fix: Preserve binary file data in non-workspace executions

When store_media_file returns a data URI (non-CoPilot graph executions
without workspace_id), the result was silently discarded. Binary files
would lose their content, keeping only a "[Binary file: N bytes]"
placeholder. Now the data URI is stored in content_str on the success
path too, not just in the except fallback.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

.github/workflows/platform-frontend-ci.yml

@@ -142,6 +142,9 @@ jobs:
 
   e2e_test:
     runs-on: big-boi
+    needs: setup
+    strategy:
+      fail-fast: false
 
     steps:
       - name: Checkout repository
@@ -171,29 +174,29 @@ jobs:
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
+
+      - name: Cache Docker layers
+        uses: actions/cache@v5
         with:
-          driver-opts: network=host
-
-      - name: Build Docker images with cache
-        working-directory: autogpt_platform
-        run: |
-          pip install pyyaml
-          python ../.github/workflows/scripts/generate-docker-ci-compose.py \
-            --source docker-compose.platform.yml \
-            --output docker-compose.ci.yml \
-            --cache-from "type=gha" \
-            --cache-to "type=gha,mode=max" \
-            --backend-scope "platform-backend-${{ hashFiles('autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/poetry.lock', 'autogpt_platform/backend/backend') }}" \
-            --frontend-scope "platform-frontend-${{ hashFiles('autogpt_platform/frontend/Dockerfile', 'autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/src') }}"
-
-          docker buildx bake --allow=fs.read=.. -f docker-compose.yml -f docker-compose.ci.yml --load
-        env:
-          NEXT_PUBLIC_PW_TEST: true
+          path: /tmp/.buildx-cache
+          key: ${{ runner.os }}-buildx-frontend-test-${{ hashFiles('autogpt_platform/docker-compose.yml', 'autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/pyproject.toml', 'autogpt_platform/backend/poetry.lock') }}
+          restore-keys: |
+            ${{ runner.os }}-buildx-frontend-test-
 
       - name: Run docker compose
-        run: docker compose -f ../docker-compose.yml up -d --no-build
+        run: |
+          NEXT_PUBLIC_PW_TEST=true docker compose -f ../docker-compose.yml up -d
         env:
-          NEXT_PUBLIC_PW_TEST: true
+          DOCKER_BUILDKIT: 1
+          BUILDX_CACHE_FROM: type=local,src=/tmp/.buildx-cache
+          BUILDX_CACHE_TO: type=local,dest=/tmp/.buildx-cache-new,mode=max
+
+      - name: Move cache
+        run: |
+          rm -rf /tmp/.buildx-cache
+          if [ -d "/tmp/.buildx-cache-new" ]; then
+            mv /tmp/.buildx-cache-new /tmp/.buildx-cache
+          fi
 
       - name: Wait for services to be ready
         run: |
@@ -227,14 +230,14 @@ jobs:
             }
           fi
 
-      - name: Cache pnpm store
+      - name: Restore dependencies cache
         uses: actions/cache@v5
         with:
           path: ~/.pnpm-store
-          # Use separate cache key for big-boi runner since it doesn't share cache with ubuntu-latest
-          key: big-boi-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
+          key: ${{ needs.setup.outputs.cache-key }}
           restore-keys: |
-            big-boi-pnpm-
+            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
+            ${{ runner.os }}-pnpm-
 
       - name: Install dependencies
         run: pnpm install --frozen-lockfile

.github/workflows/scripts/generate-docker-ci-compose.py

@@ -1,93 +0,0 @@
-#!/usr/bin/env python3
-"""
-Generate a docker-compose.ci.yml with cache configuration for all services
-that have a build key in the source compose file.
-"""
-
-import argparse
-
-import yaml
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Generate docker-compose cache override file"
-    )
-    parser.add_argument(
-        "--source",
-        default="docker-compose.platform.yml",
-        help="Source compose file to read (default: docker-compose.platform.yml)",
-    )
-    parser.add_argument(
-        "--output",
-        default="docker-compose.ci.yml",
-        help="Output compose file to write (default: docker-compose.ci.yml)",
-    )
-    parser.add_argument(
-        "--cache-from",
-        default="type=local,src=/tmp/.buildx-cache",
-        help="Cache source configuration",
-    )
-    parser.add_argument(
-        "--cache-to",
-        default="type=local,dest=/tmp/.buildx-cache-new,mode=max",
-        help="Cache destination configuration",
-    )
-    parser.add_argument(
-        "--backend-scope",
-        default="",
-        help="GHA cache scope for backend services (e.g., platform-backend-{hash})",
-    )
-    parser.add_argument(
-        "--frontend-scope",
-        default="",
-        help="GHA cache scope for frontend service (e.g., platform-frontend-{hash})",
-    )
-    args = parser.parse_args()
-
-    with open(args.source, "r") as f:
-        compose = yaml.safe_load(f)
-
-    ci_compose = {"services": {}}
-    for service_name, service_config in compose.get("services", {}).items():
-        if "build" not in service_config:
-            continue
-
-        cache_from = args.cache_from
-        cache_to = args.cache_to
-
-        # Determine scope based on Dockerfile path
-        if "type=gha" in args.cache_from or "type=gha" in args.cache_to:
-            dockerfile = service_config["build"].get("dockerfile", "Dockerfile")
-            if "frontend" in dockerfile:
-                scope = args.frontend_scope
-            elif "backend" in dockerfile:
-                scope = args.backend_scope
-            else:
-                # Skip services that don't clearly match frontend/backend
-                continue
-
-            if scope:
-                if "type=gha" in args.cache_from:
-                    cache_from = f"{args.cache_from},scope={scope}"
-                if "type=gha" in args.cache_to:
-                    cache_to = f"{args.cache_to},scope={scope}"
-
-        ci_compose["services"][service_name] = {
-            "build": {
-                "cache_from": [cache_from],
-                "cache_to": [cache_to],
-            }
-        }
-
-    with open(args.output, "w") as f:
-        yaml.dump(ci_compose, f, default_flow_style=False)
-
-    services = list(ci_compose["services"].keys())
-    print(f"Generated {args.output} with cache config for {len(services)} services:")
-    for svc in services:
-        print(f"  - {svc}")
-
-
-if __name__ == "__main__":
-    main()

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

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

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

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

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

@@ -800,13 +800,9 @@ async def stream_chat_completion(
         # Build the messages list in the correct order
         messages_to_save: list[ChatMessage] = []
 
-        # Add assistant message with tool_calls if any.
-        # Use extend (not assign) to preserve tool_calls already added by
-        # _yield_tool_call for long-running tools.
+        # Add assistant message with tool_calls if any
         if accumulated_tool_calls:
-            if not assistant_response.tool_calls:
-                assistant_response.tool_calls = []
-            assistant_response.tool_calls.extend(accumulated_tool_calls)
+            assistant_response.tool_calls = accumulated_tool_calls
             logger.info(
                 f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
             )
@@ -1408,9 +1404,13 @@ async def _yield_tool_call(
                 operation_id=operation_id,
             )
 
-            # Attach the tool_call to the current turn's assistant message
-            # (or create one if this is a tool-only response with no text).
-            session.add_tool_call_to_current_turn(tool_calls[yield_idx])
+            # Save assistant message with tool_call FIRST (required by LLM)
+            assistant_message = ChatMessage(
+                role="assistant",
+                content="",
+                tool_calls=[tool_calls[yield_idx]],
+            )
+            session.messages.append(assistant_message)
 
             # Then save pending tool result
             pending_message = ChatMessage(

autogpt_platform/backend/backend/blocks/claude_code.py

@@ -1,10 +1,10 @@
 import json
 import shlex
 import uuid
-from typing import Literal, Optional
+from typing import TYPE_CHECKING, Literal, Optional
 
 from e2b import AsyncSandbox as BaseAsyncSandbox
-from pydantic import BaseModel, SecretStr
+from pydantic import SecretStr
 
 from backend.data.block import (
     Block,
@@ -20,6 +20,13 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
+from backend.util.sandbox_files import (
+    SandboxFileOutput,
+    extract_and_store_sandbox_files,
+)
+
+if TYPE_CHECKING:
+    from backend.executor.utils import ExecutionContext
 
 
 class ClaudeCodeExecutionError(Exception):
@@ -174,22 +181,15 @@ class ClaudeCodeBlock(Block):
             advanced=True,
         )
 
-    class FileOutput(BaseModel):
-        """A file extracted from the sandbox."""
-
-        path: str
-        relative_path: str  # Path relative to working directory (for GitHub, etc.)
-        name: str
-        content: str
-
     class Output(BlockSchemaOutput):
         response: str = SchemaField(
             description="The output/response from Claude Code execution"
         )
-        files: list["ClaudeCodeBlock.FileOutput"] = SchemaField(
+        files: list[SandboxFileOutput] = SchemaField(
             description=(
                 "List of text files created/modified by Claude Code during this execution. "
-                "Each file has 'path', 'relative_path', 'name', and 'content' fields."
+                "Each file has 'path', 'relative_path', 'name', 'content', and 'workspace_ref' fields. "
+                "workspace_ref contains a workspace:// URI if the file was stored to workspace."
             )
         )
         conversation_history: str = SchemaField(
@@ -252,6 +252,7 @@ class ClaudeCodeBlock(Block):
                             "relative_path": "index.html",
                             "name": "index.html",
                             "content": "<html>Hello World</html>",
+                            "workspace_ref": None,
                         }
                     ],
                 ),
@@ -267,11 +268,12 @@ class ClaudeCodeBlock(Block):
                 "execute_claude_code": lambda *args, **kwargs: (
                     "Created index.html with hello world content",  # response
                     [
-                        ClaudeCodeBlock.FileOutput(
+                        SandboxFileOutput(
                             path="/home/user/index.html",
                             relative_path="index.html",
                             name="index.html",
                             content="<html>Hello World</html>",
+                            workspace_ref=None,
                         )
                     ],  # files
                     "User: Create a hello world HTML file\n"
@@ -294,7 +296,8 @@ class ClaudeCodeBlock(Block):
         existing_sandbox_id: str,
         conversation_history: str,
         dispose_sandbox: bool,
-    ) -> tuple[str, list["ClaudeCodeBlock.FileOutput"], str, str, str]:
+        execution_context: "ExecutionContext",
+    ) -> tuple[str, list[SandboxFileOutput], str, str, str]:
         """
         Execute Claude Code in an E2B sandbox.
 
@@ -449,14 +452,18 @@ class ClaudeCodeBlock(Block):
                 else:
                     new_conversation_history = turn_entry
 
-            # Extract files created/modified during this run
-            files = await self._extract_files(
-                sandbox, working_directory, start_timestamp
+            # Extract files created/modified during this run and store to workspace
+            sandbox_files = await extract_and_store_sandbox_files(
+                sandbox=sandbox,
+                working_directory=working_directory,
+                execution_context=execution_context,
+                since_timestamp=start_timestamp,
+                text_only=True,
             )
 
             return (
                 response,
-                files,
+                sandbox_files,  # Already SandboxFileOutput objects
                 new_conversation_history,
                 current_session_id,
                 sandbox_id,
@@ -471,140 +478,6 @@ class ClaudeCodeBlock(Block):
             if dispose_sandbox and sandbox:
                 await sandbox.kill()
 
-    async def _extract_files(
-        self,
-        sandbox: BaseAsyncSandbox,
-        working_directory: str,
-        since_timestamp: str | None = None,
-    ) -> list["ClaudeCodeBlock.FileOutput"]:
-        """
-        Extract text files created/modified during this Claude Code execution.
-
-        Args:
-            sandbox: The E2B sandbox instance
-            working_directory: Directory to search for files
-            since_timestamp: ISO timestamp - only return files modified after this time
-
-        Returns:
-            List of FileOutput objects with path, relative_path, name, and content
-        """
-        files: list[ClaudeCodeBlock.FileOutput] = []
-
-        # Text file extensions we can safely read as text
-        text_extensions = {
-            ".txt",
-            ".md",
-            ".html",
-            ".htm",
-            ".css",
-            ".js",
-            ".ts",
-            ".jsx",
-            ".tsx",
-            ".json",
-            ".xml",
-            ".yaml",
-            ".yml",
-            ".toml",
-            ".ini",
-            ".cfg",
-            ".conf",
-            ".py",
-            ".rb",
-            ".php",
-            ".java",
-            ".c",
-            ".cpp",
-            ".h",
-            ".hpp",
-            ".cs",
-            ".go",
-            ".rs",
-            ".swift",
-            ".kt",
-            ".scala",
-            ".sh",
-            ".bash",
-            ".zsh",
-            ".sql",
-            ".graphql",
-            ".env",
-            ".gitignore",
-            ".dockerfile",
-            "Dockerfile",
-            ".vue",
-            ".svelte",
-            ".astro",
-            ".mdx",
-            ".rst",
-            ".tex",
-            ".csv",
-            ".log",
-        }
-
-        try:
-            # List files recursively using find command
-            # Exclude node_modules and .git directories, but allow hidden files
-            # like .env and .gitignore (they're filtered by text_extensions later)
-            # Filter by timestamp to only get files created/modified during this run
-            safe_working_dir = shlex.quote(working_directory)
-            timestamp_filter = ""
-            if since_timestamp:
-                timestamp_filter = f"-newermt {shlex.quote(since_timestamp)} "
-            find_result = await sandbox.commands.run(
-                f"find {safe_working_dir} -type f "
-                f"{timestamp_filter}"
-                f"-not -path '*/node_modules/*' "
-                f"-not -path '*/.git/*' "
-                f"2>/dev/null"
-            )
-
-            if find_result.stdout:
-                for file_path in find_result.stdout.strip().split("\n"):
-                    if not file_path:
-                        continue
-
-                    # Check if it's a text file we can read
-                    is_text = any(
-                        file_path.endswith(ext) for ext in text_extensions
-                    ) or file_path.endswith("Dockerfile")
-
-                    if is_text:
-                        try:
-                            content = await sandbox.files.read(file_path)
-                            # Handle bytes or string
-                            if isinstance(content, bytes):
-                                content = content.decode("utf-8", errors="replace")
-
-                            # Extract filename from path
-                            file_name = file_path.split("/")[-1]
-
-                            # Calculate relative path by stripping working directory
-                            relative_path = file_path
-                            if file_path.startswith(working_directory):
-                                relative_path = file_path[len(working_directory) :]
-                                # Remove leading slash if present
-                                if relative_path.startswith("/"):
-                                    relative_path = relative_path[1:]
-
-                            files.append(
-                                ClaudeCodeBlock.FileOutput(
-                                    path=file_path,
-                                    relative_path=relative_path,
-                                    name=file_name,
-                                    content=content,
-                                )
-                            )
-                        except Exception:
-                            # Skip files that can't be read
-                            pass
-
-        except Exception:
-            # If file extraction fails, return empty results
-            pass
-
-        return files
-
     def _escape_prompt(self, prompt: str) -> str:
         """Escape the prompt for safe shell execution."""
         # Use single quotes and escape any single quotes in the prompt
@@ -617,6 +490,7 @@ class ClaudeCodeBlock(Block):
         *,
         e2b_credentials: APIKeyCredentials,
         anthropic_credentials: APIKeyCredentials,
+        execution_context: "ExecutionContext",
         **kwargs,
     ) -> BlockOutput:
         try:
@@ -637,6 +511,7 @@ class ClaudeCodeBlock(Block):
                 existing_sandbox_id=input_data.sandbox_id,
                 conversation_history=input_data.conversation_history,
                 dispose_sandbox=input_data.dispose_sandbox,
+                execution_context=execution_context,
             )
 
             yield "response", response

autogpt_platform/backend/backend/blocks/code_executor.py

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Any, Literal, Optional
+from typing import TYPE_CHECKING, Any, Literal, Optional
 
 from e2b_code_interpreter import AsyncSandbox
 from e2b_code_interpreter import Result as E2BExecutionResult
@@ -20,6 +20,13 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
+from backend.util.sandbox_files import (
+    SandboxFileOutput,
+    extract_and_store_sandbox_files,
+)
+
+if TYPE_CHECKING:
+    from backend.executor.utils import ExecutionContext
 
 TEST_CREDENTIALS = APIKeyCredentials(
     id="01234567-89ab-cdef-0123-456789abcdef",
@@ -85,6 +92,9 @@ class CodeExecutionResult(MainCodeExecutionResult):
 class BaseE2BExecutorMixin:
     """Shared implementation methods for E2B executor blocks."""
 
+    # Default working directory in E2B sandboxes
+    WORKING_DIR = "/home/user"
+
     async def execute_code(
         self,
         api_key: str,
@@ -95,14 +105,21 @@ class BaseE2BExecutorMixin:
         timeout: Optional[int] = None,
         sandbox_id: Optional[str] = None,
         dispose_sandbox: bool = False,
+        execution_context: Optional["ExecutionContext"] = None,
+        extract_files: bool = False,
     ):
         """
         Unified code execution method that handles all three use cases:
         1. Create new sandbox and execute (ExecuteCodeBlock)
         2. Create new sandbox, execute, and return sandbox_id (InstantiateCodeSandboxBlock)
         3. Connect to existing sandbox and execute (ExecuteCodeStepBlock)
+
+        Args:
+            extract_files: If True and execution_context provided, extract files
+                           created/modified during execution and store to workspace.
         """  # noqa
         sandbox = None
+        files: list[SandboxFileOutput] = []
         try:
             if sandbox_id:
                 # Connect to existing sandbox (ExecuteCodeStepBlock case)
@@ -118,6 +135,12 @@ class BaseE2BExecutorMixin:
                     for cmd in setup_commands:
                         await sandbox.commands.run(cmd)
 
+            # Capture timestamp before execution to scope file extraction
+            start_timestamp = None
+            if extract_files:
+                ts_result = await sandbox.commands.run("date -u +%Y-%m-%dT%H:%M:%S")
+                start_timestamp = ts_result.stdout.strip() if ts_result.stdout else None
+
             # Execute the code
             execution = await sandbox.run_code(
                 code,
@@ -133,7 +156,24 @@ class BaseE2BExecutorMixin:
             stdout_logs = "".join(execution.logs.stdout)
             stderr_logs = "".join(execution.logs.stderr)
 
-            return results, text_output, stdout_logs, stderr_logs, sandbox.sandbox_id
+            # Extract files created/modified during this execution
+            if extract_files and execution_context:
+                files = await extract_and_store_sandbox_files(
+                    sandbox=sandbox,
+                    working_directory=self.WORKING_DIR,
+                    execution_context=execution_context,
+                    since_timestamp=start_timestamp,
+                    text_only=False,  # Include binary files too
+                )
+
+            return (
+                results,
+                text_output,
+                stdout_logs,
+                stderr_logs,
+                sandbox.sandbox_id,
+                files,
+            )
         finally:
             # Dispose of sandbox if requested to reduce usage costs
             if dispose_sandbox and sandbox:
@@ -238,6 +278,12 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
             description="Standard output logs from execution"
         )
         stderr_logs: str = SchemaField(description="Standard error logs from execution")
+        files: list[SandboxFileOutput] = SchemaField(
+            description=(
+                "Files created or modified during execution. "
+                "Each file has path, name, content, and workspace_ref (if stored)."
+            ),
+        )
 
     def __init__(self):
         super().__init__(
@@ -259,23 +305,30 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
                 ("results", []),
                 ("response", "Hello World"),
                 ("stdout_logs", "Hello World\n"),
+                ("files", []),
             ],
             test_mock={
-                "execute_code": lambda api_key, code, language, template_id, setup_commands, timeout, dispose_sandbox: (  # noqa
+                "execute_code": lambda api_key, code, language, template_id, setup_commands, timeout, dispose_sandbox, execution_context, extract_files: (  # noqa
                     [],  # results
                     "Hello World",  # text_output
                     "Hello World\n",  # stdout_logs
                     "",  # stderr_logs
                     "sandbox_id",  # sandbox_id
+                    [],  # files
                 ),
             },
         )
 
     async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+        self,
+        input_data: Input,
+        *,
+        credentials: APIKeyCredentials,
+        execution_context: "ExecutionContext",
+        **kwargs,
     ) -> BlockOutput:
         try:
-            results, text_output, stdout, stderr, _ = await self.execute_code(
+            results, text_output, stdout, stderr, _, files = await self.execute_code(
                 api_key=credentials.api_key.get_secret_value(),
                 code=input_data.code,
                 language=input_data.language,
@@ -283,6 +336,8 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
                 setup_commands=input_data.setup_commands,
                 timeout=input_data.timeout,
                 dispose_sandbox=input_data.dispose_sandbox,
+                execution_context=execution_context,
+                extract_files=True,
             )
 
             # Determine result object shape & filter out empty formats
@@ -296,6 +351,8 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
                 yield "stdout_logs", stdout
             if stderr:
                 yield "stderr_logs", stderr
+            # Always yield files (empty list if none)
+            yield "files", [f.model_dump() for f in files]
         except Exception as e:
             yield "error", str(e)
 
@@ -393,6 +450,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
                     "Hello World\n",  # stdout_logs
                     "",  # stderr_logs
                     "sandbox_id",  # sandbox_id
+                    [],  # files
                 ),
             },
         )
@@ -401,7 +459,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            _, text_output, stdout, stderr, sandbox_id = await self.execute_code(
+            _, text_output, stdout, stderr, sandbox_id, _ = await self.execute_code(
                 api_key=credentials.api_key.get_secret_value(),
                 code=input_data.setup_code,
                 language=input_data.language,
@@ -500,6 +558,7 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
                     "Hello World\n",  # stdout_logs
                     "",  # stderr_logs
                     sandbox_id,  # sandbox_id
+                    [],  # files
                 ),
             },
         )
@@ -508,7 +567,7 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            results, text_output, stdout, stderr, _ = await self.execute_code(
+            results, text_output, stdout, stderr, _, _ = await self.execute_code(
                 api_key=credentials.api_key.get_secret_value(),
                 code=input_data.step_code,
                 language=input_data.language,

autogpt_platform/backend/backend/util/sandbox_files.py

@@ -0,0 +1,288 @@
+"""
+Shared utilities for extracting and storing files from E2B sandboxes.
+
+This module provides common file extraction and workspace storage functionality
+for blocks that run code in E2B sandboxes (Claude Code, Code Executor, etc.).
+"""
+
+import base64
+import logging
+import mimetypes
+import shlex
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+from pydantic import BaseModel
+
+from backend.util.file import store_media_file
+from backend.util.type import MediaFileType
+
+if TYPE_CHECKING:
+    from e2b import AsyncSandbox as BaseAsyncSandbox
+
+    from backend.executor.utils import ExecutionContext
+
+logger = logging.getLogger(__name__)
+
+# Text file extensions that can be safely read and stored as text
+TEXT_EXTENSIONS = {
+    ".txt",
+    ".md",
+    ".html",
+    ".htm",
+    ".css",
+    ".js",
+    ".ts",
+    ".jsx",
+    ".tsx",
+    ".json",
+    ".xml",
+    ".yaml",
+    ".yml",
+    ".toml",
+    ".ini",
+    ".cfg",
+    ".conf",
+    ".py",
+    ".rb",
+    ".php",
+    ".java",
+    ".c",
+    ".cpp",
+    ".h",
+    ".hpp",
+    ".cs",
+    ".go",
+    ".rs",
+    ".swift",
+    ".kt",
+    ".scala",
+    ".sh",
+    ".bash",
+    ".zsh",
+    ".sql",
+    ".graphql",
+    ".env",
+    ".gitignore",
+    ".dockerfile",
+    "Dockerfile",
+    ".vue",
+    ".svelte",
+    ".astro",
+    ".mdx",
+    ".rst",
+    ".tex",
+    ".csv",
+    ".log",
+}
+
+
+class SandboxFileOutput(BaseModel):
+    """A file extracted from a sandbox and optionally stored in workspace."""
+
+    path: str
+    """Full path in the sandbox."""
+
+    relative_path: str
+    """Path relative to the working directory."""
+
+    name: str
+    """Filename only."""
+
+    content: str
+    """File content as text (for backward compatibility)."""
+
+    workspace_ref: str | None = None
+    """Workspace reference (workspace://{id}#mime) if stored, None otherwise."""
+
+
+@dataclass
+class ExtractedFile:
+    """Internal representation of an extracted file before storage."""
+
+    path: str
+    relative_path: str
+    name: str
+    content: bytes
+    is_text: bool
+
+
+async def extract_sandbox_files(
+    sandbox: "BaseAsyncSandbox",
+    working_directory: str,
+    since_timestamp: str | None = None,
+    text_only: bool = True,
+) -> list[ExtractedFile]:
+    """
+    Extract files from an E2B sandbox.
+
+    Args:
+        sandbox: The E2B sandbox instance
+        working_directory: Directory to search for files
+        since_timestamp: ISO timestamp - only return files modified after this time
+        text_only: If True, only extract text files (default). If False, extract all files.
+
+    Returns:
+        List of ExtractedFile objects with path, content, and metadata
+    """
+    files: list[ExtractedFile] = []
+
+    try:
+        # Build find command
+        safe_working_dir = shlex.quote(working_directory)
+        timestamp_filter = ""
+        if since_timestamp:
+            timestamp_filter = f"-newermt {shlex.quote(since_timestamp)} "
+
+        find_result = await sandbox.commands.run(
+            f"find {safe_working_dir} -type f "
+            f"{timestamp_filter}"
+            f"-not -path '*/node_modules/*' "
+            f"-not -path '*/.git/*' "
+            f"2>/dev/null"
+        )
+
+        if not find_result.stdout:
+            return files
+
+        for file_path in find_result.stdout.strip().split("\n"):
+            if not file_path:
+                continue
+
+            # Check if it's a text file
+            is_text = any(file_path.endswith(ext) for ext in TEXT_EXTENSIONS)
+
+            # Skip non-text files if text_only mode
+            if text_only and not is_text:
+                continue
+
+            try:
+                # Read file content as bytes
+                content = await sandbox.files.read(file_path, format="bytes")
+                if isinstance(content, str):
+                    content = content.encode("utf-8")
+                elif isinstance(content, bytearray):
+                    content = bytes(content)
+
+                # Extract filename from path
+                file_name = file_path.split("/")[-1]
+
+                # Calculate relative path
+                relative_path = file_path
+                if file_path.startswith(working_directory):
+                    relative_path = file_path[len(working_directory) :]
+                    if relative_path.startswith("/"):
+                        relative_path = relative_path[1:]
+
+                files.append(
+                    ExtractedFile(
+                        path=file_path,
+                        relative_path=relative_path,
+                        name=file_name,
+                        content=content,
+                        is_text=is_text,
+                    )
+                )
+            except Exception as e:
+                logger.debug(f"Failed to read file {file_path}: {e}")
+                continue
+
+    except Exception as e:
+        logger.warning(f"File extraction failed: {e}")
+
+    return files
+
+
+async def store_sandbox_files(
+    extracted_files: list[ExtractedFile],
+    execution_context: "ExecutionContext",
+) -> list[SandboxFileOutput]:
+    """
+    Store extracted sandbox files to workspace and return output objects.
+
+    Args:
+        extracted_files: List of files extracted from sandbox
+        execution_context: Execution context for workspace storage
+
+    Returns:
+        List of SandboxFileOutput objects with workspace refs
+    """
+    outputs: list[SandboxFileOutput] = []
+
+    for file in extracted_files:
+        # Decode content for text files (for backward compat content field)
+        if file.is_text:
+            try:
+                content_str = file.content.decode("utf-8", errors="replace")
+            except Exception:
+                content_str = ""
+        else:
+            content_str = f"[Binary file: {len(file.content)} bytes]"
+
+        # Build data URI (needed for storage and as binary fallback)
+        mime_type = mimetypes.guess_type(file.name)[0] or "application/octet-stream"
+        data_uri = f"data:{mime_type};base64,{base64.b64encode(file.content).decode()}"
+
+        # Try to store in workspace
+        workspace_ref: str | None = None
+        try:
+            result = await store_media_file(
+                file=MediaFileType(data_uri),
+                execution_context=execution_context,
+                return_format="for_block_output",
+            )
+            if result.startswith("workspace://"):
+                workspace_ref = result
+            elif not file.is_text:
+                # Non-workspace context (graph execution): store_media_file
+                # returned a data URI — use it as content so binary data isn't lost.
+                content_str = result
+        except Exception as e:
+            logger.warning(f"Failed to store file {file.name} to workspace: {e}")
+            # For binary files, fall back to data URI to prevent data loss
+            if not file.is_text:
+                content_str = data_uri
+
+        outputs.append(
+            SandboxFileOutput(
+                path=file.path,
+                relative_path=file.relative_path,
+                name=file.name,
+                content=content_str,
+                workspace_ref=workspace_ref,
+            )
+        )
+
+    return outputs
+
+
+async def extract_and_store_sandbox_files(
+    sandbox: "BaseAsyncSandbox",
+    working_directory: str,
+    execution_context: "ExecutionContext",
+    since_timestamp: str | None = None,
+    text_only: bool = True,
+) -> list[SandboxFileOutput]:
+    """
+    Extract files from sandbox and store them in workspace.
+
+    This is the main entry point combining extraction and storage.
+
+    Args:
+        sandbox: The E2B sandbox instance
+        working_directory: Directory to search for files
+        execution_context: Execution context for workspace storage
+        since_timestamp: ISO timestamp - only return files modified after this time
+        text_only: If True, only extract text files
+
+    Returns:
+        List of SandboxFileOutput objects with content and workspace refs
+    """
+    extracted = await extract_sandbox_files(
+        sandbox=sandbox,
+        working_directory=working_directory,
+        since_timestamp=since_timestamp,
+        text_only=text_only,
+    )
+
+    return await store_sandbox_files(extracted, execution_context)

docs/integrations/block-integrations/llm.md

@@ -563,7 +563,7 @@ The block supports conversation continuation through three mechanisms:
 |--------|-------------|------|
 | error | Error message if execution failed | str |
 | response | The output/response from Claude Code execution | str |
-| files | List of text files created/modified by Claude Code during this execution. Each file has 'path', 'relative_path', 'name', and 'content' fields. | List[FileOutput] |
+| files | List of text files created/modified by Claude Code during this execution. Each file has 'path', 'relative_path', 'name', 'content', and 'workspace_ref' fields. workspace_ref contains a workspace:// URI if the file was stored to workspace. | List[SandboxFileOutput] |
 | conversation_history | Full conversation history including this turn. Pass this to conversation_history input to continue on a fresh sandbox if the previous sandbox timed out. | str |
 | session_id | Session ID for this conversation. Pass this back along with sandbox_id to continue the conversation. | str |
 | sandbox_id | ID of the sandbox instance. Pass this back along with session_id to continue the conversation. This is None if dispose_sandbox was True (sandbox was disposed). | str |

docs/integrations/block-integrations/misc.md

@@ -215,6 +215,7 @@ The sandbox includes pip and npm pre-installed. Set timeout to limit execution t
 | response | Text output (if any) of the main execution result | str |
 | stdout_logs | Standard output logs from execution | str |
 | stderr_logs | Standard error logs from execution | str |
+| files | Files created or modified during execution. Each file has path, name, content, and workspace_ref (if stored). | List[SandboxFileOutput] |
 
 ### Possible use case
 <!-- MANUAL: use_case -->