fix openapi mismatch

## Summary
Implements a `TextEncoderBlock` that encodes plain text into escape
sequences (the reverse of `TextDecoderBlock`).

## Changes

### Block Implementation
- Added `encoder_block.py` with `TextEncoderBlock` in
`autogpt_platform/backend/backend/blocks/`
- Uses `codecs.encode(text, "unicode_escape").decode("utf-8")` for
encoding
- Mirrors the structure and patterns of the existing `TextDecoderBlock`
- Categorised as `BlockCategory.TEXT`

### Documentation
- Added Text Encoder section to
`docs/integrations/block-integrations/text.md` (the auto-generated docs
file for TEXT category blocks)
- Expanded "How it works" with technical details on the encoding method,
validation, and edge cases
- Added 3 structured use cases per docs guidelines: JSON payload
preparation, Config/ENV generation, Snapshot fixtures
- Added Text Encoder to the overview table in
`docs/integrations/README.md`
- Removed standalone `encoder_block.md` (TEXT category blocks belong in
`text.md` per `CATEGORY_FILE_MAP` in `generate_block_docs.py`)

### Documentation Formatting (CodeRabbit feedback)
- Added blank lines around markdown tables (MD058)
- Added `text` language tags to fenced code blocks (MD040)
- Restructured use case section with bold headings per coding guidelines

## How Docs Were Synced
The `check-docs-sync` CI job runs `poetry run python
scripts/generate_block_docs.py --check` which expects blocks to be
documented in category-grouped files. Since `TextEncoderBlock` uses
`BlockCategory.TEXT`, the `CATEGORY_FILE_MAP` maps it to `text.md` — not
a standalone file. The block entry was added to `text.md` following the
exact format used by the generator (with `<!-- MANUAL -->` markers for
hand-written sections).

## Related Issue
Fixes #11111

---------

Co-authored-by: Otto <otto@agpt.co>
Co-authored-by: lif <19658300+majiayu000@users.noreply.github.com>
Co-authored-by: Aryan Kaul <134673289+aryancodes1@users.noreply.github.com>
Co-authored-by: Nicholas Tindle <nicholas.tindle@agpt.co>
Co-authored-by: Nick Tindle <nick@ntindle.com>

autogpt_platform/backend/.gitignore

@@ -19,3 +19,6 @@ load-tests/*.json
 load-tests/*.log
 load-tests/node_modules/*
 migrations/*/rollback*.sql
+
+# Workspace files
+workspaces/

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

@@ -33,7 +33,7 @@ from backend.data.understanding import (
     get_business_understanding,
 )
 from backend.util.exceptions import NotFoundError
-from backend.util.settings import Settings
+from backend.util.settings import AppEnvironment, Settings
 
 from . import db as chat_db
 from . import stream_registry
@@ -222,8 +222,18 @@ async def _get_system_prompt_template(context: str) -> str:
         try:
             # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
             # Use asyncio.to_thread to avoid blocking the event loop
+            # In non-production environments, fetch the latest prompt version
+            # instead of the production-labeled version for easier testing
+            label = (
+                None
+                if settings.config.app_env == AppEnvironment.PRODUCTION
+                else "latest"
+            )
             prompt = await asyncio.to_thread(
-                langfuse.get_prompt, config.langfuse_prompt_name, cache_ttl_seconds=0
+                langfuse.get_prompt,
+                config.langfuse_prompt_name,
+                label=label,
+                cache_ttl_seconds=0,
             )
             return prompt.compile(users_information=context)
         except Exception as e:
@@ -618,6 +628,9 @@ async def stream_chat_completion(
                         total_tokens=chunk.totalTokens,
                     )
                 )
+            elif isinstance(chunk, StreamHeartbeat):
+                # Pass through heartbeat to keep SSE connection alive
+                yield chunk
             else:
                 logger.error(f"Unknown chunk type: {type(chunk)}", exc_info=True)
 

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

@@ -7,15 +7,7 @@ from typing import Any, NotRequired, TypedDict
 
 from backend.api.features.library import db as library_db
 from backend.api.features.store import db as store_db
-from backend.data.graph import (
-    Graph,
-    Link,
-    Node,
-    create_graph,
-    get_graph,
-    get_graph_all_versions,
-    get_store_listed_graphs,
-)
+from backend.data.graph import Graph, Link, Node, get_graph, get_store_listed_graphs
 from backend.util.exceptions import DatabaseError, NotFoundError
 
 from .service import (
@@ -28,8 +20,6 @@ from .service import (
 
 logger = logging.getLogger(__name__)
 
-AGENT_EXECUTOR_BLOCK_ID = "e189baac-8c20-45a1-94a7-55177ea42565"
-
 
 class ExecutionSummary(TypedDict):
     """Summary of a single execution for quality assessment."""
@@ -669,45 +659,6 @@ def json_to_graph(agent_json: dict[str, Any]) -> Graph:
     )
 
 
-def _reassign_node_ids(graph: Graph) -> None:
-    """Reassign all node and link IDs to new UUIDs.
-
-    This is needed when creating a new version to avoid unique constraint violations.
-    """
-    id_map = {node.id: str(uuid.uuid4()) for node in graph.nodes}
-
-    for node in graph.nodes:
-        node.id = id_map[node.id]
-
-    for link in graph.links:
-        link.id = str(uuid.uuid4())
-        if link.source_id in id_map:
-            link.source_id = id_map[link.source_id]
-        if link.sink_id in id_map:
-            link.sink_id = id_map[link.sink_id]
-
-
-def _populate_agent_executor_user_ids(agent_json: dict[str, Any], user_id: str) -> None:
-    """Populate user_id in AgentExecutorBlock nodes.
-
-    The external agent generator creates AgentExecutorBlock nodes with empty user_id.
-    This function fills in the actual user_id so sub-agents run with correct permissions.
-
-    Args:
-        agent_json: Agent JSON dict (modified in place)
-        user_id: User ID to set
-    """
-    for node in agent_json.get("nodes", []):
-        if node.get("block_id") == AGENT_EXECUTOR_BLOCK_ID:
-            input_default = node.get("input_default") or {}
-            if not input_default.get("user_id"):
-                input_default["user_id"] = user_id
-                node["input_default"] = input_default
-                logger.debug(
-                    f"Set user_id for AgentExecutorBlock node {node.get('id')}"
-                )
-
-
 async def save_agent_to_library(
     agent_json: dict[str, Any], user_id: str, is_update: bool = False
 ) -> tuple[Graph, Any]:
@@ -721,35 +672,10 @@ async def save_agent_to_library(
     Returns:
         Tuple of (created Graph, LibraryAgent)
     """
-    # Populate user_id in AgentExecutorBlock nodes before conversion
-    _populate_agent_executor_user_ids(agent_json, user_id)
-
     graph = json_to_graph(agent_json)
-
     if is_update:
-        if graph.id:
-            existing_versions = await get_graph_all_versions(graph.id, user_id)
-            if existing_versions:
-                latest_version = max(v.version for v in existing_versions)
-                graph.version = latest_version + 1
-                _reassign_node_ids(graph)
-                logger.info(f"Updating agent {graph.id} to version {graph.version}")
-    else:
-        graph.id = str(uuid.uuid4())
-        graph.version = 1
-        _reassign_node_ids(graph)
-        logger.info(f"Creating new agent with ID {graph.id}")
-
-    created_graph = await create_graph(graph, user_id)
-
-    library_agents = await library_db.create_library_agent(
-        graph=created_graph,
-        user_id=user_id,
-        sensitive_action_safe_mode=True,
-        create_library_agents_for_sub_graphs=False,
-    )
-
-    return created_graph, library_agents[0]
+        return await library_db.update_graph_in_library(graph, user_id)
+    return await library_db.create_graph_in_library(graph, user_id)
 
 
 def graph_to_json(graph: Graph) -> dict[str, Any]:

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

@@ -206,9 +206,9 @@ async def search_agents(
             ]
         )
         no_results_msg = (
-            f"No agents found matching '{query}'. Try different keywords or browse the marketplace."
+            f"No agents found matching '{query}'. Let the user know they can try different keywords or browse the marketplace. Also let them know you can create a custom agent for them based on their needs."
             if source == "marketplace"
-            else f"No agents matching '{query}' found in your library."
+            else f"No agents matching '{query}' found in your library. Let the user know you can create a custom agent for them based on their needs."
         )
         return NoResultsResponse(
             message=no_results_msg, session_id=session_id, suggestions=suggestions
@@ -224,10 +224,10 @@ async def search_agents(
     message = (
         "Now you have found some options for the user to choose from. "
         "You can add a link to a recommended agent at: /marketplace/agent/agent_id "
-        "Please ask the user if they would like to use any of these agents."
+        "Please ask the user if they would like to use any of these agents. Let the user know we can create a custom agent for them based on their needs."
         if source == "marketplace"
         else "Found agents in the user's library. You can provide a link to view an agent at: "
-        "/library/agents/{agent_id}. Use agent_output to get execution results, or run_agent to execute."
+        "/library/agents/{agent_id}. Use agent_output to get execution results, or run_agent to execute. Let the user know we can create a custom agent for them based on their needs."
     )
 
     return AgentsFoundResponse(

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

@@ -8,7 +8,12 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data import graph as graph_db
 from backend.data.graph import GraphModel
-from backend.data.model import Credentials, CredentialsFieldInfo, CredentialsMetaInput
+from backend.data.model import (
+    CredentialsFieldInfo,
+    CredentialsMetaInput,
+    HostScopedCredentials,
+    OAuth2Credentials,
+)
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import NotFoundError
 
@@ -123,7 +128,7 @@ def build_missing_credentials_from_graph(
 
     return {
         field_key: _serialize_missing_credential(field_key, field_info)
-        for field_key, (field_info, _node_fields) in aggregated_fields.items()
+        for field_key, (field_info, _, _) in aggregated_fields.items()
         if field_key not in matched_keys
     }
 
@@ -264,7 +269,8 @@ async def match_user_credentials_to_graph(
     # provider is in the set of acceptable providers.
     for credential_field_name, (
         credential_requirements,
-        _node_fields,
+        _,
+        _,
     ) in aggregated_creds.items():
         # Find first matching credential by provider, type, and scopes
         matching_cred = next(
@@ -273,7 +279,14 @@ async def match_user_credentials_to_graph(
                 for cred in available_creds
                 if cred.provider in credential_requirements.provider
                 and cred.type in credential_requirements.supported_types
-                and _credential_has_required_scopes(cred, credential_requirements)
+                and (
+                    cred.type != "oauth2"
+                    or _credential_has_required_scopes(cred, credential_requirements)
+                )
+                and (
+                    cred.type != "host_scoped"
+                    or _credential_is_for_host(cred, credential_requirements)
+                )
             ),
             None,
         )
@@ -318,19 +331,10 @@ async def match_user_credentials_to_graph(
 
 
 def _credential_has_required_scopes(
-    credential: Credentials,
+    credential: OAuth2Credentials,
     requirements: CredentialsFieldInfo,
 ) -> bool:
-    """
-    Check if a credential has all the scopes required by the block.
-
-    For OAuth2 credentials, verifies that the credential's scopes are a superset
-    of the required scopes. For other credential types, returns True (no scope check).
-    """
-    # Only OAuth2 credentials have scopes to check
-    if credential.type != "oauth2":
-        return True
-
+    """Check if an OAuth2 credential has all the scopes required by the input."""
     # If no scopes are required, any credential matches
     if not requirements.required_scopes:
         return True
@@ -339,6 +343,22 @@ def _credential_has_required_scopes(
     return set(credential.scopes).issuperset(requirements.required_scopes)
 
 
+def _credential_is_for_host(
+    credential: HostScopedCredentials,
+    requirements: CredentialsFieldInfo,
+) -> bool:
+    """Check if a host-scoped credential matches the host required by the input."""
+    # We need to know the host to match host-scoped credentials to.
+    # Graph.aggregate_credentials_inputs() adds the node's set URL value (if any)
+    # to discriminator_values. No discriminator_values -> no host to match against.
+    if not requirements.discriminator_values:
+        return True
+
+    # Check that credential host matches required host.
+    # Host-scoped credential inputs are grouped by host, so any item from the set works.
+    return credential.matches_url(list(requirements.discriminator_values)[0])
+
+
 async def check_user_has_required_credentials(
     user_id: str,
     required_credentials: list[CredentialsMetaInput],

autogpt_platform/backend/backend/api/features/library/db.py

@@ -19,7 +19,10 @@ from backend.data.graph import GraphSettings
 from backend.data.includes import AGENT_PRESET_INCLUDE, library_agent_include
 from backend.data.model import CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
-from backend.integrations.webhooks.graph_lifecycle_hooks import on_graph_activate
+from backend.integrations.webhooks.graph_lifecycle_hooks import (
+    on_graph_activate,
+    on_graph_deactivate,
+)
 from backend.util.clients import get_scheduler_client
 from backend.util.exceptions import DatabaseError, InvalidInputError, NotFoundError
 from backend.util.json import SafeJson
@@ -537,6 +540,92 @@ async def update_agent_version_in_library(
     return library_model.LibraryAgent.from_db(lib)
 
 
+async def create_graph_in_library(
+    graph: graph_db.Graph,
+    user_id: str,
+) -> tuple[graph_db.GraphModel, library_model.LibraryAgent]:
+    """Create a new graph and add it to the user's library."""
+    graph.version = 1
+    graph_model = graph_db.make_graph_model(graph, user_id)
+    graph_model.reassign_ids(user_id=user_id, reassign_graph_id=True)
+
+    created_graph = await graph_db.create_graph(graph_model, user_id)
+
+    library_agents = await create_library_agent(
+        graph=created_graph,
+        user_id=user_id,
+        sensitive_action_safe_mode=True,
+        create_library_agents_for_sub_graphs=False,
+    )
+
+    if created_graph.is_active:
+        created_graph = await on_graph_activate(created_graph, user_id=user_id)
+
+    return created_graph, library_agents[0]
+
+
+async def update_graph_in_library(
+    graph: graph_db.Graph,
+    user_id: str,
+) -> tuple[graph_db.GraphModel, library_model.LibraryAgent]:
+    """Create a new version of an existing graph and update the library entry."""
+    existing_versions = await graph_db.get_graph_all_versions(graph.id, user_id)
+    current_active_version = (
+        next((v for v in existing_versions if v.is_active), None)
+        if existing_versions
+        else None
+    )
+    graph.version = (
+        max(v.version for v in existing_versions) + 1 if existing_versions else 1
+    )
+
+    graph_model = graph_db.make_graph_model(graph, user_id)
+    graph_model.reassign_ids(user_id=user_id, reassign_graph_id=False)
+
+    created_graph = await graph_db.create_graph(graph_model, user_id)
+
+    library_agent = await get_library_agent_by_graph_id(user_id, created_graph.id)
+    if not library_agent:
+        raise NotFoundError(f"Library agent not found for graph {created_graph.id}")
+
+    library_agent = await update_library_agent_version_and_settings(
+        user_id, created_graph
+    )
+
+    if created_graph.is_active:
+        created_graph = await on_graph_activate(created_graph, user_id=user_id)
+        await graph_db.set_graph_active_version(
+            graph_id=created_graph.id,
+            version=created_graph.version,
+            user_id=user_id,
+        )
+        if current_active_version:
+            await on_graph_deactivate(current_active_version, user_id=user_id)
+
+    return created_graph, library_agent
+
+
+async def update_library_agent_version_and_settings(
+    user_id: str, agent_graph: graph_db.GraphModel
+) -> library_model.LibraryAgent:
+    """Update library agent to point to new graph version and sync settings."""
+    library = await update_agent_version_in_library(
+        user_id, agent_graph.id, agent_graph.version
+    )
+    updated_settings = GraphSettings.from_graph(
+        graph=agent_graph,
+        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
+        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
+    )
+    if updated_settings != library.settings:
+        library = await update_library_agent(
+            library_agent_id=library.id,
+            user_id=user_id,
+            settings=updated_settings,
+        )
+    return library
+
+
 async def update_library_agent(
     library_agent_id: str,
     user_id: str,

autogpt_platform/backend/backend/api/features/v1.py

@@ -101,7 +101,6 @@ from backend.util.timezone_utils import (
 from backend.util.virus_scanner import scan_content_safe
 
 from .library import db as library_db
-from .library import model as library_model
 from .store.model import StoreAgentDetails
 
 
@@ -823,18 +822,16 @@ async def update_graph(
     graph: graph_db.Graph,
     user_id: Annotated[str, Security(get_user_id)],
 ) -> graph_db.GraphModel:
-    # Sanity check
     if graph.id and graph.id != graph_id:
         raise HTTPException(400, detail="Graph ID does not match ID in URI")
 
-    # Determine new version
     existing_versions = await graph_db.get_graph_all_versions(graph_id, user_id=user_id)
     if not existing_versions:
         raise HTTPException(404, detail=f"Graph #{graph_id} not found")
-    latest_version_number = max(g.version for g in existing_versions)
-    graph.version = latest_version_number + 1
 
+    graph.version = max(g.version for g in existing_versions) + 1
     current_active_version = next((v for v in existing_versions if v.is_active), None)
+
     graph = graph_db.make_graph_model(graph, user_id)
     graph.reassign_ids(user_id=user_id, reassign_graph_id=False)
     graph.validate_graph(for_run=False)
@@ -842,27 +839,23 @@ async def update_graph(
     new_graph_version = await graph_db.create_graph(graph, user_id=user_id)
 
     if new_graph_version.is_active:
-        # Keep the library agent up to date with the new active version
-        await _update_library_agent_version_and_settings(user_id, new_graph_version)
-
-        # Handle activation of the new graph first to ensure continuity
+        await library_db.update_library_agent_version_and_settings(
+            user_id, new_graph_version
+        )
         new_graph_version = await on_graph_activate(new_graph_version, user_id=user_id)
-        # Ensure new version is the only active version
         await graph_db.set_graph_active_version(
             graph_id=graph_id, version=new_graph_version.version, user_id=user_id
         )
         if current_active_version:
-            # Handle deactivation of the previously active version
             await on_graph_deactivate(current_active_version, user_id=user_id)
 
-    # Fetch new graph version *with sub-graphs* (needed for credentials input schema)
     new_graph_version_with_subgraphs = await graph_db.get_graph(
         graph_id,
         new_graph_version.version,
         user_id=user_id,
         include_subgraphs=True,
     )
-    assert new_graph_version_with_subgraphs  # make type checker happy
+    assert new_graph_version_with_subgraphs
     return new_graph_version_with_subgraphs
 
 
@@ -900,33 +893,15 @@ async def set_graph_active_version(
     )
 
     # Keep the library agent up to date with the new active version
-    await _update_library_agent_version_and_settings(user_id, new_active_graph)
+    await library_db.update_library_agent_version_and_settings(
+        user_id, new_active_graph
+    )
 
     if current_active_graph and current_active_graph.version != new_active_version:
         # Handle deactivation of the previously active version
         await on_graph_deactivate(current_active_graph, user_id=user_id)
 
 
-async def _update_library_agent_version_and_settings(
-    user_id: str, agent_graph: graph_db.GraphModel
-) -> library_model.LibraryAgent:
-    library = await library_db.update_agent_version_in_library(
-        user_id, agent_graph.id, agent_graph.version
-    )
-    updated_settings = GraphSettings.from_graph(
-        graph=agent_graph,
-        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
-        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
-    )
-    if updated_settings != library.settings:
-        library = await library_db.update_library_agent(
-            library_agent_id=library.id,
-            user_id=user_id,
-            settings=updated_settings,
-        )
-    return library
-
-
 @v1_router.patch(
     path="/graphs/{graph_id}/settings",
     summary="Update graph settings",

autogpt_platform/backend/backend/blocks/encoder_block.py

@@ -0,0 +1,77 @@
+"""Text encoding block for converting special characters to escape sequences."""
+
+import codecs
+
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.model import SchemaField
+
+
+class TextEncoderBlock(Block):
+    """
+    Encodes a string by converting special characters into escape sequences.
+
+    This block is the inverse of TextDecoderBlock. It takes text containing
+    special characters (like newlines, tabs, etc.) and converts them into
+    their escape sequence representations (e.g., newline becomes \\n).
+    """
+
+    class Input(BlockSchemaInput):
+        """Input schema for TextEncoderBlock."""
+
+        text: str = SchemaField(
+            description="A string containing special characters to be encoded",
+            placeholder="Your text with newlines and quotes to encode",
+        )
+
+    class Output(BlockSchemaOutput):
+        """Output schema for TextEncoderBlock."""
+
+        encoded_text: str = SchemaField(
+            description="The encoded text with special characters converted to escape sequences"
+        )
+        error: str = SchemaField(description="Error message if encoding fails")
+
+    def __init__(self):
+        super().__init__(
+            id="5185f32e-4b65-4ecf-8fbb-873f003f09d6",
+            description="Encodes a string by converting special characters into escape sequences",
+            categories={BlockCategory.TEXT},
+            input_schema=TextEncoderBlock.Input,
+            output_schema=TextEncoderBlock.Output,
+            test_input={
+                "text": """Hello
+World!
+This is a "quoted" string."""
+            },
+            test_output=[
+                (
+                    "encoded_text",
+                    """Hello\\nWorld!\\nThis is a "quoted" string.""",
+                )
+            ],
+        )
+
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        """
+        Encode the input text by converting special characters to escape sequences.
+
+        Args:
+            input_data: The input containing the text to encode.
+            **kwargs: Additional keyword arguments (unused).
+
+        Yields:
+            The encoded text with escape sequences, or an error message if encoding fails.
+        """
+        try:
+            encoded_text = codecs.encode(input_data.text, "unicode_escape").decode(
+                "utf-8"
+            )
+            yield "encoded_text", encoded_text
+        except Exception as e:
+            yield "error", f"Encoding error: {str(e)}"

autogpt_platform/backend/backend/blocks/linear/_api.py

@@ -162,8 +162,16 @@ class LinearClient:
                 "searchTerm": team_name,
             }
 
-            team_id = await self.query(query, variables)
-            return team_id["teams"]["nodes"][0]["id"]
+            result = await self.query(query, variables)
+            nodes = result["teams"]["nodes"]
+
+            if not nodes:
+                raise LinearAPIException(
+                    f"Team '{team_name}' not found. Check the team name or key and try again.",
+                    status_code=404,
+                )
+
+            return nodes[0]["id"]
         except LinearAPIException as e:
             raise e
 
@@ -240,17 +248,44 @@ class LinearClient:
         except LinearAPIException as e:
             raise e
 
-    async def try_search_issues(self, term: str) -> list[Issue]:
+    async def try_search_issues(
+        self,
+        term: str,
+        max_results: int = 10,
+        team_id: str | None = None,
+    ) -> list[Issue]:
         try:
             query = """
-                query SearchIssues($term: String!, $includeComments: Boolean!) {
-                    searchIssues(term: $term, includeComments: $includeComments) {
+                query SearchIssues(
+                    $term: String!,
+                    $first: Int,
+                    $teamId: String
+                ) {
+                    searchIssues(
+                        term: $term,
+                        first: $first,
+                        teamId: $teamId
+                    ) {
                         nodes {
                             id
                             identifier
                             title
                             description
                             priority
+                            createdAt
+                            state {
+                                id
+                                name
+                                type
+                            }
+                            project {
+                                id
+                                name
+                            }
+                            assignee {
+                                id
+                                name
+                            }
                         }
                     }
                 }
@@ -258,7 +293,8 @@ class LinearClient:
 
             variables: dict[str, Any] = {
                 "term": term,
-                "includeComments": True,
+                "first": max_results,
+                "teamId": team_id,
             }
 
             issues = await self.query(query, variables)

autogpt_platform/backend/backend/blocks/linear/issues.py

@@ -17,7 +17,7 @@ from ._config import (
     LinearScope,
     linear,
 )
-from .models import CreateIssueResponse, Issue
+from .models import CreateIssueResponse, Issue, State
 
 
 class LinearCreateIssueBlock(Block):
@@ -135,9 +135,20 @@ class LinearSearchIssuesBlock(Block):
             description="Linear credentials with read permissions",
             required_scopes={LinearScope.READ},
         )
+        max_results: int = SchemaField(
+            description="Maximum number of results to return",
+            default=10,
+            ge=1,
+            le=100,
+        )
+        team_name: str | None = SchemaField(
+            description="Optional team name to filter results (e.g., 'Internal', 'Open Source')",
+            default=None,
+        )
 
     class Output(BlockSchemaOutput):
         issues: list[Issue] = SchemaField(description="List of issues")
+        error: str = SchemaField(description="Error message if the search failed")
 
     def __init__(self):
         super().__init__(
@@ -145,8 +156,11 @@ class LinearSearchIssuesBlock(Block):
             description="Searches for issues on Linear",
             input_schema=self.Input,
             output_schema=self.Output,
+            categories={BlockCategory.PRODUCTIVITY, BlockCategory.ISSUE_TRACKING},
             test_input={
                 "term": "Test issue",
+                "max_results": 10,
+                "team_name": None,
                 "credentials": TEST_CREDENTIALS_INPUT_OAUTH,
             },
             test_credentials=TEST_CREDENTIALS_OAUTH,
@@ -156,10 +170,14 @@ class LinearSearchIssuesBlock(Block):
                     [
                         Issue(
                             id="abc123",
-                            identifier="abc123",
+                            identifier="TST-123",
                             title="Test issue",
                             description="Test description",
                             priority=1,
+                            state=State(
+                                id="state1", name="In Progress", type="started"
+                            ),
+                            createdAt="2026-01-15T10:00:00.000Z",
                         )
                     ],
                 )
@@ -168,10 +186,12 @@ class LinearSearchIssuesBlock(Block):
                 "search_issues": lambda *args, **kwargs: [
                     Issue(
                         id="abc123",
-                        identifier="abc123",
+                        identifier="TST-123",
                         title="Test issue",
                         description="Test description",
                         priority=1,
+                        state=State(id="state1", name="In Progress", type="started"),
+                        createdAt="2026-01-15T10:00:00.000Z",
                     )
                 ]
             },
@@ -181,10 +201,22 @@ class LinearSearchIssuesBlock(Block):
     async def search_issues(
         credentials: OAuth2Credentials | APIKeyCredentials,
         term: str,
+        max_results: int = 10,
+        team_name: str | None = None,
     ) -> list[Issue]:
         client = LinearClient(credentials=credentials)
-        response: list[Issue] = await client.try_search_issues(term=term)
-        return response
+
+        # Resolve team name to ID if provided
+        # Raises LinearAPIException with descriptive message if team not found
+        team_id: str | None = None
+        if team_name:
+            team_id = await client.try_get_team_by_name(team_name=team_name)
+
+        return await client.try_search_issues(
+            term=term,
+            max_results=max_results,
+            team_id=team_id,
+        )
 
     async def run(
         self,
@@ -196,7 +228,10 @@ class LinearSearchIssuesBlock(Block):
         """Execute the issue search"""
         try:
             issues = await self.search_issues(
-                credentials=credentials, term=input_data.term
+                credentials=credentials,
+                term=input_data.term,
+                max_results=input_data.max_results,
+                team_name=input_data.team_name,
             )
             yield "issues", issues
         except LinearAPIException as e:

autogpt_platform/backend/backend/blocks/linear/models.py

@@ -36,12 +36,21 @@ class Project(BaseModel):
     content: str | None = None
 
 
+class State(BaseModel):
+    id: str
+    name: str
+    type: str | None = (
+        None  # Workflow state type (e.g., "triage", "backlog", "started", "completed", "canceled")
+    )
+
+
 class Issue(BaseModel):
     id: str
     identifier: str
     title: str
     description: str | None
     priority: int
+    state: State | None = None
     project: Project | None = None
     createdAt: str | None = None
     comments: list[Comment] | None = None

autogpt_platform/backend/backend/blocks/test/test_text_encoder.py

@@ -0,0 +1,77 @@
+import pytest
+
+from backend.blocks.encoder_block import TextEncoderBlock
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_basic():
+    """Test basic encoding of newlines and special characters."""
+    block = TextEncoderBlock()
+    result = []
+    async for output in block.run(TextEncoderBlock.Input(text="Hello\nWorld")):
+        result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "encoded_text"
+    assert result[0][1] == "Hello\\nWorld"
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_multiple_escapes():
+    """Test encoding of multiple escape sequences."""
+    block = TextEncoderBlock()
+    result = []
+    async for output in block.run(
+        TextEncoderBlock.Input(text="Line1\nLine2\tTabbed\rCarriage")
+    ):
+        result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "encoded_text"
+    assert "\\n" in result[0][1]
+    assert "\\t" in result[0][1]
+    assert "\\r" in result[0][1]
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_unicode():
+    """Test that unicode characters are handled correctly."""
+    block = TextEncoderBlock()
+    result = []
+    async for output in block.run(TextEncoderBlock.Input(text="Hello 世界\n")):
+        result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "encoded_text"
+    # Unicode characters should be escaped as \uXXXX sequences
+    assert "\\n" in result[0][1]
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_empty_string():
+    """Test encoding of an empty string."""
+    block = TextEncoderBlock()
+    result = []
+    async for output in block.run(TextEncoderBlock.Input(text="")):
+        result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "encoded_text"
+    assert result[0][1] == ""
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_error_handling():
+    """Test that encoding errors are handled gracefully."""
+    from unittest.mock import patch
+
+    block = TextEncoderBlock()
+    result = []
+
+    with patch("codecs.encode", side_effect=Exception("Mocked encoding error")):
+        async for output in block.run(TextEncoderBlock.Input(text="test")):
+            result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "error"
+    assert "Mocked encoding error" in result[0][1]

autogpt_platform/backend/backend/blocks/youtube.py

@@ -165,10 +165,13 @@ class TranscribeYoutubeVideoBlock(Block):
         credentials: WebshareProxyCredentials,
         **kwargs,
     ) -> BlockOutput:
-        video_id = self.extract_video_id(input_data.youtube_url)
-        yield "video_id", video_id
+        try:
+            video_id = self.extract_video_id(input_data.youtube_url)
+            transcript = self.get_transcript(video_id, credentials)
+            transcript_text = self.format_transcript(transcript=transcript)
 
-        transcript = self.get_transcript(video_id, credentials)
-        transcript_text = self.format_transcript(transcript=transcript)
-
-        yield "transcript", transcript_text
+            # Only yield after all operations succeed
+            yield "video_id", video_id
+            yield "transcript", transcript_text
+        except Exception as e:
+            yield "error", str(e)

autogpt_platform/backend/backend/data/block.py

@@ -246,7 +246,9 @@ class BlockSchema(BaseModel):
                         f"is not of type {CredentialsMetaInput.__name__}"
                     )
 
-                credentials_fields[field_name].validate_credentials_field_schema(cls)
+                CredentialsMetaInput.validate_credentials_field_schema(
+                    cls.get_field_schema(field_name), field_name
+                )
 
             elif field_name in credentials_fields:
                 raise KeyError(

autogpt_platform/backend/backend/data/credit_test.py

@@ -134,6 +134,16 @@ async def test_block_credit_reset(server: SpinTestServer):
         month1 = datetime.now(timezone.utc).replace(month=1, day=1)
         user_credit.time_now = lambda: month1
 
+        # IMPORTANT: Set updatedAt to December of previous year to ensure it's
+        # in a different month than month1 (January). This fixes a timing bug
+        # where if the test runs in early February, 35 days ago would be January,
+        # matching the mocked month1 and preventing the refill from triggering.
+        dec_previous_year = month1.replace(year=month1.year - 1, month=12, day=15)
+        await UserBalance.prisma().update(
+            where={"userId": DEFAULT_USER_ID},
+            data={"updatedAt": dec_previous_year},
+        )
+
         # First call in month 1 should trigger refill
         balance = await user_credit.get_credits(DEFAULT_USER_ID)
         assert balance == REFILL_VALUE  # Should get 1000 credits

autogpt_platform/backend/backend/data/graph.py

@@ -20,7 +20,7 @@ from prisma.types import (
     AgentNodeLinkCreateInput,
     StoreListingVersionWhereInput,
 )
-from pydantic import BaseModel, BeforeValidator, Field, create_model
+from pydantic import BaseModel, BeforeValidator, Field
 from pydantic.fields import computed_field
 
 from backend.blocks.agent import AgentExecutorBlock
@@ -30,7 +30,6 @@ from backend.data.db import prisma as db
 from backend.data.dynamic_fields import is_tool_pin, sanitize_pin_name
 from backend.data.includes import MAX_GRAPH_VERSIONS_FETCH
 from backend.data.model import (
-    CredentialsField,
     CredentialsFieldInfo,
     CredentialsMetaInput,
     is_credentials_field_name,
@@ -45,7 +44,6 @@ from .block import (
     AnyBlockSchema,
     Block,
     BlockInput,
-    BlockSchema,
     BlockType,
     EmptySchema,
     get_block,
@@ -366,39 +364,8 @@ class Graph(BaseGraph):
     @computed_field
     @property
     def credentials_input_schema(self) -> dict[str, Any]:
-        schema = self._credentials_input_schema.jsonschema()
-
-        # Determine which credential fields are required based on credentials_optional metadata
         graph_credentials_inputs = self.aggregate_credentials_inputs()
-        required_fields = []
 
-        # Build a map of node_id -> node for quick lookup
-        all_nodes = {node.id: node for node in self.nodes}
-        for sub_graph in self.sub_graphs:
-            for node in sub_graph.nodes:
-                all_nodes[node.id] = node
-
-        for field_key, (
-            _field_info,
-            node_field_pairs,
-        ) in graph_credentials_inputs.items():
-            # A field is required if ANY node using it has credentials_optional=False
-            is_required = False
-            for node_id, _field_name in node_field_pairs:
-                node = all_nodes.get(node_id)
-                if node and not node.credentials_optional:
-                    is_required = True
-                    break
-
-            if is_required:
-                required_fields.append(field_key)
-
-        schema["required"] = required_fields
-        return schema
-
-    @property
-    def _credentials_input_schema(self) -> type[BlockSchema]:
-        graph_credentials_inputs = self.aggregate_credentials_inputs()
         logger.debug(
             f"Combined credentials input fields for graph #{self.id} ({self.name}): "
             f"{graph_credentials_inputs}"
@@ -406,8 +373,8 @@ class Graph(BaseGraph):
 
         # Warn if same-provider credentials inputs can't be combined (= bad UX)
         graph_cred_fields = list(graph_credentials_inputs.values())
-        for i, (field, keys) in enumerate(graph_cred_fields):
-            for other_field, other_keys in list(graph_cred_fields)[i + 1 :]:
+        for i, (field, keys, _) in enumerate(graph_cred_fields):
+            for other_field, other_keys, _ in list(graph_cred_fields)[i + 1 :]:
                 if field.provider != other_field.provider:
                     continue
                 if ProviderName.HTTP in field.provider:
@@ -423,31 +390,78 @@ class Graph(BaseGraph):
                     f"keys: {keys} <> {other_keys}."
                 )
 
-        fields: dict[str, tuple[type[CredentialsMetaInput], CredentialsMetaInput]] = {
-            agg_field_key: (
-                CredentialsMetaInput[
-                    Literal[tuple(field_info.provider)],  # type: ignore
-                    Literal[tuple(field_info.supported_types)],  # type: ignore
-                ],
-                CredentialsField(
-                    required_scopes=set(field_info.required_scopes or []),
-                    discriminator=field_info.discriminator,
-                    discriminator_mapping=field_info.discriminator_mapping,
-                    discriminator_values=field_info.discriminator_values,
-                ),
-            )
-            for agg_field_key, (field_info, _) in graph_credentials_inputs.items()
-        }
+        # Build JSON schema directly to avoid expensive create_model + validation overhead
+        properties = {}
+        required_fields = []
 
-        return create_model(
-            self.name.replace(" ", "") + "CredentialsInputSchema",
-            __base__=BlockSchema,
-            **fields,  # type: ignore
-        )
+        for agg_field_key, (
+            field_info,
+            _,
+            is_required,
+        ) in graph_credentials_inputs.items():
+            providers = list(field_info.provider)
+            cred_types = list(field_info.supported_types)
+
+            field_schema: dict[str, Any] = {
+                "credentials_provider": providers,
+                "credentials_types": cred_types,
+                "type": "object",
+                "properties": {
+                    "id": {"title": "Id", "type": "string"},
+                    "title": {
+                        "anyOf": [{"type": "string"}, {"type": "null"}],
+                        "default": None,
+                        "title": "Title",
+                    },
+                    "provider": {
+                        "title": "Provider",
+                        "type": "string",
+                        **(
+                            {"enum": providers}
+                            if len(providers) > 1
+                            else {"const": providers[0]}
+                        ),
+                    },
+                    "type": {
+                        "title": "Type",
+                        "type": "string",
+                        **(
+                            {"enum": cred_types}
+                            if len(cred_types) > 1
+                            else {"const": cred_types[0]}
+                        ),
+                    },
+                },
+                "required": ["id", "provider", "type"],
+            }
+
+            # Add other (optional) field info items
+            field_schema.update(
+                field_info.model_dump(
+                    by_alias=True,
+                    exclude_defaults=True,
+                    exclude={"provider", "supported_types"},  # already included above
+                )
+            )
+
+            # Ensure field schema is well-formed
+            CredentialsMetaInput.validate_credentials_field_schema(
+                field_schema, agg_field_key
+            )
+
+            properties[agg_field_key] = field_schema
+            if is_required:
+                required_fields.append(agg_field_key)
+
+        return {
+            "type": "object",
+            "properties": properties,
+            "required": required_fields,
+        }
 
     def aggregate_credentials_inputs(
         self,
-    ) -> dict[str, tuple[CredentialsFieldInfo, set[tuple[str, str]]]]:
+    ) -> dict[str, tuple[CredentialsFieldInfo, set[tuple[str, str]], bool]]:
         """
         Returns:
             dict[aggregated_field_key, tuple(
@@ -455,13 +469,19 @@ class Graph(BaseGraph):
                     (now includes discriminator_values from matching nodes)
                 set[(node_id, field_name)]: Node credentials fields that are
                     compatible with this aggregated field spec
+                bool: True if the field is required (any node has credentials_optional=False)
             )]
         """
         # First collect all credential field data with input defaults
-        node_credential_data = []
+        # Track (field_info, (node_id, field_name), is_required) for each credential field
+        node_credential_data: list[tuple[CredentialsFieldInfo, tuple[str, str]]] = []
+        node_required_map: dict[str, bool] = {}  # node_id -> is_required
 
         for graph in [self] + self.sub_graphs:
             for node in graph.nodes:
+                # Track if this node requires credentials (credentials_optional=False means required)
+                node_required_map[node.id] = not node.credentials_optional
+
                 for (
                     field_name,
                     field_info,
@@ -485,7 +505,21 @@ class Graph(BaseGraph):
                     )
 
         # Combine credential field info (this will merge discriminator_values automatically)
-        return CredentialsFieldInfo.combine(*node_credential_data)
+        combined = CredentialsFieldInfo.combine(*node_credential_data)
+
+        # Add is_required flag to each aggregated field
+        # A field is required if ANY node using it has credentials_optional=False
+        return {
+            key: (
+                field_info,
+                node_field_pairs,
+                any(
+                    node_required_map.get(node_id, True)
+                    for node_id, _ in node_field_pairs
+                ),
+            )
+            for key, (field_info, node_field_pairs) in combined.items()
+        }
 
 
 class GraphModel(Graph):
@@ -832,16 +866,55 @@ class GraphModel(Graph):
         )
 
 
-class GraphMeta(Graph):
+class GraphMeta(BaseModel):
+    """
+    Graph metadata without nodes/links, used for list endpoints.
+
+    This is a flat, lightweight model (not inheriting from Graph) to avoid recomputing
+    expensive computed fields. Values are copied from GraphModel.
+    """
+
+    id: str
+    version: int = 1
+    is_active: bool = True
+    name: str
+    description: str
+    instructions: str | None = None
+    recommended_schedule_cron: str | None = None
+    forked_from_id: str | None = None
+    forked_from_version: int | None = None
     user_id: str
 
-    # Easy work-around to prevent exposing nodes and links in the API response
-    nodes: list[NodeModel] = Field(default=[], exclude=True)  # type: ignore
-    links: list[Link] = Field(default=[], exclude=True)
+    input_schema: dict[str, Any]
+    output_schema: dict[str, Any]
+    credentials_input_schema: dict[str, Any]
+    has_external_trigger: bool
+    has_human_in_the_loop: bool
+    has_sensitive_action: bool
+    trigger_setup_info: Optional["GraphTriggerInfo"]
 
     @staticmethod
-    def from_graph(graph: GraphModel) -> "GraphMeta":
-        return GraphMeta(**graph.model_dump())
+    def from_graph(graph: "GraphModel") -> "GraphMeta":
+        return GraphMeta(
+            id=graph.id,
+            version=graph.version,
+            is_active=graph.is_active,
+            name=graph.name,
+            description=graph.description,
+            instructions=graph.instructions,
+            recommended_schedule_cron=graph.recommended_schedule_cron,
+            forked_from_id=graph.forked_from_id,
+            forked_from_version=graph.forked_from_version,
+            user_id=graph.user_id,
+            # Pre-computed values (were @computed_field on Graph)
+            input_schema=graph.input_schema,
+            output_schema=graph.output_schema,
+            has_external_trigger=graph.has_external_trigger,
+            has_human_in_the_loop=graph.has_human_in_the_loop,
+            has_sensitive_action=graph.has_sensitive_action,
+            trigger_setup_info=graph.trigger_setup_info,
+            credentials_input_schema=graph.credentials_input_schema,
+        )
 
 
 class GraphsPaginated(BaseModel):
@@ -920,9 +993,9 @@ async def list_graphs_paginated(
     graph_models: list[GraphMeta] = []
     for graph in graphs:
         try:
+            # GraphMeta.from_graph() accesses all computed fields on the GraphModel,
+            # which validates that the graph is well formed (e.g. no unknown block_ids).
             graph_meta = GraphModel.from_db(graph).meta()
-            # Trigger serialization to validate that the graph is well formed
-            graph_meta.model_dump()
             graph_models.append(graph_meta)
         except Exception as e:
             logger.error(f"Error processing graph {graph.id}: {e}")

autogpt_platform/backend/backend/data/model.py

@@ -19,7 +19,6 @@ from typing import (
     cast,
     get_args,
 )
-from urllib.parse import urlparse
 from uuid import uuid4
 
 from prisma.enums import CreditTransactionType, OnboardingStep
@@ -42,6 +41,7 @@ from typing_extensions import TypedDict
 
 from backend.integrations.providers import ProviderName
 from backend.util.json import loads as json_loads
+from backend.util.request import parse_url
 from backend.util.settings import Secrets
 
 # Type alias for any provider name (including custom ones)
@@ -163,7 +163,6 @@ class User(BaseModel):
 if TYPE_CHECKING:
     from prisma.models import User as PrismaUser
 
-    from backend.data.block import BlockSchema
 
 T = TypeVar("T")
 logger = logging.getLogger(__name__)
@@ -397,19 +396,25 @@ class HostScopedCredentials(_BaseCredentials):
     def matches_url(self, url: str) -> bool:
         """Check if this credential should be applied to the given URL."""
 
-        parsed_url = urlparse(url)
-        # Extract hostname without port
-        request_host = parsed_url.hostname
+        request_host, request_port = _extract_host_from_url(url)
+        cred_scope_host, cred_scope_port = _extract_host_from_url(self.host)
         if not request_host:
             return False
 
-        # Simple host matching - exact match or wildcard subdomain match
-        if self.host == request_host:
+        # If a port is specified in credential host, the request host port must match
+        if cred_scope_port is not None and request_port != cred_scope_port:
+            return False
+        # Non-standard ports are only allowed if explicitly specified in credential host
+        elif cred_scope_port is None and request_port not in (80, 443, None):
+            return False
+
+        # Simple host matching
+        if cred_scope_host == request_host:
             return True
 
         # Support wildcard matching (e.g., "*.example.com" matches "api.example.com")
-        if self.host.startswith("*."):
-            domain = self.host[2:]  # Remove "*."
+        if cred_scope_host.startswith("*."):
+            domain = cred_scope_host[2:]  # Remove "*."
             return request_host.endswith(f".{domain}") or request_host == domain
 
         return False
@@ -502,15 +507,13 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
     def allowed_cred_types(cls) -> tuple[CredentialsType, ...]:
         return get_args(cls.model_fields["type"].annotation)
 
-    @classmethod
-    def validate_credentials_field_schema(cls, model: type["BlockSchema"]):
+    @staticmethod
+    def validate_credentials_field_schema(
+        field_schema: dict[str, Any], field_name: str
+    ):
         """Validates the schema of a credentials input field"""
-        field_name = next(
-            name for name, type in model.get_credentials_fields().items() if type is cls
-        )
-        field_schema = model.jsonschema()["properties"][field_name]
         try:
-            schema_extra = CredentialsFieldInfo[CP, CT].model_validate(field_schema)
+            field_info = CredentialsFieldInfo[CP, CT].model_validate(field_schema)
         except ValidationError as e:
             if "Field required [type=missing" not in str(e):
                 raise
@@ -520,11 +523,11 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
                 f"{field_schema}"
             ) from e
 
-        providers = cls.allowed_providers()
+        providers = field_info.provider
         if (
             providers is not None
             and len(providers) > 1
-            and not schema_extra.discriminator
+            and not field_info.discriminator
         ):
             raise TypeError(
                 f"Multi-provider CredentialsField '{field_name}' "
@@ -551,13 +554,13 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
     )
 
 
-def _extract_host_from_url(url: str) -> str:
-    """Extract host from URL for grouping host-scoped credentials."""
+def _extract_host_from_url(url: str) -> tuple[str, int | None]:
+    """Extract host and port from URL for grouping host-scoped credentials."""
     try:
-        parsed = urlparse(url)
-        return parsed.hostname or url
+        parsed = parse_url(url)
+        return parsed.hostname or url, parsed.port
     except Exception:
-        return ""
+        return "", None
 
 
 class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
@@ -606,7 +609,7 @@ class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
                 providers = frozenset(
                     [cast(CP, "http")]
                     + [
-                        cast(CP, _extract_host_from_url(str(value)))
+                        cast(CP, parse_url(str(value)).netloc)
                         for value in field.discriminator_values
                     ]
                 )

autogpt_platform/backend/backend/data/model_test.py

@@ -79,10 +79,23 @@ class TestHostScopedCredentials:
             headers={"Authorization": SecretStr("Bearer token")},
         )
 
-        assert creds.matches_url("http://localhost:8080/api/v1")
+        # Non-standard ports require explicit port in credential host
+        assert not creds.matches_url("http://localhost:8080/api/v1")
         assert creds.matches_url("https://localhost:443/secure/endpoint")
         assert creds.matches_url("http://localhost/simple")
 
+    def test_matches_url_with_explicit_port(self):
+        """Test URL matching with explicit port in credential host."""
+        creds = HostScopedCredentials(
+            provider="custom",
+            host="localhost:8080",
+            headers={"Authorization": SecretStr("Bearer token")},
+        )
+
+        assert creds.matches_url("http://localhost:8080/api/v1")
+        assert not creds.matches_url("http://localhost:3000/api/v1")
+        assert not creds.matches_url("http://localhost/simple")
+
     def test_empty_headers_dict(self):
         """Test HostScopedCredentials with empty headers."""
         creds = HostScopedCredentials(
@@ -128,8 +141,20 @@ class TestHostScopedCredentials:
             ("*.example.com", "https://sub.api.example.com/test", True),
             ("*.example.com", "https://example.com/test", True),
             ("*.example.com", "https://example.org/test", False),
-            ("localhost", "http://localhost:3000/test", True),
+            # Non-standard ports require explicit port in credential host
+            ("localhost", "http://localhost:3000/test", False),
+            ("localhost:3000", "http://localhost:3000/test", True),
             ("localhost", "http://127.0.0.1:3000/test", False),
+            # IPv6 addresses (frontend stores with brackets via URL.hostname)
+            ("[::1]", "http://[::1]/test", True),
+            ("[::1]", "http://[::1]:80/test", True),
+            ("[::1]", "https://[::1]:443/test", True),
+            ("[::1]", "http://[::1]:8080/test", False),  # Non-standard port
+            ("[::1]:8080", "http://[::1]:8080/test", True),
+            ("[::1]:8080", "http://[::1]:9090/test", False),
+            ("[2001:db8::1]", "http://[2001:db8::1]/path", True),
+            ("[2001:db8::1]", "https://[2001:db8::1]:443/path", True),
+            ("[2001:db8::1]", "http://[2001:db8::ff]/path", False),
         ],
     )
     def test_url_matching_parametrized(self, host: str, test_url: str, expected: bool):

autogpt_platform/backend/backend/executor/utils.py

@@ -373,7 +373,7 @@ def make_node_credentials_input_map(
     # Get aggregated credentials fields for the graph
     graph_cred_inputs = graph.aggregate_credentials_inputs()
 
-    for graph_input_name, (_, compatible_node_fields) in graph_cred_inputs.items():
+    for graph_input_name, (_, compatible_node_fields, _) in graph_cred_inputs.items():
         # Best-effort map: skip missing items
         if graph_input_name not in graph_credentials_input:
             continue

autogpt_platform/backend/backend/util/request.py

@@ -157,12 +157,7 @@ async def validate_url(
         is_trusted: Boolean indicating if the hostname is in trusted_origins
         ip_addresses: List of IP addresses for the host; empty if the host is trusted
     """
-    # Canonicalize URL
-    url = url.strip("/ ").replace("\\", "/")
-    parsed = urlparse(url)
-    if not parsed.scheme:
-        url = f"http://{url}"
-        parsed = urlparse(url)
+    parsed = parse_url(url)
 
     # Check scheme
     if parsed.scheme not in ALLOWED_SCHEMES:
@@ -220,6 +215,17 @@ async def validate_url(
     )
 
 
+def parse_url(url: str) -> URL:
+    """Canonicalizes and parses a URL string."""
+    url = url.strip("/ ").replace("\\", "/")
+
+    # Ensure scheme is present for proper parsing
+    if not re.match(r"[a-z0-9+.\-]+://", url):
+        url = f"http://{url}"
+
+    return urlparse(url)
+
+
 def pin_url(url: URL, ip_addresses: Optional[list[str]] = None) -> URL:
     """
     Pins a URL to a specific IP address to prevent DNS rebinding attacks.

autogpt_platform/backend/snapshots/grph_single

@@ -3,7 +3,6 @@
   "credentials_input_schema": {
     "properties": {},
     "required": [],
-    "title": "TestGraphCredentialsInputSchema",
     "type": "object"
   },
   "description": "A test graph",

autogpt_platform/backend/snapshots/grphs_all

@@ -3,7 +3,6 @@
     "credentials_input_schema": {
       "properties": {},
       "required": [],
-      "title": "TestGraphCredentialsInputSchema",
       "type": "object"
     },
     "description": "A test graph",
@@ -27,7 +26,6 @@
       "type": "object"
     },
     "recommended_schedule_cron": null,
-    "sub_graphs": [],
     "trigger_setup_info": null,
     "user_id": "3e53486c-cf57-477e-ba2a-cb02dc828e1a",
     "version": 1

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

@@ -7804,68 +7804,57 @@
             "anyOf": [{ "type": "integer" }, { "type": "null" }],
             "title": "Forked From Version"
           },
-          "sub_graphs": {
-            "items": { "$ref": "#/components/schemas/BaseGraph-Output" },
-            "type": "array",
-            "title": "Sub Graphs",
-            "default": []
-          },
           "user_id": { "type": "string", "title": "User Id" },
           "input_schema": {
             "additionalProperties": true,
             "type": "object",
-            "title": "Input Schema",
-            "readOnly": true
+            "title": "Input Schema"
           },
           "output_schema": {
             "additionalProperties": true,
             "type": "object",
-            "title": "Output Schema",
-            "readOnly": true
+            "title": "Output Schema"
+          },
+          "credentials_input_schema": {
+            "additionalProperties": true,
+            "type": "object",
+            "title": "Credentials Input Schema"
           },
           "has_external_trigger": {
             "type": "boolean",
-            "title": "Has External Trigger",
-            "readOnly": true
+            "title": "Has External Trigger"
           },
           "has_human_in_the_loop": {
             "type": "boolean",
-            "title": "Has Human In The Loop",
-            "readOnly": true
+            "title": "Has Human In The Loop"
           },
           "has_sensitive_action": {
             "type": "boolean",
-            "title": "Has Sensitive Action",
-            "readOnly": true
+            "title": "Has Sensitive Action"
           },
           "trigger_setup_info": {
             "anyOf": [
               { "$ref": "#/components/schemas/GraphTriggerInfo" },
               { "type": "null" }
-            ],
-            "readOnly": true
-          },
-          "credentials_input_schema": {
-            "additionalProperties": true,
-            "type": "object",
-            "title": "Credentials Input Schema",
-            "readOnly": true
+            ]
           }
         },
         "type": "object",
         "required": [
+          "id",
           "name",
           "description",
           "user_id",
           "input_schema",
           "output_schema",
+          "credentials_input_schema",
           "has_external_trigger",
           "has_human_in_the_loop",
           "has_sensitive_action",
-          "trigger_setup_info",
-          "credentials_input_schema"
+          "trigger_setup_info"
         ],
-        "title": "GraphMeta"
+        "title": "GraphMeta",
+        "description": "Graph metadata without nodes/links, used for list endpoints.\n\nThis is a flat, lightweight model (not inheriting from Graph) to avoid recomputing\nexpensive computed fields. Values are copied from GraphModel."
       },
       "GraphModel": {
         "properties": {

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/ChatMessage.tsx

@@ -346,6 +346,7 @@ export function ChatMessage({
           toolId={message.toolId}
           toolName={message.toolName}
           result={message.result}
+          onSendMessage={onSendMessage}
         />
       </div>
     );

autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/MessageList.tsx

@@ -73,6 +73,7 @@ export function MessageList({
                     key={index}
                     message={message}
                     prevMessage={messages[index - 1]}
+                    onSendMessage={onSendMessage}
                   />
                 );
               }

autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/components/LastToolResponse/LastToolResponse.tsx

@@ -5,11 +5,13 @@ import { shouldSkipAgentOutput } from "../../helpers";
 export interface LastToolResponseProps {
   message: ChatMessageData;
   prevMessage: ChatMessageData | undefined;
+  onSendMessage?: (content: string) => void;
 }
 
 export function LastToolResponse({
   message,
   prevMessage,
+  onSendMessage,
 }: LastToolResponseProps) {
   if (message.type !== "tool_response") return null;
 
@@ -21,6 +23,7 @@ export function LastToolResponse({
         toolId={message.toolId}
         toolName={message.toolName}
         result={message.result}
+        onSendMessage={onSendMessage}
       />
     </div>
   );

autogpt_platform/frontend/src/components/contextual/Chat/components/ThinkingMessage/ThinkingMessage.tsx

@@ -1,6 +1,8 @@
+import { Progress } from "@/components/atoms/Progress/Progress";
 import { cn } from "@/lib/utils";
 import { useEffect, useRef, useState } from "react";
 import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
+import { useAsymptoticProgress } from "../ToolCallMessage/useAsymptoticProgress";
 
 export interface ThinkingMessageProps {
   className?: string;
@@ -11,18 +13,19 @@ export function ThinkingMessage({ className }: ThinkingMessageProps) {
   const [showCoffeeMessage, setShowCoffeeMessage] = useState(false);
   const timerRef = useRef<NodeJS.Timeout | null>(null);
   const coffeeTimerRef = useRef<NodeJS.Timeout | null>(null);
+  const progress = useAsymptoticProgress(showCoffeeMessage);
 
   useEffect(() => {
     if (timerRef.current === null) {
       timerRef.current = setTimeout(() => {
         setShowSlowLoader(true);
-      }, 8000);
+      }, 3000);
     }
 
     if (coffeeTimerRef.current === null) {
       coffeeTimerRef.current = setTimeout(() => {
         setShowCoffeeMessage(true);
-      }, 10000);
+      }, 8000);
     }
 
     return () => {
@@ -49,9 +52,18 @@ export function ThinkingMessage({ className }: ThinkingMessageProps) {
           <AIChatBubble>
             <div className="transition-all duration-500 ease-in-out">
               {showCoffeeMessage ? (
-                <span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
-                  This could take a few minutes, grab a coffee ☕️
-                </span>
+                <div className="flex flex-col items-center gap-3">
+                  <div className="flex w-full max-w-[280px] flex-col gap-1.5">
+                    <div className="flex items-center justify-between text-xs text-neutral-500">
+                      <span>Working on it...</span>
+                      <span>{Math.round(progress)}%</span>
+                    </div>
+                    <Progress value={progress} className="h-2 w-full" />
+                  </div>
+                  <span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
+                    This could take a few minutes, grab a coffee ☕️
+                  </span>
+                </div>
               ) : showSlowLoader ? (
                 <span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
                   Taking a bit more time...

autogpt_platform/frontend/src/components/contextual/Chat/components/ToolCallMessage/useAsymptoticProgress.ts

@@ -0,0 +1,50 @@
+import { useEffect, useRef, useState } from "react";
+
+/**
+ * Hook that returns a progress value that starts fast and slows down,
+ * asymptotically approaching but never reaching the max value.
+ *
+ * Uses a half-life formula: progress = max * (1 - 0.5^(time/halfLife))
+ * This creates the "game loading bar" effect where:
+ * - 50% is reached at halfLifeSeconds
+ * - 75% is reached at 2 * halfLifeSeconds
+ * - 87.5% is reached at 3 * halfLifeSeconds
+ * - and so on...
+ *
+ * @param isActive - Whether the progress should be animating
+ * @param halfLifeSeconds - Time in seconds to reach 50% progress (default: 30)
+ * @param maxProgress - Maximum progress value to approach (default: 100)
+ * @param intervalMs - Update interval in milliseconds (default: 100)
+ * @returns Current progress value (0-maxProgress)
+ */
+export function useAsymptoticProgress(
+  isActive: boolean,
+  halfLifeSeconds = 30,
+  maxProgress = 100,
+  intervalMs = 100,
+) {
+  const [progress, setProgress] = useState(0);
+  const elapsedTimeRef = useRef(0);
+
+  useEffect(() => {
+    if (!isActive) {
+      setProgress(0);
+      elapsedTimeRef.current = 0;
+      return;
+    }
+
+    const interval = setInterval(() => {
+      elapsedTimeRef.current += intervalMs / 1000;
+      // Half-life approach: progress = max * (1 - 0.5^(time/halfLife))
+      // At t=halfLife: 50%, at t=2*halfLife: 75%, at t=3*halfLife: 87.5%, etc.
+      const newProgress =
+        maxProgress *
+        (1 - Math.pow(0.5, elapsedTimeRef.current / halfLifeSeconds));
+      setProgress(newProgress);
+    }, intervalMs);
+
+    return () => clearInterval(interval);
+  }, [isActive, halfLifeSeconds, maxProgress, intervalMs]);
+
+  return progress;
+}

autogpt_platform/frontend/src/components/contextual/Chat/components/ToolResponseMessage/AgentCreatedPrompt.tsx

@@ -0,0 +1,128 @@
+"use client";
+
+import { useGetV2GetLibraryAgent } from "@/app/api/__generated__/endpoints/library/library";
+import { GraphExecutionJobInfo } from "@/app/api/__generated__/models/graphExecutionJobInfo";
+import { GraphExecutionMeta } from "@/app/api/__generated__/models/graphExecutionMeta";
+import { RunAgentModal } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/RunAgentModal";
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import {
+  CheckCircleIcon,
+  PencilLineIcon,
+  PlayIcon,
+} from "@phosphor-icons/react";
+import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
+
+interface Props {
+  agentName: string;
+  libraryAgentId: string;
+  onSendMessage?: (content: string) => void;
+}
+
+export function AgentCreatedPrompt({
+  agentName,
+  libraryAgentId,
+  onSendMessage,
+}: Props) {
+  // Fetch library agent eagerly so modal is ready when user clicks
+  const { data: libraryAgentResponse, isLoading } = useGetV2GetLibraryAgent(
+    libraryAgentId,
+    {
+      query: {
+        enabled: !!libraryAgentId,
+      },
+    },
+  );
+
+  const libraryAgent =
+    libraryAgentResponse?.status === 200 ? libraryAgentResponse.data : null;
+
+  function handleRunWithPlaceholders() {
+    onSendMessage?.(
+      `Run the agent "${agentName}" with placeholder/example values so I can test it.`,
+    );
+  }
+
+  function handleRunCreated(execution: GraphExecutionMeta) {
+    onSendMessage?.(
+      `I've started the agent "${agentName}". The execution ID is ${execution.id}. Please monitor its progress and let me know when it completes.`,
+    );
+  }
+
+  function handleScheduleCreated(schedule: GraphExecutionJobInfo) {
+    const scheduleInfo = schedule.cron
+      ? `with cron schedule "${schedule.cron}"`
+      : "to run on the specified schedule";
+    onSendMessage?.(
+      `I've scheduled the agent "${agentName}" ${scheduleInfo}. The schedule ID is ${schedule.id}.`,
+    );
+  }
+
+  return (
+    <AIChatBubble>
+      <div className="flex flex-col gap-4">
+        <div className="flex items-center gap-2">
+          <div className="flex h-8 w-8 items-center justify-center rounded-full bg-green-100">
+            <CheckCircleIcon
+              size={18}
+              weight="fill"
+              className="text-green-600"
+            />
+          </div>
+          <div>
+            <Text variant="body-medium" className="text-neutral-900">
+              Agent Created Successfully
+            </Text>
+            <Text variant="small" className="text-neutral-500">
+              &quot;{agentName}&quot; is ready to test
+            </Text>
+          </div>
+        </div>
+
+        <div className="flex flex-col gap-2">
+          <Text variant="small-medium" className="text-neutral-700">
+            Ready to test?
+          </Text>
+          <div className="flex flex-wrap gap-2">
+            <Button
+              variant="outline"
+              size="small"
+              onClick={handleRunWithPlaceholders}
+              className="gap-2"
+            >
+              <PlayIcon size={16} />
+              Run with example values
+            </Button>
+            {libraryAgent ? (
+              <RunAgentModal
+                triggerSlot={
+                  <Button variant="outline" size="small" className="gap-2">
+                    <PencilLineIcon size={16} />
+                    Run with my inputs
+                  </Button>
+                }
+                agent={libraryAgent}
+                onRunCreated={handleRunCreated}
+                onScheduleCreated={handleScheduleCreated}
+              />
+            ) : (
+              <Button
+                variant="outline"
+                size="small"
+                loading={isLoading}
+                disabled
+                className="gap-2"
+              >
+                <PencilLineIcon size={16} />
+                Run with my inputs
+              </Button>
+            )}
+          </div>
+          <Text variant="small" className="text-neutral-500">
+            or just ask me
+          </Text>
+        </div>
+      </div>
+    </AIChatBubble>
+  );
+}

autogpt_platform/frontend/src/components/contextual/Chat/components/ToolResponseMessage/ToolResponseMessage.tsx

@@ -2,11 +2,13 @@ import { Text } from "@/components/atoms/Text/Text";
 import { cn } from "@/lib/utils";
 import type { ToolResult } from "@/types/chat";
 import { WarningCircleIcon } from "@phosphor-icons/react";
+import { AgentCreatedPrompt } from "./AgentCreatedPrompt";
 import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
 import { MarkdownContent } from "../MarkdownContent/MarkdownContent";
 import {
   formatToolResponse,
   getErrorMessage,
+  isAgentSavedResponse,
   isErrorResponse,
 } from "./helpers";
 
@@ -16,6 +18,7 @@ export interface ToolResponseMessageProps {
   result?: ToolResult;
   success?: boolean;
   className?: string;
+  onSendMessage?: (content: string) => void;
 }
 
 export function ToolResponseMessage({
@@ -24,6 +27,7 @@ export function ToolResponseMessage({
   result,
   success: _success,
   className,
+  onSendMessage,
 }: ToolResponseMessageProps) {
   if (isErrorResponse(result)) {
     const errorMessage = getErrorMessage(result);
@@ -43,6 +47,18 @@ export function ToolResponseMessage({
     );
   }
 
+  // Check for agent_saved response - show special prompt
+  const agentSavedData = isAgentSavedResponse(result);
+  if (agentSavedData.isSaved) {
+    return (
+      <AgentCreatedPrompt
+        agentName={agentSavedData.agentName}
+        libraryAgentId={agentSavedData.libraryAgentId}
+        onSendMessage={onSendMessage}
+      />
+    );
+  }
+
   const formattedText = formatToolResponse(result, toolName);
 
   return (

autogpt_platform/frontend/src/components/contextual/Chat/components/ToolResponseMessage/helpers.ts

@@ -6,6 +6,43 @@ function stripInternalReasoning(content: string): string {
     .trim();
 }
 
+export interface AgentSavedData {
+  isSaved: boolean;
+  agentName: string;
+  agentId: string;
+  libraryAgentId: string;
+  libraryAgentLink: string;
+}
+
+export function isAgentSavedResponse(result: unknown): AgentSavedData {
+  if (typeof result !== "object" || result === null) {
+    return {
+      isSaved: false,
+      agentName: "",
+      agentId: "",
+      libraryAgentId: "",
+      libraryAgentLink: "",
+    };
+  }
+  const response = result as Record<string, unknown>;
+  if (response.type === "agent_saved") {
+    return {
+      isSaved: true,
+      agentName: (response.agent_name as string) || "Agent",
+      agentId: (response.agent_id as string) || "",
+      libraryAgentId: (response.library_agent_id as string) || "",
+      libraryAgentLink: (response.library_agent_link as string) || "",
+    };
+  }
+  return {
+    isSaved: false,
+    agentName: "",
+    agentId: "",
+    libraryAgentId: "",
+    libraryAgentLink: "",
+  };
+}
+
 export function isErrorResponse(result: unknown): boolean {
   if (typeof result === "string") {
     const lower = result.toLowerCase();

autogpt_platform/frontend/src/components/contextual/CredentialsInput/components/HotScopedCredentialsModal/HotScopedCredentialsModal.tsx

@@ -41,7 +41,17 @@ export function HostScopedCredentialsModal({
   const currentHost = currentUrl ? getHostFromUrl(currentUrl) : "";
 
   const formSchema = z.object({
-    host: z.string().min(1, "Host is required"),
+    host: z
+      .string()
+      .min(1, "Host is required")
+      .refine((val) => !/^[a-zA-Z][a-zA-Z\d+\-.]*:\/\//.test(val), {
+        message: "Enter only the host (e.g. api.example.com), not a full URL",
+      })
+      .refine((val) => !val.includes("/"), {
+        message:
+          "Enter only the host (e.g. api.example.com), without a trailing path. " +
+          "You may specify a port (e.g. api.example.com:8080) if needed.",
+      }),
     title: z.string().optional(),
     headers: z.record(z.string()).optional(),
   });

docs/integrations/README.md

@@ -62,7 +62,6 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Get Store Agent Details](block-integrations/system/store_operations.md#get-store-agent-details) | Get detailed information about an agent from the store |
 | [Get Weather Information](block-integrations/basic.md#get-weather-information) | Retrieves weather information for a specified location using OpenWeatherMap API |
 | [Human In The Loop](block-integrations/basic.md#human-in-the-loop) | Pause execution and wait for human approval or modification of data |
-| [Linear Search Issues](block-integrations/linear/issues.md#linear-search-issues) | Searches for issues on Linear |
 | [List Is Empty](block-integrations/basic.md#list-is-empty) | Checks if a list is empty |
 | [List Library Agents](block-integrations/system/library_operations.md#list-library-agents) | List all agents in your personal library |
 | [Note](block-integrations/basic.md#note) | A visual annotation block that displays a sticky note in the workflow editor for documentation and organization purposes |
@@ -193,6 +192,7 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Get Current Time](block-integrations/text.md#get-current-time) | This block outputs the current time |
 | [Match Text Pattern](block-integrations/text.md#match-text-pattern) | Matches text against a regex pattern and forwards data to positive or negative output based on the match |
 | [Text Decoder](block-integrations/text.md#text-decoder) | Decodes a string containing escape sequences into actual text |
+| [Text Encoder](block-integrations/text.md#text-encoder) | Encodes a string by converting special characters into escape sequences |
 | [Text Replace](block-integrations/text.md#text-replace) | This block is used to replace a text with a new text |
 | [Text Split](block-integrations/text.md#text-split) | This block is used to split a text into a list of strings |
 | [Word Character Count](block-integrations/text.md#word-character-count) | Counts the number of words and characters in a given text |
@@ -571,6 +571,7 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Linear Create Comment](block-integrations/linear/comment.md#linear-create-comment) | Creates a new comment on a Linear issue |
 | [Linear Create Issue](block-integrations/linear/issues.md#linear-create-issue) | Creates a new issue on Linear |
 | [Linear Get Project Issues](block-integrations/linear/issues.md#linear-get-project-issues) | Gets issues from a Linear project filtered by status and assignee |
+| [Linear Search Issues](block-integrations/linear/issues.md#linear-search-issues) | Searches for issues on Linear |
 | [Linear Search Projects](block-integrations/linear/projects.md#linear-search-projects) | Searches for projects on Linear |
 
 ## Hardware

docs/integrations/block-integrations/linear/issues.md

@@ -90,9 +90,9 @@ Searches for issues on Linear
 
 ### How it works
 <!-- MANUAL: how_it_works -->
-This block searches for issues in Linear using a text query. It searches across issue titles, descriptions, and other fields to find matching issues.
+This block searches for issues in Linear using a text query. It searches across issue titles, descriptions, and other fields to find matching issues. You can limit the number of results returned using the `max_results` parameter (default: 10, max: 100) to control token consumption and response size.
 
-Returns a list of issues matching the search term.
+Optionally filter results by team name to narrow searches to specific workspaces. If a team name is provided, the block resolves it to a team ID before searching. Returns matching issues with their state, creation date, project, and assignee information. If the search or team resolution fails, an error message is returned.
 <!-- END MANUAL -->
 
 ### Inputs
@@ -100,12 +100,14 @@ Returns a list of issues matching the search term.
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
 | term | Term to search for issues | str | Yes |
+| max_results | Maximum number of results to return | int | No |
+| team_name | Optional team name to filter results (e.g., 'Internal', 'Open Source') | str | No |
 
 ### Outputs
 
 | Output | Description | Type |
 |--------|-------------|------|
-| error | Error message if the operation failed | str |
+| error | Error message if the search failed | str |
 | issues | List of issues | List[Issue] |
 
 ### Possible use case

docs/integrations/block-integrations/text.md

@@ -380,6 +380,42 @@ This is useful when working with data from APIs or files where escape sequences
 
 ---
 
+## Text Encoder
+
+### What it is
+Encodes a string by converting special characters into escape sequences
+
+### How it works
+<!-- MANUAL: how_it_works -->
+The Text Encoder takes the input string and applies Python's `unicode_escape` encoding (equivalent to `codecs.encode(text, "unicode_escape").decode("utf-8")`) to transform special characters like newlines, tabs, and backslashes into their escaped forms.
+
+The block relies on the input schema to ensure the value is a string; non-string inputs are rejected by validation, and any encoding failures surface as block errors. Non-ASCII characters are emitted as `\uXXXX` sequences, which is useful for ASCII-only payloads.
+<!-- END MANUAL -->
+
+### Inputs
+
+| Input | Description | Type | Required |
+|-------|-------------|------|----------|
+| text | A string containing special characters to be encoded | str | Yes |
+
+### Outputs
+
+| Output | Description | Type |
+|--------|-------------|------|
+| error | Error message if encoding fails | str |
+| encoded_text | The encoded text with special characters converted to escape sequences | str |
+
+### Possible use case
+<!-- MANUAL: use_case -->
+**JSON Payload Preparation**: Encode multiline or quoted text before embedding it in JSON string fields to ensure proper escaping.
+
+**Config/ENV Generation**: Convert template text into escaped strings for `.env` or YAML values that require special character handling.
+
+**Snapshot Fixtures**: Produce stable escaped strings for golden files or API tests where consistent text representation is needed.
+<!-- END MANUAL -->
+
+---
+
 ## Text Replace
 
 ### What it is