1. DSL Import fix: change self._session.commit() to self._session.flush()
in app_dsl_service.py _create_or_update_app() to avoid "closed transaction"
error. DSL import now works: export agent app -> import -> new app created.
2. Memory loading attempt: added _load_memory_messages() to AgentV2Node
that loads TokenBufferMemory from conversation history. However, chatflow
engine manages conversations differently from easy-UI (conversation may
not be in DB at query time, or uses ConversationVariablePersistenceLayer
instead of Message table). Memory needs further investigation.
Test results:
- Multi-turn memory: Turn 1 OK, Turn 2 LLM doesn't see history (needs deeper fix)
- Service API with API Key: PASSED (answer="Sixteen" for 8+8)
- DSL Import: PASSED (status=completed, new app created)
- Token aggregation: PASSED (node=49, workflow=49)
Known: memory in multi-turn chatflow needs to use graphon's built-in
memory mechanism (MemoryConfig on node + ConversationVariablePersistenceLayer)
rather than direct DB query.
Made-with: Cursor
1. Fix workflow-level total_tokens=0:
Call graph_runtime_state.add_tokens(usage.total_tokens) in both
_run_without_tools and _run_with_tools paths after node execution.
Previously only graphon's internal ModelInvokeCompletedEvent handler
called add_tokens, which agent-v2 doesn't emit.
2. Fix Turn 2 SSE empty response:
Set PUBSUB_REDIS_CHANNEL_TYPE=streams in .env. Redis Streams
provides durable event delivery (consumers can replay past events),
solving the pub/sub at-most-once timing issue.
3. Skill -> Agent runtime integration:
SandboxBuilder.build() now auto-includes SkillInitializer if not
already present. This ensures sandbox.attrs has the skill bundle
loaded for downstream consumers (tool execution in sandbox).
4. LegacyResponseAdapter:
New module at core/app/apps/common/legacy_response_adapter.py.
Filters workflow-specific SSE events (workflow_started, node_started,
node_finished, workflow_finished) from the stream, passing through
only message/message_end/agent_log/error/ping events that old
clients expect.
46 unit tests pass.
Made-with: Cursor
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
FunctionCallStrategy and ReActStrategy were passing user=self.context.user_id
to ModelInstance.invoke_llm() which doesn't accept that parameter.
This caused tool-using agent runs to fail with:
"ModelInstance.invoke_llm() got an unexpected keyword argument 'user'"
Verified: Agent V2 with current_time tool now works end-to-end:
ROUND 1: LLM thought -> CALL current_time -> got time
ROUND 2: LLM generates answer with time info
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
VirtualWorkflowSynthesizer.ensure_workflow() creates a real draft
workflow on first call for a legacy app, persisting it to the database.
On subsequent calls, returns the existing draft.
This is needed because AdvancedChatAppGenerator's worker thread looks
up workflows from the database by ID. Instead of hacking the generator
to skip DB lookups, we treat this as a lazy one-time upgrade: the old
app gets a real workflow that can also be edited in the workflow editor.
Verified: old chat app created on main branch ("What is 2+2?" -> "Four")
and old agent-chat app ("Say hello" -> "Hello!") both successfully
execute through the Agent V2 engine with AGENT_V2_TRANSPARENT_UPGRADE=true.
Made-with: Cursor
Co-authored-by: Brian Wang <BrianWang1990@users.noreply.github.com>
Co-authored-by: test <test@testdeMac-mini.local>
Co-authored-by: BrianWang1990 <512dabing99@163.com>
Co-authored-by: Stephen Zhou <hi@hyoban.cc>
Co-authored-by: Stephen Zhou <38493346+hyoban@users.noreply.github.com>
Add two feature-flag-controlled upgrade paths that allow existing apps
and LLM nodes to transparently run through the Agent V2 engine without
any database migration:
1. AGENT_V2_TRANSPARENT_UPGRADE (default: off):
When enabled, old apps (chat/completion/agent-chat) bypass legacy
Easy-UI runners. VirtualWorkflowSynthesizer converts AppModelConfig
to an in-memory Workflow (start -> agent-v2 -> answer) at runtime,
then executes via AdvancedChatAppGenerator. Falls back to legacy
path on any synthesis error.
VirtualWorkflowSynthesizer maps:
- model JSON -> ModelConfig
- pre_prompt/chat_prompt_config -> prompt_template
- agent_mode.tools -> ToolMetadata[]
- agent_mode.strategy -> agent_strategy
- dataset_configs -> context
- file_upload -> vision
2. AGENT_V2_REPLACES_LLM (default: off):
When enabled, DifyNodeFactory.create_node() transparently remaps
nodes with type="llm" to type="agent-v2" before class resolution.
Since AgentV2NodeData is a strict superset of LLMNodeData, the
mapping is lossless. With tools=[], Agent V2 behaves identically
to LLM Node.
Both flags default to False for safety. Turn off = instant rollback.
46 existing tests pass. Flask starts successfully.
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
