The EventAdapter was converting every LLMResultChunk from the agent
strategy into StreamChunkEvent. Combined with the answer node's
{{#agent.text#}} variable output, this caused the final answer to
appear twice (e.g., "It is 2026-04-09 04:27:45.It is 2026-04-09 04:27:45.").
Now LLMResultChunk from strategy output is silently consumed (text still
accumulates in AgentResult.text via the strategy). Only AgentLogEvent
(thought/tool_call/round) is forwarded to the pipeline.
Known remaining issues:
- workflow/message level total_tokens=0 (node level is correct at 33)
because pipeline aggregation doesn't include agent-v2 node tokens
- Turn 2 SSE delivery timing with Redis pubsub (celery executes OK)
Made-with: Cursor
1. Remove StreamChunkEvent from AgentV2Node._run_without_tools():
The agent-v2 node was yielding StreamChunkEvent during LLM streaming,
AND the downstream answer node was outputting the same text via
{{#agent.text#}} variable reference, causing "FourFour" duplication.
Now text only flows through outputs.text -> answer node (single path).
2. Map inputs to query for completion app transparent upgrade:
Completion apps send {inputs: {query: "..."}} not {query: "..."}.
VirtualWorkflowSynthesizer route now extracts query from inputs
when the top-level query is missing.
Verified:
- Old chat app: "What is 2+2?" -> "Four" (was "FourFour")
- Old completion app: {inputs: {query: "What is 3+3?"}} -> "3 + 3 = 6" (was failing)
- Old agent-chat app: still works
Made-with: Cursor
Replace the hardcoded FunctionCallAgentRunner / CotChatAgentRunner /
CotCompletionAgentRunner selection in AgentChatAppRunner with the new
AgentAppRunner class that uses StrategyFactory from Phase 1.
Before: AgentChatAppRunner manually selects FC/CoT runner class based on
model features and LLM mode, then instantiates it directly.
After: AgentChatAppRunner instantiates AgentAppRunner (from sandbox branch),
which internally uses StrategyFactory.create_strategy() to auto-select
the right strategy, and uses ToolInvokeHook for proper agent_invoke
with file handling and thought persistence.
This unifies the agent execution engine: both the new Agent V2 workflow
node and the legacy agent-chat app now use the same StrategyFactory
and AgentPattern implementations.
Also fix: command and file_upload nodes use string node_type instead of
BuiltinNodeTypes.COMMAND/FILE_UPLOAD (not in current graphon version).
46 tests pass. Flask starts successfully.
Made-with: Cursor
Integrate the ported sandbox system with Agent V2 node:
- Add DIFY_SANDBOX_CONTEXT_KEY to app_invoke_entities for passing
sandbox through run_context without modifying graphon
- DifyNodeFactory._resolve_sandbox() extracts sandbox from run_context
and passes it to AgentV2Node constructor
- AgentV2Node accepts optional sandbox parameter
- AgentV2ToolManager supports dual execution paths:
- _invoke_tool_directly(): standard ToolEngine.generic_invoke (no sandbox)
- _invoke_tool_in_sandbox(): delegates to SandboxBashSession.run_tool()
which uses DifyCli to call back to Dify API from inside the sandbox
- Graceful fallback: if sandbox execution fails, logs warning and returns
error message (does not crash the agent loop)
To enable sandbox for an Agent workflow:
1. Create a Sandbox via SandboxBuilder
2. Add it to run_context under DIFY_SANDBOX_CONTEXT_KEY
3. Agent V2 nodes will automatically use sandbox for tool execution
46 existing tests still pass.
Made-with: Cursor
Fixes discovered during end-to-end testing of Agent workflow execution:
1. ModelManager instantiation: use ModelManager.for_tenant() instead of
ModelManager() which requires a ProviderManager argument
2. Variable template resolution: use VariableTemplateParser(template).format()
instead of non-existent resolve_template() static method
3. invoke_llm() signature: remove unsupported 'user' keyword argument
4. Event dispatch: remove ModelInvokeCompletedEvent from _run() yield
(graphon base Node._dispatch doesn't support it via singledispatch)
5. NodeRunResult metadata: use WorkflowNodeExecutionMetadataKey enum keys
(TOTAL_TOKENS, TOTAL_PRICE, CURRENCY) instead of arbitrary string keys
6. SSE topic mismatch: use AppMode.AGENT (not ADVANCED_CHAT) in
retrieve_events() so publisher and subscriber share the same channel
7. Celery task routing: add AppMode.AGENT to workflow_execute_task._run_app()
alongside ADVANCED_CHAT
All issues verified fixed: Agent V2 node successfully invokes LLM and
returns "Hello there!" through the full SSE streaming pipeline.
Made-with: Cursor
Ensure new Agent apps (AppMode.AGENT) can access all workflow-related
APIs and Service API chat endpoints:
- Add AppMode.AGENT to 13 workflow controller mode checks
- Add AppMode.AGENT to 4 workflow_run controller mode checks
- Add AppMode.AGENT to workflow_draft_variable controller
- Add AppMode.AGENT to Service API chat, conversation, message endpoints
- Add AgentV2Node.get_default_config() with prompt templates and strategy defaults
- 46 unit tests all passing (8 new Phase 7 tests)
Old agent/agent-chat paths remain completely unchanged.
Made-with: Cursor
The frontend and backend implementation for the human input node.
Co-authored-by: twwu <twwu@dify.ai>
Co-authored-by: JzoNg <jzongcode@gmail.com>
Co-authored-by: yyh <92089059+lyzno1@users.noreply.github.com>
Co-authored-by: zhsama <torvalds@linux.do>
The backend part of the human in the loop (HITL) feature and relevant architecture / workflow engine changes.
Signed-off-by: yihong0618 <zouzou0208@gmail.com>
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: -LAN- <laipz8200@outlook.com>
Co-authored-by: 盐粒 Yanli <yanli@dify.ai>
Co-authored-by: CrabSAMA <40541269+CrabSAMA@users.noreply.github.com>
Co-authored-by: Stephen Zhou <38493346+hyoban@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: yihong <zouzou0208@gmail.com>
Co-authored-by: Joel <iamjoel007@gmail.com>
