id"] for node in nodes if node.get("block_id") == CONDITION_BLOCK_ID
-    }
-
-    if not condition_node_ids:
-        return agent
-
-    new_nodes = []
-    new_links = []
-    processed_conditions = set()
-
-    for link in links:
-        sink_id = link.get("sink_id")
-        sink_name = link.get("sink_name")
-
-        # Check if this link goes to a ConditionBlock's value2
-        if sink_id in condition_node_ids and sink_name == "value2":
-            source_node = next(
-                (n for n in nodes if n["id"] == link.get("source_id")), None
-            )
-
-            # Skip if source is already a StoreValueBlock
-            if source_node and source_node.get("block_id") == STORE_VALUE_BLOCK_ID:
-                continue
-
-            # Skip if we already processed this condition
-            if sink_id in processed_conditions:
-                continue
-
-            processed_conditions.add(sink_id)
-
-            # Create StoreValueBlock
-            store_node_id = str(uuid.uuid4())
-            store_node = {
-                "id": store_node_id,
-                "block_id": STORE_VALUE_BLOCK_ID,
-                "input_default": {"data": None},
-                "metadata": {"position": {"x": 0, "y": -100}},
-            }
-            new_nodes.append(store_node)
-
-            # Create link: original source -> StoreValueBlock
-            new_links.append(
-                {
-                    "id": str(uuid.uuid4()),
-                    "source_id": link["source_id"],
-                    "source_name": link["source_name"],
-                    "sink_id": store_node_id,
-                    "sink_name": "input",
-                    "is_static": False,
-                }
-            )
-
-            # Update original link: StoreValueBlock -> ConditionBlock
-            link["source_id"] = store_node_id
-            link["source_name"] = "output"
-
-            logger.debug(f"Added StoreValueBlock before ConditionBlock {sink_id}")
-
-    if new_nodes:
-        agent["nodes"] = nodes + new_nodes
-
-    return agent
-
-
-def fix_addtolist_blocks(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix AddToList blocks by adding prerequisite empty AddToList block.
-
-    When an AddToList block is found:
-    1. Checks if there's a CreateListBlock before it
-    2. Removes CreateListBlock if linked directly to AddToList
-    3. Adds an empty AddToList block before the original
-    4. Ensures the original has a self-referencing link
-    """
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    new_nodes = []
-    original_addtolist_ids = set()
-    nodes_to_remove = set()
-    links_to_remove = []
-
-    # First pass: identify CreateListBlock nodes to remove
-    for link in links:
-        source_node = next(
-            (n for n in nodes if n.get("id") == link.get("source_id")), None
-        )
-        sink_node = next((n for n in nodes if n.get("id") == link.get("sink_id")), None)
-
-        if (
-            source_node
-            and sink_node
-            and source_node.get("block_id") == CREATELIST_BLOCK_ID
-            and sink_node.get("block_id") == ADDTOLIST_BLOCK_ID
-        ):
-            nodes_to_remove.add(source_node.get("id"))
-            links_to_remove.append(link)
-            logger.debug(f"Removing CreateListBlock {source_node.get('id')}")
-
-    # Second pass: process AddToList blocks
-    filtered_nodes = []
-    for node in nodes:
-        if node.get("id") in nodes_to_remove:
-            continue
-
-        if node.get("block_id") == ADDTOLIST_BLOCK_ID:
-            original_addtolist_ids.add(node.get("id"))
-            node_id = node.get("id")
-            pos = node.get("metadata", {}).get("position", {"x": 0, "y": 0})
-
-            # Check if already has prerequisite
-            has_prereq = any(
-                link.get("sink_id") == node_id
-                and link.get("sink_name") == "list"
-                and link.get("source_name") == "updated_list"
-                for link in links
-            )
-
-            if not has_prereq:
-                # Remove links to "list" input (except self-reference)
-                for link in links:
-                    if (
-                        link.get("sink_id") == node_id
-                        and link.get("sink_name") == "list"
-                        and link.get("source_id") != node_id
-                        and link not in links_to_remove
-                    ):
-                        links_to_remove.append(link)
-
-                # Create prerequisite AddToList block
-                prereq_id = str(uuid.uuid4())
-                prereq_node = {
-                    "id": prereq_id,
-                    "block_id": ADDTOLIST_BLOCK_ID,
-                    "input_default": {"list": [], "entry": None, "entries": []},
-                    "metadata": {
-                        "position": {"x": pos.get("x", 0) - 800, "y": pos.get("y", 0)}
-                    },
-                }
-                new_nodes.append(prereq_node)
-
-                # Link prerequisite to original
-                links.append(
-                    {
-                        "id": str(uuid.uuid4()),
-                        "source_id": prereq_id,
-                        "source_name": "updated_list",
-                        "sink_id": node_id,
-                        "sink_name": "list",
-                        "is_static": False,
-                    }
-                )
-                logger.debug(f"Added prerequisite AddToList block for {node_id}")
-
-        filtered_nodes.append(node)
-
-    # Remove marked links
-    filtered_links = [link for link in links if link not in links_to_remove]
-
-    # Add self-referencing links for original AddToList blocks
-    for node in filtered_nodes + new_nodes:
-        if (
-            node.get("block_id") == ADDTOLIST_BLOCK_ID
-            and node.get("id") in original_addtolist_ids
-        ):
-            node_id = node.get("id")
-            has_self_ref = any(
-                link["source_id"] == node_id
-                and link["sink_id"] == node_id
-                and link["source_name"] == "updated_list"
-                and link["sink_name"] == "list"
-                for link in filtered_links
-            )
-            if not has_self_ref:
-                filtered_links.append(
-                    {
-                        "id": str(uuid.uuid4()),
-                        "source_id": node_id,
-                        "source_name": "updated_list",
-                        "sink_id": node_id,
-                        "sink_name": "list",
-                        "is_static": False,
-                    }
-                )
-                logger.debug(f"Added self-reference for AddToList {node_id}")
-
-    agent["nodes"] = filtered_nodes + new_nodes
-    agent["links"] = filtered_links
-    return agent
-
-
-def fix_addtodictionary_blocks(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix AddToDictionary blocks by removing empty CreateDictionary nodes."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    nodes_to_remove = set()
-    links_to_remove = []
-
-    for link in links:
-        source_node = next(
-            (n for n in nodes if n.get("id") == link.get("source_id")), None
-        )
-        sink_node = next((n for n in nodes if n.get("id") == link.get("sink_id")), None)
-
-        if (
-            source_node
-            and sink_node
-            and source_node.get("block_id") == CREATEDICT_BLOCK_ID
-            and sink_node.get("block_id") == ADDTODICTIONARY_BLOCK_ID
-        ):
-            nodes_to_remove.add(source_node.get("id"))
-            links_to_remove.append(link)
-            logger.debug(f"Removing CreateDictionary {source_node.get('id')}")
-
-    agent["nodes"] = [n for n in nodes if n.get("id") not in nodes_to_remove]
-    agent["links"] = [link for link in links if link not in links_to_remove]
-    return agent
-
-
-def fix_code_execution_output(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix CodeExecutionBlock output: change 'response' to 'stdout_logs'."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-
-    for link in links:
-        source_node = next(
-            (n for n in nodes if n.get("id") == link.get("source_id")), None
-        )
-        if (
-            source_node
-            and source_node.get("block_id") == CODE_EXECUTION_BLOCK_ID
-            and link.get("source_name") == "response"
-        ):
-            link["source_name"] = "stdout_logs"
-            logger.debug("Fixed CodeExecutionBlock output: response -> stdout_logs")
-
-    return agent
-
-
-def fix_data_sampling_sample_size(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix DataSamplingBlock by setting sample_size to 1 as default."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    links_to_remove = []
-
-    for node in nodes:
-        if node.get("block_id") == DATA_SAMPLING_BLOCK_ID:
-            node_id = node.get("id")
-            input_default = node.get("input_default", {})
-
-            # Remove links to sample_size
-            for link in links:
-                if (
-                    link.get("sink_id") == node_id
-                    and link.get("sink_name") == "sample_size"
-                ):
-                    links_to_remove.append(link)
-
-            # Set default
-            input_default["sample_size"] = 1
-            node["input_default"] = input_default
-            logger.debug(f"Fixed DataSamplingBlock {node_id} sample_size to 1")
-
-    if links_to_remove:
-        agent["links"] = [link for link in links if link not in links_to_remove]
-
-    return agent
-
-
-def fix_node_x_coordinates(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix node x-coordinates to ensure 800+ unit spacing between linked nodes."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    node_lookup = {n.get("id"): n for n in nodes}
-
-    for link in links:
-        source_id = link.get("source_id")
-        sink_id = link.get("sink_id")
-
-        source_node = node_lookup.get(source_id)
-        sink_node = node_lookup.get(sink_id)
-
-        if not source_node or not sink_node:
-            continue
-
-        source_pos = source_node.get("metadata", {}).get("position", {})
-        sink_pos = sink_node.get("metadata", {}).get("position", {})
-
-        source_x = source_pos.get("x", 0)
-        sink_x = sink_pos.get("x", 0)
-
-        if abs(sink_x - source_x) < 800:
-            new_x = source_x + 800
-            if "metadata" not in sink_node:
-                sink_node["metadata"] = {}
-            if "position" not in sink_node["metadata"]:
-                sink_node["metadata"]["position"] = {}
-            sink_node["metadata"]["position"]["x"] = new_x
-            logger.debug(f"Fixed node {sink_id} x: {sink_x} -> {new_x}")
-
-    return agent
-
-
-def fix_getcurrentdate_offset(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix GetCurrentDateBlock offset to ensure it's positive."""
-    for node in agent.get("nodes", []):
-        if node.get("block_id") == GET_CURRENT_DATE_BLOCK_ID:
-            input_default = node.get("input_default", {})
-            if "offset" in input_default:
-                offset = input_default["offset"]
-                if isinstance(offset, (int, float)) and offset < 0:
-                    input_default["offset"] = abs(offset)
-                    logger.debug(f"Fixed offset: {offset} -> {abs(offset)}")
-
-    return agent
-
-
-def fix_ai_model_parameter(
-    agent: dict[str, Any],
-    blocks_info: list[dict[str, Any]],
-    default_model: str = "gpt-4o",
-) -> dict[str, Any]:
-    """Add default model parameter to AI blocks if missing."""
-    block_map = {b.get("id"): b for b in blocks_info}
-
-    for node in agent.get("nodes", []):
-        block_id = node.get("block_id")
-        block = block_map.get(block_id)
-
-        if not block:
-            continue
-
-        # Check if block has AI category
-        categories = block.get("categories", [])
-        is_ai_block = any(
-            cat.get("category") == "AI" for cat in categories if isinstance(cat, dict)
-        )
-
-        if is_ai_block:
-            input_default = node.get("input_default", {})
-            if "model" not in input_default:
-                input_default["model"] = default_model
-                node["input_default"] = input_default
-                logger.debug(
-                    f"Added model '{default_model}' to AI block {node.get('id')}"
-                )
-
-    return agent
-
-
-def fix_link_static_properties(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-) -> dict[str, Any]:
-    """Fix is_static property based on source block's staticOutput."""
-    block_map = {b.get("id"): b for b in blocks_info}
-    node_lookup = {n.get("id"): n for n in agent.get("nodes", [])}
-
-    for link in agent.get("links", []):
-        source_node = node_lookup.get(link.get("source_id"))
-        if not source_node:
-            continue
-
-        source_block = block_map.get(source_node.get("block_id"))
-        if not source_block:
-            continue
-
-        static_output = source_block.get("staticOutput", False)
-        if link.get("is_static") != static_output:
-            link["is_static"] = static_output
-            logger.debug(f"Fixed link {link.get('id')} is_static to {static_output}")
-
-    return agent
-
-
-def fix_data_type_mismatch(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-) -> dict[str, Any]:
-    """Fix data type mismatches by inserting UniversalTypeConverterBlock."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    block_map = {b.get("id"): b for b in blocks_info}
-    node_lookup = {n.get("id"): n for n in nodes}
-
-    def get_property_type(schema: dict, name: str) -> str | None:
-        if "_#_" in name:
-            parent, child = name.split("_#_", 1)
-            parent_schema = schema.get(parent, {})
-            if "properties" in parent_schema:
-                return parent_schema["properties"].get(child, {}).get("type")
-            return None
-        return schema.get(name, {}).get("type")
-
-    def are_types_compatible(src: str, sink: str) -> bool:
-        if {src, sink} <= {"integer", "number"}:
-            return True
-        return src == sink
-
-    type_mapping = {
-        "string": "string",
-        "text": "string",
-        "integer": "number",
-        "number": "number",
-        "float": "number",
-        "boolean": "boolean",
-        "bool": "boolean",
-        "array": "list",
-        "list": "list",
-        "object": "dictionary",
-        "dict": "dictionary",
-        "dictionary": "dictionary",
-    }
-
-    new_links = []
-    nodes_to_add = []
-
-    for link in links:
-        source_node = node_lookup.get(link.get("source_id"))
-        sink_node = node_lookup.get(link.get("sink_id"))
-
-        if not source_node or not sink_node:
-            new_links.append(link)
-            continue
-
-        source_block = block_map.get(source_node.get("block_id"))
-        sink_block = block_map.get(sink_node.get("block_id"))
-
-        if not source_block or not sink_block:
-            new_links.append(link)
-            continue
-
-        source_outputs = source_block.get("outputSchema", {}).get("properties", {})
-        sink_inputs = sink_block.get("inputSchema", {}).get("properties", {})
-
-        source_type = get_property_type(source_outputs, link.get("source_name", ""))
-        sink_type = get_property_type(sink_inputs, link.get("sink_name", ""))
-
-        if (
-            source_type
-            and sink_type
-            and not are_types_compatible(source_type, sink_type)
-        ):
-            # Insert type converter
-            converter_id = str(uuid.uuid4())
-            target_type = type_mapping.get(sink_type, sink_type)
-
-            converter_node = {
-                "id": converter_id,
-                "block_id": UNIVERSAL_TYPE_CONVERTER_BLOCK_ID,
-                "input_default": {"type": target_type},
-                "metadata": {"position": {"x": 0, "y": 100}},
-            }
-            nodes_to_add.append(converter_node)
-
-            # source -> converter
-            new_links.append(
-                {
-                    "id": str(uuid.uuid4()),
-                    "source_id": link["source_id"],
-                    "source_name": link["source_name"],
-                    "sink_id": converter_id,
-                    "sink_name": "value",
-                    "is_static": False,
-                }
-            )
-
-            # converter -> sink
-            new_links.append(
-                {
-                    "id": str(uuid.uuid4()),
-                    "source_id": converter_id,
-                    "source_name": "value",
-                    "sink_id": link["sink_id"],
-                    "sink_name": link["sink_name"],
-                    "is_static": False,
-                }
-            )
-
-            logger.debug(f"Inserted type converter: {source_type} -> {target_type}")
-        else:
-            new_links.append(link)
-
-    if nodes_to_add:
-        agent["nodes"] = nodes + nodes_to_add
-        agent["links"] = new_links
-
-    return agent
-
-
-def apply_all_fixes(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]] | None = None
-) -> dict[str, Any]:
-    """Apply all fixes to an agent JSON.
-
-    Args:
-        agent: Agent JSON dict
-        blocks_info: Optional list of block info dicts for advanced fixes
-
-    Returns:
-        Fixed agent JSON
-    """
-    # Basic fixes (no block info needed)
-    agent = fix_agent_ids(agent)
-    agent = fix_double_curly_braces(agent)
-    agent = fix_storevalue_before_condition(agent)
-    agent = fix_addtolist_blocks(agent)
-    agent = fix_addtodictionary_blocks(agent)
-    agent = fix_code_execution_output(agent)
-    agent = fix_data_sampling_sample_size(agent)
-    agent = fix_node_x_coordinates(agent)
-    agent = fix_getcurrentdate_offset(agent)
-
-    # Advanced fixes (require block info)
-    if blocks_info is None:
-        blocks_info = get_blocks_info()
-
-    agent = fix_ai_model_parameter(agent, blocks_info)
-    agent = fix_link_static_properties(agent, blocks_info)
-    agent = fix_data_type_mismatch(agent, blocks_info)
-
-    return agent

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/prompts.py

@@ -1,225 +0,0 @@
-"""Prompt templates for agent generation."""
-
-DECOMPOSITION_PROMPT = """
-You are an expert AutoGPT Workflow Decomposer. Your task is to analyze a user's high-level goal and break it down into a clear, step-by-step plan using the available blocks.
-
-Each step should represent a distinct, automatable action suitable for execution by an AI automation system.
-
----
-
-FIRST: Analyze the user's goal and determine:
-1) Design-time configuration (fixed settings that won't change per run)
-2) Runtime inputs (values the agent's end-user will provide each time it runs)
-
-For anything that can vary per run (email addresses, names, dates, search terms, etc.):
-- DO NOT ask for the actual value
-- Instead, define it as an Agent Input with a clear name, type, and description
-
-Only ask clarifying questions about design-time config that affects how you build the workflow:
-- Which external service to use (e.g., "Gmail vs Outlook", "Notion vs Google Docs")
-- Required formats or structures (e.g., "CSV, JSON, or PDF output?")
-- Business rules that must be hard-coded
-
-IMPORTANT CLARIFICATIONS POLICY:
-- Ask no more than five essential questions
-- Do not ask for concrete values that can be provided at runtime as Agent Inputs
-- Do not ask for API keys or credentials; the platform handles those directly
-- If there is enough information to infer reasonable defaults, prefer to propose defaults
-
----
-
-GUIDELINES:
-1. List each step as a numbered item
-2. Describe the action clearly and specify inputs/outputs
-3. Ensure steps are in logical, sequential order
-4. Mention block names naturally (e.g., "Use GetWeatherByLocationBlock to...")
-5. Help the user reach their goal efficiently
-
----
-
-RULES:
-1. OUTPUT FORMAT: Only output either clarifying questions OR step-by-step instructions, not both
-2. USE ONLY THE BLOCKS PROVIDED
-3. ALL required_input fields must be provided
-4. Data types of linked properties must match
-5. Write expert-level prompts for AI-related blocks
-
----
-
-CRITICAL BLOCK RESTRICTIONS:
-1. AddToListBlock: Outputs updated list EVERY addition, not after all additions
-2. SendEmailBlock: Draft the email for user review; set SMTP config based on email type
-3. ConditionBlock: value2 is reference, value1 is contrast
-4. CodeExecutionBlock: DO NOT USE - use AI blocks instead
-5. ReadCsvBlock: Only use the 'rows' output, not 'row'
-
----
-
-OUTPUT FORMAT:
-
-If more information is needed:
-```json
-{{
-  "type": "clarifying_questions",
-  "questions": [
-    {{
-      "question": "Which email provider should be used? (Gmail, Outlook, custom SMTP)",
-      "keyword": "email_provider",
-      "example": "Gmail"
-    }}
-  ]
-}}
-```
-
-If ready to proceed:
-```json
-{{
-  "type": "instructions",
-  "steps": [
-    {{
-      "step_number": 1,
-      "block_name": "AgentShortTextInputBlock",
-      "description": "Get the URL of the content to analyze.",
-      "inputs": [{{"name": "name", "value": "URL"}}],
-      "outputs": [{{"name": "result", "description": "The URL entered by user"}}]
-    }}
-  ]
-}}
-```
-
----
-
-AVAILABLE BLOCKS:
-{block_summaries}
-"""
-
-GENERATION_PROMPT = """
-You are an expert AI workflow builder. Generate a valid agent JSON from the given instructions.
-
----
-
-NODES:
-Each node must include:
-- `id`: Unique UUID v4 (e.g. `a8f5b1e2-c3d4-4e5f-8a9b-0c1d2e3f4a5b`)
-- `block_id`: The block identifier (must match an Allowed Block)
-- `input_default`: Dict of inputs (can be empty if no static inputs needed)
-- `metadata`: Must contain:
-  - `position`: {{"x": number, "y": number}} - adjacent nodes should differ by 800+ in X
-  - `customized_name`: Clear name describing this block's purpose in the workflow
-
----
-
-LINKS:
-Each link connects a source node's output to a sink node's input:
-- `id`: MUST be UUID v4 (NOT "link-1", "link-2", etc.)
-- `source_id`: ID of the source node
-- `source_name`: Output field name from the source block
-- `sink_id`: ID of the sink node
-- `sink_name`: Input field name on the sink block
-- `is_static`: true only if source block has static_output: true
-
-CRITICAL: All IDs must be valid UUID v4 format!
-
----
-
-AGENT (GRAPH):
-Wrap nodes and links in:
-- `id`: UUID of the agent
-- `name`: Short, generic name (avoid specific company names, URLs)
-- `description`: Short, generic description
-- `nodes`: List of all nodes
-- `links`: List of all links
-- `version`: 1
-- `is_active`: true
-
----
-
-TIPS:
-- All required_input fields must be provided via input_default or a valid link
-- Ensure consistent source_id and sink_id references
-- Avoid dangling links
-- Input/output pins must match block schemas
-- Do not invent unknown block_ids
-
----
-
-ALLOWED BLOCKS:
-{block_summaries}
-
----
-
-Generate the complete agent JSON. Output ONLY valid JSON, no explanation.
-"""
-
-PATCH_PROMPT = """
-You are an expert at modifying AutoGPT agent workflows. Given the current agent and a modification request, generate a JSON patch to update the agent.
-
-CURRENT AGENT:
-{current_agent}
-
-AVAILABLE BLOCKS:
-{block_summaries}
-
----
-
-PATCH FORMAT:
-Return a JSON object with the following structure:
-
-```json
-{{
-  "type": "patch",
-  "intent": "Brief description of what the patch does",
-  "patches": [
-    {{
-      "type": "modify",
-      "node_id": "uuid-of-node-to-modify",
-      "changes": {{
-        "input_default": {{"field": "new_value"}},
-        "metadata": {{"customized_name": "New Name"}}
-      }}
-    }},
-    {{
-      "type": "add",
-      "new_nodes": [
-        {{
-          "id": "new-uuid",
-          "block_id": "block-uuid",
-          "input_default": {{}},
-          "metadata": {{"position": {{"x": 0, "y": 0}}, "customized_name": "Name"}}
-        }}
-      ],
-      "new_links": [
-        {{
-          "id": "link-uuid",
-          "source_id": "source-node-id",
-          "source_name": "output_field",
-          "sink_id": "sink-node-id",
-          "sink_name": "input_field"
-        }}
-      ]
-    }},
-    {{
-      "type": "remove",
-      "node_ids": ["uuid-of-node-to-remove"],
-      "link_ids": ["uuid-of-link-to-remove"]
-    }}
-  ]
-}}
-```
-
-If you need more information, return:
-```json
-{{
-  "type": "clarifying_questions",
-  "questions": [
-    {{
-      "question": "What specific change do you want?",
-      "keyword": "change_type",
-      "example": "Add error handling"
-    }}
-  ]
-}}
-```
-
-Generate the minimal patch needed. Output ONLY valid JSON.
-"""

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py

@@ -0,0 +1,269 @@
+"""External Agent Generator service client.
+
+This module provides a client for communicating with the external Agent Generator
+microservice. When AGENTGENERATOR_HOST is configured, the agent generation functions
+will delegate to the external service instead of using the built-in LLM-based implementation.
+"""
+
+import logging
+from typing import Any
+
+import httpx
+
+from backend.util.settings import Settings
+
+logger = logging.getLogger(__name__)
+
+_client: httpx.AsyncClient | None = None
+_settings: Settings | None = None
+
+
+def _get_settings() -> Settings:
+    """Get or create settings singleton."""
+    global _settings
+    if _settings is None:
+        _settings = Settings()
+    return _settings
+
+
+def is_external_service_configured() -> bool:
+    """Check if external Agent Generator service is configured."""
+    settings = _get_settings()
+    return bool(settings.config.agentgenerator_host)
+
+
+def _get_base_url() -> str:
+    """Get the base URL for the external service."""
+    settings = _get_settings()
+    host = settings.config.agentgenerator_host
+    port = settings.config.agentgenerator_port
+    return f"http://{host}:{port}"
+
+
+def _get_client() -> httpx.AsyncClient:
+    """Get or create the HTTP client for the external service."""
+    global _client
+    if _client is None:
+        settings = _get_settings()
+        _client = httpx.AsyncClient(
+            base_url=_get_base_url(),
+            timeout=httpx.Timeout(settings.config.agentgenerator_timeout),
+        )
+    return _client
+
+
+async def decompose_goal_external(
+    description: str, context: str = ""
+) -> dict[str, Any] | None:
+    """Call the external service to decompose a goal.
+
+    Args:
+        description: Natural language goal description
+        context: Additional context (e.g., answers to previous questions)
+
+    Returns:
+        Dict with either:
+        - {"type": "clarifying_questions", "questions": [...]}
+        - {"type": "instructions", "steps": [...]}
+        - {"type": "unachievable_goal", ...}
+        - {"type": "vague_goal", ...}
+        Or None on error
+    """
+    client = _get_client()
+
+    # Build the request payload
+    payload: dict[str, Any] = {"description": description}
+    if context:
+        # The external service uses user_instruction for additional context
+        payload["user_instruction"] = context
+
+    try:
+        response = await client.post("/api/decompose-description", json=payload)
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error(f"External service returned error: {data.get('error')}")
+            return None
+
+        # Map the response to the expected format
+        response_type = data.get("type")
+        if response_type == "instructions":
+            return {"type": "instructions", "steps": data.get("steps", [])}
+        elif response_type == "clarifying_questions":
+            return {
+                "type": "clarifying_questions",
+                "questions": data.get("questions", []),
+            }
+        elif response_type == "unachievable_goal":
+            return {
+                "type": "unachievable_goal",
+                "reason": data.get("reason"),
+                "suggested_goal": data.get("suggested_goal"),
+            }
+        elif response_type == "vague_goal":
+            return {
+                "type": "vague_goal",
+                "suggested_goal": data.get("suggested_goal"),
+            }
+        else:
+            logger.error(
+                f"Unknown response type from external service: {response_type}"
+            )
+            return None
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error calling external agent generator: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error calling external agent generator: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error calling external agent generator: {e}")
+        return None
+
+
+async def generate_agent_external(
+    instructions: dict[str, Any]
+) -> dict[str, Any] | None:
+    """Call the external service to generate an agent from instructions.
+
+    Args:
+        instructions: Structured instructions from decompose_goal
+
+    Returns:
+        Agent JSON dict or None on error
+    """
+    client = _get_client()
+
+    try:
+        response = await client.post(
+            "/api/generate-agent", json={"instructions": instructions}
+        )
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error(f"External service returned error: {data.get('error')}")
+            return None
+
+        return data.get("agent_json")
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error calling external agent generator: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error calling external agent generator: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error calling external agent generator: {e}")
+        return None
+
+
+async def generate_agent_patch_external(
+    update_request: str, current_agent: dict[str, Any]
+) -> dict[str, Any] | None:
+    """Call the external service to generate a patch for an existing agent.
+
+    Args:
+        update_request: Natural language description of changes
+        current_agent: Current agent JSON
+
+    Returns:
+        Updated agent JSON, clarifying questions dict, or None on error
+    """
+    client = _get_client()
+
+    try:
+        response = await client.post(
+            "/api/update-agent",
+            json={
+                "update_request": update_request,
+                "current_agent_json": current_agent,
+            },
+        )
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error(f"External service returned error: {data.get('error')}")
+            return None
+
+        # Check if it's clarifying questions
+        if data.get("type") == "clarifying_questions":
+            return {
+                "type": "clarifying_questions",
+                "questions": data.get("questions", []),
+            }
+
+        # Otherwise return the updated agent JSON
+        return data.get("agent_json")
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error calling external agent generator: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error calling external agent generator: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error calling external agent generator: {e}")
+        return None
+
+
+async def get_blocks_external() -> list[dict[str, Any]] | None:
+    """Get available blocks from the external service.
+
+    Returns:
+        List of block info dicts or None on error
+    """
+    client = _get_client()
+
+    try:
+        response = await client.get("/api/blocks")
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error("External service returned error getting blocks")
+            return None
+
+        return data.get("blocks", [])
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error getting blocks from external service: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error getting blocks from external service: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error getting blocks from external service: {e}")
+        return None
+
+
+async def health_check() -> bool:
+    """Check if the external service is healthy.
+
+    Returns:
+        True if healthy, False otherwise
+    """
+    if not is_external_service_configured():
+        return False
+
+    client = _get_client()
+
+    try:
+        response = await client.get("/health")
+        response.raise_for_status()
+        data = response.json()
+        return data.get("status") == "healthy" and data.get("blocks_loaded", False)
+    except Exception as e:
+        logger.warning(f"External agent generator health check failed: {e}")
+        return False
+
+
+async def close_client() -> None:
+    """Close the HTTP client."""
+    global _client
+    if _client is not None:
+        await _client.aclose()
+        _client = None

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/utils.py

@@ -1,213 +0,0 @@
-"""Utilities for agent generation."""
-
-import json
-import re
-from typing import Any
-
-from backend.data.block import get_blocks
-
-# UUID validation regex
-UUID_REGEX = re.compile(
-    r"^[a-f0-9]{8}-[a-f0-9]{4}-4[a-f0-9]{3}-[89ab][a-f0-9]{3}-[a-f0-9]{12}$"
-)
-
-# Block IDs for various fixes
-STORE_VALUE_BLOCK_ID = "1ff065e9-88e8-4358-9d82-8dc91f622ba9"
-CONDITION_BLOCK_ID = "715696a0-e1da-45c8-b209-c2fa9c3b0be6"
-ADDTOLIST_BLOCK_ID = "aeb08fc1-2fc1-4141-bc8e-f758f183a822"
-ADDTODICTIONARY_BLOCK_ID = "31d1064e-7446-4693-a7d4-65e5ca1180d1"
-CREATELIST_BLOCK_ID = "a912d5c7-6e00-4542-b2a9-8034136930e4"
-CREATEDICT_BLOCK_ID = "b924ddf4-de4f-4b56-9a85-358930dcbc91"
-CODE_EXECUTION_BLOCK_ID = "0b02b072-abe7-11ef-8372-fb5d162dd712"
-DATA_SAMPLING_BLOCK_ID = "4a448883-71fa-49cf-91cf-70d793bd7d87"
-UNIVERSAL_TYPE_CONVERTER_BLOCK_ID = "95d1b990-ce13-4d88-9737-ba5c2070c97b"
-GET_CURRENT_DATE_BLOCK_ID = "b29c1b50-5d0e-4d9f-8f9d-1b0e6fcbf0b1"
-
-DOUBLE_CURLY_BRACES_BLOCK_IDS = [
-    "44f6c8ad-d75c-4ae1-8209-aad1c0326928",  # FillTextTemplateBlock
-    "6ab085e2-20b3-4055-bc3e-08036e01eca6",
-    "90f8c45e-e983-4644-aa0b-b4ebe2f531bc",
-    "363ae599-353e-4804-937e-b2ee3cef3da4",  # AgentOutputBlock
-    "3b191d9f-356f-482d-8238-ba04b6d18381",
-    "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
-    "3a7c4b8d-6e2f-4a5d-b9c1-f8d23c5a9b0e",
-    "ed1ae7a0-b770-4089-b520-1f0005fad19a",
-    "a892b8d9-3e4e-4e9c-9c1e-75f8efcf1bfa",
-    "b29c1b50-5d0e-4d9f-8f9d-1b0e6fcbf0b1",
-    "716a67b3-6760-42e7-86dc-18645c6e00fc",
-    "530cf046-2ce0-4854-ae2c-659db17c7a46",
-    "ed55ac19-356e-4243-a6cb-bc599e9b716f",
-    "1f292d4a-41a4-4977-9684-7c8d560b9f91",  # LLM blocks
-    "32a87eab-381e-4dd4-bdb8-4c47151be35a",
-]
-
-
-def is_valid_uuid(value: str) -> bool:
-    """Check if a string is a valid UUID v4."""
-    return isinstance(value, str) and UUID_REGEX.match(value) is not None
-
-
-def _compact_schema(schema: dict) -> dict[str, str]:
-    """Extract compact type info from a JSON schema properties dict.
-
-    Returns a dict of {field_name: type_string} for essential info only.
-    """
-    props = schema.get("properties", {})
-    result = {}
-
-    for name, prop in props.items():
-        # Skip internal/complex fields
-        if name.startswith("_"):
-            continue
-
-        # Get type string
-        type_str = prop.get("type", "any")
-
-        # Handle anyOf/oneOf (optional types)
-        if "anyOf" in prop:
-            types = [t.get("type", "?") for t in prop["anyOf"] if t.get("type")]
-            type_str = "|".join(types) if types else "any"
-        elif "allOf" in prop:
-            type_str = "object"
-
-        # Add array item type if present
-        if type_str == "array" and "items" in prop:
-            items = prop["items"]
-            if isinstance(items, dict):
-                item_type = items.get("type", "any")
-                type_str = f"array[{item_type}]"
-
-        result[name] = type_str
-
-    return result
-
-
-def get_block_summaries(include_schemas: bool = True) -> str:
-    """Generate compact block summaries for prompts.
-
-    Args:
-        include_schemas: Whether to include input/output type info
-
-    Returns:
-        Formatted string of block summaries (compact format)
-    """
-    blocks = get_blocks()
-    summaries = []
-
-    for block_id, block_cls in blocks.items():
-        block = block_cls()
-        name = block.name
-        desc = getattr(block, "description", "") or ""
-
-        # Truncate description
-        if len(desc) > 150:
-            desc = desc[:147] + "..."
-
-        if not include_schemas:
-            summaries.append(f"- {name} (id: {block_id}): {desc}")
-        else:
-            # Compact format with type info only
-            inputs = {}
-            outputs = {}
-            required = []
-
-            if hasattr(block, "input_schema"):
-                try:
-                    schema = block.input_schema.jsonschema()
-                    inputs = _compact_schema(schema)
-                    required = schema.get("required", [])
-                except Exception:
-                    pass
-
-            if hasattr(block, "output_schema"):
-                try:
-                    schema = block.output_schema.jsonschema()
-                    outputs = _compact_schema(schema)
-                except Exception:
-                    pass
-
-            # Build compact line format
-            # Format: NAME (id): desc | in: {field:type, ...} [required] | out: {field:type}
-            in_str = ", ".join(f"{k}:{v}" for k, v in inputs.items())
-            out_str = ", ".join(f"{k}:{v}" for k, v in outputs.items())
-            req_str = f" req=[{','.join(required)}]" if required else ""
-
-            static = " [static]" if getattr(block, "static_output", False) else ""
-
-            line = f"- {name} (id: {block_id}): {desc}"
-            if in_str:
-                line += f"\n  in: {{{in_str}}}{req_str}"
-            if out_str:
-                line += f"\n  out: {{{out_str}}}{static}"
-
-            summaries.append(line)
-
-    return "\n".join(summaries)
-
-
-def get_blocks_info() -> list[dict[str, Any]]:
-    """Get block information with schemas for validation and fixing."""
-    blocks = get_blocks()
-    blocks_info = []
-    for block_id, block_cls in blocks.items():
-        block = block_cls()
-        blocks_info.append(
-            {
-                "id": block_id,
-                "name": block.name,
-                "description": getattr(block, "description", ""),
-                "categories": getattr(block, "categories", []),
-                "staticOutput": getattr(block, "static_output", False),
-                "inputSchema": (
-                    block.input_schema.jsonschema()
-                    if hasattr(block, "input_schema")
-                    else {}
-                ),
-                "outputSchema": (
-                    block.output_schema.jsonschema()
-                    if hasattr(block, "output_schema")
-                    else {}
-                ),
-            }
-        )
-    return blocks_info
-
-
-def parse_json_from_llm(text: str) -> dict[str, Any] | None:
-    """Extract JSON from LLM response (handles markdown code blocks)."""
-    if not text:
-        return None
-
-    # Try fenced code block
-    match = re.search(r"```(?:json)?\s*([\s\S]*?)```", text, re.IGNORECASE)
-    if match:
-        try:
-            return json.loads(match.group(1).strip())
-        except json.JSONDecodeError:
-            pass
-
-    # Try raw text
-    try:
-        return json.loads(text.strip())
-    except json.JSONDecodeError:
-        pass
-
-    # Try finding {...} span
-    start = text.find("{")
-    end = text.rfind("}")
-    if start != -1 and end > start:
-        try:
-            return json.loads(text[start : end + 1])
-        except json.JSONDecodeError:
-            pass
-
-    # Try finding [...] span
-    start = text.find("[")
-    end = text.rfind("]")
-    if start != -1 and end > start:
-        try:
-            return json.loads(text[start : end + 1])
-        except json.JSONDecodeError:
-            pass
-
-    return None

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/validator.py

@@ -1,279 +0,0 @@
-"""Agent validator - Validates agent structure and connections."""
-
-import logging
-import re
-from typing import Any
-
-from .utils import get_blocks_info
-
-logger = logging.getLogger(__name__)
-
-
-class AgentValidator:
-    """Validator for AutoGPT agents with detailed error reporting."""
-
-    def __init__(self):
-        self.errors: list[str] = []
-
-    def add_error(self, error: str) -> None:
-        """Add an error message."""
-        self.errors.append(error)
-
-    def validate_block_existence(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate all block IDs exist in the blocks library."""
-        valid = True
-        valid_block_ids = {b.get("id") for b in blocks_info if b.get("id")}
-
-        for node in agent.get("nodes", []):
-            block_id = node.get("block_id")
-            node_id = node.get("id")
-
-            if not block_id:
-                self.add_error(f"Node '{node_id}' is missing 'block_id' field.")
-                valid = False
-                continue
-
-            if block_id not in valid_block_ids:
-                self.add_error(
-                    f"Node '{node_id}' references block_id '{block_id}' which does not exist."
-                )
-                valid = False
-
-        return valid
-
-    def validate_link_node_references(self, agent: dict[str, Any]) -> bool:
-        """Validate all node IDs referenced in links exist."""
-        valid = True
-        valid_node_ids = {n.get("id") for n in agent.get("nodes", []) if n.get("id")}
-
-        for link in agent.get("links", []):
-            link_id = link.get("id", "Unknown")
-            source_id = link.get("source_id")
-            sink_id = link.get("sink_id")
-
-            if not source_id:
-                self.add_error(f"Link '{link_id}' is missing 'source_id'.")
-                valid = False
-            elif source_id not in valid_node_ids:
-                self.add_error(
-                    f"Link '{link_id}' references non-existent source_id '{source_id}'."
-                )
-                valid = False
-
-            if not sink_id:
-                self.add_error(f"Link '{link_id}' is missing 'sink_id'.")
-                valid = False
-            elif sink_id not in valid_node_ids:
-                self.add_error(
-                    f"Link '{link_id}' references non-existent sink_id '{sink_id}'."
-                )
-                valid = False
-
-        return valid
-
-    def validate_required_inputs(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate required inputs are provided."""
-        valid = True
-        block_map = {b.get("id"): b for b in blocks_info}
-
-        for node in agent.get("nodes", []):
-            block_id = node.get("block_id")
-            block = block_map.get(block_id)
-
-            if not block:
-                continue
-
-            required_inputs = block.get("inputSchema", {}).get("required", [])
-            input_defaults = node.get("input_default", {})
-            node_id = node.get("id")
-
-            # Get linked inputs
-            linked_inputs = {
-                link["sink_name"]
-                for link in agent.get("links", [])
-                if link.get("sink_id") == node_id
-            }
-
-            for req_input in required_inputs:
-                if (
-                    req_input not in input_defaults
-                    and req_input not in linked_inputs
-                    and req_input != "credentials"
-                ):
-                    block_name = block.get("name", "Unknown Block")
-                    self.add_error(
-                        f"Node '{node_id}' ({block_name}) is missing required input '{req_input}'."
-                    )
-                    valid = False
-
-        return valid
-
-    def validate_data_type_compatibility(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate linked data types are compatible."""
-        valid = True
-        block_map = {b.get("id"): b for b in blocks_info}
-        node_lookup = {n.get("id"): n for n in agent.get("nodes", [])}
-
-        def get_type(schema: dict, name: str) -> str | None:
-            if "_#_" in name:
-                parent, child = name.split("_#_", 1)
-                parent_schema = schema.get(parent, {})
-                if "properties" in parent_schema:
-                    return parent_schema["properties"].get(child, {}).get("type")
-                return None
-            return schema.get(name, {}).get("type")
-
-        def are_compatible(src: str, sink: str) -> bool:
-            if {src, sink} <= {"integer", "number"}:
-                return True
-            return src == sink
-
-        for link in agent.get("links", []):
-            source_node = node_lookup.get(link.get("source_id"))
-            sink_node = node_lookup.get(link.get("sink_id"))
-
-            if not source_node or not sink_node:
-                continue
-
-            source_block = block_map.get(source_node.get("block_id"))
-            sink_block = block_map.get(sink_node.get("block_id"))
-
-            if not source_block or not sink_block:
-                continue
-
-            source_outputs = source_block.get("outputSchema", {}).get("properties", {})
-            sink_inputs = sink_block.get("inputSchema", {}).get("properties", {})
-
-            source_type = get_type(source_outputs, link.get("source_name", ""))
-            sink_type = get_type(sink_inputs, link.get("sink_name", ""))
-
-            if source_type and sink_type and not are_compatible(source_type, sink_type):
-                self.add_error(
-                    f"Type mismatch: {source_block.get('name')} output '{link['source_name']}' "
-                    f"({source_type}) -> {sink_block.get('name')} input '{link['sink_name']}' ({sink_type})."
-                )
-                valid = False
-
-        return valid
-
-    def validate_nested_sink_links(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate nested sink links (with _#_ notation)."""
-        valid = True
-        block_map = {b.get("id"): b for b in blocks_info}
-        node_lookup = {n.get("id"): n for n in agent.get("nodes", [])}
-
-        for link in agent.get("links", []):
-            sink_name = link.get("sink_name", "")
-
-            if "_#_" in sink_name:
-                parent, child = sink_name.split("_#_", 1)
-
-                sink_node = node_lookup.get(link.get("sink_id"))
-                if not sink_node:
-                    continue
-
-                block = block_map.get(sink_node.get("block_id"))
-                if not block:
-                    continue
-
-                input_props = block.get("inputSchema", {}).get("properties", {})
-                parent_schema = input_props.get(parent)
-
-                if not parent_schema:
-                    self.add_error(
-                        f"Invalid nested link '{sink_name}': parent '{parent}' not found."
-                    )
-                    valid = False
-                    continue
-
-                if not parent_schema.get("additionalProperties"):
-                    if not (
-                        isinstance(parent_schema, dict)
-                        and "properties" in parent_schema
-                        and child in parent_schema.get("properties", {})
-                    ):
-                        self.add_error(
-                            f"Invalid nested link '{sink_name}': child '{child}' not found in '{parent}'."
-                        )
-                        valid = False
-
-        return valid
-
-    def validate_prompt_spaces(self, agent: dict[str, Any]) -> bool:
-        """Validate prompts don't have spaces in template variables."""
-        valid = True
-
-        for node in agent.get("nodes", []):
-            input_default = node.get("input_default", {})
-            prompt = input_default.get("prompt", "")
-
-            if not isinstance(prompt, str):
-                continue
-
-            # Find {{...}} with spaces
-            matches = re.finditer(r"\{\{([^}]+)\}\}", prompt)
-            for match in matches:
-                content = match.group(1)
-                if " " in content:
-                    self.add_error(
-                        f"Node '{node.get('id')}' has spaces in template variable: "
-                        f"'{{{{{content}}}}}' should be '{{{{{content.replace(' ', '_')}}}}}'."
-                    )
-                    valid = False
-
-        return valid
-
-    def validate(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]] | None = None
-    ) -> tuple[bool, str | None]:
-        """Run all validations.
-
-        Returns:
-            Tuple of (is_valid, error_message)
-        """
-        self.errors = []
-
-        if blocks_info is None:
-            blocks_info = get_blocks_info()
-
-        checks = [
-            self.validate_block_existence(agent, blocks_info),
-            self.validate_link_node_references(agent),
-            self.validate_required_inputs(agent, blocks_info),
-            self.validate_data_type_compatibility(agent, blocks_info),
-            self.validate_nested_sink_links(agent, blocks_info),
-            self.validate_prompt_spaces(agent),
-        ]
-
-        all_passed = all(checks)
-
-        if all_passed:
-            logger.info("Agent validation successful")
-            return True, None
-
-        error_message = "Agent validation failed:\n"
-        for i, error in enumerate(self.errors, 1):
-            error_message += f"{i}. {error}\n"
-
-        logger.warning(f"Agent validation failed with {len(self.errors)} errors")
-        return False, error_message
-
-
-def validate_agent(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]] | None = None
-) -> tuple[bool, str | None]:
-    """Convenience function to validate an agent.
-
-    Returns:
-        Tuple of (is_valid, error_message)
-    """
-    validator = AgentValidator()
-    return validator.validate(agent, blocks_info)

autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py

@@ -8,12 +8,10 @@ from langfuse import observe
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
-    apply_all_fixes,
+    AgentGeneratorNotConfiguredError,
     decompose_goal,
     generate_agent,
-    get_blocks_info,
     save_agent_to_library,
-    validate_agent,
 )
 from .base import BaseTool
 from .models import (
@@ -27,9 +25,6 @@ from .models import (
 
 logger = logging.getLogger(__name__)
 
-# Maximum retries for agent generation with validation feedback
-MAX_GENERATION_RETRIES = 2
-
 
 class CreateAgentTool(BaseTool):
     """Tool for creating agents from natural language descriptions."""
@@ -91,9 +86,8 @@ class CreateAgentTool(BaseTool):
 
         Flow:
         1. Decompose the description into steps (may return clarifying questions)
-        2. Generate agent JSON from the steps
-        3. Apply fixes to correct common LLM errors
-        4. Preview or save based on the save parameter
+        2. Generate agent JSON (external service handles fixing and validation)
+        3. Preview or save based on the save parameter
         """
         description = kwargs.get("description", "").strip()
         context = kwargs.get("context", "")
@@ -110,11 +104,13 @@ class CreateAgentTool(BaseTool):
         # Step 1: Decompose goal into steps
         try:
             decomposition_result = await decompose_goal(description, context)
-        except ValueError as e:
-            # Handle missing API key or configuration errors
+        except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
-                message=f"Agent generation is not configured: {str(e)}",
-                error="configuration_error",
+                message=(
+                    "Agent generation is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
                 session_id=session_id,
             )
 
@@ -171,72 +167,32 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Step 2: Generate agent JSON with retry on validation failure
-        blocks_info = get_blocks_info()
-        agent_json = None
-        validation_errors = None
-
-        for attempt in range(MAX_GENERATION_RETRIES + 1):
-            # Generate agent (include validation errors from previous attempt)
-            if attempt == 0:
-                agent_json = await generate_agent(decomposition_result)
-            else:
-                # Retry with validation error feedback
-                logger.info(
-                    f"Retry {attempt}/{MAX_GENERATION_RETRIES} with validation feedback"
-                )
-                retry_instructions = {
-                    **decomposition_result,
-                    "previous_errors": validation_errors,
-                    "retry_instructions": (
-                        "The previous generation had validation errors. "
-                        "Please fix these issues in the new generation:\n"
-                        f"{validation_errors}"
-                    ),
-                }
-                agent_json = await generate_agent(retry_instructions)
-
-            if agent_json is None:
-                if attempt == MAX_GENERATION_RETRIES:
-                    return ErrorResponse(
-                        message="Failed to generate the agent. Please try again.",
-                        error="Generation failed",
-                        session_id=session_id,
-                    )
-                continue
-
-            # Step 3: Apply fixes to correct common errors
-            agent_json = apply_all_fixes(agent_json, blocks_info)
-
-            # Step 4: Validate the agent
-            is_valid, validation_errors = validate_agent(agent_json, blocks_info)
-
-            if is_valid:
-                logger.info(f"Agent generated successfully on attempt {attempt + 1}")
-                break
-
-            logger.warning(
-                f"Validation failed on attempt {attempt + 1}: {validation_errors}"
+        # Step 2: Generate agent JSON (external service handles fixing and validation)
+        try:
+            agent_json = await generate_agent(decomposition_result)
+        except AgentGeneratorNotConfiguredError:
+            return ErrorResponse(
+                message=(
+                    "Agent generation is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
+                session_id=session_id,
             )
 
-            if attempt == MAX_GENERATION_RETRIES:
-                # Return error with validation details
-                return ErrorResponse(
-                    message=(
-                        f"Generated agent has validation errors after {MAX_GENERATION_RETRIES + 1} attempts. "
-                        f"Please try rephrasing your request or simplify the workflow."
-                    ),
-                    error="validation_failed",
-                    details={"validation_errors": validation_errors},
-                    session_id=session_id,
-                )
+        if agent_json is None:
+            return ErrorResponse(
+                message="Failed to generate the agent. Please try again.",
+                error="Generation failed",
+                session_id=session_id,
+            )
 
         agent_name = agent_json.get("name", "Generated Agent")
         agent_description = agent_json.get("description", "")
         node_count = len(agent_json.get("nodes", []))
         link_count = len(agent_json.get("links", []))
 
-        # Step 4: Preview or save
+        # Step 3: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(

autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py

@@ -8,13 +8,10 @@ from langfuse import observe
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
-    apply_agent_patch,
-    apply_all_fixes,
+    AgentGeneratorNotConfiguredError,
     generate_agent_patch,
     get_agent_as_json,
-    get_blocks_info,
     save_agent_to_library,
-    validate_agent,
 )
 from .base import BaseTool
 from .models import (
@@ -28,9 +25,6 @@ from .models import (
 
 logger = logging.getLogger(__name__)
 
-# Maximum retries for patch generation with validation feedback
-MAX_GENERATION_RETRIES = 2
-
 
 class EditAgentTool(BaseTool):
     """Tool for editing existing agents using natural language."""
@@ -43,7 +37,7 @@ class EditAgentTool(BaseTool):
     def description(self) -> str:
         return (
             "Edit an existing agent from the user's library using natural language. "
-            "Generates a patch to update the agent while preserving unchanged parts."
+            "Generates updates to the agent while preserving unchanged parts."
         )
 
     @property
@@ -98,9 +92,8 @@ class EditAgentTool(BaseTool):
 
         Flow:
         1. Fetch the current agent
-        2. Generate a patch based on the requested changes
-        3. Apply the patch to create an updated agent
-        4. Preview or save based on the save parameter
+        2. Generate updated agent (external service handles fixing and validation)
+        3. Preview or save based on the save parameter
         """
         agent_id = kwargs.get("agent_id", "").strip()
         changes = kwargs.get("changes", "").strip()
@@ -137,121 +130,58 @@ class EditAgentTool(BaseTool):
         if context:
             update_request = f"{changes}\n\nAdditional context:\n{context}"
 
-        # Step 2: Generate patch with retry on validation failure
-        blocks_info = get_blocks_info()
-        updated_agent = None
-        validation_errors = None
-        intent = "Applied requested changes"
-
-        for attempt in range(MAX_GENERATION_RETRIES + 1):
-            # Generate patch (include validation errors from previous attempt)
-            try:
-                if attempt == 0:
-                    patch_result = await generate_agent_patch(
-                        update_request, current_agent
-                    )
-                else:
-                    # Retry with validation error feedback
-                    logger.info(
-                        f"Retry {attempt}/{MAX_GENERATION_RETRIES} with validation feedback"
-                    )
-                    retry_request = (
-                        f"{update_request}\n\n"
-                        f"IMPORTANT: The previous edit had validation errors. "
-                        f"Please fix these issues:\n{validation_errors}"
-                    )
-                    patch_result = await generate_agent_patch(
-                        retry_request, current_agent
-                    )
-            except ValueError as e:
-                # Handle missing API key or configuration errors
-                return ErrorResponse(
-                    message=f"Agent generation is not configured: {str(e)}",
-                    error="configuration_error",
-                    session_id=session_id,
-                )
-
-            if patch_result is None:
-                if attempt == MAX_GENERATION_RETRIES:
-                    return ErrorResponse(
-                        message="Failed to generate changes. Please try rephrasing.",
-                        error="Patch generation failed",
-                        session_id=session_id,
-                    )
-                continue
-
-            # Check if LLM returned clarifying questions
-            if patch_result.get("type") == "clarifying_questions":
-                questions = patch_result.get("questions", [])
-                return ClarificationNeededResponse(
-                    message=(
-                        "I need some more information about the changes. "
-                        "Please answer the following questions:"
-                    ),
-                    questions=[
-                        ClarifyingQuestion(
-                            question=q.get("question", ""),
-                            keyword=q.get("keyword", ""),
-                            example=q.get("example"),
-                        )
-                        for q in questions
-                    ],
-                    session_id=session_id,
-                )
-
-            # Step 3: Apply patch and fixes
-            try:
-                updated_agent = apply_agent_patch(current_agent, patch_result)
-                updated_agent = apply_all_fixes(updated_agent, blocks_info)
-            except Exception as e:
-                if attempt == MAX_GENERATION_RETRIES:
-                    return ErrorResponse(
-                        message=f"Failed to apply changes: {str(e)}",
-                        error="patch_apply_failed",
-                        details={"exception": str(e)},
-                        session_id=session_id,
-                    )
-                validation_errors = str(e)
-                continue
-
-            # Step 4: Validate the updated agent
-            is_valid, validation_errors = validate_agent(updated_agent, blocks_info)
-
-            if is_valid:
-                logger.info(f"Agent edited successfully on attempt {attempt + 1}")
-                intent = patch_result.get("intent", "Applied requested changes")
-                break
-
-            logger.warning(
-                f"Validation failed on attempt {attempt + 1}: {validation_errors}"
+        # Step 2: Generate updated agent (external service handles fixing and validation)
+        try:
+            result = await generate_agent_patch(update_request, current_agent)
+        except AgentGeneratorNotConfiguredError:
+            return ErrorResponse(
+                message=(
+                    "Agent editing is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
+                session_id=session_id,
             )
 
-            if attempt == MAX_GENERATION_RETRIES:
-                # Return error with validation details
-                return ErrorResponse(
-                    message=(
-                        f"Updated agent has validation errors after "
-                        f"{MAX_GENERATION_RETRIES + 1} attempts. "
-                        f"Please try rephrasing your request or simplify the changes."
-                    ),
-                    error="validation_failed",
-                    details={"validation_errors": validation_errors},
-                    session_id=session_id,
-                )
+        if result is None:
+            return ErrorResponse(
+                message="Failed to generate changes. Please try rephrasing.",
+                error="Update generation failed",
+                session_id=session_id,
+            )
 
-        # At this point, updated_agent is guaranteed to be set (we return on all failure paths)
-        assert updated_agent is not None
+        # Check if LLM returned clarifying questions
+        if result.get("type") == "clarifying_questions":
+            questions = result.get("questions", [])
+            return ClarificationNeededResponse(
+                message=(
+                    "I need some more information about the changes. "
+                    "Please answer the following questions:"
+                ),
+                questions=[
+                    ClarifyingQuestion(
+                        question=q.get("question", ""),
+                        keyword=q.get("keyword", ""),
+                        example=q.get("example"),
+                    )
+                    for q in questions
+                ],
+                session_id=session_id,
+            )
+
+        # Result is the updated agent JSON
+        updated_agent = result
 
         agent_name = updated_agent.get("name", "Updated Agent")
         agent_description = updated_agent.get("description", "")
         node_count = len(updated_agent.get("nodes", []))
         link_count = len(updated_agent.get("links", []))
 
-        # Step 5: Preview or save
+        # Step 3: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(
-                    f"I've updated the agent. Changes: {intent}. "
+                    f"I've updated the agent. "
                     f"The agent now has {node_count} blocks. "
                     f"Review it and call edit_agent with save=true to save the changes."
                 ),
@@ -277,10 +207,7 @@ class EditAgentTool(BaseTool):
             )
 
             return AgentSavedResponse(
-                message=(
-                    f"Updated agent '{created_graph.name}' has been saved to your library! "
-                    f"Changes: {intent}"
-                ),
+                message=f"Updated agent '{created_graph.name}' has been saved to your library!",
                 agent_id=created_graph.id,
                 agent_name=created_graph.name,
                 library_agent_id=library_agent.id,

autogpt_platform/backend/backend/api/features/executions/review/model.py

@@ -179,14 +179,6 @@ class ReviewRequest(BaseModel):
     reviews: List[ReviewItem] = Field(
         description="All reviews with their approval status, data, and messages"
     )
-    auto_approve_future_actions: bool = Field(
-        default=False,
-        description=(
-            "If true, future reviews from the same blocks (nodes) being approved "
-            "will be automatically approved for the remainder of this execution. "
-            "This only affects the current execution run."
-        ),
-    )
 
     @model_validator(mode="after")
     def validate_review_completeness(self):

autogpt_platform/backend/backend/api/features/executions/review/review_routes_test.py

@@ -490,321 +490,3 @@ def test_process_review_action_invalid_node_exec_id(
     # Should be a 400 Bad Request, not 500 Internal Server Error
     assert response.status_code == 400
     assert "Invalid node execution ID format" in response.json()["detail"]
-
-
-def test_process_review_action_auto_approve_creates_auto_approval_records(
-    mocker: pytest_mock.MockerFixture,
-    sample_pending_review: PendingHumanReviewModel,
-    test_user_id: str,
-) -> None:
-    """Test that auto_approve_future_actions flag creates auto-approval records"""
-    from backend.data.execution import ExecutionContext, NodeExecutionResult
-    from backend.data.graph import GraphSettings
-
-    # Mock process_all_reviews
-    mock_process_all_reviews = mocker.patch(
-        "backend.api.features.executions.review.routes.process_all_reviews_for_execution"
-    )
-    approved_review = PendingHumanReviewModel(
-        node_exec_id="test_node_123",
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        payload={"data": "test payload"},
-        instructions="Please review",
-        editable=True,
-        status=ReviewStatus.APPROVED,
-        review_message="Approved",
-        was_edited=False,
-        processed=False,
-        created_at=FIXED_NOW,
-        updated_at=FIXED_NOW,
-        reviewed_at=FIXED_NOW,
-    )
-    mock_process_all_reviews.return_value = {"test_node_123": approved_review}
-
-    # Mock get_node_execution to return node_id
-    mock_get_node_execution = mocker.patch(
-        "backend.api.features.executions.review.routes.get_node_execution"
-    )
-    mock_node_exec = mocker.Mock(spec=NodeExecutionResult)
-    mock_node_exec.node_id = "test_node_def_456"
-    mock_get_node_execution.return_value = mock_node_exec
-
-    # Mock create_auto_approval_record
-    mock_create_auto_approval = mocker.patch(
-        "backend.api.features.executions.review.routes.create_auto_approval_record"
-    )
-
-    # Mock has_pending_reviews_for_graph_exec
-    mock_has_pending = mocker.patch(
-        "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
-    )
-    mock_has_pending.return_value = False
-
-    # Mock get_graph_settings to return custom settings
-    mock_get_settings = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_settings"
-    )
-    mock_get_settings.return_value = GraphSettings(
-        human_in_the_loop_safe_mode=True,
-        sensitive_action_safe_mode=True,
-    )
-
-    # Mock add_graph_execution
-    mock_add_execution = mocker.patch(
-        "backend.api.features.executions.review.routes.add_graph_execution"
-    )
-
-    request_data = {
-        "reviews": [
-            {
-                "node_exec_id": "test_node_123",
-                "approved": True,
-                "message": "Approved",
-            }
-        ],
-        "auto_approve_future_actions": True,
-    }
-
-    response = client.post("/api/review/action", json=request_data)
-
-    assert response.status_code == 200
-
-    # Verify process_all_reviews_for_execution was called (without auto_approve param)
-    mock_process_all_reviews.assert_called_once()
-
-    # Verify create_auto_approval_record was called for the approved review
-    mock_create_auto_approval.assert_called_once_with(
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        node_id="test_node_def_456",
-        payload={"data": "test payload"},
-    )
-
-    # Verify get_graph_settings was called with correct parameters
-    mock_get_settings.assert_called_once_with(
-        user_id=test_user_id, graph_id="test_graph_789"
-    )
-
-    # Verify add_graph_execution was called with proper ExecutionContext
-    mock_add_execution.assert_called_once()
-    call_kwargs = mock_add_execution.call_args.kwargs
-    execution_context = call_kwargs["execution_context"]
-
-    assert isinstance(execution_context, ExecutionContext)
-    assert execution_context.human_in_the_loop_safe_mode is True
-    assert execution_context.sensitive_action_safe_mode is True
-
-
-def test_process_review_action_without_auto_approve_still_loads_settings(
-    mocker: pytest_mock.MockerFixture,
-    sample_pending_review: PendingHumanReviewModel,
-    test_user_id: str,
-) -> None:
-    """Test that execution context is created with settings even without auto-approve"""
-    from backend.data.execution import ExecutionContext
-    from backend.data.graph import GraphSettings
-
-    # Mock process_all_reviews
-    mock_process_all_reviews = mocker.patch(
-        "backend.api.features.executions.review.routes.process_all_reviews_for_execution"
-    )
-    approved_review = PendingHumanReviewModel(
-        node_exec_id="test_node_123",
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        payload={"data": "test payload"},
-        instructions="Please review",
-        editable=True,
-        status=ReviewStatus.APPROVED,
-        review_message="Approved",
-        was_edited=False,
-        processed=False,
-        created_at=FIXED_NOW,
-        updated_at=FIXED_NOW,
-        reviewed_at=FIXED_NOW,
-    )
-    mock_process_all_reviews.return_value = {"test_node_123": approved_review}
-
-    # Mock create_auto_approval_record - should NOT be called when auto_approve is False
-    mock_create_auto_approval = mocker.patch(
-        "backend.api.features.executions.review.routes.create_auto_approval_record"
-    )
-
-    # Mock has_pending_reviews_for_graph_exec
-    mock_has_pending = mocker.patch(
-        "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
-    )
-    mock_has_pending.return_value = False
-
-    # Mock get_graph_settings with sensitive_action_safe_mode enabled
-    mock_get_settings = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_settings"
-    )
-    mock_get_settings.return_value = GraphSettings(
-        human_in_the_loop_safe_mode=False,
-        sensitive_action_safe_mode=True,
-    )
-
-    # Mock add_graph_execution
-    mock_add_execution = mocker.patch(
-        "backend.api.features.executions.review.routes.add_graph_execution"
-    )
-
-    # Request WITHOUT auto_approve_future_actions
-    request_data = {
-        "reviews": [
-            {
-                "node_exec_id": "test_node_123",
-                "approved": True,
-                "message": "Approved",
-            }
-        ],
-        "auto_approve_future_actions": False,
-    }
-
-    response = client.post("/api/review/action", json=request_data)
-
-    assert response.status_code == 200
-
-    # Verify process_all_reviews_for_execution was called
-    mock_process_all_reviews.assert_called_once()
-
-    # Verify create_auto_approval_record was NOT called (auto_approve_future_actions=False)
-    mock_create_auto_approval.assert_not_called()
-
-    # Verify settings were loaded
-    mock_get_settings.assert_called_once()
-
-    # Verify ExecutionContext has proper settings
-    mock_add_execution.assert_called_once()
-    call_kwargs = mock_add_execution.call_args.kwargs
-    execution_context = call_kwargs["execution_context"]
-
-    assert isinstance(execution_context, ExecutionContext)
-    assert execution_context.human_in_the_loop_safe_mode is False
-    assert execution_context.sensitive_action_safe_mode is True
-
-
-def test_process_review_action_auto_approve_only_applies_to_approved_reviews(
-    mocker: pytest_mock.MockerFixture,
-    test_user_id: str,
-) -> None:
-    """Test that auto_approve record is created only for approved reviews"""
-    from backend.data.execution import ExecutionContext, NodeExecutionResult
-    from backend.data.graph import GraphSettings
-
-    # Create two reviews - one approved, one rejected
-    approved_review = PendingHumanReviewModel(
-        node_exec_id="node_exec_approved",
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        payload={"data": "approved"},
-        instructions="Review",
-        editable=True,
-        status=ReviewStatus.APPROVED,
-        review_message=None,
-        was_edited=False,
-        processed=False,
-        created_at=FIXED_NOW,
-        updated_at=FIXED_NOW,
-        reviewed_at=FIXED_NOW,
-    )
-    rejected_review = PendingHumanReviewModel(
-        node_exec_id="node_exec_rejected",
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        payload={"data": "rejected"},
-        instructions="Review",
-        editable=True,
-        status=ReviewStatus.REJECTED,
-        review_message="Rejected",
-        was_edited=False,
-        processed=False,
-        created_at=FIXED_NOW,
-        updated_at=FIXED_NOW,
-        reviewed_at=FIXED_NOW,
-    )
-
-    # Mock process_all_reviews
-    mock_process_all_reviews = mocker.patch(
-        "backend.api.features.executions.review.routes.process_all_reviews_for_execution"
-    )
-    mock_process_all_reviews.return_value = {
-        "node_exec_approved": approved_review,
-        "node_exec_rejected": rejected_review,
-    }
-
-    # Mock get_node_execution to return node_id (only called for approved review)
-    mock_get_node_execution = mocker.patch(
-        "backend.api.features.executions.review.routes.get_node_execution"
-    )
-    mock_node_exec = mocker.Mock(spec=NodeExecutionResult)
-    mock_node_exec.node_id = "test_node_def_approved"
-    mock_get_node_execution.return_value = mock_node_exec
-
-    # Mock create_auto_approval_record
-    mock_create_auto_approval = mocker.patch(
-        "backend.api.features.executions.review.routes.create_auto_approval_record"
-    )
-
-    # Mock has_pending_reviews_for_graph_exec
-    mock_has_pending = mocker.patch(
-        "backend.api.features.executions.review.routes.has_pending_reviews_for_graph_exec"
-    )
-    mock_has_pending.return_value = False
-
-    # Mock get_graph_settings
-    mock_get_settings = mocker.patch(
-        "backend.api.features.executions.review.routes.get_graph_settings"
-    )
-    mock_get_settings.return_value = GraphSettings()
-
-    # Mock add_graph_execution
-    mock_add_execution = mocker.patch(
-        "backend.api.features.executions.review.routes.add_graph_execution"
-    )
-
-    request_data = {
-        "reviews": [
-            {"node_exec_id": "node_exec_approved", "approved": True},
-            {"node_exec_id": "node_exec_rejected", "approved": False},
-        ],
-        "auto_approve_future_actions": True,
-    }
-
-    response = client.post("/api/review/action", json=request_data)
-
-    assert response.status_code == 200
-
-    # Verify process_all_reviews_for_execution was called
-    mock_process_all_reviews.assert_called_once()
-
-    # Verify create_auto_approval_record was called ONLY for the approved review
-    # (not for the rejected one)
-    mock_create_auto_approval.assert_called_once_with(
-        user_id=test_user_id,
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        node_id="test_node_def_approved",
-        payload={"data": "approved"},
-    )
-
-    # Verify get_node_execution was called only for approved review
-    mock_get_node_execution.assert_called_once_with("node_exec_approved")
-
-    # Verify ExecutionContext was created (auto-approval is now DB-based)
-    call_kwargs = mock_add_execution.call_args.kwargs
-    execution_context = call_kwargs["execution_context"]
-    assert isinstance(execution_context, ExecutionContext)

autogpt_platform/backend/backend/api/features/executions/review/routes.py

@@ -5,14 +5,8 @@ import autogpt_libs.auth as autogpt_auth_lib
 from fastapi import APIRouter, HTTPException, Query, Security, status
 from prisma.enums import ReviewStatus
 
-from backend.data.execution import (
-    ExecutionContext,
-    get_graph_execution_meta,
-    get_node_execution,
-)
-from backend.data.graph import get_graph_settings
+from backend.data.execution import get_graph_execution_meta
 from backend.data.human_review import (
-    create_auto_approval_record,
     get_pending_reviews_for_execution,
     get_pending_reviews_for_user,
     has_pending_reviews_for_graph_exec,
@@ -134,20 +128,14 @@ async def process_review_action(
         )
 
     # Build review decisions map
-    # When auto_approve_future_actions is true, ignore any edited data
-    # (auto-approved reviews should use original data for consistency)
     review_decisions = {}
     for review in request.reviews:
         review_status = (
             ReviewStatus.APPROVED if review.approved else ReviewStatus.REJECTED
         )
-        # If auto-approving future actions, don't allow data modifications
-        reviewed_data = (
-            None if request.auto_approve_future_actions else review.reviewed_data
-        )
         review_decisions[review.node_exec_id] = (
             review_status,
-            reviewed_data,
+            review.reviewed_data,
             review.message,
         )
 
@@ -157,22 +145,6 @@ async def process_review_action(
         review_decisions=review_decisions,
     )
 
-    # Create auto-approval records for approved reviews if requested
-    if request.auto_approve_future_actions:
-        for node_exec_id, review in updated_reviews.items():
-            if review.status == ReviewStatus.APPROVED:
-                # Look up the node_id from the node execution
-                node_exec = await get_node_execution(node_exec_id)
-                if node_exec:
-                    await create_auto_approval_record(
-                        user_id=user_id,
-                        graph_exec_id=review.graph_exec_id,
-                        graph_id=review.graph_id,
-                        graph_version=review.graph_version,
-                        node_id=node_exec.node_id,
-                        payload=review.payload,
-                    )
-
     # Count results
     approved_count = sum(
         1
@@ -197,24 +169,10 @@ async def process_review_action(
         if not still_has_pending:
             # Resume execution
             try:
-                # Load graph settings to create proper execution context
-                settings = await get_graph_settings(
-                    user_id=user_id, graph_id=first_review.graph_id
-                )
-
-                # Create execution context with settings
-                # Note: auto-approval is now handled via database lookup in
-                # check_auto_approval(), no need to pass auto_approved_node_ids
-                execution_context = ExecutionContext(
-                    human_in_the_loop_safe_mode=settings.human_in_the_loop_safe_mode,
-                    sensitive_action_safe_mode=settings.sensitive_action_safe_mode,
-                )
-
                 await add_graph_execution(
                     graph_id=first_review.graph_id,
                     user_id=user_id,
                     graph_exec_id=graph_exec_id,
-                    execution_context=execution_context,
                 )
                 logger.info(f"Resumed execution {graph_exec_id}")
             except Exception as e:

autogpt_platform/backend/backend/blocks/basic.py

@@ -116,7 +116,6 @@ class PrintToConsoleBlock(Block):
             input_schema=PrintToConsoleBlock.Input,
             output_schema=PrintToConsoleBlock.Output,
             test_input={"text": "Hello, World!"},
-            is_sensitive_action=True,
             test_output=[
                 ("output", "Hello, World!"),
                 ("status", "printed"),

autogpt_platform/backend/backend/blocks/helpers/review.py

@@ -10,7 +10,7 @@ from prisma.enums import ReviewStatus
 from pydantic import BaseModel
 
 from backend.data.execution import ExecutionContext, ExecutionStatus
-from backend.data.human_review import ReviewResult, check_auto_approval
+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
 
@@ -55,7 +55,6 @@ class HITLReviewHelper:
     async def _handle_review_request(
         input_data: Any,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -70,7 +69,6 @@ class HITLReviewHelper:
         Args:
             input_data: The input data to be reviewed
             user_id: ID of the user requesting the review
-            node_id: ID of the node in the graph definition
             node_exec_id: ID of the node execution
             graph_exec_id: ID of the graph execution
             graph_id: ID of the graph
@@ -85,27 +83,15 @@ class HITLReviewHelper:
         Raises:
             Exception: If review creation or status update fails
         """
-        # 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 auto-approval for specific nodes.
-
-        # Check if this node has been auto-approved in a previous review
-        auto_approval = await check_auto_approval(
-            graph_exec_id=graph_exec_id,
-            node_id=node_id,
-        )
-        if auto_approval:
+        # Skip review if safe mode is disabled - return auto-approved result
+        if not execution_context.human_in_the_loop_safe_mode:
             logger.info(
-                f"Block {block_name} skipping review for node {node_exec_id} - "
-                f"node {node_id} has auto-approval from previous review"
+                f"Block {block_name} skipping review for node {node_exec_id} - safe mode disabled"
             )
-            # Return a new ReviewResult with the current node_exec_id but approved status
             return ReviewResult(
                 data=input_data,
                 status=ReviewStatus.APPROVED,
-                message="Auto-approved (user approved all future actions for this block)",
+                message="Auto-approved (safe mode disabled)",
                 processed=True,
                 node_exec_id=node_exec_id,
             )
@@ -143,7 +129,6 @@ class HITLReviewHelper:
     async def handle_review_decision(
         input_data: Any,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -158,7 +143,6 @@ class HITLReviewHelper:
         Args:
             input_data: The input data to be reviewed
             user_id: ID of the user requesting the review
-            node_id: ID of the node in the graph definition
             node_exec_id: ID of the node execution
             graph_exec_id: ID of the graph execution
             graph_id: ID of the graph
@@ -174,7 +158,6 @@ class HITLReviewHelper:
         review_result = await HITLReviewHelper._handle_review_request(
             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,

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -97,7 +97,6 @@ class HumanInTheLoopBlock(Block):
         input_data: Input,
         *,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -116,7 +115,6 @@ class HumanInTheLoopBlock(Block):
         decision = await self.handle_review_decision(
             input_data=input_data.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,

autogpt_platform/backend/backend/data/block.py

@@ -441,7 +441,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         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.
@@ -474,8 +473,8 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         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()
+        self.is_sensitive_action: bool = False
 
         if self.webhook_config:
             if isinstance(self.webhook_config, BlockWebhookConfig):
@@ -623,7 +622,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         input_data: BlockInput,
         *,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -650,7 +648,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         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,

autogpt_platform/backend/backend/data/human_review.py

@@ -32,87 +32,6 @@ class ReviewResult(BaseModel):
     node_exec_id: str
 
 
-def get_auto_approve_key(graph_exec_id: str, node_id: str) -> str:
-    """Generate the special nodeExecId key for auto-approval records."""
-    return f"auto_approve_{graph_exec_id}_{node_id}"
-
-
-async def check_auto_approval(
-    graph_exec_id: str,
-    node_id: str,
-) -> Optional[ReviewResult]:
-    """
-    Check if there's an existing auto-approval record for this node in this execution.
-
-    Auto-approval records are stored as PendingHumanReview entries with a special
-    nodeExecId pattern: "auto_approve_{graph_exec_id}_{node_id}"
-
-    Args:
-        graph_exec_id: ID of the graph execution
-        node_id: ID of the node definition (not execution)
-
-    Returns:
-        ReviewResult if auto-approval found, None otherwise
-    """
-    auto_approve_key = get_auto_approve_key(graph_exec_id, node_id)
-
-    # Look for the auto-approval record by its special key
-    auto_approved_review = await PendingHumanReview.prisma().find_unique(
-        where={"nodeExecId": auto_approve_key},
-    )
-
-    if auto_approved_review and auto_approved_review.status == ReviewStatus.APPROVED:
-        logger.info(
-            f"Found auto-approval for node {node_id} in execution {graph_exec_id}"
-        )
-        return ReviewResult(
-            data=auto_approved_review.payload,
-            status=ReviewStatus.APPROVED,
-            message="Auto-approved (user approved all future actions for this block)",
-            processed=True,
-            node_exec_id=auto_approve_key,
-        )
-
-    return None
-
-
-async def create_auto_approval_record(
-    user_id: str,
-    graph_exec_id: str,
-    graph_id: str,
-    graph_version: int,
-    node_id: str,
-    payload: SafeJsonData,
-) -> None:
-    """
-    Create an auto-approval record for a node in this execution.
-
-    This is stored as a PendingHumanReview with a special nodeExecId pattern
-    and status=APPROVED, so future executions of the same node can skip review.
-    """
-    auto_approve_key = get_auto_approve_key(graph_exec_id, node_id)
-
-    await PendingHumanReview.prisma().upsert(
-        where={"nodeExecId": auto_approve_key},
-        data={
-            "create": {
-                "nodeExecId": auto_approve_key,
-                "userId": user_id,
-                "graphExecId": graph_exec_id,
-                "graphId": graph_id,
-                "graphVersion": graph_version,
-                "payload": SafeJson(payload),
-                "instructions": "Auto-approval record",
-                "editable": False,
-                "status": ReviewStatus.APPROVED,
-                "processed": True,
-                "reviewedAt": datetime.now(timezone.utc),
-            },
-            "update": {},  # Already exists, no update needed
-        },
-    )
-
-
 async def get_or_create_human_review(
     user_id: str,
     node_exec_id: str,

autogpt_platform/backend/backend/data/human_review_test.py

@@ -46,8 +46,8 @@ async def test_get_or_create_human_review_new(
     sample_db_review.status = ReviewStatus.WAITING
     sample_db_review.processed = False
 
-    mock_prisma = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_prisma.return_value.upsert = AsyncMock(return_value=sample_db_review)
+    mock_upsert = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
+    mock_upsert.return_value.upsert = AsyncMock(return_value=sample_db_review)
 
     result = await get_or_create_human_review(
         user_id="test-user-123",
@@ -75,8 +75,8 @@ async def test_get_or_create_human_review_approved(
     sample_db_review.processed = False
     sample_db_review.reviewMessage = "Looks good"
 
-    mock_prisma = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_prisma.return_value.upsert = AsyncMock(return_value=sample_db_review)
+    mock_upsert = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
+    mock_upsert.return_value.upsert = AsyncMock(return_value=sample_db_review)
 
     result = await get_or_create_human_review(
         user_id="test-user-123",

autogpt_platform/backend/backend/util/settings.py

@@ -350,6 +350,19 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
         description="Whether to mark failed scans as clean or not",
     )
 
+    agentgenerator_host: str = Field(
+        default="",
+        description="The host for the Agent Generator service (empty to use built-in)",
+    )
+    agentgenerator_port: int = Field(
+        default=8000,
+        description="The port for the Agent Generator service",
+    )
+    agentgenerator_timeout: int = Field(
+        default=120,
+        description="The timeout in seconds for Agent Generator service requests",
+    )
+
     enable_example_blocks: bool = Field(
         default=False,
         description="Whether to enable example blocks in production",

autogpt_platform/backend/migrations/20260121200000_remove_node_execution_fk_from_pending_human_review/migration.sql

@@ -1,7 +0,0 @@
--- Remove NodeExecution foreign key from PendingHumanReview
--- The nodeExecId column remains as the primary key, but we remove the FK constraint
--- to AgentNodeExecution since PendingHumanReview records can persist after node
--- execution records are deleted.
-
--- Drop foreign key constraint that linked PendingHumanReview.nodeExecId to AgentNodeExecution.id
-ALTER TABLE "platform"."PendingHumanReview" DROP CONSTRAINT IF EXISTS "PendingHumanReview_nodeExecId_fkey";

autogpt_platform/backend/schema.prisma

@@ -517,6 +517,8 @@ model AgentNodeExecution {
 
   stats Json?
 
+  PendingHumanReview PendingHumanReview?
+
   @@index([agentGraphExecutionId, agentNodeId, executionStatus])
   @@index([agentNodeId, executionStatus])
   @@index([addedTime, queuedTime])
@@ -565,7 +567,6 @@ enum ReviewStatus {
 }
 
 // Pending human reviews for Human-in-the-loop blocks
-// Also stores auto-approval records with special nodeExecId patterns (e.g., "auto_approve_{graph_exec_id}_{node_id}")
 model PendingHumanReview {
   nodeExecId    String       @id
   userId        String
@@ -584,6 +585,7 @@ model PendingHumanReview {
   reviewedAt    DateTime?
 
   User           User                @relation(fields: [userId], references: [id], onDelete: Cascade)
+  NodeExecution  AgentNodeExecution  @relation(fields: [nodeExecId], references: [id], onDelete: Cascade)
   GraphExecution AgentGraphExecution @relation(fields: [graphExecId], references: [id], onDelete: Cascade)
 
   @@unique([nodeExecId]) // One pending review per node execution

autogpt_platform/backend/test/agent_generator/__init__.py

@@ -0,0 +1 @@
+"""Tests for agent generator module."""

autogpt_platform/backend/test/agent_generator/test_core_integration.py

@@ -0,0 +1,273 @@
+"""
+Tests for the Agent Generator core module.
+
+This test suite verifies that the core functions correctly delegate to
+the external Agent Generator service.
+"""
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from backend.api.features.chat.tools.agent_generator import core
+from backend.api.features.chat.tools.agent_generator.core import (
+    AgentGeneratorNotConfiguredError,
+)
+
+
+class TestServiceNotConfigured:
+    """Test that functions raise AgentGeneratorNotConfiguredError when service is not configured."""
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_raises_when_not_configured(self):
+        """Test that decompose_goal raises error when service not configured."""
+        with patch.object(core, "is_external_service_configured", return_value=False):
+            with pytest.raises(AgentGeneratorNotConfiguredError):
+                await core.decompose_goal("Build a chatbot")
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_raises_when_not_configured(self):
+        """Test that generate_agent raises error when service not configured."""
+        with patch.object(core, "is_external_service_configured", return_value=False):
+            with pytest.raises(AgentGeneratorNotConfiguredError):
+                await core.generate_agent({"steps": []})
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_patch_raises_when_not_configured(self):
+        """Test that generate_agent_patch raises error when service not configured."""
+        with patch.object(core, "is_external_service_configured", return_value=False):
+            with pytest.raises(AgentGeneratorNotConfiguredError):
+                await core.generate_agent_patch("Add a node", {"nodes": []})
+
+
+class TestDecomposeGoal:
+    """Test decompose_goal function service delegation."""
+
+    @pytest.mark.asyncio
+    async def test_calls_external_service(self):
+        """Test that decompose_goal calls the external service."""
+        expected_result = {"type": "instructions", "steps": ["Step 1"]}
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "decompose_goal_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            result = await core.decompose_goal("Build a chatbot")
+
+            mock_external.assert_called_once_with("Build a chatbot", "")
+            assert result == expected_result
+
+    @pytest.mark.asyncio
+    async def test_passes_context_to_external_service(self):
+        """Test that decompose_goal passes context to external service."""
+        expected_result = {"type": "instructions", "steps": ["Step 1"]}
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "decompose_goal_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            await core.decompose_goal("Build a chatbot", "Use Python")
+
+            mock_external.assert_called_once_with("Build a chatbot", "Use Python")
+
+    @pytest.mark.asyncio
+    async def test_returns_none_on_service_failure(self):
+        """Test that decompose_goal returns None when external service fails."""
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "decompose_goal_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = None
+
+            result = await core.decompose_goal("Build a chatbot")
+
+            assert result is None
+
+
+class TestGenerateAgent:
+    """Test generate_agent function service delegation."""
+
+    @pytest.mark.asyncio
+    async def test_calls_external_service(self):
+        """Test that generate_agent calls the external service."""
+        expected_result = {"name": "Test Agent", "nodes": [], "links": []}
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            instructions = {"type": "instructions", "steps": ["Step 1"]}
+            result = await core.generate_agent(instructions)
+
+            mock_external.assert_called_once_with(instructions)
+            # Result should have id, version, is_active added if not present
+            assert result is not None
+            assert result["name"] == "Test Agent"
+            assert "id" in result
+            assert result["version"] == 1
+            assert result["is_active"] is True
+
+    @pytest.mark.asyncio
+    async def test_preserves_existing_id_and_version(self):
+        """Test that external service result preserves existing id and version."""
+        expected_result = {
+            "id": "existing-id",
+            "version": 3,
+            "is_active": False,
+            "name": "Test Agent",
+        }
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result.copy()
+
+            result = await core.generate_agent({"steps": []})
+
+            assert result is not None
+            assert result["id"] == "existing-id"
+            assert result["version"] == 3
+            assert result["is_active"] is False
+
+    @pytest.mark.asyncio
+    async def test_returns_none_when_external_service_fails(self):
+        """Test that generate_agent returns None when external service fails."""
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = None
+
+            result = await core.generate_agent({"steps": []})
+
+            assert result is None
+
+
+class TestGenerateAgentPatch:
+    """Test generate_agent_patch function service delegation."""
+
+    @pytest.mark.asyncio
+    async def test_calls_external_service(self):
+        """Test that generate_agent_patch calls the external service."""
+        expected_result = {"name": "Updated Agent", "nodes": [], "links": []}
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_patch_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            current_agent = {"nodes": [], "links": []}
+            result = await core.generate_agent_patch("Add a node", current_agent)
+
+            mock_external.assert_called_once_with("Add a node", current_agent)
+            assert result == expected_result
+
+    @pytest.mark.asyncio
+    async def test_returns_clarifying_questions(self):
+        """Test that generate_agent_patch returns clarifying questions."""
+        expected_result = {
+            "type": "clarifying_questions",
+            "questions": [{"question": "What type of node?"}],
+        }
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_patch_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            result = await core.generate_agent_patch("Add a node", {"nodes": []})
+
+            assert result == expected_result
+
+    @pytest.mark.asyncio
+    async def test_returns_none_when_external_service_fails(self):
+        """Test that generate_agent_patch returns None when service fails."""
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_patch_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = None
+
+            result = await core.generate_agent_patch("Add a node", {"nodes": []})
+
+            assert result is None
+
+
+class TestJsonToGraph:
+    """Test json_to_graph function."""
+
+    def test_converts_agent_json_to_graph(self):
+        """Test conversion of agent JSON to Graph model."""
+        agent_json = {
+            "id": "test-id",
+            "version": 2,
+            "is_active": True,
+            "name": "Test Agent",
+            "description": "A test agent",
+            "nodes": [
+                {
+                    "id": "node1",
+                    "block_id": "block1",
+                    "input_default": {"key": "value"},
+                    "metadata": {"x": 100},
+                }
+            ],
+            "links": [
+                {
+                    "id": "link1",
+                    "source_id": "node1",
+                    "sink_id": "output",
+                    "source_name": "result",
+                    "sink_name": "input",
+                    "is_static": False,
+                }
+            ],
+        }
+
+        graph = core.json_to_graph(agent_json)
+
+        assert graph.id == "test-id"
+        assert graph.version == 2
+        assert graph.is_active is True
+        assert graph.name == "Test Agent"
+        assert graph.description == "A test agent"
+        assert len(graph.nodes) == 1
+        assert graph.nodes[0].id == "node1"
+        assert graph.nodes[0].block_id == "block1"
+        assert len(graph.links) == 1
+        assert graph.links[0].source_id == "node1"
+
+    def test_generates_ids_if_missing(self):
+        """Test that missing IDs are generated."""
+        agent_json = {
+            "name": "Test Agent",
+            "nodes": [{"block_id": "block1"}],
+            "links": [],
+        }
+
+        graph = core.json_to_graph(agent_json)
+
+        assert graph.id is not None
+        assert graph.nodes[0].id is not None
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

autogpt_platform/backend/test/agent_generator/test_service.py

@@ -0,0 +1,422 @@
+"""
+Tests for the Agent Generator external service client.
+
+This test suite verifies the external Agent Generator service integration,
+including service detection, API calls, and error handling.
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import httpx
+import pytest
+
+from backend.api.features.chat.tools.agent_generator import service
+
+
+class TestServiceConfiguration:
+    """Test service configuration detection."""
+
+    def setup_method(self):
+        """Reset settings singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    def test_external_service_not_configured_when_host_empty(self):
+        """Test that external service is not configured when host is empty."""
+        mock_settings = MagicMock()
+        mock_settings.config.agentgenerator_host = ""
+
+        with patch.object(service, "_get_settings", return_value=mock_settings):
+            assert service.is_external_service_configured() is False
+
+    def test_external_service_configured_when_host_set(self):
+        """Test that external service is configured when host is set."""
+        mock_settings = MagicMock()
+        mock_settings.config.agentgenerator_host = "agent-generator.local"
+
+        with patch.object(service, "_get_settings", return_value=mock_settings):
+            assert service.is_external_service_configured() is True
+
+    def test_get_base_url(self):
+        """Test base URL construction."""
+        mock_settings = MagicMock()
+        mock_settings.config.agentgenerator_host = "agent-generator.local"
+        mock_settings.config.agentgenerator_port = 8000
+
+        with patch.object(service, "_get_settings", return_value=mock_settings):
+            url = service._get_base_url()
+            assert url == "http://agent-generator.local:8000"
+
+
+class TestDecomposeGoalExternal:
+    """Test decompose_goal_external function."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_returns_instructions(self):
+        """Test successful decomposition returning instructions."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "instructions",
+            "steps": ["Step 1", "Step 2"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build a chatbot")
+
+        assert result == {"type": "instructions", "steps": ["Step 1", "Step 2"]}
+        mock_client.post.assert_called_once_with(
+            "/api/decompose-description", json={"description": "Build a chatbot"}
+        )
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_returns_clarifying_questions(self):
+        """Test decomposition returning clarifying questions."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "clarifying_questions",
+            "questions": ["What platform?", "What language?"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build something")
+
+        assert result == {
+            "type": "clarifying_questions",
+            "questions": ["What platform?", "What language?"],
+        }
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_with_context(self):
+        """Test decomposition with additional context."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "instructions",
+            "steps": ["Step 1"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            await service.decompose_goal_external(
+                "Build a chatbot", context="Use Python"
+            )
+
+        mock_client.post.assert_called_once_with(
+            "/api/decompose-description",
+            json={"description": "Build a chatbot", "user_instruction": "Use Python"},
+        )
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_returns_unachievable_goal(self):
+        """Test decomposition returning unachievable goal response."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "unachievable_goal",
+            "reason": "Cannot do X",
+            "suggested_goal": "Try Y instead",
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Do something impossible")
+
+        assert result == {
+            "type": "unachievable_goal",
+            "reason": "Cannot do X",
+            "suggested_goal": "Try Y instead",
+        }
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_handles_http_error(self):
+        """Test decomposition handles HTTP errors gracefully."""
+        mock_client = AsyncMock()
+        mock_client.post.side_effect = httpx.HTTPStatusError(
+            "Server error", request=MagicMock(), response=MagicMock()
+        )
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build a chatbot")
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_handles_request_error(self):
+        """Test decomposition handles request errors gracefully."""
+        mock_client = AsyncMock()
+        mock_client.post.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build a chatbot")
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_handles_service_error(self):
+        """Test decomposition handles service returning error."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": False,
+            "error": "Internal error",
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build a chatbot")
+
+        assert result is None
+
+
+class TestGenerateAgentExternal:
+    """Test generate_agent_external function."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_success(self):
+        """Test successful agent generation."""
+        agent_json = {
+            "name": "Test Agent",
+            "nodes": [],
+            "links": [],
+        }
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "agent_json": agent_json,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        instructions = {"type": "instructions", "steps": ["Step 1"]}
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.generate_agent_external(instructions)
+
+        assert result == agent_json
+        mock_client.post.assert_called_once_with(
+            "/api/generate-agent", json={"instructions": instructions}
+        )
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_handles_error(self):
+        """Test agent generation handles errors gracefully."""
+        mock_client = AsyncMock()
+        mock_client.post.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.generate_agent_external({"steps": []})
+
+        assert result is None
+
+
+class TestGenerateAgentPatchExternal:
+    """Test generate_agent_patch_external function."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_generate_patch_returns_updated_agent(self):
+        """Test successful patch generation returning updated agent."""
+        updated_agent = {
+            "name": "Updated Agent",
+            "nodes": [{"id": "1", "block_id": "test"}],
+            "links": [],
+        }
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "agent_json": updated_agent,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        current_agent = {"name": "Old Agent", "nodes": [], "links": []}
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.generate_agent_patch_external(
+                "Add a new node", current_agent
+            )
+
+        assert result == updated_agent
+        mock_client.post.assert_called_once_with(
+            "/api/update-agent",
+            json={
+                "update_request": "Add a new node",
+                "current_agent_json": current_agent,
+            },
+        )
+
+    @pytest.mark.asyncio
+    async def test_generate_patch_returns_clarifying_questions(self):
+        """Test patch generation returning clarifying questions."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "clarifying_questions",
+            "questions": ["What type of node?"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.generate_agent_patch_external(
+                "Add something", {"nodes": []}
+            )
+
+        assert result == {
+            "type": "clarifying_questions",
+            "questions": ["What type of node?"],
+        }
+
+
+class TestHealthCheck:
+    """Test health_check function."""
+
+    def setup_method(self):
+        """Reset singletons before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_health_check_returns_false_when_not_configured(self):
+        """Test health check returns False when service not configured."""
+        with patch.object(
+            service, "is_external_service_configured", return_value=False
+        ):
+            result = await service.health_check()
+            assert result is False
+
+    @pytest.mark.asyncio
+    async def test_health_check_returns_true_when_healthy(self):
+        """Test health check returns True when service is healthy."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "status": "healthy",
+            "blocks_loaded": True,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.get.return_value = mock_response
+
+        with patch.object(service, "is_external_service_configured", return_value=True):
+            with patch.object(service, "_get_client", return_value=mock_client):
+                result = await service.health_check()
+
+        assert result is True
+        mock_client.get.assert_called_once_with("/health")
+
+    @pytest.mark.asyncio
+    async def test_health_check_returns_false_when_not_healthy(self):
+        """Test health check returns False when service is not healthy."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "status": "unhealthy",
+            "blocks_loaded": False,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.get.return_value = mock_response
+
+        with patch.object(service, "is_external_service_configured", return_value=True):
+            with patch.object(service, "_get_client", return_value=mock_client):
+                result = await service.health_check()
+
+        assert result is False
+
+    @pytest.mark.asyncio
+    async def test_health_check_returns_false_on_error(self):
+        """Test health check returns False on connection error."""
+        mock_client = AsyncMock()
+        mock_client.get.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "is_external_service_configured", return_value=True):
+            with patch.object(service, "_get_client", return_value=mock_client):
+                result = await service.health_check()
+
+        assert result is False
+
+
+class TestGetBlocksExternal:
+    """Test get_blocks_external function."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_get_blocks_success(self):
+        """Test successful blocks retrieval."""
+        blocks = [
+            {"id": "block1", "name": "Block 1"},
+            {"id": "block2", "name": "Block 2"},
+        ]
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "blocks": blocks,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.get.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.get_blocks_external()
+
+        assert result == blocks
+        mock_client.get.assert_called_once_with("/api/blocks")
+
+    @pytest.mark.asyncio
+    async def test_get_blocks_handles_error(self):
+        """Test blocks retrieval handles errors gracefully."""
+        mock_client = AsyncMock()
+        mock_client.get.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.get_blocks_external()
+
+        assert result is None
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

autogpt_platform/frontend/src/app/(platform)/build/components/FloatingSafeModeToogle.tsx

@@ -86,6 +86,7 @@ export function FloatingSafeModeToggle({
   const {
     currentHITLSafeMode,
     showHITLToggle,
+    isHITLStateUndetermined,
     handleHITLToggle,
     currentSensitiveActionSafeMode,
     showSensitiveActionToggle,
@@ -98,9 +99,16 @@ export function FloatingSafeModeToggle({
     return null;
   }
 
+  const showHITL = showHITLToggle && !isHITLStateUndetermined;
+  const showSensitive = showSensitiveActionToggle;
+
+  if (!showHITL && !showSensitive) {
+    return null;
+  }
+
   return (
     <div className={cn("fixed z-50 flex flex-col gap-2", className)}>
-      {showHITLToggle && (
+      {showHITL && (
         <SafeModeButton
           isEnabled={currentHITLSafeMode}
           label="Human in the loop block approval"
@@ -111,7 +119,7 @@ export function FloatingSafeModeToggle({
           fullWidth={fullWidth}
         />
       )}
-      {showSensitiveActionToggle && (
+      {showSensitive && (
         <SafeModeButton
           isEnabled={currentSensitiveActionSafeMode}
           label="Sensitive actions blocks approval"

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/RunAgentModal.tsx

@@ -14,10 +14,6 @@ import {
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { useEffect, useRef, useState } from "react";
 import { ScheduleAgentModal } from "../ScheduleAgentModal/ScheduleAgentModal";
-import {
-  AIAgentSafetyPopup,
-  useAIAgentSafetyPopup,
-} from "./components/AIAgentSafetyPopup/AIAgentSafetyPopup";
 import { ModalHeader } from "./components/ModalHeader/ModalHeader";
 import { ModalRunSection } from "./components/ModalRunSection/ModalRunSection";
 import { RunActions } from "./components/RunActions/RunActions";
@@ -87,17 +83,8 @@ export function RunAgentModal({
 
   const [isScheduleModalOpen, setIsScheduleModalOpen] = useState(false);
   const [hasOverflow, setHasOverflow] = useState(false);
-  const [isSafetyPopupOpen, setIsSafetyPopupOpen] = useState(false);
-  const [pendingRunAction, setPendingRunAction] = useState<(() => void) | null>(
-    null,
-  );
   const contentRef = useRef<HTMLDivElement>(null);
 
-  const { shouldShowPopup, dismissPopup } = useAIAgentSafetyPopup(
-    agent.has_sensitive_action,
-    agent.has_human_in_the_loop,
-  );
-
   const hasAnySetupFields =
     Object.keys(agentInputFields || {}).length > 0 ||
     Object.keys(agentCredentialsInputFields || {}).length > 0;
@@ -178,24 +165,6 @@ export function RunAgentModal({
     onScheduleCreated?.(schedule);
   }
 
-  function handleRunWithSafetyCheck() {
-    if (shouldShowPopup) {
-      setPendingRunAction(() => handleRun);
-      setIsSafetyPopupOpen(true);
-    } else {
-      handleRun();
-    }
-  }
-
-  function handleSafetyPopupAcknowledge() {
-    setIsSafetyPopupOpen(false);
-    dismissPopup();
-    if (pendingRunAction) {
-      pendingRunAction();
-      setPendingRunAction(null);
-    }
-  }
-
   return (
     <>
       <Dialog
@@ -279,7 +248,7 @@ export function RunAgentModal({
                 )}
                 <RunActions
                   defaultRunType={defaultRunType}
-                  onRun={handleRunWithSafetyCheck}
+                  onRun={handleRun}
                   isExecuting={isExecuting}
                   isSettingUpTrigger={isSettingUpTrigger}
                   isRunReady={allRequiredInputsAreSet}
@@ -297,11 +266,6 @@ export function RunAgentModal({
           </div>
         </Dialog.Content>
       </Dialog>
-
-      <AIAgentSafetyPopup
-        isOpen={isSafetyPopupOpen}
-        onAcknowledge={handleSafetyPopupAcknowledge}
-      />
     </>
   );
 }

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/components/AIAgentSafetyPopup/AIAgentSafetyPopup.tsx

@@ -1,95 +0,0 @@
-"use client";
-
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Key, storage } from "@/services/storage/local-storage";
-import { ShieldCheckIcon } from "@phosphor-icons/react";
-import { useCallback, useEffect, useState } from "react";
-
-interface Props {
-  onAcknowledge: () => void;
-  isOpen: boolean;
-}
-
-export function AIAgentSafetyPopup({ onAcknowledge, isOpen }: Props) {
-  function handleAcknowledge() {
-    // Mark popup as shown so it won't appear again
-    storage.set(Key.AI_AGENT_SAFETY_POPUP_SHOWN, "true");
-    onAcknowledge();
-  }
-
-  if (!isOpen) return null;
-
-  return (
-    <Dialog
-      controlled={{ isOpen, set: () => {} }}
-      styling={{ maxWidth: "480px" }}
-    >
-      <Dialog.Content>
-        <div className="flex flex-col items-center p-6 text-center">
-          <div className="mb-6 flex h-16 w-16 items-center justify-center rounded-full bg-blue-50">
-            <ShieldCheckIcon
-              weight="fill"
-              size={32}
-              className="text-blue-600"
-            />
-          </div>
-
-          <Text variant="h3" className="mb-4">
-            Safety Checks Enabled
-          </Text>
-
-          <Text variant="body" className="mb-2 text-zinc-700">
-            AI-generated agents may take actions that affect your data or
-            external systems.
-          </Text>
-
-          <Text variant="body" className="mb-8 text-zinc-700">
-            AutoGPT includes safety checks so you&apos;ll always have the
-            opportunity to review and approve sensitive actions before they
-            happen.
-          </Text>
-
-          <Button
-            variant="primary"
-            size="large"
-            className="w-full"
-            onClick={handleAcknowledge}
-          >
-            Got it
-          </Button>
-        </div>
-      </Dialog.Content>
-    </Dialog>
-  );
-}
-
-export function useAIAgentSafetyPopup(
-  hasSensitiveAction: boolean,
-  hasHumanInTheLoop: boolean,
-) {
-  const [shouldShowPopup, setShouldShowPopup] = useState(false);
-  const [hasChecked, setHasChecked] = useState(false);
-
-  useEffect(() => {
-    // Only check once after mount (to avoid SSR issues)
-    if (hasChecked) return;
-
-    const hasSeenPopup =
-      storage.get(Key.AI_AGENT_SAFETY_POPUP_SHOWN) === "true";
-    const isRelevantAgent = hasSensitiveAction || hasHumanInTheLoop;
-
-    setShouldShowPopup(!hasSeenPopup && isRelevantAgent);
-    setHasChecked(true);
-  }, [hasSensitiveAction, hasHumanInTheLoop, hasChecked]);
-
-  const dismissPopup = useCallback(() => {
-    setShouldShowPopup(false);
-  }, []);
-
-  return {
-    shouldShowPopup,
-    dismissPopup,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedRunView/components/SafeModeToggle.tsx

@@ -69,6 +69,7 @@ export function SafeModeToggle({ graph, className }: Props) {
   const {
     currentHITLSafeMode,
     showHITLToggle,
+    isHITLStateUndetermined,
     handleHITLToggle,
     currentSensitiveActionSafeMode,
     showSensitiveActionToggle,
@@ -77,13 +78,20 @@ export function SafeModeToggle({ graph, className }: Props) {
     shouldShowToggle,
   } = useAgentSafeMode(graph);
 
-  if (!shouldShowToggle) {
+  if (!shouldShowToggle || isHITLStateUndetermined) {
+    return null;
+  }
+
+  const showHITL = showHITLToggle && !isHITLStateUndetermined;
+  const showSensitive = showSensitiveActionToggle;
+
+  if (!showHITL && !showSensitive) {
     return null;
   }
 
   return (
     <div className={cn("flex gap-1", className)}>
-      {showHITLToggle && (
+      {showHITL && (
         <SafeModeIconButton
           isEnabled={currentHITLSafeMode}
           label="Human-in-the-loop"
@@ -93,7 +101,7 @@ export function SafeModeToggle({ graph, className }: Props) {
           isPending={isPending}
         />
       )}
-      {showSensitiveActionToggle && (
+      {showSensitive && (
         <SafeModeIconButton
           isEnabled={currentSensitiveActionSafeMode}
           label="Sensitive actions"

autogpt_platform/frontend/src/app/api/openapi.json

@@ -9425,12 +9425,6 @@
             "type": "array",
             "title": "Reviews",
             "description": "All reviews with their approval status, data, and messages"
-          },
-          "auto_approve_future_actions": {
-            "type": "boolean",
-            "title": "Auto Approve Future Actions",
-            "description": "If true, future reviews from the same blocks (nodes) being approved will be automatically approved for the remainder of this execution. This only affects the current execution run.",
-            "default": false
           }
         },
         "type": "object",

autogpt_platform/frontend/src/components/organisms/FloatingReviewsPanel/FloatingReviewsPanel.tsx

@@ -31,29 +31,6 @@ export function FloatingReviewsPanel({
       query: {
         enabled: !!(graphId && executionId),
         select: okData,
-        // Poll while execution is in progress to detect status changes
-        refetchInterval: (q) => {
-          // Note: refetchInterval callback receives raw data before select transform
-          const rawData = q.state.data as
-            | { status: number; data?: { status?: string } }
-            | undefined;
-          if (rawData?.status !== 200) return false;
-
-          const status = rawData?.data?.status;
-          if (!status) return false;
-
-          // Poll every 2 seconds while running or in review
-          if (
-            status === AgentExecutionStatus.RUNNING ||
-            status === AgentExecutionStatus.QUEUED ||
-            status === AgentExecutionStatus.INCOMPLETE ||
-            status === AgentExecutionStatus.REVIEW
-          ) {
-            return 2000;
-          }
-          return false;
-        },
-        refetchIntervalInBackground: true,
       },
     },
   );
@@ -63,27 +40,23 @@ export function FloatingReviewsPanel({
     useShallow((state) => state.graphExecutionStatus),
   );
 
-  // Determine if we should poll for pending reviews
-  const isInReviewStatus =
-    executionDetails?.status === AgentExecutionStatus.REVIEW ||
-    graphExecutionStatus === AgentExecutionStatus.REVIEW;
-
   const { pendingReviews, isLoading, refetch } = usePendingReviewsForExecution(
     executionId || "",
-    {
-      enabled: !!executionId,
-      // Poll every 2 seconds when in REVIEW status to catch new reviews
-      refetchInterval: isInReviewStatus ? 2000 : false,
-    },
   );
 
-  // Refetch pending reviews when execution status changes
   useEffect(() => {
-    if (executionId && executionDetails?.status) {
+    if (executionId) {
       refetch();
     }
   }, [executionDetails?.status, executionId, refetch]);
 
+  // Refetch when graph execution status changes to REVIEW
+  useEffect(() => {
+    if (graphExecutionStatus === AgentExecutionStatus.REVIEW && executionId) {
+      refetch();
+    }
+  }, [graphExecutionStatus, executionId, refetch]);
+
   if (
     !executionId ||
     (!isLoading &&

autogpt_platform/frontend/src/components/organisms/PendingReviewsList/PendingReviewsList.tsx

@@ -1,9 +1,8 @@
-import { useState, useCallback } from "react";
+import { useState } from "react";
 import { PendingHumanReviewModel } from "@/app/api/__generated__/models/pendingHumanReviewModel";
 import { PendingReviewCard } from "@/components/organisms/PendingReviewCard/PendingReviewCard";
 import { Text } from "@/components/atoms/Text/Text";
 import { Button } from "@/components/atoms/Button/Button";
-import { Switch } from "@/components/atoms/Switch/Switch";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { ClockIcon, WarningIcon } from "@phosphor-icons/react";
 import { usePostV2ProcessReviewAction } from "@/app/api/__generated__/endpoints/executions/executions";
@@ -41,8 +40,6 @@ export function PendingReviewsList({
     "approve" | "reject" | null
   >(null);
 
-  const [autoApproveFuture, setAutoApproveFuture] = useState(false);
-
   const { toast } = useToast();
 
   const reviewActionMutation = usePostV2ProcessReviewAction({
@@ -95,26 +92,6 @@ export function PendingReviewsList({
     setReviewMessageMap((prev) => ({ ...prev, [nodeExecId]: message }));
   }
 
-  // Reset data to original values when toggling auto-approve
-  const handleAutoApproveFutureToggle = useCallback(
-    (enabled: boolean) => {
-      setAutoApproveFuture(enabled);
-      if (enabled) {
-        // Reset all data to original values
-        const originalData: Record<string, string> = {};
-        reviews.forEach((review) => {
-          originalData[review.node_exec_id] = JSON.stringify(
-            review.payload,
-            null,
-            2,
-          );
-        });
-        setReviewDataMap(originalData);
-      }
-    },
-    [reviews],
-  );
-
   function processReviews(approved: boolean) {
     if (reviews.length === 0) {
       toast({
@@ -132,31 +109,22 @@ export function PendingReviewsList({
       const reviewData = reviewDataMap[review.node_exec_id];
       const reviewMessage = reviewMessageMap[review.node_exec_id];
 
-      // When auto-approving future actions, send undefined (use original data)
-      // Otherwise, parse and send the edited data if available
-      let parsedData: any = undefined;
+      let parsedData: any = review.payload; // Default to original payload
 
-      if (!autoApproveFuture) {
-        // For regular approve/reject, use edited data if available
-        if (review.editable && reviewData) {
-          try {
-            parsedData = JSON.parse(reviewData);
-          } catch (error) {
-            toast({
-              title: "Invalid JSON",
-              description: `Please fix the JSON format in review for node ${review.node_exec_id}: ${error instanceof Error ? error.message : "Invalid syntax"}`,
-              variant: "destructive",
-            });
-            setPendingAction(null);
-            return;
-          }
-        } else {
-          // No edits, use original payload
-          parsedData = review.payload;
+      // Parse edited data if available and editable
+      if (review.editable && reviewData) {
+        try {
+          parsedData = JSON.parse(reviewData);
+        } catch (error) {
+          toast({
+            title: "Invalid JSON",
+            description: `Please fix the JSON format in review for node ${review.node_exec_id}: ${error instanceof Error ? error.message : "Invalid syntax"}`,
+            variant: "destructive",
+          });
+          setPendingAction(null);
+          return;
         }
       }
-      // When autoApproveFuture is true, parsedData stays undefined
-      // Backend will use the original payload stored in the database
 
       reviewItems.push({
         node_exec_id: review.node_exec_id,
@@ -169,7 +137,6 @@ export function PendingReviewsList({
     reviewActionMutation.mutate({
       data: {
         reviews: reviewItems,
-        auto_approve_future_actions: autoApproveFuture && approved,
       },
     });
   }
@@ -215,37 +182,21 @@ export function PendingReviewsList({
       <div className="space-y-7">
         {reviews.map((review) => (
           <PendingReviewCard
-            key={`${review.node_exec_id}-${autoApproveFuture}`}
+            key={review.node_exec_id}
             review={review}
             onReviewDataChange={handleReviewDataChange}
             onReviewMessageChange={handleReviewMessageChange}
             reviewMessage={reviewMessageMap[review.node_exec_id] || ""}
-            isDisabled={autoApproveFuture}
           />
         ))}
       </div>
 
-      <div className="space-y-4">
-        {/* Auto-approve toggle */}
-        <div className="flex items-center gap-3">
-          <Switch
-            checked={autoApproveFuture}
-            onCheckedChange={handleAutoApproveFutureToggle}
-            disabled={reviewActionMutation.isPending}
-          />
-          <Text variant="body" className="text-textBlack">
-            Auto-approve all future actions from these blocks
-          </Text>
-        </div>
+      <div className="space-y-7">
+        <Text variant="body" className="text-textGrey">
+          Note: Changes you make here apply only to this task
+        </Text>
 
-        {autoApproveFuture && (
-          <Text variant="small" className="text-amber-600">
-            Editing is disabled. Original data will be used for this and all
-            future reviews from these blocks.
-          </Text>
-        )}
-
-        <div className="flex flex-wrap gap-2">
+        <div className="flex gap-2">
           <Button
             onClick={() => processReviews(true)}
             disabled={reviewActionMutation.isPending || reviews.length === 0}
@@ -269,11 +220,6 @@ export function PendingReviewsList({
             Reject
           </Button>
         </div>
-
-        <Text variant="small" className="text-textGrey">
-          You can turn auto-approval on or off anytime in this agent&apos;s
-          settings.
-        </Text>
       </div>
     </div>
   );

autogpt_platform/frontend/src/hooks/usePendingReviews.ts

@@ -15,22 +15,8 @@ export function usePendingReviews() {
   };
 }
 
-interface UsePendingReviewsForExecutionOptions {
-  enabled?: boolean;
-  refetchInterval?: number | false;
-}
-
-export function usePendingReviewsForExecution(
-  graphExecId: string,
-  options?: UsePendingReviewsForExecutionOptions,
-) {
-  const query = useGetV2GetPendingReviewsForExecution(graphExecId, {
-    query: {
-      enabled: options?.enabled ?? !!graphExecId,
-      refetchInterval: options?.refetchInterval,
-      refetchIntervalInBackground: !!options?.refetchInterval,
-    },
-  });
+export function usePendingReviewsForExecution(graphExecId: string) {
+  const query = useGetV2GetPendingReviewsForExecution(graphExecId);
 
   return {
     pendingReviews: okData(query.data) || [],

autogpt_platform/frontend/src/services/storage/local-storage.ts

@@ -10,7 +10,6 @@ export enum Key {
   LIBRARY_AGENTS_CACHE = "library-agents-cache",
   CHAT_SESSION_ID = "chat_session_id",
   COOKIE_CONSENT = "autogpt_cookie_consent",
-  AI_AGENT_SAFETY_POPUP_SHOWN = "ai-agent-safety-popup-shown",
 }
 
 function get(key: Key) {