diff --git a/typescript-sdk/apps/dojo/src/agents.ts b/typescript-sdk/apps/dojo/src/agents.ts index 69d97f773..859794523 100644 --- a/typescript-sdk/apps/dojo/src/agents.ts +++ b/typescript-sdk/apps/dojo/src/agents.ts @@ -8,7 +8,7 @@ import { MastraClient } from "@mastra/client-js"; import { MastraAgent } from "@ag-ui/mastra"; import { VercelAISDKAgent } from "@ag-ui/vercel-ai-sdk"; import { openai } from "@ai-sdk/openai"; -import { LangGraphAgent } from "@ag-ui/langgraph"; +import { LangGraphAgent, LangGraphHttpAgent } from "@ag-ui/langgraph"; import { AgnoAgent } from "@ag-ui/agno"; import { LlamaIndexAgent } from "@ag-ui/llamaindex"; import { CrewAIAgent } from "@ag-ui/crewai"; @@ -113,6 +113,31 @@ export const agentsIntegrations: AgentIntegrationConfig[] = [ }; }, }, + { + id: "langgraph-fastapi", + agents: async () => { + return { + agentic_chat: new LangGraphHttpAgent({ + url: "http://localhost:8000/agent/agentic_chat", + }), + agentic_generative_ui: new LangGraphHttpAgent({ + url: "http://localhost:8000/agent/agentic_generative_ui", + }), + human_in_the_loop: new LangGraphHttpAgent({ + url: "http://localhost:8000/agent/human_in_the_loop", + }), + predictive_state_updates: new LangGraphHttpAgent({ + url: "http://localhost:8000/agent/predictive_state_updates", + }), + shared_state: new LangGraphHttpAgent({ + url: "http://localhost:8000/agent/shared_state", + }), + tool_based_generative_ui: new LangGraphHttpAgent({ + url: "http://localhost:8000/agent/tool_based_generative_ui", + }), + }; + }, + }, { id: "agno", agents: async () => { diff --git a/typescript-sdk/apps/dojo/src/menu.ts b/typescript-sdk/apps/dojo/src/menu.ts index e3a6067a4..632c9d720 100644 --- a/typescript-sdk/apps/dojo/src/menu.ts +++ b/typescript-sdk/apps/dojo/src/menu.ts @@ -50,6 +50,18 @@ export const menuIntegrations: MenuIntegrationConfig[] = [ "shared_state", ], }, + { + id: "langgraph-fastapi", + name: "LangGraph FastAPI", + features: [ + "agentic_chat", + "human_in_the_loop", + "agentic_generative_ui", + "tool_based_generative_ui", + "predictive_state_updates", + "shared_state", + ], + }, { id: "agno", name: "Agno", diff --git a/typescript-sdk/integrations/langgraph/python/.gitignore b/typescript-sdk/integrations/langgraph/python/.gitignore new file mode 100644 index 000000000..c6f079331 --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/.gitignore @@ -0,0 +1,140 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# OS +.DS_Store +Thumbs.db diff --git a/python-sdk/ag_ui/__init__.py b/typescript-sdk/integrations/langgraph/python/README.md similarity index 100% rename from python-sdk/ag_ui/__init__.py rename to typescript-sdk/integrations/langgraph/python/README.md diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/__init__.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/__init__.py new file mode 100644 index 000000000..2b69e1a3f --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/__init__.py @@ -0,0 +1,35 @@ +from .agent import LangGraphAgent +from .types import ( + LangGraphEventTypes, + CustomEventNames, + State, + SchemaKeys, + MessageInProgress, + RunMetadata, + MessagesInProgressRecord, + ToolCall, + BaseLangGraphPlatformMessage, + LangGraphPlatformResultMessage, + LangGraphPlatformActionExecutionMessage, + LangGraphPlatformMessage, + PredictStateTool +) +from .endpoint import add_langgraph_fastapi_endpoint + +__all__ = [ + "LangGraphAgent", + "LangGraphEventTypes", + "CustomEventNames", + "State", + "SchemaKeys", + "MessageInProgress", + "RunMetadata", + "MessagesInProgressRecord", + "ToolCall", + "BaseLangGraphPlatformMessage", + "LangGraphPlatformResultMessage", + "LangGraphPlatformActionExecutionMessage", + "LangGraphPlatformMessage", + "PredictStateTool", + "add_langgraph_fastapi_endpoint" +] diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/agent.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/agent.py new file mode 100644 index 000000000..310a85436 --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/agent.py @@ -0,0 +1,682 @@ +import uuid +import json +from typing import Optional, List, Any, Union, AsyncGenerator, Generator + +from langgraph.graph.state import CompiledStateGraph +from langchain.schema import BaseMessage, SystemMessage +from langchain_core.runnables import RunnableConfig, ensure_config +from langchain_core.messages import HumanMessage +from langgraph.types import Command + +from .types import ( + State, + LangGraphPlatformMessage, + MessagesInProgressRecord, + SchemaKeys, + MessageInProgress, + RunMetadata, + LangGraphEventTypes, + CustomEventNames, + LangGraphReasoning +) +from .utils import ( + agui_messages_to_langchain, + DEFAULT_SCHEMA_KEYS, + filter_object_by_schema_keys, + get_stream_payload_input, + langchain_messages_to_agui, + resolve_reasoning_content, + resolve_message_content, + camel_to_snake +) + +from ag_ui.core import ( + EventType, + CustomEvent, + MessagesSnapshotEvent, + RawEvent, + RunAgentInput, + RunErrorEvent, + RunFinishedEvent, + RunStartedEvent, + StateDeltaEvent, + StateSnapshotEvent, + StepFinishedEvent, + StepStartedEvent, + TextMessageContentEvent, + TextMessageEndEvent, + TextMessageStartEvent, + ToolCallArgsEvent, + ToolCallEndEvent, + ToolCallStartEvent, + ThinkingTextMessageStartEvent, + ThinkingTextMessageContentEvent, + ThinkingTextMessageEndEvent, + ThinkingStartEvent, + ThinkingEndEvent, +) +from ag_ui.encoder import EventEncoder + +ProcessedEvents = Union[ + TextMessageStartEvent, + TextMessageContentEvent, + TextMessageEndEvent, + ToolCallStartEvent, + ToolCallArgsEvent, + ToolCallEndEvent, + StateSnapshotEvent, + StateDeltaEvent, + MessagesSnapshotEvent, + RawEvent, + CustomEvent, + RunStartedEvent, + RunFinishedEvent, + RunErrorEvent, + StepStartedEvent, + StepFinishedEvent, +] + +class LangGraphAgent: + def __init__(self, *, name: str, graph: CompiledStateGraph, description: Optional[str] = None, config: Union[Optional[RunnableConfig], dict] = None): + self.name = name + self.description = description + self.graph = graph + self.config = config or {} + self.messages_in_process: MessagesInProgressRecord = {} + self.active_run: Optional[RunMetadata] = None + self.constant_schema_keys = ['messages', 'tools'] + + def _dispatch_event(self, event: ProcessedEvents) -> str: + return event # Fallback if no encoder + + async def run(self, input: RunAgentInput) -> AsyncGenerator[str, None]: + forwarded_props = {} + if hasattr(input, "forwarded_props") and input.forwarded_props: + forwarded_props = { + camel_to_snake(k): v for k, v in input.forwarded_props.items() + } + async for event_str in self._handle_stream_events(input.copy(update={"forwarded_props": forwarded_props})): + yield event_str + + async def _handle_stream_events(self, input: RunAgentInput) -> AsyncGenerator[str, None]: + thread_id = input.thread_id or str(uuid.uuid4()) + self.active_run = { + "id": input.run_id, + "thread_id": thread_id, + "thinking_process": None, + } + + messages = input.messages or [] + forwarded_props = input.forwarded_props + node_name_input = forwarded_props.get('node_name', None) if forwarded_props else None + + self.active_run["manually_emitted_state"] = None + self.active_run["node_name"] = node_name_input + if self.active_run["node_name"] == "__end__": + self.active_run["node_name"] = None + + config = ensure_config(self.config.copy() if self.config else {}) + config["configurable"] = {**(config.get('configurable', {})), "thread_id": thread_id} + + agent_state = await self.graph.aget_state(config) + self.active_run["mode"] = "continue" if thread_id and self.active_run.get("node_name") != "__end__" and self.active_run.get("node_name") else "start" + prepared_stream_response = await self.prepare_stream(input=input, agent_state=agent_state, config=config) + + yield self._dispatch_event( + RunStartedEvent(type=EventType.RUN_STARTED, thread_id=thread_id, run_id=self.active_run["id"]) + ) + + langchain_messages = agui_messages_to_langchain(messages) + non_system_messages = [msg for msg in langchain_messages if not isinstance(msg, SystemMessage)] + + if len(agent_state.values.get("messages", [])) > len(non_system_messages): + # Find the last user message by working backwards from the last message + last_user_message = None + for i in range(len(langchain_messages) - 1, -1, -1): + if isinstance(langchain_messages[i], HumanMessage): + last_user_message = langchain_messages[i] + break + + if last_user_message: + prepared_stream_response = await self.prepare_regenerate_stream( + input=input, + message_checkpoint=last_user_message, + config=config + ) + + state = prepared_stream_response["state"] + stream = prepared_stream_response["stream"] + config = prepared_stream_response["config"] + events_to_dispatch = prepared_stream_response.get('events_to_dispatch', None) + + if events_to_dispatch is not None and len(events_to_dispatch) > 0: + for event in events_to_dispatch: + yield self._dispatch_event(event) + return + + should_exit = False + current_graph_state = state + async for event in stream: + if event["event"] == "error": + yield self._dispatch_event( + RunErrorEvent(type=EventType.RUN_ERROR, message=event["data"]["message"], raw_event=event) + ) + break + + current_node_name = event.get("metadata", {}).get("langgraph_node") + event_type = event.get("event") + self.active_run["id"] = event.get("run_id") + exiting_node = False + + if event_type == "on_chain_end" and isinstance( + event.get("data", {}).get("output"), dict + ): + current_graph_state.update(event["data"]["output"]) + exiting_node = self.active_run["node_name"] == current_node_name + + should_exit = should_exit or ( + event_type == "on_custom_event" and + event["name"] == "exit" + ) + + if current_node_name and current_node_name != self.active_run.get("node_name"): + if self.active_run["node_name"] and self.active_run["node_name"] != node_name_input: + yield self._dispatch_event( + StepFinishedEvent(type=EventType.STEP_FINISHED, step_name=self.active_run["node_name"]) + ) + self.active_run["node_name"] = None + + yield self._dispatch_event( + StepStartedEvent(type=EventType.STEP_STARTED, step_name=current_node_name) + ) + self.active_run["node_name"] = current_node_name + + updated_state = self.active_run.get("manually_emitted_state") or current_graph_state + has_state_diff = updated_state != state + if exiting_node or (has_state_diff and not self.get_message_in_progress(self.active_run["id"])): + state = updated_state + self.active_run["prev_node_name"] = self.active_run["node_name"] + current_graph_state.update(updated_state) + yield self._dispatch_event( + StateSnapshotEvent( + type=EventType.STATE_SNAPSHOT, + snapshot=self.get_state_snapshot(state), + raw_event=event, + ) + ) + + yield self._dispatch_event( + RawEvent(type=EventType.RAW, event=event) + ) + + async for single_event in self._handle_single_event(event, state): + yield single_event + + state = await self.graph.aget_state(config) + + tasks = state.tasks if len(state.tasks) > 0 else None + interrupts = tasks[0].interrupts if tasks else [] + + writes = state.metadata.get("writes", {}) or {} + node_name = self.active_run["node_name"] if interrupts else next(iter(writes), None) + next_nodes = state.next or () + is_end_node = len(next_nodes) == 0 and not interrupts + + node_name = "__end__" if is_end_node else node_name + + for interrupt in interrupts: + yield self._dispatch_event( + CustomEvent( + type=EventType.CUSTOM, + name=LangGraphEventTypes.OnInterrupt.value, + value=json.dumps(interrupt.value) if not isinstance(interrupt.value, str) else interrupt.value, + raw_event=interrupt, + ) + ) + + if self.active_run.get("node_name") != node_name: + yield self._dispatch_event( + StepFinishedEvent(type=EventType.STEP_FINISHED, step_name=self.active_run["node_name"]) + ) + self.active_run["node_name"] = node_name + yield self._dispatch_event( + StepStartedEvent(type=EventType.STEP_STARTED, step_name=self.active_run["node_name"]) + ) + + state_values = state.values if state.values else state + yield self._dispatch_event( + StateSnapshotEvent(type=EventType.STATE_SNAPSHOT, snapshot=self.get_state_snapshot(state_values)) + ) + + yield self._dispatch_event( + MessagesSnapshotEvent( + type=EventType.MESSAGES_SNAPSHOT, + messages=langchain_messages_to_agui(state_values.get("messages", [])), + ) + ) + + yield self._dispatch_event( + StepFinishedEvent(type=EventType.STEP_FINISHED, step_name=self.active_run["node_name"]) + ) + self.active_run["node_name"] = None + + yield self._dispatch_event( + RunFinishedEvent(type=EventType.RUN_FINISHED, thread_id=thread_id, run_id=self.active_run["id"]) + ) + self.active_run = None + + + async def prepare_stream(self, input: RunAgentInput, agent_state: State, config: RunnableConfig): + state_input = input.state or {} + messages = input.messages or [] + tools = input.tools or [] + forwarded_props = input.forwarded_props or {} + thread_id = input.thread_id + + state_input["messages"] = agent_state.values.get("messages", []) + self.active_run["current_graph_state"] = agent_state.values + langchain_messages = agui_messages_to_langchain(messages) + state = self.langgraph_default_merge_state(state_input, langchain_messages, tools) + self.active_run["current_graph_state"].update(state) + config["configurable"]["thread_id"] = thread_id + interrupts = agent_state.tasks[0].interrupts if agent_state.tasks and len(agent_state.tasks) > 0 else [] + has_active_interrupts = len(interrupts) > 0 + resume_input = forwarded_props.get('command', {}).get('resume', None) + + events_to_dispatch = [] + if has_active_interrupts and not resume_input: + events_to_dispatch.append( + RunStartedEvent(type=EventType.RUN_STARTED, thread_id=thread_id, run_id=self.active_run["id"]) + ) + + for interrupt in interrupts: + events_to_dispatch.append( + CustomEvent( + type=EventType.CUSTOM, + name=LangGraphEventTypes.OnInterrupt.value, + value=json.dumps(interrupt.value) if not isinstance(interrupt.value, str) else interrupt.value, + raw_event=interrupt, + ) + ) + + events_to_dispatch.append( + RunFinishedEvent(type=EventType.RUN_FINISHED, thread_id=thread_id, run_id=self.active_run["id"]) + ) + return { + "stream": None, + "state": None, + "config": None, + "events_to_dispatch": events_to_dispatch, + } + + if self.active_run["mode"] == "continue": + await self.graph.aupdate_state(config, state, as_node=self.active_run.get("node_name")) + + self.active_run["schema_keys"] = self.get_schema_keys(config) + + if resume_input: + stream_input = Command(resume=resume_input) + else: + payload_input = get_stream_payload_input( + mode=self.active_run["mode"], + state=state, + schema_keys=self.active_run["schema_keys"], + ) + stream_input = {**forwarded_props, **payload_input} if payload_input else None + + return { + "stream": self.graph.astream_events(stream_input, config, version="v2"), + "state": state, + "config": config + } + + async def prepare_regenerate_stream( # pylint: disable=too-many-arguments + self, + input: RunAgentInput, + message_checkpoint: HumanMessage, + config: RunnableConfig + ): + tools = input.tools or [] + thread_id = input.thread_id + + time_travel_checkpoint = await self.get_checkpoint_before_message(message_checkpoint.id, thread_id) + if time_travel_checkpoint is None: + return None + + fork = await self.graph.aupdate_state( + time_travel_checkpoint.config, + time_travel_checkpoint.values, + as_node=time_travel_checkpoint.next[0] if time_travel_checkpoint.next else "__start__" + ) + + stream_input = self.langgraph_default_merge_state(time_travel_checkpoint.values, [message_checkpoint], tools) + stream = self.graph.astream_events(stream_input, fork, version="v2") + + return { + "stream": stream, + "state": time_travel_checkpoint.values, + "config": config + } + + def get_message_in_progress(self, run_id: str) -> Optional[MessageInProgress]: + return self.messages_in_process.get(run_id) + + def set_message_in_progress(self, run_id: str, data: MessageInProgress): + current_message_in_progress = self.messages_in_process.get(run_id, {}) + self.messages_in_process[run_id] = { + **current_message_in_progress, + **data, + } + + def get_schema_keys(self, config) -> SchemaKeys: + try: + input_schema = self.graph.get_input_jsonschema(config) + output_schema = self.graph.get_output_jsonschema(config) + config_schema = self.graph.config_schema().schema() + + input_schema_keys = list(input_schema["properties"].keys()) if "properties" in input_schema else [] + output_schema_keys = list(output_schema["properties"].keys()) if "properties" in output_schema else [] + config_schema_keys = list(config_schema["properties"].keys()) if "properties" in config_schema else [] + + return { + "input": [*input_schema_keys, *self.constant_schema_keys], + "output": [*output_schema_keys, *self.constant_schema_keys], + "config": config_schema_keys, + } + except Exception: + return { + "input": self.constant_schema_keys, + "output": self.constant_schema_keys, + "config": [], + } + + def langgraph_default_merge_state(self, state: State, messages: List[BaseMessage], tools: Any) -> State: + if messages and isinstance(messages[0], SystemMessage): + messages = messages[1:] + + existing_messages: List[LangGraphPlatformMessage] = state.get("messages", []) + existing_message_ids = {msg.id for msg in existing_messages} + + new_messages = [msg for msg in messages if msg.id not in existing_message_ids] + + tools_as_dicts = [] + if tools: + for tool in tools: + if hasattr(tool, "model_dump"): + tools_as_dicts.append(tool.model_dump()) + elif hasattr(tool, "dict"): + tools_as_dicts.append(tool.dict()) + else: + tools_as_dicts.append(tool) + + return { + **state, + "messages": new_messages, + "tools": [*state.get("tools", []), *tools_as_dicts], + } + + def get_state_snapshot(self, state: State) -> State: + schema_keys = self.active_run["schema_keys"] + if schema_keys and schema_keys.get("output"): + state = filter_object_by_schema_keys(state, [*DEFAULT_SCHEMA_KEYS, *schema_keys["output"]]) + return state + + async def _handle_single_event(self, event: Any, state: State) -> AsyncGenerator[str, None]: + event_type = event.get("event") + if event_type == LangGraphEventTypes.OnChatModelStream: + should_emit_messages = event["metadata"].get("emit-messages", True) + should_emit_tool_calls = event["metadata"].get("emit-tool-calls", True) + + if event["data"]["chunk"].response_metadata.get('finish_reason', None): + return + + current_stream = self.get_message_in_progress(self.active_run["id"]) + has_current_stream = bool(current_stream and current_stream.get("id")) + tool_call_data = event["data"]["chunk"].tool_call_chunks[0] if event["data"]["chunk"].tool_call_chunks else None + predict_state_metadata = event["metadata"].get("predict_state", []) + tool_call_used_to_predict_state = False + if tool_call_data and tool_call_data.get("name") and predict_state_metadata: + tool_call_used_to_predict_state = any( + predict_tool.get("tool") == tool_call_data["name"] + for predict_tool in predict_state_metadata + ) + + is_tool_call_start_event = not has_current_stream and tool_call_data and tool_call_data.get("name") + is_tool_call_args_event = has_current_stream and current_stream.get("tool_call_id") and tool_call_data and tool_call_data.get("args") + is_tool_call_end_event = has_current_stream and current_stream.get("tool_call_id") and not tool_call_data + + reasoning_data = resolve_reasoning_content(event["data"]["chunk"]) if event["data"]["chunk"] else None + message_content = resolve_message_content(event["data"]["chunk"].content) if event["data"]["chunk"] and event["data"]["chunk"].content else None + is_message_content_event = tool_call_data is None and message_content + is_message_end_event = has_current_stream and not current_stream.get("tool_call_id") and not is_message_content_event + + if reasoning_data: + self.handle_thinking_event(reasoning_data) + return + + if reasoning_data is None and self.active_run.get('thinking_process', None) is not None: + yield self._dispatch_event( + ThinkingTextMessageEndEvent( + type=EventType.THINKING_TEXT_MESSAGE_END, + ) + ) + yield self._dispatch_event( + ThinkingEndEvent( + type=EventType.THINKING_END, + ) + ) + self.active_run["thinking_process"] = None + + if tool_call_used_to_predict_state: + yield self._dispatch_event( + CustomEvent( + type=EventType.CUSTOM, + name="PredictState", + value=predict_state_metadata, + raw_event=event + ) + ) + + if is_tool_call_end_event: + yield self._dispatch_event( + ToolCallEndEvent(type=EventType.TOOL_CALL_END, tool_call_id=current_stream["tool_call_id"], raw_event=event) + ) + self.messages_in_process[self.active_run["id"]] = None + return + + + if is_message_end_event: + yield self._dispatch_event( + TextMessageEndEvent(type=EventType.TEXT_MESSAGE_END, message_id=current_stream["id"], raw_event=event) + ) + self.messages_in_process[self.active_run["id"]] = None + return + + if is_tool_call_start_event and should_emit_tool_calls: + yield self._dispatch_event( + ToolCallStartEvent( + type=EventType.TOOL_CALL_START, + tool_call_id=tool_call_data["id"], + tool_call_name=tool_call_data["name"], + parent_message_id=event["data"]["chunk"].id, + raw_event=event, + ) + ) + self.set_message_in_progress( + self.active_run["id"], + MessageInProgress(id=event["data"]["chunk"].id, tool_call_id=tool_call_data["id"], tool_call_name=tool_call_data["name"]) + ) + return + + if is_tool_call_args_event and should_emit_tool_calls: + yield self._dispatch_event( + ToolCallArgsEvent( + type=EventType.TOOL_CALL_ARGS, + tool_call_id=current_stream["tool_call_id"], + delta=tool_call_data["args"], + raw_event=event + ) + ) + return + + if is_message_content_event and should_emit_messages: + if bool(current_stream and current_stream.get("id")) == False: + yield self._dispatch_event( + TextMessageStartEvent( + type=EventType.TEXT_MESSAGE_START, + role="assistant", + message_id=event["data"]["chunk"].id, + raw_event=event, + ) + ) + self.set_message_in_progress( + self.active_run["id"], + MessageInProgress( + id=event["data"]["chunk"].id, + tool_call_id=None, + tool_call_name=None + ) + ) + current_stream = self.get_message_in_progress(self.active_run["id"]) + + yield self._dispatch_event( + TextMessageContentEvent( + type=EventType.TEXT_MESSAGE_CONTENT, + message_id=current_stream["id"], + delta=event["data"]["chunk"].content, + raw_event=event, + ) + ) + return + + elif event_type == LangGraphEventTypes.OnChatModelEnd: + if self.get_message_in_progress(self.active_run["id"]) and self.get_message_in_progress(self.active_run["id"]).get("tool_call_id"): + resolved = self._dispatch_event( + ToolCallEndEvent(type=EventType.TOOL_CALL_END, tool_call_id=self.get_message_in_progress(self.active_run["id"])["tool_call_id"], raw_event=event) + ) + if resolved: + self.messages_in_process[self.active_run["id"]] = None + yield resolved + elif self.get_message_in_progress(self.active_run["id"]) and self.get_message_in_progress(self.active_run["id"]).get("id"): + resolved = self._dispatch_event( + TextMessageEndEvent(type=EventType.TEXT_MESSAGE_END, message_id=self.get_message_in_progress(self.active_run["id"])["id"], raw_event=event) + ) + if resolved: + self.messages_in_process[self.active_run["id"]] = None + yield resolved + + elif event_type == LangGraphEventTypes.OnCustomEvent: + if event["name"] == CustomEventNames.ManuallyEmitMessage: + yield self._dispatch_event( + TextMessageStartEvent(type=EventType.TEXT_MESSAGE_START, role="assistant", message_id=event["data"]["message_id"], raw_event=event) + ) + yield self._dispatch_event( + TextMessageContentEvent( + type=EventType.TEXT_MESSAGE_CONTENT, + message_id=event["data"]["message_id"], + delta=event["data"]["message"], + raw_event=event, + ) + ) + yield self._dispatch_event( + TextMessageEndEvent(type=EventType.TEXT_MESSAGE_END, message_id=event["data"]["message_id"], raw_event=event) + ) + + elif event["name"] == CustomEventNames.ManuallyEmitToolCall: + yield self._dispatch_event( + ToolCallStartEvent( + type=EventType.TOOL_CALL_START, + tool_call_id=event["data"]["id"], + tool_call_name=event["data"]["name"], + parent_message_id=event["data"]["id"], + raw_event=event, + ) + ) + yield self._dispatch_event( + ToolCallArgsEvent(type=EventType.TOOL_CALL_ARGS, tool_call_id=event["data"]["id"], delta=event["data"]["args"], raw_event=event) + ) + yield self._dispatch_event( + ToolCallEndEvent(type=EventType.TOOL_CALL_END, tool_call_id=event["data"]["id"], raw_event=event) + ) + + elif event["name"] == CustomEventNames.ManuallyEmitState: + self.active_run["manually_emitted_state"] = event["data"] + yield self._dispatch_event( + StateSnapshotEvent(type=EventType.STATE_SNAPSHOT, snapshot=self.get_state_snapshot(state), raw_event=event) + ) + + yield self._dispatch_event( + CustomEvent(type=EventType.CUSTOM, name=event["name"], value=event["data"], raw_event=event) + ) + + def handle_thinking_event(self, reasoning_data: LangGraphReasoning) -> Generator[str, Any, str | None]: + if not reasoning_data or "type" not in reasoning_data or "text" not in reasoning_data: + return "" + + thinking_step_index = reasoning_data.get("index") + + if (self.active_run.get("thinking_process") and + self.active_run["thinking_process"].get("index") and + self.active_run["thinking_process"]["index"] != thinking_step_index): + + if self.active_run["thinking_process"].get("type"): + yield self._dispatch_event( + ThinkingTextMessageEndEvent( + type=EventType.THINKING_TEXT_MESSAGE_END, + ) + ) + yield self._dispatch_event( + ThinkingEndEvent( + type=EventType.THINKING_END, + ) + ) + self.active_run["thinking_process"] = None + + if not self.active_run.get("thinking_process"): + yield self._dispatch_event( + ThinkingStartEvent( + type=EventType.THINKING_START, + ) + ) + self.active_run["thinking_process"] = { + "index": thinking_step_index + } + + if self.active_run["thinking_process"].get("type") != reasoning_data["type"]: + yield self._dispatch_event( + ThinkingTextMessageStartEvent( + type=EventType.THINKING_TEXT_MESSAGE_START, + ) + ) + self.active_run["thinking_process"]["type"] = reasoning_data["type"] + + if self.active_run["thinking_process"].get("type"): + yield self._dispatch_event( + ThinkingTextMessageContentEvent( + type=EventType.THINKING_TEXT_MESSAGE_CONTENT, + delta=reasoning_data["text"] + ) + ) + + async def get_checkpoint_before_message(self, message_id: str, thread_id: str): + if not thread_id: + raise ValueError("Missing thread_id in config") + + history_list = [] + async for snapshot in self.graph.aget_state_history({"configurable": {"thread_id": thread_id}}): + history_list.append(snapshot) + + history_list.reverse() + for idx, snapshot in enumerate(history_list): + messages = snapshot.values.get("messages", []) + if any(getattr(m, "id", None) == message_id for m in messages): + if idx == 0: + # No snapshot before this + # Return synthetic "empty before" version + empty_snapshot = snapshot + empty_snapshot.values["messages"] = [] + return empty_snapshot + return history_list[idx - 1] # return one snapshot *before* the one that includes the message + + raise ValueError("Message ID not found in history") + diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/endpoint.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/endpoint.py new file mode 100644 index 000000000..89c6e06be --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/endpoint.py @@ -0,0 +1,27 @@ +from fastapi import FastAPI, HTTPException, Request +from fastapi.responses import StreamingResponse + +from ag_ui.core.types import RunAgentInput +from ag_ui.encoder import EventEncoder + +from .agent import LangGraphAgent + +def add_langgraph_fastapi_endpoint(app: FastAPI, agent: LangGraphAgent, path: str = "/"): + """Adds an endpoint to the FastAPI app.""" + + @app.post(path) + async def langgraph_agent_endpoint(input_data: RunAgentInput, request: Request): + # Get the accept header from the request + accept_header = request.headers.get("accept") + + # Create an event encoder to properly format SSE events + encoder = EventEncoder(accept=accept_header) + + async def event_generator(): + async for event in agent.run(input_data): + yield encoder.encode(event) + + return StreamingResponse( + event_generator(), + media_type=encoder.get_content_type() + ) \ No newline at end of file diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/.env.example b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/.env.example new file mode 100644 index 000000000..3ae27e109 --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/.env.example @@ -0,0 +1,2 @@ +OPENAI_API_KEY= +LANGSMITH_API_KEY= diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/README.md b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/__init__.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/agentic_chat.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/agentic_chat.py new file mode 100644 index 000000000..30c31a177 --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/agentic_chat.py @@ -0,0 +1,81 @@ +""" +A simple agentic chat flow using LangGraph instead of CrewAI. +""" + +from typing import Dict, List, Any, Optional + +# Updated imports for LangGraph +from langchain_core.runnables import RunnableConfig +from langgraph.graph import StateGraph, END, START +from langgraph.graph import MessagesState +from langgraph.types import Command +from typing_extensions import Literal +from langchain_openai import ChatOpenAI +from langchain_core.messages import SystemMessage +from langgraph.checkpoint.memory import MemorySaver + +class AgentState(MessagesState): + tools: List[Any] + +async def chat_node(state: AgentState, config: RunnableConfig): + """ + Standard chat node based on the ReAct design pattern. It handles: + - The model to use (and binds in CopilotKit actions and the tools defined above) + - The system prompt + - Getting a response from the model + - Handling tool calls + + For more about the ReAct design pattern, see: + https://www.perplexity.ai/search/react-agents-NcXLQhreS0WDzpVaS4m9Cg + """ + + # 1. Define the model + model = ChatOpenAI(model="gpt-4o") + + # Define config for the model + if config is None: + config = RunnableConfig(recursion_limit=25) + + # 2. Bind the tools to the model + model_with_tools = model.bind_tools( + [ + *state["tools"], + # your_tool_here + ], + + # 2.1 Disable parallel tool calls to avoid race conditions, + # enable this for faster performance if you want to manage + # the complexity of running tool calls in parallel. + parallel_tool_calls=False, + ) + + # 3. Define the system message by which the chat model will be run + system_message = SystemMessage( + content=f"You are a helpful assistant. ." + ) + + # 4. Run the model to generate a response + response = await model_with_tools.ainvoke([ + system_message, + *state["messages"], + ], config) + + # 6. We've handled all tool calls, so we can end the graph. + return Command( + goto=END, + update={ + "messages": response + } + ) + +# Define a new graph +workflow = StateGraph(AgentState) +workflow.add_node("chat_node", chat_node) +workflow.set_entry_point("chat_node") + +# Add explicit edges, matching the pattern in other examples +workflow.add_edge(START, "chat_node") +workflow.add_edge("chat_node", END) + +# Compile the graph +agentic_chat_graph = workflow.compile(checkpointer=MemorySaver()) \ No newline at end of file diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/agentic_generative_ui.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/agentic_generative_ui.py new file mode 100644 index 000000000..d3047220a --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/agentic_generative_ui.py @@ -0,0 +1,193 @@ +""" +An example demonstrating agentic generative UI using LangGraph. +""" + +import json +import asyncio +from typing import Dict, List, Any, Optional, Literal +# LangGraph imports +from langchain_core.runnables import RunnableConfig +from langgraph.graph import StateGraph, END, START +from langgraph.types import Command +from langchain_core.callbacks.manager import adispatch_custom_event +from langgraph.graph import MessagesState +from langgraph.checkpoint.memory import MemorySaver +# OpenAI imports +from langchain_openai import ChatOpenAI +from langchain_core.messages import SystemMessage + +# This tool simulates performing a task on the server. +# The tool call will be streamed to the frontend as it is being generated. +PERFORM_TASK_TOOL = { + "type": "function", + "function": { + "name": "generate_task_steps_generative_ui", + "description": "Make up 10 steps (only a couple of words per step) that are required for a task. The step should be in gerund form (i.e. Digging hole, opening door, ...)", + "parameters": { + "type": "object", + "properties": { + "steps": { + "type": "array", + "items": { + "type": "object", + "properties": { + "description": { + "type": "string", + "description": "The text of the step in gerund form" + }, + "status": { + "type": "string", + "enum": ["pending"], + "description": "The status of the step, always 'pending'" + } + }, + "required": ["description", "status"] + }, + "description": "An array of 10 step objects, each containing text and status" + } + }, + "required": ["steps"] + } + } +} + + +class AgentState(MessagesState): + steps: List[dict] = [] + tools: List[Any] + + +async def start_flow(state: AgentState, config: RunnableConfig): + """ + This is the entry point for the flow. + """ + + if "steps" not in state: + state["steps"] = [] + + return Command( + goto="chat_node", + update={ + "messages": state["messages"], + "steps": state["steps"] + } + ) + + +async def chat_node(state: AgentState, config: RunnableConfig): + """ + Standard chat node. + """ + system_prompt = """ + You are a helpful assistant assisting with any task. + When asked to do something, you MUST call the function `generate_task_steps_generative_ui` + that was provided to you. + If you called the function, you MUST NOT repeat the steps in your next response to the user. + Just give a very brief summary (one sentence) of what you did with some emojis. + Always say you actually did the steps, not merely generated them. + """ + + # Define the model + model = ChatOpenAI(model="gpt-4o") + + # Define config for the model with emit_intermediate_state to stream tool calls to frontend + if config is None: + config = RunnableConfig(recursion_limit=25) + + # Use "predict_state" metadata to set up streaming for the write_document tool + config["metadata"]["predict_state"] = [{ + "state_key": "steps", + "tool": "generate_task_steps_generative_ui", + "tool_argument": "steps", + }] + + # Bind the tools to the model + model_with_tools = model.bind_tools( + [ + *state["tools"], + PERFORM_TASK_TOOL + ], + # Disable parallel tool calls to avoid race conditions + parallel_tool_calls=False, + ) + + # Run the model to generate a response + response = await model_with_tools.ainvoke([ + SystemMessage(content=system_prompt), + *state["messages"], + ], config) + + messages = state["messages"] + [response] + + # Extract any tool calls from the response + if hasattr(response, "tool_calls") and response.tool_calls and len(response.tool_calls) > 0: + tool_call = response.tool_calls[0] + + # Handle tool_call as a dictionary rather than an object + if isinstance(tool_call, dict): + tool_call_id = tool_call["id"] + tool_call_name = tool_call["name"] + tool_call_args = tool_call["args"] + else: + # Handle as an object (backward compatibility) + tool_call_id = tool_call.id + tool_call_name = tool_call.name + tool_call_args = tool_call.args + + if tool_call_name == "generate_task_steps_generative_ui": + steps = [{"description": step["description"], "status": step["status"]} for step in tool_call_args["steps"]] + + # Add the tool response to messages + tool_response = { + "role": "tool", + "content": "Steps executed.", + "tool_call_id": tool_call_id + } + + messages = messages + [tool_response] + + # Return Command to route to simulate_task_node + for i, step in enumerate(steps): + # simulate executing the step + await asyncio.sleep(1) + steps[i]["status"] = "completed" + # Update the state with the completed step - using config as first parameter + state["steps"] = steps + await adispatch_custom_event( + "manually_emit_state", + state, + config=config, + ) + + return Command( + goto='start_flow', + update={ + "messages": messages, + "steps": state["steps"] + } + ) + + return Command( + goto=END, + update={ + "messages": messages, + "steps": state["steps"] + } + ) + + +# Define the graph +workflow = StateGraph(AgentState) + +# Add nodes +workflow.add_node("start_flow", start_flow) +workflow.add_node("chat_node", chat_node) + +# Add edges (equivalent to the routing in CrewAI) +workflow.set_entry_point("start_flow") +workflow.add_edge(START, "start_flow") +workflow.add_edge("start_flow", "chat_node") +workflow.add_edge("chat_node", END) + +# Compile the graph +graph = workflow.compile(checkpointer=MemorySaver()) diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/dojo.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/dojo.py new file mode 100644 index 000000000..878b20a99 --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/dojo.py @@ -0,0 +1,96 @@ +import os +import uvicorn +from fastapi import FastAPI + +from dotenv import load_dotenv +load_dotenv() + +from ag_ui_langgraph import LangGraphAgent, add_langgraph_fastapi_endpoint +from .human_in_the_loop import human_in_the_loop_graph +from .predictive_state_updates import predictive_state_updates_graph +from .shared_state import shared_state_graph +from .tool_based_generative_ui import tool_based_generative_ui_graph +from .agentic_chat import agentic_chat_graph +from .agentic_generative_ui import graph + +app = FastAPI(title="LangGraph Dojo Example Server") + +agents = { + # Register the LangGraph agent using the LangGraphAgent class + "agentic_chat": LangGraphAgent( + name="agentic_chat", + description="An example for an agentic chat flow using LangGraph.", + graph=agentic_chat_graph + ), + "tool_based_generative_ui": LangGraphAgent( + name="tool_based_generative_ui", + description="An example for a tool-based generative UI flow.", + graph=tool_based_generative_ui_graph, + ), + "agentic_generative_ui": LangGraphAgent( + name="agentic_generative_ui", + description="An example for an agentic generative UI flow.", + graph=graph, + ), + "human_in_the_loop": LangGraphAgent( + name="human_in_the_loop", + description="An example for a human in the loop flow.", + graph=human_in_the_loop_graph, + ), + "shared_state": LangGraphAgent( + name="shared_state", + description="An example for a shared state flow.", + graph=shared_state_graph, + ), + "predictive_state_updates": LangGraphAgent( + name="predictive_state_updates", + description="An example for a predictive state updates flow.", + graph=predictive_state_updates_graph, + ) +} + +add_langgraph_fastapi_endpoint( + app=app, + agent=agents["agentic_chat"], + path="/agent/agentic_chat" +) + +add_langgraph_fastapi_endpoint( + app=app, + agent=agents["tool_based_generative_ui"], + path="/agent/tool_based_generative_ui" +) + +add_langgraph_fastapi_endpoint( + app=app, + agent=agents["agentic_generative_ui"], + path="/agent/agentic_generative_ui" +) + +add_langgraph_fastapi_endpoint( + app=app, + agent=agents["human_in_the_loop"], + path="/agent/human_in_the_loop" +) + +add_langgraph_fastapi_endpoint( + app=app, + agent=agents["shared_state"], + path="/agent/shared_state" +) + +add_langgraph_fastapi_endpoint( + app=app, + agent=agents["predictive_state_updates"], + path="/agent/predictive_state_updates" +) + +def main(): + """Run the uvicorn server.""" + port = int(os.getenv("PORT", "8000")) + uvicorn.run( + "agents.dojo:app", + host="0.0.0.0", + port=port, + reload=True + ) diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/human_in_the_loop.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/human_in_the_loop.py new file mode 100644 index 000000000..ccea26c49 --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/human_in_the_loop.py @@ -0,0 +1,274 @@ +""" +A LangGraph implementation of the human-in-the-loop agent. +""" + +import json +from typing import Dict, List, Any + +# LangGraph imports +from langchain_core.runnables import RunnableConfig +from langgraph.graph import StateGraph, END, START +from langgraph.types import Command, interrupt +from langgraph.graph import MessagesState +from langgraph.checkpoint.memory import MemorySaver + +# LLM imports +from langchain_openai import ChatOpenAI +from langchain_core.messages import SystemMessage + +DEFINE_TASK_TOOL = { + "type": "function", + "function": { + "name": "generate_task_steps", + "description": "Make up 10 steps (only a couple of words per step) that are required for a task. The step should be in imperative form (i.e. Dig hole, Open door, ...)", + "parameters": { + "type": "object", + "properties": { + "steps": { + "type": "array", + "items": { + "type": "object", + "properties": { + "description": { + "type": "string", + "description": "The text of the step in imperative form" + }, + "status": { + "type": "string", + "enum": ["enabled"], + "description": "The status of the step, always 'enabled'" + } + }, + "required": ["description", "status"] + }, + "description": "An array of 10 step objects, each containing text and status" + } + }, + "required": ["steps"] + } + } +} + +class AgentState(MessagesState): + steps: List[Dict[str, str]] = [] + tools: List[Any] + +async def start_flow(state: Dict[str, Any], config: RunnableConfig): + """ + This is the entry point for the flow. + """ + + # Initialize steps list if not exists + if "steps" not in state: + state["steps"] = [] + + + return Command( + goto="chat_node", + update={ + "messages": state["messages"], + "steps": state["steps"], + } + ) + + +async def chat_node(state: Dict[str, Any], config: RunnableConfig): + """ + Standard chat node where the agent processes messages and generates responses. + If task steps are defined, the user can enable/disable them using interrupts. + """ + system_prompt = """ + You are a helpful assistant that can perform any task. + You MUST call the `generate_task_steps` function when the user asks you to perform a task. + Always make sure you will provide tasks based on the user query + """ + + # Define the model + model = ChatOpenAI(model="gpt-4o-mini") + + # Define config for the model + if config is None: + config = RunnableConfig(recursion_limit=25) + + # Use "predict_state" metadata to set up streaming for the write_document tool + config["metadata"]["predict_state"] = [{ + "state_key": "steps", + "tool": "generate_task_steps", + "tool_argument": "steps" + }] + + # Bind the tools to the model + model_with_tools = model.bind_tools( + [ + *state["tools"], + DEFINE_TASK_TOOL + ], + # Disable parallel tool calls to avoid race conditions + parallel_tool_calls=False, + ) + + # Run the model and generate a response + response = await model_with_tools.ainvoke([ + SystemMessage(content=system_prompt), + *state["messages"], + ], config) + + # Update messages with the response + messages = state["messages"] + [response] + + # Handle tool calls + if hasattr(response, "tool_calls") and response.tool_calls and len(response.tool_calls) > 0: + tool_call = response.tool_calls[0] + # Extract tool call information + if hasattr(tool_call, "id"): + tool_call_id = tool_call.id + tool_call_name = tool_call.name + tool_call_args = tool_call.args if not isinstance(tool_call.args, str) else json.loads(tool_call.args) + else: + tool_call_id = tool_call.get("id", "") + tool_call_name = tool_call.get("name", "") + args = tool_call.get("args", {}) + tool_call_args = args if not isinstance(args, str) else json.loads(args) + + if tool_call_name == "generate_task_steps": + # Get the steps from the tool call + steps_raw = tool_call_args.get("steps", []) + + # Set initial status to "enabled" for all steps + steps_data = [] + + # Handle different potential formats of steps data + if isinstance(steps_raw, list): + for step in steps_raw: + if isinstance(step, dict) and "description" in step: + steps_data.append({ + "description": step["description"], + "status": "enabled" + }) + elif isinstance(step, str): + steps_data.append({ + "description": step, + "status": "enabled" + }) + + # If no steps were processed correctly, return to END with the updated messages + if not steps_data: + return Command( + goto=END, + update={ + "messages": messages, + "steps": state["steps"], + } + ) + # Update steps in state and emit to frontend + state["steps"] = steps_data + + # Add a tool response to satisfy OpenAI's requirements + tool_response = { + "role": "tool", + "content": "Task steps generated.", + "tool_call_id": tool_call_id + } + + messages = messages + [tool_response] + + # Move to the process_steps_node which will handle the interrupt and final response + return Command( + goto="process_steps_node", + update={ + "messages": messages, + "steps": state["steps"], + } + ) + + # If no tool calls or not generate_task_steps, return to END with the updated messages + return Command( + goto=END, + update={ + "messages": messages, + "steps": state["steps"], + } + ) + + +async def process_steps_node(state: Dict[str, Any], config: RunnableConfig): + """ + This node handles the user interrupt for step customization and generates the final response. + """ + + # Check if we already have a user_response in the state + # This happens when the node restarts after an interrupt + if "user_response" in state and state["user_response"]: + user_response = state["user_response"] + else: + # Use LangGraph interrupt to get user input on steps + # This will pause execution and wait for user input in the frontend + user_response = interrupt({"steps": state["steps"]}) + # Store the user response in state for when the node restarts + state["user_response"] = user_response + + # Generate the creative completion response + final_prompt = """ + Provide a textual description of how you are performing the task. + If the user has disabled a step, you are not allowed to perform that step. + However, you should find a creative workaround to perform the task, and if an essential step is disabled, you can even use + some humor in the description of how you are performing the task. + Don't just repeat a list of steps, come up with a creative but short description (3 sentences max) of how you are performing the task. + """ + + final_response = await ChatOpenAI(model="gpt-4o").ainvoke([ + SystemMessage(content=final_prompt), + {"role": "user", "content": user_response} + ], config) + + # Add the final response to messages + messages = state["messages"] + [final_response] + + # Clear the user_response from state to prepare for future interactions + if "user_response" in state: + state.pop("user_response") + + # Return to END with the updated messages + return Command( + goto=END, + update={ + "messages": messages, + "steps": state["steps"], + } + ) + + +# Define the graph +workflow = StateGraph(AgentState) + +# Add nodes +workflow.add_node("start_flow", start_flow) +workflow.add_node("chat_node", chat_node) +workflow.add_node("process_steps_node", process_steps_node) + +# Add edges +workflow.set_entry_point("start_flow") +workflow.add_edge(START, "start_flow") +workflow.add_edge("start_flow", "chat_node") +# workflow.add_edge("chat_node", "process_steps_node") # Removed unconditional edge +workflow.add_edge("process_steps_node", END) +# workflow.add_edge("chat_node", END) # Removed unconditional edge + +# Add conditional edges from chat_node +def should_continue(command: Command): + if command.goto == "process_steps_node": + return "process_steps_node" + else: + return END + +workflow.add_conditional_edges( + "chat_node", + should_continue, + { + "process_steps_node": "process_steps_node", + END: END, + }, +) + +# Compile the graph +human_in_the_loop_graph = workflow.compile(checkpointer=MemorySaver()) \ No newline at end of file diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/predictive_state_updates.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/predictive_state_updates.py new file mode 100644 index 000000000..a7b435741 --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/predictive_state_updates.py @@ -0,0 +1,179 @@ +""" +A demo of predictive state updates using LangGraph. +""" + +import json +import uuid +from typing import Dict, List, Any, Optional + +# LangGraph imports +from langchain_core.runnables import RunnableConfig +from langgraph.graph import StateGraph, END, START +from langgraph.types import Command +from langgraph.graph import MessagesState +from langgraph.checkpoint.memory import MemorySaver +# OpenAI imports +from langchain_openai import ChatOpenAI +from langchain_core.messages import SystemMessage + +WRITE_DOCUMENT_TOOL = { + "type": "function", + "function": { + "name": "write_document", + "description": " ".join(""" + Write a document. Use markdown formatting to format the document. + It's good to format the document extensively so it's easy to read. + You can use all kinds of markdown. + However, do not use italic or strike-through formatting, it's reserved for another purpose. + You MUST write the full document, even when changing only a few words. + When making edits to the document, try to make them minimal - do not change every word. + Keep stories SHORT! + """.split()), + "parameters": { + "type": "object", + "properties": { + "document": { + "type": "string", + "description": "The document to write" + }, + }, + } + } +} + + +class AgentState(MessagesState): + """ + The state of the agent. + """ + document: Optional[str] = None + tools: List[Any] + + +async def start_flow(state: AgentState, config: RunnableConfig): + """ + This is the entry point for the flow. + """ + return Command( + goto="chat_node" + ) + + +async def chat_node(state: AgentState, config: RunnableConfig): + """ + Standard chat node. + """ + + system_prompt = f""" + You are a helpful assistant for writing documents. + To write the document, you MUST use the write_document tool. + You MUST write the full document, even when changing only a few words. + When you wrote the document, DO NOT repeat it as a message. + Just briefly summarize the changes you made. 2 sentences max. + This is the current state of the document: ----\n {state.get('document')}\n----- + """ + + # Define the model + model = ChatOpenAI(model="gpt-4o") + + # Define config for the model with emit_intermediate_state to stream tool calls to frontend + if config is None: + config = RunnableConfig(recursion_limit=25) + + # Use "predict_state" metadata to set up streaming for the write_document tool + config["metadata"]["predict_state"] = [{ + "state_key": "document", + "tool": "write_document", + "tool_argument": "document" + }] + + # Bind the tools to the model + model_with_tools = model.bind_tools( + [ + *state["tools"], + WRITE_DOCUMENT_TOOL + ], + # Disable parallel tool calls to avoid race conditions + parallel_tool_calls=False, + ) + + # Run the model to generate a response + response = await model_with_tools.ainvoke([ + SystemMessage(content=system_prompt), + *state["messages"], + ], config) + + # Update messages with the response + messages = state["messages"] + [response] + + # Extract any tool calls from the response + if hasattr(response, "tool_calls") and response.tool_calls: + tool_call = response.tool_calls[0] + + # Handle tool_call as a dictionary or an object + if isinstance(tool_call, dict): + tool_call_id = tool_call["id"] + tool_call_name = tool_call["name"] + tool_call_args = tool_call["args"] + else: + # Handle as an object (backward compatibility) + tool_call_id = tool_call.id + tool_call_name = tool_call.name + tool_call_args = tool_call.args + + if tool_call_name == "write_document": + # Add the tool response to messages + tool_response = { + "role": "tool", + "content": "Document written.", + "tool_call_id": tool_call_id + } + + # Add confirmation tool call + confirm_tool_call = { + "role": "assistant", + "content": "", + "tool_calls": [{ + "id": str(uuid.uuid4()), + "function": { + "name": "confirm_changes", + "arguments": "{}" + } + }] + } + + messages = messages + [tool_response, confirm_tool_call] + + # Return Command to route to end + return Command( + goto=END, + update={ + "messages": messages, + "document": tool_call_args["document"] + } + ) + + # If no tool was called, go to end + return Command( + goto=END, + update={ + "messages": messages + } + ) + + +# Define the graph +workflow = StateGraph(AgentState) + +# Add nodes +workflow.add_node("start_flow", start_flow) +workflow.add_node("chat_node", chat_node) + +# Add edges +workflow.set_entry_point("start_flow") +workflow.add_edge(START, "start_flow") +workflow.add_edge("start_flow", "chat_node") +workflow.add_edge("chat_node", END) + +# Compile the graph +predictive_state_updates_graph = workflow.compile(checkpointer=MemorySaver()) diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/shared_state.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/shared_state.py new file mode 100644 index 000000000..493bbee05 --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/shared_state.py @@ -0,0 +1,295 @@ +""" +A demo of shared state between the agent and CopilotKit using LangGraph. +""" + +import json +from enum import Enum +from typing import Dict, List, Any, Optional + +# LangGraph imports +from langchain_core.runnables import RunnableConfig +from langgraph.graph import StateGraph, END, START +from langgraph.types import Command +from langchain_core.callbacks.manager import adispatch_custom_event +from langgraph.graph import MessagesState +from langgraph.checkpoint.memory import MemorySaver +# OpenAI imports +from langchain_openai import ChatOpenAI +from langchain_core.messages import SystemMessage + +class SkillLevel(str, Enum): + """ + The level of skill required for the recipe. + """ + BEGINNER = "Beginner" + INTERMEDIATE = "Intermediate" + ADVANCED = "Advanced" + + +class SpecialPreferences(str, Enum): + """ + Special preferences for the recipe. + """ + HIGH_PROTEIN = "High Protein" + LOW_CARB = "Low Carb" + SPICY = "Spicy" + BUDGET_FRIENDLY = "Budget-Friendly" + ONE_POT_MEAL = "One-Pot Meal" + VEGETARIAN = "Vegetarian" + VEGAN = "Vegan" + + +class CookingTime(str, Enum): + """ + The cooking time of the recipe. + """ + FIVE_MIN = "5 min" + FIFTEEN_MIN = "15 min" + THIRTY_MIN = "30 min" + FORTY_FIVE_MIN = "45 min" + SIXTY_PLUS_MIN = "60+ min" + + +GENERATE_RECIPE_TOOL = { + "type": "function", + "function": { + "name": "generate_recipe", + "description": " ".join("""Using the existing (if any) ingredients and instructions, proceed with the recipe to finish it. + Make sure the recipe is complete. ALWAYS provide the entire recipe, not just the changes.""".split()), + "parameters": { + "type": "object", + "properties": { + "recipe": { + "type": "object", + "properties": { + "skill_level": { + "type": "string", + "enum": [level.value for level in SkillLevel], + "description": "The skill level required for the recipe" + }, + "special_preferences": { + "type": "array", + "items": { + "type": "string", + "enum": [preference.value for preference in SpecialPreferences] + }, + "description": "A list of special preferences for the recipe" + }, + "cooking_time": { + "type": "string", + "enum": [time.value for time in CookingTime], + "description": "The cooking time of the recipe" + }, + "ingredients": { + "type": "array", + "items": {"icon": "string", "name": "string", "amount": "string"}, + "description": "Entire list of ingredients for the recipe, including the new ingredients and the ones that are already in the recipe: Icon: the actual emoji (not emoji code like '\x1f35e', but the actual emoji like 🥕), name and amount. Like so: 🥕 Carrots (250g)" + }, + "instructions": { + "type": "array", + "items": {"type": "string"}, + "description": "Entire list of instructions for the recipe, including the new instructions and the ones that are already there" + }, + "changes": { + "type": "string", + "description": "A description of the changes made to the recipe" + } + }, + } + }, + "required": ["recipe"] + } + } +} + + +class AgentState(MessagesState): + """ + The state of the recipe. + """ + recipe: Optional[Dict[str, Any]] = None + tools: List[Any] + + +async def start_flow(state: Dict[str, Any], config: RunnableConfig): + """ + This is the entry point for the flow. + """ + + # Initialize recipe if not exists + if "recipe" not in state or state["recipe"] is None: + state["recipe"] = { + "skill_level": SkillLevel.BEGINNER.value, + "special_preferences": [], + "cooking_time": CookingTime.FIFTEEN_MIN.value, + "ingredients": [{"icon": "🍴", "name": "Sample Ingredient", "amount": "1 unit"}], + "instructions": ["First step instruction"] + } + # Emit the initial state to ensure it's properly shared with the frontend + await adispatch_custom_event( + "manually_emit_intermediate_state", + state, + config=config, + ) + + return Command( + goto="chat_node", + update={ + "messages": state["messages"], + "recipe": state["recipe"] + } + ) + + +async def chat_node(state: Dict[str, Any], config: RunnableConfig): + """ + Standard chat node. + """ + # Create a safer serialization of the recipe + recipe_json = "No recipe yet" + if "recipe" in state and state["recipe"] is not None: + try: + recipe_json = json.dumps(state["recipe"], indent=2) + except Exception as e: + recipe_json = f"Error serializing recipe: {str(e)}" + + system_prompt = f"""You are a helpful assistant for creating recipes. + This is the current state of the recipe: {recipe_json} + You can improve the recipe by calling the generate_recipe tool. + + IMPORTANT: + 1. Create a recipe using the existing ingredients and instructions. Make sure the recipe is complete. + 2. For ingredients, append new ingredients to the existing ones. + 3. For instructions, append new steps to the existing ones. + 4. 'ingredients' is always an array of objects with 'icon', 'name', and 'amount' fields + 5. 'instructions' is always an array of strings + + If you have just created or modified the recipe, just answer in one sentence what you did. dont describe the recipe, just say what you did. + """ + + # Define the model + model = ChatOpenAI(model="gpt-4o-mini") + + # Define config for the model + if config is None: + config = RunnableConfig(recursion_limit=25) + + # Use "predict_state" metadata to set up streaming for the write_document tool + config["metadata"]["predict_state"] = [{ + "state_key": "recipe", + "tool": "generate_recipe", + "tool_argument": "recipe" + }] + + # Bind the tools to the model + model_with_tools = model.bind_tools( + [ + *state["tools"], + GENERATE_RECIPE_TOOL + ], + # Disable parallel tool calls to avoid race conditions + parallel_tool_calls=False, + ) + + # Run the model and generate a response + response = await model_with_tools.ainvoke([ + SystemMessage(content=system_prompt), + *state["messages"], + ], config) + + # Update messages with the response + messages = state["messages"] + [response] + + # Handle tool calls + if hasattr(response, "tool_calls") and response.tool_calls: + tool_call = response.tool_calls[0] + + # Handle tool_call as a dictionary or an object + if isinstance(tool_call, dict): + tool_call_id = tool_call["id"] + tool_call_name = tool_call["name"] + # Check if args is already a dict or needs to be parsed + if isinstance(tool_call["args"], dict): + tool_call_args = tool_call["args"] + else: + tool_call_args = json.loads(tool_call["args"]) + else: + # Handle as an object + tool_call_id = tool_call.id + tool_call_name = tool_call.name + # Check if args is already a dict or needs to be parsed + if isinstance(tool_call.args, dict): + tool_call_args = tool_call.args + else: + tool_call_args = json.loads(tool_call.args) + + if tool_call_name == "generate_recipe": + # Update recipe state with tool_call_args + recipe_data = tool_call_args["recipe"] + + # If we have an existing recipe, update it + if "recipe" in state and state["recipe"] is not None: + recipe = state["recipe"] + for key, value in recipe_data.items(): + if value is not None: # Only update fields that were provided + recipe[key] = value + else: + # Create a new recipe + recipe = { + "skill_level": recipe_data.get("skill_level", SkillLevel.BEGINNER.value), + "special_preferences": recipe_data.get("special_preferences", []), + "cooking_time": recipe_data.get("cooking_time", CookingTime.FIFTEEN_MIN.value), + "ingredients": recipe_data.get("ingredients", []), + "instructions": recipe_data.get("instructions", []) + } + + # Add tool response to messages + tool_response = { + "role": "tool", + "content": "Recipe generated.", + "tool_call_id": tool_call_id + } + + messages = messages + [tool_response] + + # Explicitly emit the updated state to ensure it's shared with frontend + state["recipe"] = recipe + await adispatch_custom_event( + "manually_emit_intermediate_state", + state, + config=config, + ) + + # Return command with updated recipe + return Command( + goto="start_flow", + update={ + "messages": messages, + "recipe": recipe + } + ) + + return Command( + goto=END, + update={ + "messages": messages, + "recipe": state["recipe"] + } + ) + + +# Define the graph +workflow = StateGraph(AgentState) + +# Add nodes +workflow.add_node("start_flow", start_flow) +workflow.add_node("chat_node", chat_node) + +# Add edges +workflow.set_entry_point("start_flow") +workflow.add_edge(START, "start_flow") +workflow.add_edge("start_flow", "chat_node") +workflow.add_edge("chat_node", END) + +# Compile the graph +shared_state_graph = workflow.compile(checkpointer=MemorySaver()) \ No newline at end of file diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/tool_based_generative_ui.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/tool_based_generative_ui.py new file mode 100644 index 000000000..b286c4dbf --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/examples/agents/tool_based_generative_ui.py @@ -0,0 +1,129 @@ +""" +An example demonstrating tool-based generative UI using LangGraph. +""" + +from typing import Dict, List, Any, Optional + +# LangGraph imports +from langchain_core.runnables import RunnableConfig +from langgraph.graph import StateGraph, END, START +from langgraph.types import Command +from langgraph.graph import MessagesState +from langgraph.checkpoint.memory import MemorySaver +# OpenAI imports +from langchain_openai import ChatOpenAI +from langchain_core.messages import SystemMessage + +# List of available images (modify path if needed) +IMAGE_LIST = [ + "Osaka_Castle_Turret_Stone_Wall_Pine_Trees_Daytime.jpg", + "Tokyo_Skyline_Night_Tokyo_Tower_Mount_Fuji_View.jpg", + "Itsukushima_Shrine_Miyajima_Floating_Torii_Gate_Sunset_Long_Exposure.jpg", + "Takachiho_Gorge_Waterfall_River_Lush_Greenery_Japan.jpg", + "Bonsai_Tree_Potted_Japanese_Art_Green_Foliage.jpeg", + "Shirakawa-go_Gassho-zukuri_Thatched_Roof_Village_Aerial_View.jpg", + "Ginkaku-ji_Silver_Pavilion_Kyoto_Japanese_Garden_Pond_Reflection.jpg", + "Senso-ji_Temple_Asakusa_Cherry_Blossoms_Kimono_Umbrella.jpg", + "Cherry_Blossoms_Sakura_Night_View_City_Lights_Japan.jpg", + "Mount_Fuji_Lake_Reflection_Cherry_Blossoms_Sakura_Spring.jpg" +] + +# This tool generates a haiku on the server. +# The tool call will be streamed to the frontend as it is being generated. +GENERATE_HAIKU_TOOL = { + "type": "function", + "function": { + "name": "generate_haiku", + "description": "Generate a haiku in Japanese and its English translation. Also select exactly 3 relevant images from the provided list based on the haiku's theme.", + "parameters": { + "type": "object", + "properties": { + "japanese": { + "type": "array", + "items": { + "type": "string" + }, + "description": "An array of three lines of the haiku in Japanese" + }, + "english": { + "type": "array", + "items": { + "type": "string" + }, + "description": "An array of three lines of the haiku in English" + }, + "image_names": { + "type": "array", + "items": { + "type": "string" + }, + "description": "An array of EXACTLY THREE image filenames from the provided list that are most relevant to the haiku." + } + }, + "required": ["japanese", "english", "image_names"] + } + } +} + +class AgentState(MessagesState): + tools: List[Any] + +async def chat_node(state: AgentState, config: RunnableConfig): + """ + The main function handling chat and tool calls. + """ + # Prepare the image list string for the prompt + image_list_str = "\n".join([f"- {img}" for img in IMAGE_LIST]) + + system_prompt = f""" + You assist the user in generating a haiku. + When generating a haiku using the 'generate_haiku' tool, you MUST also select exactly 3 image filenames from the following list that are most relevant to the haiku's content or theme. Return the filenames in the 'image_names' parameter. + + Available images: + {image_list_str} + + Dont provide the relavent image names in your final response to the user. + """ + + # Define the model + model = ChatOpenAI(model="gpt-4o") + + # Define config for the model + if config is None: + config = RunnableConfig(recursion_limit=25) + + # Bind the tools to the model + model_with_tools = model.bind_tools( + [GENERATE_HAIKU_TOOL], + # Disable parallel tool calls to avoid race conditions + parallel_tool_calls=False, + ) + + # Run the model to generate a response + response = await model_with_tools.ainvoke([ + SystemMessage(content=system_prompt), + *state["messages"], + ], config) + + # Return Command to end with updated messages + return Command( + goto=END, + update={ + "messages": state["messages"] + [response] + } + ) + +# Define the graph +workflow = StateGraph(AgentState) + +# Add nodes +workflow.add_node("chat_node", chat_node) + +# Add edges 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"] +readme = "README.md" +packages = [{ include = "agents" }] + +[tool.poetry.dependencies] +python = "<3.14,>=3.10" +ag-ui-langgraph = { version = "0.0.1a4", extras = ["fastapi"] } +python-dotenv = "^1.0.0" +fastapi = "^0.115.12" +uvicorn = "^0.34.3" +langchain = ">=0.3.0" +langchain-core = ">=0.1.5" +langchain-community = ">=0.0.1" +langchain-experimental = ">=0.0.11" +langchain-openai = ">=0.0.1" +langgraph = ">=0.3.25,<=0.5.0" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.scripts] +dev = "agents.dojo:main" \ No newline at end of file diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/types.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/types.py new file mode 100644 index 000000000..7d08d6bf3 --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/types.py @@ -0,0 +1,91 @@ +from typing import TypedDict, Optional, List, Any, Dict, Union, Literal +from typing_extensions import NotRequired +from enum import Enum + +class LangGraphEventTypes(str, Enum): + OnChainStart = "on_chain_start" + OnChainStream = "on_chain_stream" + OnChainEnd = "on_chain_end" + OnChatModelStart = "on_chat_model_start" + OnChatModelStream = "on_chat_model_stream" + OnChatModelEnd = "on_chat_model_end" + OnToolStart = "on_tool_start" + OnToolEnd = "on_tool_end" + OnCustomEvent = "on_custom_event" + OnInterrupt = "on_interrupt" + +class CustomEventNames(str, Enum): + ManuallyEmitMessage = "manually_emit_message" + ManuallyEmitToolCall = "manually_emit_tool_call" + ManuallyEmitState = "manually_emit_state" + Exit = "exit" + +State = Dict[str, Any] + +SchemaKeys = TypedDict("SchemaKeys", { + "input": NotRequired[Optional[List[str]]], + "output": NotRequired[Optional[List[str]]], + "config": NotRequired[Optional[List[str]]] +}) + +ThinkingProcess = TypedDict("ThinkingProcess", { + "index": int, + "type": NotRequired[Optional[Literal['text']]], +}) + +MessageInProgress = TypedDict("MessageInProgress", { + "id": str, + "tool_call_id": NotRequired[Optional[str]], + "tool_call_name": NotRequired[Optional[str]] +}) + +RunMetadata = TypedDict("RunMetadata", { + "id": str, + "schema_keys": NotRequired[Optional[SchemaKeys]], + "node_name": NotRequired[Optional[str]], + "prev_node_name": NotRequired[Optional[str]], + "exiting_node": NotRequired[bool], + "manually_emitted_state": NotRequired[Optional[State]], + "thread_id": NotRequired[Optional[ThinkingProcess]], + "thinking_process": NotRequired[Optional[str]] +}) + +MessagesInProgressRecord = Dict[str, Optional[MessageInProgress]] + +ToolCall = TypedDict("ToolCall", { + "id": str, + "name": str, + "args": Dict[str, Any] +}) + +class BaseLangGraphPlatformMessage(TypedDict): + content: str + role: str + additional_kwargs: NotRequired[Dict[str, Any]] + type: str + id: str + +class LangGraphPlatformResultMessage(BaseLangGraphPlatformMessage): + tool_call_id: str + name: str + +class LangGraphPlatformActionExecutionMessage(BaseLangGraphPlatformMessage): + tool_calls: List[ToolCall] + +LangGraphPlatformMessage = Union[ + LangGraphPlatformActionExecutionMessage, + LangGraphPlatformResultMessage, + BaseLangGraphPlatformMessage, +] + +PredictStateTool = TypedDict("PredictStateTool", { + "tool": str, + "state_key": str, + "tool_argument": str +}) + +LangGraphReasoning = TypedDict("LangGraphReasoning", { + "type": str, + "text": str, + "index": int +}) diff --git a/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/utils.py b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/utils.py new file mode 100644 index 000000000..c1c50466f --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/ag_ui_langgraph/utils.py @@ -0,0 +1,179 @@ +import json +import re +from typing import List, Any, Dict, Union + +from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, SystemMessage, ToolMessage +from ag_ui.core import ( + Message as AGUIMessage, + UserMessage as AGUIUserMessage, + AssistantMessage as AGUIAssistantMessage, + SystemMessage as AGUISystemMessage, + ToolMessage as AGUIToolMessage, + ToolCall as AGUIToolCall, + FunctionCall as AGUIFunctionCall, +) +from .types import State, SchemaKeys, LangGraphReasoning + +DEFAULT_SCHEMA_KEYS = ["tools"] + +def filter_object_by_schema_keys(obj: Dict[str, Any], schema_keys: List[str]) -> Dict[str, Any]: + if not obj: + return {} + return {k: v for k, v in obj.items() if k in schema_keys} + +def get_stream_payload_input( + *, + mode: str, + state: State, + schema_keys: SchemaKeys, +) -> Union[State, None]: + input_payload = state if mode == "start" else None + if input_payload and schema_keys and schema_keys.get("input"): + input_payload = filter_object_by_schema_keys(input_payload, [*DEFAULT_SCHEMA_KEYS, *schema_keys["input"]]) + return input_payload + +def stringify_if_needed(item: Any) -> str: + if item is None: + return '' + if isinstance(item, str): + return item + return json.dumps(item) + +def langchain_messages_to_agui(messages: List[BaseMessage]) -> List[AGUIMessage]: + agui_messages: List[AGUIMessage] = [] + for message in messages: + if isinstance(message, HumanMessage): + agui_messages.append(AGUIUserMessage( + id=str(message.id), + role="user", + content=stringify_if_needed(resolve_message_content(message.content)), + name=message.name, + )) + elif isinstance(message, AIMessage): + tool_calls = None + if message.tool_calls: + tool_calls = [ + AGUIToolCall( + id=str(tc["id"]), + type="function", + function=AGUIFunctionCall( + name=tc["name"], + arguments=json.dumps(tc.get("args", {})), + ), + ) + for tc in message.tool_calls + ] + + agui_messages.append(AGUIAssistantMessage( + id=str(message.id), + role="assistant", + content=stringify_if_needed(resolve_message_content(message.content)), + tool_calls=tool_calls, + name=message.name, + )) + elif isinstance(message, SystemMessage): + agui_messages.append(AGUISystemMessage( + id=str(message.id), + role="system", + content=stringify_if_needed(resolve_message_content(message.content)), + name=message.name, + )) + elif isinstance(message, ToolMessage): + agui_messages.append(AGUIToolMessage( + id=str(message.id), + role="tool", + content=stringify_if_needed(resolve_message_content(message.content)), + tool_call_id=message.tool_call_id, + )) + else: + raise TypeError(f"Unsupported message type: {type(message)}") + return agui_messages + +def agui_messages_to_langchain(messages: List[AGUIMessage]) -> List[BaseMessage]: + langchain_messages = [] + for message in messages: + role = message.role + if role == "user": + langchain_messages.append(HumanMessage( + id=message.id, + content=message.content, + name=message.name, + )) + elif role == "assistant": + tool_calls = [] + if hasattr(message, "tool_calls") and message.tool_calls: + for tc in message.tool_calls: + tool_calls.append({ + "id": tc.id, + "name": tc.function.name, + "args": json.loads(tc.function.arguments) if hasattr(tc, "function") and tc.function.arguments else {}, + "type": "tool_call", + }) + langchain_messages.append(AIMessage( + id=message.id, + content=message.content or "", + tool_calls=tool_calls, + name=message.name, + )) + elif role == "system": + langchain_messages.append(SystemMessage( + id=message.id, + content=message.content, + name=message.name, + )) + elif role == "tool": + langchain_messages.append(ToolMessage( + id=message.id, + content=message.content, + tool_call_id=message.tool_call_id, + )) + else: + raise ValueError(f"Unsupported message role: {role}") + return langchain_messages + +def resolve_reasoning_content(chunk: Any) -> LangGraphReasoning | None: + content = chunk.content + if not content: + return None + + # Anthropic reasoning response + if isinstance(content, list) and content and content[0]: + if not content[0].get("thinking"): + return None + return LangGraphReasoning( + text=content[0]["thinking"], + type="text", + 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a/typescript-sdk/integrations/langgraph/python/pyproject.toml b/typescript-sdk/integrations/langgraph/python/pyproject.toml new file mode 100644 index 000000000..eda8e96f4 --- /dev/null +++ b/typescript-sdk/integrations/langgraph/python/pyproject.toml @@ -0,0 +1,27 @@ +[tool.poetry] +name = "ag-ui-langgraph" +version = "0.0.1" +description = "" +authors = ["Ran Shem Tov "] +readme = "README.md" +exclude = [ + "ag_ui_langgraph/examples/**", +] + +[tool.poetry.dependencies] +python = "<3.14,>=3.10" +ag-ui-protocol = "==0.1.7" +fastapi = { version = "^0.115.12", optional = true } +langchain = ">=0.3.0" +langchain-core = ">=0.3.0" +langgraph = ">=0.3.25,<=0.5.0" + +[tool.poetry.extras] +fastapi = ["fastapi"] + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.scripts] +dev = "ag_ui_langgraph.dojo:main" \ No newline at end of file diff --git a/typescript-sdk/integrations/langgraph/python/tests/__init__.py b/typescript-sdk/integrations/langgraph/python/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/typescript-sdk/integrations/langgraph/src/agent.ts b/typescript-sdk/integrations/langgraph/src/agent.ts new file mode 100644 index 000000000..ec61999ca --- /dev/null +++ b/typescript-sdk/integrations/langgraph/src/agent.ts @@ -0,0 +1,863 @@ +import { Observable, Subscriber } from "rxjs"; +import { + Client as LangGraphClient, + EventsStreamEvent, + StreamMode, + Config as LangGraphConfig, + ThreadState, + Assistant, + Message as LangGraphMessage, + Config, + Interrupt, + Thread +} from "@langchain/langgraph-sdk"; +import { randomUUID } from "node:crypto"; +import { RemoveMessage } from "@langchain/core/messages"; +import { + LangGraphPlatformMessage, + CustomEventNames, + LangGraphEventTypes, + State, + MessagesInProgressRecord, + ThinkingInProgress, + SchemaKeys, + MessageInProgress, + RunMetadata, + PredictStateTool, + LangGraphReasoning +} from "./types"; +import { + AbstractAgent, + AgentConfig, + CustomEvent, + EventType, + MessagesSnapshotEvent, + RawEvent, + RunAgentInput, + RunErrorEvent, + RunFinishedEvent, + RunStartedEvent, + StateDeltaEvent, + StateSnapshotEvent, + StepFinishedEvent, + StepStartedEvent, + TextMessageContentEvent, + TextMessageEndEvent, + TextMessageStartEvent, + ToolCallArgsEvent, + ToolCallEndEvent, + ToolCallStartEvent, + ThinkingTextMessageStartEvent, + ThinkingTextMessageContentEvent, + ThinkingTextMessageEndEvent, + ThinkingStartEvent, + ThinkingEndEvent, +} from "@ag-ui/client"; +import { RunsStreamPayload } from "@langchain/langgraph-sdk/dist/types"; +import { + aguiMessagesToLangChain, + DEFAULT_SCHEMA_KEYS, + filterObjectBySchemaKeys, + getStreamPayloadInput, + langchainMessagesToAgui, + resolveMessageContent, + resolveReasoningContent +} from "@/utils"; + +export type ProcessedEvents = + | TextMessageStartEvent + | TextMessageContentEvent + | TextMessageEndEvent + | ThinkingTextMessageStartEvent + | ThinkingTextMessageContentEvent + | ThinkingTextMessageEndEvent + | ToolCallStartEvent + | ToolCallArgsEvent + | ToolCallEndEvent + | ThinkingStartEvent + | ThinkingEndEvent + | StateSnapshotEvent + | StateDeltaEvent + | MessagesSnapshotEvent + | RawEvent + | CustomEvent + | RunStartedEvent + | RunFinishedEvent + | RunErrorEvent + | StepStartedEvent + | StepFinishedEvent; + +type RunAgentExtendedInput< + TStreamMode extends StreamMode | StreamMode[] = StreamMode, + TSubgraphs extends boolean = false, +> = Omit & { + forwardedProps?: Omit, "input"> & { + nodeName?: string; + threadMetadata?: Record; + }; +}; + +export interface LangGraphAgentConfig extends AgentConfig { + client?: LangGraphClient; + deploymentUrl: string; + langsmithApiKey?: string; + propertyHeaders?: Record; + assistantConfig?: LangGraphConfig; + agentName?: string; + graphId: string; +} + +export class LangGraphAgent extends AbstractAgent { + client: LangGraphClient; + assistantConfig?: LangGraphConfig; + agentName?: string; + graphId: string; + assistant?: Assistant; + messagesInProcess: MessagesInProgressRecord; + thinkingProcess: null | ThinkingInProgress; + activeRun?: RunMetadata; + // @ts-expect-error no need to initialize subscriber right now + subscriber: Subscriber; + + constructor(config: LangGraphAgentConfig) { + super(config); + this.messagesInProcess = {}; + this.agentName = config.agentName; + this.graphId = config.graphId; + this.assistantConfig = config.assistantConfig; + this.thinkingProcess = null + this.client = + config?.client ?? + new LangGraphClient({ + apiUrl: config.deploymentUrl, + apiKey: config.langsmithApiKey, + defaultHeaders: { ...(config.propertyHeaders ?? {}) }, + }); + } + + dispatchEvent(event: ProcessedEvents) { + this.subscriber.next(event); + return true; + } + + run(input: RunAgentInput) { + this.activeRun = { + id: input.runId, + threadId: input.threadId, + }; + return new Observable((subscriber) => { + this.handleStreamEvents(input, subscriber); + return () => {}; + }); + } + + async handleStreamEvents(input: RunAgentExtendedInput, subscriber: Subscriber) { + let { threadId: inputThreadId, state, messages, tools, context, forwardedProps } = input; + this.subscriber = subscriber; + let shouldExit = false; + // If a manual emittance happens, it is the ultimate source of truth of state, unless a node has exited. + // Therefore, this value should either hold null, or the only edition of state that should be used. + this.activeRun!.manuallyEmittedState = null; + + const nodeNameInput = forwardedProps?.nodeName; + this.activeRun!.nodeName = nodeNameInput; + if (this.activeRun!.nodeName === '__end__') { + this.activeRun!.nodeName = undefined; + } + + const threadId = inputThreadId ?? randomUUID(); + + if (!this.assistant) { + this.assistant = await this.getAssistant(); + } + + const thread = await this.getOrCreateThread(threadId, forwardedProps?.threadMetadata); + this.activeRun!.threadId = thread.thread_id; + const agentState = await this.client.threads.getState(thread.thread_id) ?? { values: {} } as ThreadState + + const agentStateValues = agentState.values as State; + const aguiToLangChainMessage = aguiMessagesToLangChain(messages); + + state.messages = agentStateValues.messages; + state = this.langGraphDefaultMergeState(state, aguiToLangChainMessage, tools); + const graphInfo = await this.client.assistants.getGraph(this.assistant.assistant_id); + + const mode = + threadId && this.activeRun!.nodeName != "__end__" && this.activeRun!.nodeName + ? "continue" + : "start"; + + if (mode === "continue" && !forwardedProps?.command?.resume) { + const nodeBefore = graphInfo.edges.find(e => e.target === this.activeRun!.nodeName); + await this.client.threads.updateState(threadId, { + values: state, + asNode: nodeBefore?.source, + }); + } + this.activeRun!.schemaKeys = await this.getSchemaKeys(); + + const payloadInput = getStreamPayloadInput({ + mode, + state, + schemaKeys: this.activeRun!.schemaKeys, + }); + + let payloadConfig: LangGraphConfig | undefined; + const configsToMerge = [this.assistantConfig, forwardedProps?.config].filter( + Boolean, + ) as LangGraphConfig[]; + if (configsToMerge.length) { + payloadConfig = await this.mergeConfigs({ + configs: configsToMerge, + assistant: this.assistant, + schemaKeys: this.activeRun!.schemaKeys, + }); + } + const payload = { + ...forwardedProps, + streamMode: + forwardedProps?.streamMode ?? (["events", "values", "updates"] satisfies StreamMode[]), + input: payloadInput, + config: payloadConfig, + }; + + // If there are still outstanding unresolved interrupts, we must force resolution of them before moving forward + const interrupts = (agentState.tasks?.[0]?.interrupts ?? []) as Interrupt[]; + if (interrupts?.length && !forwardedProps?.command?.resume) { + this.dispatchEvent({ + type: EventType.RUN_STARTED, + threadId, + runId: input.runId, + }); + + interrupts.forEach((interrupt) => { + this.dispatchEvent({ + type: EventType.CUSTOM, + name: LangGraphEventTypes.OnInterrupt, + value: + typeof interrupt.value === "string" ? interrupt.value : JSON.stringify(interrupt.value), + rawEvent: interrupt, + }); + }); + + this.dispatchEvent({ + type: EventType.RUN_FINISHED, + threadId, + runId: input.runId, + }); + return subscriber.complete(); + } + + const streamResponse = this.client.runs.stream(threadId, this.assistant.assistant_id, payload); + + this.activeRun!.prevNodeName = null; + let latestStateValues = {}; + let updatedState = state; + + try { + this.dispatchEvent({ + type: EventType.RUN_STARTED, + threadId, + runId: this.activeRun!.id, + }); + + for await (let streamResponseChunk of streamResponse) { + // @ts-ignore + if (!payload.streamMode.includes(streamResponseChunk.event as StreamMode)) { + continue; + } + + // Force event type, as data is not properly defined on the LG side. + type EventsChunkData = { + __interrupt__?: any; + metadata: Record; + event: string; + data: any; + [key: string]: unknown; + }; + const chunk = streamResponseChunk as EventsStreamEvent & { data: EventsChunkData }; + + if (streamResponseChunk.event === "error") { + this.dispatchEvent({ + type: EventType.RUN_ERROR, + message: streamResponseChunk.data.message, + rawEvent: streamResponseChunk, + }); + break; + } + + if (streamResponseChunk.event === "updates") continue; + + if (streamResponseChunk.event === "values") { + latestStateValues = chunk.data; + continue; + } + + const chunkData = chunk.data; + const currentNodeName = chunkData.metadata.langgraph_node; + const eventType = chunkData.event; + const metadata = chunkData.metadata; + this.activeRun!.id = metadata.run_id; + + if (currentNodeName && currentNodeName !== this.activeRun!.nodeName) { + if (this.activeRun!.nodeName && this.activeRun!.nodeName !== nodeNameInput) { + this.endStep() + } + + this.startStep(currentNodeName) + } + + shouldExit = + shouldExit || + (eventType === LangGraphEventTypes.OnCustomEvent && + chunkData.name === CustomEventNames.Exit); + + this.activeRun!.exitingNode = + this.activeRun!.nodeName === currentNodeName && + eventType === LangGraphEventTypes.OnChainEnd; + if (this.activeRun!.exitingNode) { + this.activeRun!.manuallyEmittedState = null; + } + + // we only want to update the node name under certain conditions + // since we don't need any internal node names to be sent to the frontend + if (graphInfo["nodes"].some((node) => node.id === currentNodeName)) { + this.activeRun!.nodeName = currentNodeName + } + + updatedState = this.activeRun!.manuallyEmittedState ?? latestStateValues; + + if (!this.activeRun!.nodeName) { + continue; + } + + const hasStateDiff = JSON.stringify(updatedState) !== JSON.stringify(state); + // We should not update snapshot while a message is in progress. + if ( + (hasStateDiff || + this.activeRun!.prevNodeName != this.activeRun!.nodeName || + this.activeRun!.exitingNode) && + !Boolean(this.getMessageInProgress(this.activeRun!.id)) + ) { + state = updatedState; + this.activeRun!.prevNodeName = this.activeRun!.nodeName; + + this.dispatchEvent({ + type: EventType.STATE_SNAPSHOT, + snapshot: this.getStateSnapshot(state), + rawEvent: chunk, + }); + } + + this.dispatchEvent({ + type: EventType.RAW, + event: chunkData, + }); + + this.handleSingleEvent(chunkData, state); + } + + state = await this.client.threads.getState(threadId); + const tasks = state.tasks + const interrupts = (tasks?.[0]?.interrupts ?? []) as Interrupt[]; + const isEndNode = state.next.length === 0 + const writes = state.metadata.writes ?? {} + + let newNodeName = this.activeRun!.nodeName! + + if (!interrupts?.length) { + newNodeName = isEndNode ? '__end__' : (state.next[0] ?? Object.keys(writes)[0]); + } + + + interrupts.forEach((interrupt) => { + this.dispatchEvent({ + type: EventType.CUSTOM, + name: LangGraphEventTypes.OnInterrupt, + value: + typeof interrupt.value === "string" ? interrupt.value : JSON.stringify(interrupt.value), + rawEvent: interrupt, + }); + }); + + if (this.activeRun!.nodeName != newNodeName) { + this.endStep() + this.startStep(newNodeName) + } + + this.endStep() + this.dispatchEvent({ + type: EventType.STATE_SNAPSHOT, + snapshot: this.getStateSnapshot(state.values), + }); + this.dispatchEvent({ + type: EventType.MESSAGES_SNAPSHOT, + messages: langchainMessagesToAgui(state.values.messages ?? []), + }); + + this.dispatchEvent({ + type: EventType.RUN_FINISHED, + threadId, + runId: this.activeRun!.id, + }); + this.activeRun = undefined; + return subscriber.complete(); + } catch (e) { + return subscriber.error(e); + } + } + + handleSingleEvent(event: any, state: State): void { + switch (event.event) { + case LangGraphEventTypes.OnChatModelStream: + let shouldEmitMessages = event.metadata["emit-messages"] ?? true; + let shouldEmitToolCalls = event.metadata["emit-tool-calls"] ?? true; + + if (event.data.chunk.response_metadata.finish_reason) return; + let currentStream = this.getMessageInProgress(this.activeRun!.id); + const hasCurrentStream = Boolean(currentStream?.id); + const toolCallData = event.data.chunk.tool_call_chunks?.[0]; + const toolCallUsedToPredictState = event.metadata["predict_state"]?.some( + (predictStateTool: PredictStateTool) => predictStateTool.tool === toolCallData?.name, + ); + + const isToolCallStartEvent = !hasCurrentStream && toolCallData?.name; + const isToolCallArgsEvent = + hasCurrentStream && currentStream?.toolCallId && toolCallData?.args; + const isToolCallEndEvent = hasCurrentStream && currentStream?.toolCallId && !toolCallData; + + const reasoningData = resolveReasoningContent(event.data); + const messageContent = resolveMessageContent(event.data.chunk.content); + const isMessageContentEvent = Boolean(!toolCallData && messageContent); + + const isMessageEndEvent = + hasCurrentStream && !currentStream?.toolCallId && !isMessageContentEvent; + + if (reasoningData) { + this.handleThinkingEvent(reasoningData) + break; + } + + if (!reasoningData && this.thinkingProcess) { + this.dispatchEvent({ + type: EventType.THINKING_TEXT_MESSAGE_END, + }) + this.dispatchEvent({ + type: EventType.THINKING_END, + }) + this.thinkingProcess = null; + } + + if (toolCallUsedToPredictState) { + this.dispatchEvent({ + type: EventType.CUSTOM, + name: "PredictState", + value: event.metadata["predict_state"], + }); + } + + if (isToolCallEndEvent) { + const resolved = this.dispatchEvent({ + type: EventType.TOOL_CALL_END, + toolCallId: currentStream?.toolCallId!, + rawEvent: event, + }); + if (resolved) { + this.messagesInProcess[this.activeRun!.id] = null; + } + break; + } + + if (isMessageEndEvent) { + const resolved = this.dispatchEvent({ + type: EventType.TEXT_MESSAGE_END, + messageId: currentStream!.id, + rawEvent: event, + }); + if (resolved) { + this.messagesInProcess[this.activeRun!.id] = null; + } + break; + } + + if (isToolCallStartEvent && shouldEmitToolCalls) { + const resolved = this.dispatchEvent({ + type: EventType.TOOL_CALL_START, + toolCallId: toolCallData.id, + toolCallName: toolCallData.name, + parentMessageId: event.data.chunk.id, + rawEvent: event, + }); + if (resolved) { + this.setMessageInProgress(this.activeRun!.id, { + id: event.data.chunk.id, + toolCallId: toolCallData.id, + toolCallName: toolCallData.name, + }); + } + break; + } + + // Tool call args: emit ActionExecutionArgs + if (isToolCallArgsEvent && shouldEmitToolCalls) { + this.dispatchEvent({ + type: EventType.TOOL_CALL_ARGS, + toolCallId: currentStream?.toolCallId!, + delta: toolCallData.args, + rawEvent: event, + }); + break; + } + + // Message content: emit TextMessageContent + if (isMessageContentEvent && shouldEmitMessages) { + // No existing message yet, also init the message + if (!currentStream) { + this.dispatchEvent({ + type: EventType.TEXT_MESSAGE_START, + role: "assistant", + messageId: event.data.chunk.id, + rawEvent: event, + }); + this.setMessageInProgress(this.activeRun!.id, { + id: event.data.chunk.id, + toolCallId: null, + toolCallName: null, + }); + currentStream = this.getMessageInProgress(this.activeRun!.id); + } + + this.dispatchEvent({ + type: EventType.TEXT_MESSAGE_CONTENT, + messageId: currentStream!.id, + delta: messageContent!, + rawEvent: event, + }); + break; + } + + break; + case LangGraphEventTypes.OnChatModelEnd: + if (this.getMessageInProgress(this.activeRun!.id)?.toolCallId) { + const resolved = this.dispatchEvent({ + type: EventType.TOOL_CALL_END, + toolCallId: this.getMessageInProgress(this.activeRun!.id)!.toolCallId!, + rawEvent: event, + }); + if (resolved) { + this.messagesInProcess[this.activeRun!.id] = null; + } + break; + } + if (this.getMessageInProgress(this.activeRun!.id)?.id) { + const resolved = this.dispatchEvent({ + type: EventType.TEXT_MESSAGE_END, + messageId: this.getMessageInProgress(this.activeRun!.id)!.id, + rawEvent: event, + }); + if (resolved) { + this.messagesInProcess[this.activeRun!.id] = null; + } + break; + } + break; + case LangGraphEventTypes.OnCustomEvent: + if (event.name === CustomEventNames.ManuallyEmitMessage) { + this.dispatchEvent({ + type: EventType.TEXT_MESSAGE_START, + role: "assistant", + messageId: event.data.message_id, + rawEvent: event, + }); + this.dispatchEvent({ + type: EventType.TEXT_MESSAGE_CONTENT, + messageId: event.data.message_id, + delta: event.data.message, + rawEvent: event, + }); + this.dispatchEvent({ + type: EventType.TEXT_MESSAGE_END, + messageId: event.data.message_id, + rawEvent: event, + }); + break; + } + + if (event.name === CustomEventNames.ManuallyEmitToolCall) { + this.dispatchEvent({ + type: EventType.TOOL_CALL_START, + toolCallId: event.data.id, + toolCallName: event.data.name, + parentMessageId: event.data.id, + rawEvent: event, + }); + this.dispatchEvent({ + type: EventType.TOOL_CALL_ARGS, + toolCallId: event.data.id, + delta: event.data.args, + rawEvent: event, + }); + this.dispatchEvent({ + type: EventType.TOOL_CALL_END, + toolCallId: event.data.id, + rawEvent: event, + }); + break; + } + + if (event.name === CustomEventNames.ManuallyEmitState) { + this.activeRun!.manuallyEmittedState = event.data; + this.dispatchEvent({ + type: EventType.STATE_SNAPSHOT, + snapshot: this.getStateSnapshot(state), + rawEvent: event, + }); + } + + this.dispatchEvent({ + type: EventType.CUSTOM, + name: event.name, + value: event.data, + rawEvent: event, + }); + break; + } + } + + handleThinkingEvent(reasoningData: LangGraphReasoning) { + if (!reasoningData || !reasoningData.type || !reasoningData.text) { + return; + } + + const thinkingStepIndex = reasoningData.index; + + if (this.thinkingProcess?.index && this.thinkingProcess.index !== thinkingStepIndex) { + if (this.thinkingProcess.type) { + this.dispatchEvent({ + type: EventType.THINKING_TEXT_MESSAGE_END, + }) + } + this.dispatchEvent({ + type: EventType.THINKING_END, + }) + this.thinkingProcess = null; + } + + if (!this.thinkingProcess) { + // No thinking step yet. Start a new one + this.dispatchEvent({ + type: EventType.THINKING_START, + }) + this.thinkingProcess = { + index: thinkingStepIndex, + }; + } + + + if (this.thinkingProcess.type !== reasoningData.type) { + this.dispatchEvent({ + type: EventType.THINKING_TEXT_MESSAGE_START, + }) + this.thinkingProcess.type = reasoningData.type + } + + if (this.thinkingProcess.type) { + this.dispatchEvent({ + type: EventType.THINKING_TEXT_MESSAGE_CONTENT, + delta: reasoningData.text + }) + } + } + + getStateSnapshot(state: State) { + const schemaKeys = this.activeRun!.schemaKeys!; + // Do not emit state keys that are not part of the output schema + if (schemaKeys?.output) { + state = filterObjectBySchemaKeys(state, [...DEFAULT_SCHEMA_KEYS, ...schemaKeys.output]); + } + // return state + return state; + } + + async getOrCreateThread(threadId: string, threadMetadata?: Record): Promise { + let thread: Thread; + try { + try { + thread = await this.getThread(threadId); + } catch (error) { + thread = await this.createThread({ + threadId, + metadata: threadMetadata + }); + } + } catch (error: unknown) { + throw new Error(`Failed to create thread: ${(error as Error).message}`); + } + + return thread; + } + + async getThread(threadId: string) { + return this.client.threads.get(threadId); + } + + async createThread(payload?: Parameters[0]) { + return this.client.threads.create(payload); + } + + async mergeConfigs({ + configs, + assistant, + schemaKeys, + }: { + configs: Config[]; + assistant: Assistant; + schemaKeys: SchemaKeys; + }) { + return configs.reduce((acc, cfg) => { + let filteredConfigurable = acc.configurable; + + if (cfg.configurable) { + filteredConfigurable = schemaKeys?.config + ? filterObjectBySchemaKeys(cfg?.configurable, [ + ...DEFAULT_SCHEMA_KEYS, + ...(schemaKeys?.config ?? []), + ]) + : cfg?.configurable; + } + + const newConfig = { + ...acc, + ...cfg, + configurable: filteredConfigurable, + }; + + // LG does not return recursion limit if it's the default, therefore we check: if no recursion limit is currently set, and the user asked for 25, there is no change. + const isRecursionLimitSetToDefault = + acc.recursion_limit == null && cfg.recursion_limit === 25; + // Deep compare configs to avoid unnecessary update calls + const configsAreDifferent = JSON.stringify(newConfig) !== JSON.stringify(acc); + + // Check if the only difference is the recursion_limit being set to default + const isOnlyRecursionLimitDifferent = + isRecursionLimitSetToDefault && + JSON.stringify({ ...newConfig, recursion_limit: null }) === + JSON.stringify({ ...acc, recursion_limit: null }); + + if (configsAreDifferent && !isOnlyRecursionLimitDifferent) { + return { + ...acc, + ...newConfig, + }; + } + + return acc; + }, assistant.config); + } + + getMessageInProgress(runId: string) { + return this.messagesInProcess[runId]; + } + + setMessageInProgress(runId: string, data: MessageInProgress) { + this.messagesInProcess = { + ...this.messagesInProcess, + [runId]: { + ...(this.messagesInProcess[runId] as MessageInProgress), + ...data, + }, + }; + } + + async getAssistant(): Promise { + const assistants = await this.client.assistants.search(); + const retrievedAssistant = assistants.find( + (searchResult) => + searchResult.graph_id === this.graphId, + ); + if (!retrievedAssistant) { + console.error(` + No agent found with graph ID ${this.graphId} found..\n + + These are the available agents: [${assistants.map((a) => `${a.graph_id} (ID: ${a.assistant_id})`).join(", ")}] + `); + throw new Error("No agent id found"); + } + + return retrievedAssistant; + } + + async getSchemaKeys(): Promise { + const CONSTANT_KEYS = ["messages"]; + + try { + const graphSchema = await this.client.assistants.getSchemas(this.assistant!.assistant_id); + let configSchema = null; + if (graphSchema.config_schema?.properties) { + configSchema = Object.keys(graphSchema.config_schema.properties); + } + if (!graphSchema.input_schema?.properties || !graphSchema.output_schema?.properties) { + return { config: [], input: CONSTANT_KEYS, output: CONSTANT_KEYS }; + } + const inputSchema = Object.keys(graphSchema.input_schema.properties); + const outputSchema = Object.keys(graphSchema.output_schema.properties); + + return { + input: inputSchema && inputSchema.length ? [...inputSchema, ...CONSTANT_KEYS] : null, + output: outputSchema && outputSchema.length ? [...outputSchema, ...CONSTANT_KEYS] : null, + config: configSchema, + }; + } catch (e) { + return { config: [], input: CONSTANT_KEYS, output: CONSTANT_KEYS }; + } + } + + langGraphDefaultMergeState(state: State, messages: LangGraphMessage[], tools: any): State { + if (messages.length > 0 && "role" in messages[0] && messages[0].role === "system") { + // remove system message + messages = messages.slice(1); + } + + // merge with existing messages + const existingMessages: LangGraphPlatformMessage[] = state.messages || []; + const existingMessageIds = new Set(existingMessages.map((message) => message.id)); + const messageIds = new Set(messages.map((message) => message.id)); + + let removedMessages: RemoveMessage[] = []; + if (messages.length < existingMessages.length) { + // Messages were removed + removedMessages = existingMessages + .filter((m) => !messageIds.has(m.id)) + .map((m) => new RemoveMessage({ id: m.id! })); + } + + const newMessages = messages.filter((message) => !existingMessageIds.has(message.id)); + + return { + ...state, + messages: [...removedMessages, ...newMessages], + tools: [...(state.tools ?? []), ...tools], + }; + } + + startStep(nodeName: string) { + this.dispatchEvent({ + type: EventType.STEP_STARTED, + stepName: nodeName, + }); + this.activeRun!.nodeName = nodeName; + } + + endStep() { + if (!this.activeRun!.nodeName) { + throw new Error("No active step to end"); + } + this.dispatchEvent({ + type: EventType.STEP_FINISHED, + stepName: this.activeRun!.nodeName!, + }); + this.activeRun!.nodeName = undefined; + } +} + +export * from "./types"; diff --git a/typescript-sdk/integrations/langgraph/src/index.ts b/typescript-sdk/integrations/langgraph/src/index.ts index 1e725bd00..6b1faf49b 100644 --- a/typescript-sdk/integrations/langgraph/src/index.ts +++ b/typescript-sdk/integrations/langgraph/src/index.ts @@ -1,863 +1,4 @@ -import { Observable, Subscriber } from "rxjs"; -import { - Client as LangGraphClient, - EventsStreamEvent, - StreamMode, - Config as LangGraphConfig, - ThreadState, - Assistant, - Message as LangGraphMessage, - Config, - Interrupt, - Thread -} from "@langchain/langgraph-sdk"; -import { randomUUID } from "node:crypto"; -import { RemoveMessage } from "@langchain/core/messages"; -import { - LangGraphPlatformMessage, - CustomEventNames, - LangGraphEventTypes, - State, - MessagesInProgressRecord, - ThinkingInProgress, - SchemaKeys, - MessageInProgress, - RunMetadata, - PredictStateTool, - LangGraphReasoning -} from "./types"; -import { - AbstractAgent, - AgentConfig, - CustomEvent, - EventType, - MessagesSnapshotEvent, - RawEvent, - RunAgentInput, - RunErrorEvent, - RunFinishedEvent, - RunStartedEvent, - StateDeltaEvent, - StateSnapshotEvent, - StepFinishedEvent, - StepStartedEvent, - TextMessageContentEvent, - TextMessageEndEvent, - TextMessageStartEvent, - ToolCallArgsEvent, - ToolCallEndEvent, - ToolCallStartEvent, - ThinkingTextMessageStartEvent, - ThinkingTextMessageContentEvent, - ThinkingTextMessageEndEvent, - ThinkingStartEvent, - ThinkingEndEvent, -} from "@ag-ui/client"; -import { RunsStreamPayload } from "@langchain/langgraph-sdk/dist/types"; -import { - aguiMessagesToLangChain, - DEFAULT_SCHEMA_KEYS, - filterObjectBySchemaKeys, - getStreamPayloadInput, - langchainMessagesToAgui, - resolveMessageContent, - resolveReasoningContent -} from "@/utils"; +import { HttpAgent } from "@ag-ui/client/src"; -export type ProcessedEvents = - | TextMessageStartEvent - | TextMessageContentEvent - | TextMessageEndEvent - | ThinkingTextMessageStartEvent - | ThinkingTextMessageContentEvent - | ThinkingTextMessageEndEvent - | ToolCallStartEvent - | ToolCallArgsEvent - | ToolCallEndEvent - | ThinkingStartEvent - | ThinkingEndEvent - | StateSnapshotEvent - | StateDeltaEvent - | MessagesSnapshotEvent - | RawEvent - | CustomEvent - | RunStartedEvent - | RunFinishedEvent - | RunErrorEvent - | StepStartedEvent - | StepFinishedEvent; - -type RunAgentExtendedInput< - TStreamMode extends StreamMode | StreamMode[] = StreamMode, - TSubgraphs extends boolean = false, -> = Omit & { - forwardedProps?: Omit, "input"> & { - nodeName?: string; - threadMetadata?: Record; - }; -}; - -export interface LangGraphAgentConfig extends AgentConfig { - client?: LangGraphClient; - deploymentUrl: string; - langsmithApiKey?: string; - propertyHeaders?: Record; - assistantConfig?: LangGraphConfig; - agentName?: string; - graphId: string; -} - -export class LangGraphAgent extends AbstractAgent { - client: LangGraphClient; - assistantConfig?: LangGraphConfig; - agentName?: string; - graphId: string; - assistant?: Assistant; - messagesInProcess: MessagesInProgressRecord; - thinkingProcess: null | ThinkingInProgress; - activeRun?: RunMetadata; - // @ts-expect-error no need to initialize subscriber right now - subscriber: Subscriber; - - constructor(config: LangGraphAgentConfig) { - super(config); - this.messagesInProcess = {}; - this.agentName = config.agentName; - this.graphId = config.graphId; - this.assistantConfig = config.assistantConfig; - this.thinkingProcess = null - this.client = - config?.client ?? - new LangGraphClient({ - apiUrl: config.deploymentUrl, - apiKey: config.langsmithApiKey, - defaultHeaders: { ...(config.propertyHeaders ?? {}) }, - }); - } - - dispatchEvent(event: ProcessedEvents) { - this.subscriber.next(event); - return true; - } - - run(input: RunAgentInput) { - this.activeRun = { - id: input.runId, - threadId: input.threadId, - }; - return new Observable((subscriber) => { - this.handleStreamEvents(input, subscriber); - return () => {}; - }); - } - - async handleStreamEvents(input: RunAgentExtendedInput, subscriber: Subscriber) { - let { threadId: inputThreadId, state, messages, tools, context, forwardedProps } = input; - this.subscriber = subscriber; - let shouldExit = false; - // If a manual emittance happens, it is the ultimate source of truth of state, unless a node has exited. - // Therefore, this value should either hold null, or the only edition of state that should be used. - this.activeRun!.manuallyEmittedState = null; - - const nodeNameInput = forwardedProps?.nodeName; - this.activeRun!.nodeName = nodeNameInput; - if (this.activeRun!.nodeName === '__end__') { - this.activeRun!.nodeName = undefined; - } - - const threadId = inputThreadId ?? randomUUID(); - - if (!this.assistant) { - this.assistant = await this.getAssistant(); - } - - const thread = await this.getOrCreateThread(threadId, forwardedProps?.threadMetadata); - this.activeRun!.threadId = thread.thread_id; - const agentState = await this.client.threads.getState(thread.thread_id) ?? { values: {} } as ThreadState - - const agentStateValues = agentState.values as State; - const aguiToLangChainMessage = aguiMessagesToLangChain(messages); - - state.messages = agentStateValues.messages; - state = this.langGraphDefaultMergeState(state, aguiToLangChainMessage, tools); - const graphInfo = await this.client.assistants.getGraph(this.assistant.assistant_id); - - const mode = - threadId && this.activeRun!.nodeName != "__end__" && this.activeRun!.nodeName - ? "continue" - : "start"; - - if (mode === "continue" && !forwardedProps?.command?.resume) { - const nodeBefore = graphInfo.edges.find(e => e.target === this.activeRun!.nodeName); - await this.client.threads.updateState(threadId, { - values: state, - asNode: nodeBefore?.source, - }); - } - this.activeRun!.schemaKeys = await this.getSchemaKeys(); - - const payloadInput = getStreamPayloadInput({ - mode, - state, - schemaKeys: this.activeRun!.schemaKeys, - }); - - let payloadConfig: LangGraphConfig | undefined; - const configsToMerge = [this.assistantConfig, forwardedProps?.config].filter( - Boolean, - ) as LangGraphConfig[]; - if (configsToMerge.length) { - payloadConfig = await this.mergeConfigs({ - configs: configsToMerge, - assistant: this.assistant, - schemaKeys: this.activeRun!.schemaKeys, - }); - } - const payload = { - ...forwardedProps, - streamMode: - forwardedProps?.streamMode ?? (["events", "values", "updates"] satisfies StreamMode[]), - input: payloadInput, - config: payloadConfig, - }; - - // If there are still outstanding unresolved interrupts, we must force resolution of them before moving forward - const interrupts = (agentState.tasks?.[0]?.interrupts ?? []) as Interrupt[]; - if (interrupts?.length && !forwardedProps?.command?.resume) { - this.dispatchEvent({ - type: EventType.RUN_STARTED, - threadId, - runId: input.runId, - }); - - interrupts.forEach((interrupt) => { - this.dispatchEvent({ - type: EventType.CUSTOM, - name: LangGraphEventTypes.OnInterrupt, - value: - typeof interrupt.value === "string" ? interrupt.value : JSON.stringify(interrupt.value), - rawEvent: interrupt, - }); - }); - - this.dispatchEvent({ - type: EventType.RUN_FINISHED, - threadId, - runId: input.runId, - }); - return subscriber.complete(); - } - - const streamResponse = this.client.runs.stream(threadId, this.assistant.assistant_id, payload); - - this.activeRun!.prevNodeName = null; - let latestStateValues = {}; - let updatedState = state; - - try { - this.dispatchEvent({ - type: EventType.RUN_STARTED, - threadId, - runId: this.activeRun!.id, - }); - - for await (let streamResponseChunk of streamResponse) { - // @ts-ignore - if (!payload.streamMode.includes(streamResponseChunk.event as StreamMode)) { - continue; - } - - // Force event type, as data is not properly defined on the LG side. - type EventsChunkData = { - __interrupt__?: any; - metadata: Record; - event: string; - data: any; - [key: string]: unknown; - }; - const chunk = streamResponseChunk as EventsStreamEvent & { data: EventsChunkData }; - - if (streamResponseChunk.event === "error") { - this.dispatchEvent({ - type: EventType.RUN_ERROR, - message: streamResponseChunk.data.message, - rawEvent: streamResponseChunk, - }); - break; - } - - if (streamResponseChunk.event === "updates") continue; - - if (streamResponseChunk.event === "values") { - latestStateValues = chunk.data; - continue; - } - - const chunkData = chunk.data; - const currentNodeName = chunkData.metadata.langgraph_node; - const eventType = chunkData.event; - const metadata = chunkData.metadata; - this.activeRun!.id = metadata.run_id; - - if (currentNodeName && currentNodeName !== this.activeRun!.nodeName) { - if (this.activeRun!.nodeName && this.activeRun!.nodeName !== nodeNameInput) { - this.endStep() - } - - this.startStep(currentNodeName) - } - - shouldExit = - shouldExit || - (eventType === LangGraphEventTypes.OnCustomEvent && - chunkData.name === CustomEventNames.Exit); - - this.activeRun!.exitingNode = - this.activeRun!.nodeName === currentNodeName && - eventType === LangGraphEventTypes.OnChainEnd; - if (this.activeRun!.exitingNode) { - this.activeRun!.manuallyEmittedState = null; - } - - // we only want to update the node name under certain conditions - // since we don't need any internal node names to be sent to the frontend - if (graphInfo["nodes"].some((node) => node.id === currentNodeName)) { - this.activeRun!.nodeName = currentNodeName - } - - updatedState = this.activeRun!.manuallyEmittedState ?? latestStateValues; - - if (!this.activeRun!.nodeName) { - continue; - } - - const hasStateDiff = JSON.stringify(updatedState) !== JSON.stringify(state); - // We should not update snapshot while a message is in progress. - if ( - (hasStateDiff || - this.activeRun!.prevNodeName != this.activeRun!.nodeName || - this.activeRun!.exitingNode) && - !Boolean(this.getMessageInProgress(this.activeRun!.id)) - ) { - state = updatedState; - this.activeRun!.prevNodeName = this.activeRun!.nodeName; - - this.dispatchEvent({ - type: EventType.STATE_SNAPSHOT, - snapshot: this.getStateSnapshot(state), - rawEvent: chunk, - }); - } - - this.dispatchEvent({ - type: EventType.RAW, - event: chunkData, - }); - - this.handleSingleEvent(chunkData, state); - } - - state = await this.client.threads.getState(threadId); - const tasks = state.tasks - const interrupts = (tasks?.[0]?.interrupts ?? []) as Interrupt[]; - const isEndNode = state.next.length === 0 - const writes = state.metadata.writes ?? {} - - let newNodeName = this.activeRun!.nodeName! - - if (!interrupts?.length) { - newNodeName = isEndNode ? '__end__' : (state.next[0] ?? Object.keys(writes)[0]); - } - - - interrupts.forEach((interrupt) => { - this.dispatchEvent({ - type: EventType.CUSTOM, - name: LangGraphEventTypes.OnInterrupt, - value: - typeof interrupt.value === "string" ? interrupt.value : JSON.stringify(interrupt.value), - rawEvent: interrupt, - }); - }); - - if (this.activeRun!.nodeName != newNodeName) { - this.endStep() - this.startStep(newNodeName) - } - - this.endStep() - this.dispatchEvent({ - type: EventType.STATE_SNAPSHOT, - snapshot: this.getStateSnapshot(state.values), - }); - this.dispatchEvent({ - type: EventType.MESSAGES_SNAPSHOT, - messages: langchainMessagesToAgui(state.values.messages ?? []), - }); - - this.dispatchEvent({ - type: EventType.RUN_FINISHED, - threadId, - runId: this.activeRun!.id, - }); - this.activeRun = undefined; - return subscriber.complete(); - } catch (e) { - return subscriber.error(e); - } - } - - handleSingleEvent(event: any, state: State): void { - switch (event.event) { - case LangGraphEventTypes.OnChatModelStream: - let shouldEmitMessages = event.metadata["emit-messages"] ?? true; - let shouldEmitToolCalls = event.metadata["emit-tool-calls"] ?? true; - - if (event.data.chunk.response_metadata.finish_reason) return; - let currentStream = this.getMessageInProgress(this.activeRun!.id); - const hasCurrentStream = Boolean(currentStream?.id); - const toolCallData = event.data.chunk.tool_call_chunks?.[0]; - const toolCallUsedToPredictState = event.metadata["predict_state"]?.some( - (predictStateTool: PredictStateTool) => predictStateTool.tool === toolCallData?.name, - ); - - const isToolCallStartEvent = !hasCurrentStream && toolCallData?.name; - const isToolCallArgsEvent = - hasCurrentStream && currentStream?.toolCallId && toolCallData?.args; - const isToolCallEndEvent = hasCurrentStream && currentStream?.toolCallId && !toolCallData; - - const reasoningData = resolveReasoningContent(event.data); - const messageContent = resolveMessageContent(event.data.chunk.content); - const isMessageContentEvent = Boolean(!toolCallData && messageContent); - - const isMessageEndEvent = - hasCurrentStream && !currentStream?.toolCallId && !isMessageContentEvent; - - if (reasoningData) { - this.handleThinkingEvent(reasoningData) - break; - } - - if (!reasoningData && this.thinkingProcess) { - this.dispatchEvent({ - type: EventType.THINKING_TEXT_MESSAGE_END, - }) - this.dispatchEvent({ - type: EventType.THINKING_END, - }) - this.thinkingProcess = null; - } - - if (toolCallUsedToPredictState) { - this.dispatchEvent({ - type: EventType.CUSTOM, - name: "PredictState", - value: event.metadata["predict_state"], - }); - } - - if (isToolCallEndEvent) { - const resolved = this.dispatchEvent({ - type: EventType.TOOL_CALL_END, - toolCallId: currentStream?.toolCallId!, - rawEvent: event, - }); - if (resolved) { - this.messagesInProcess[this.activeRun!.id] = null; - } - break; - } - - if (isMessageEndEvent) { - const resolved = this.dispatchEvent({ - type: EventType.TEXT_MESSAGE_END, - messageId: currentStream!.id, - rawEvent: event, - }); - if (resolved) { - this.messagesInProcess[this.activeRun!.id] = null; - } - break; - } - - if (isToolCallStartEvent && shouldEmitToolCalls) { - const resolved = this.dispatchEvent({ - type: EventType.TOOL_CALL_START, - toolCallId: toolCallData.id, - toolCallName: toolCallData.name, - parentMessageId: event.data.chunk.id, - rawEvent: event, - }); - if (resolved) { - this.setMessageInProgress(this.activeRun!.id, { - id: event.data.chunk.id, - toolCallId: toolCallData.id, - toolCallName: toolCallData.name, - }); - } - break; - } - - // Tool call args: emit ActionExecutionArgs - if (isToolCallArgsEvent && shouldEmitToolCalls) { - this.dispatchEvent({ - type: EventType.TOOL_CALL_ARGS, - toolCallId: currentStream?.toolCallId!, - delta: toolCallData.args, - rawEvent: event, - }); - break; - } - - // Message content: emit TextMessageContent - if (isMessageContentEvent && shouldEmitMessages) { - // No existing message yet, also init the message - if (!currentStream) { - this.dispatchEvent({ - type: EventType.TEXT_MESSAGE_START, - role: "assistant", - messageId: event.data.chunk.id, - rawEvent: event, - }); - this.setMessageInProgress(this.activeRun!.id, { - id: event.data.chunk.id, - toolCallId: null, - toolCallName: null, - }); - currentStream = this.getMessageInProgress(this.activeRun!.id); - } - - this.dispatchEvent({ - type: EventType.TEXT_MESSAGE_CONTENT, - messageId: currentStream!.id, - delta: messageContent!, - rawEvent: event, - }); - break; - } - - break; - case LangGraphEventTypes.OnChatModelEnd: - if (this.getMessageInProgress(this.activeRun!.id)?.toolCallId) { - const resolved = this.dispatchEvent({ - type: EventType.TOOL_CALL_END, - toolCallId: this.getMessageInProgress(this.activeRun!.id)!.toolCallId!, - rawEvent: event, - }); - if (resolved) { - this.messagesInProcess[this.activeRun!.id] = null; - } - break; - } - if (this.getMessageInProgress(this.activeRun!.id)?.id) { - const resolved = this.dispatchEvent({ - type: EventType.TEXT_MESSAGE_END, - messageId: this.getMessageInProgress(this.activeRun!.id)!.id, - rawEvent: event, - }); - if (resolved) { - this.messagesInProcess[this.activeRun!.id] = null; - } - break; - } - break; - case LangGraphEventTypes.OnCustomEvent: - if (event.name === CustomEventNames.ManuallyEmitMessage) { - this.dispatchEvent({ - type: EventType.TEXT_MESSAGE_START, - role: "assistant", - messageId: event.data.message_id, - rawEvent: event, - }); - this.dispatchEvent({ - type: EventType.TEXT_MESSAGE_CONTENT, - messageId: event.data.message_id, - delta: event.data.message, - rawEvent: event, - }); - this.dispatchEvent({ - type: EventType.TEXT_MESSAGE_END, - messageId: event.data.message_id, - rawEvent: event, - }); - break; - } - - if (event.name === CustomEventNames.ManuallyEmitToolCall) { - this.dispatchEvent({ - type: EventType.TOOL_CALL_START, - toolCallId: event.data.id, - toolCallName: event.data.name, - parentMessageId: event.data.id, - rawEvent: event, - }); - this.dispatchEvent({ - type: EventType.TOOL_CALL_ARGS, - toolCallId: event.data.id, - delta: event.data.args, - rawEvent: event, - }); - this.dispatchEvent({ - type: EventType.TOOL_CALL_END, - toolCallId: event.data.id, - rawEvent: event, - }); - break; - } - - if (event.name === CustomEventNames.ManuallyEmitState) { - this.activeRun!.manuallyEmittedState = event.data; - this.dispatchEvent({ - type: EventType.STATE_SNAPSHOT, - snapshot: this.getStateSnapshot(state), - rawEvent: event, - }); - } - - this.dispatchEvent({ - type: EventType.CUSTOM, - name: event.name, - value: event.data, - rawEvent: event, - }); - break; - } - } - - handleThinkingEvent(reasoningData: LangGraphReasoning) { - if (!reasoningData || !reasoningData.type || !reasoningData.text) { - return; - } - - const thinkingStepIndex = reasoningData.index; - - if (this.thinkingProcess?.index && this.thinkingProcess.index !== thinkingStepIndex) { - if (this.thinkingProcess.type) { - this.dispatchEvent({ - type: EventType.THINKING_TEXT_MESSAGE_END, - }) - } - this.dispatchEvent({ - type: EventType.THINKING_END, - }) - this.thinkingProcess = null; - } - - if (!this.thinkingProcess) { - // No thinking step yet. Start a new one - this.dispatchEvent({ - type: EventType.THINKING_START, - }) - this.thinkingProcess = { - index: thinkingStepIndex, - }; - } - - - if (this.thinkingProcess.type !== reasoningData.type) { - this.dispatchEvent({ - type: EventType.THINKING_TEXT_MESSAGE_START, - }) - this.thinkingProcess.type = reasoningData.type - } - - if (this.thinkingProcess.type) { - this.dispatchEvent({ - type: EventType.THINKING_TEXT_MESSAGE_CONTENT, - delta: reasoningData.text - }) - } - } - - getStateSnapshot(state: State) { - const schemaKeys = this.activeRun!.schemaKeys!; - // Do not emit state keys that are not part of the output schema - if (schemaKeys?.output) { - state = filterObjectBySchemaKeys(state, [...DEFAULT_SCHEMA_KEYS, ...schemaKeys.output]); - } - // return state - return state; - } - - async getOrCreateThread(threadId: string, threadMetadata?: Record): Promise { - let thread: Thread; - try { - try { - thread = await this.getThread(threadId); - } catch (error) { - thread = await this.createThread({ - threadId, - metadata: threadMetadata - }); - } - } catch (error: unknown) { - throw new Error(`Failed to create thread: ${(error as Error).message}`); - } - - return thread; - } - - async getThread(threadId: string) { - return this.client.threads.get(threadId); - } - - async createThread(payload?: Parameters[0]) { - return this.client.threads.create(payload); - } - - async mergeConfigs({ - configs, - assistant, - schemaKeys, - }: { - configs: Config[]; - assistant: Assistant; - schemaKeys: SchemaKeys; - }) { - return configs.reduce((acc, cfg) => { - let filteredConfigurable = acc.configurable; - - if (cfg.configurable) { - filteredConfigurable = schemaKeys?.config - ? filterObjectBySchemaKeys(cfg?.configurable, [ - ...DEFAULT_SCHEMA_KEYS, - ...(schemaKeys?.config ?? []), - ]) - : cfg?.configurable; - } - - const newConfig = { - ...acc, - ...cfg, - configurable: filteredConfigurable, - }; - - // LG does not return recursion limit if it's the default, therefore we check: if no recursion limit is currently set, and the user asked for 25, there is no change. - const isRecursionLimitSetToDefault = - acc.recursion_limit == null && cfg.recursion_limit === 25; - // Deep compare configs to avoid unnecessary update calls - const configsAreDifferent = JSON.stringify(newConfig) !== JSON.stringify(acc); - - // Check if the only difference is the recursion_limit being set to default - const isOnlyRecursionLimitDifferent = - isRecursionLimitSetToDefault && - JSON.stringify({ ...newConfig, recursion_limit: null }) === - JSON.stringify({ ...acc, recursion_limit: null }); - - if (configsAreDifferent && !isOnlyRecursionLimitDifferent) { - return { - ...acc, - ...newConfig, - }; - } - - return acc; - }, assistant.config); - } - - getMessageInProgress(runId: string) { - return this.messagesInProcess[runId]; - } - - setMessageInProgress(runId: string, data: MessageInProgress) { - this.messagesInProcess = { - ...this.messagesInProcess, - [runId]: { - ...(this.messagesInProcess[runId] as MessageInProgress), - ...data, - }, - }; - } - - async getAssistant(): Promise { - const assistants = await this.client.assistants.search(); - const retrievedAssistant = assistants.find( - (searchResult) => - searchResult.graph_id === this.graphId, - ); - if (!retrievedAssistant) { - console.error(` - No agent found with graph ID ${this.graphId} found..\n - - These are the available agents: [${assistants.map((a) => `${a.graph_id} (ID: ${a.assistant_id})`).join(", ")}] - `); - throw new Error("No agent id found"); - } - - return retrievedAssistant; - } - - async getSchemaKeys(): Promise { - const CONSTANT_KEYS = ["messages"]; - - try { - const graphSchema = await this.client.assistants.getSchemas(this.assistant!.assistant_id); - let configSchema = null; - if (graphSchema.config_schema?.properties) { - configSchema = Object.keys(graphSchema.config_schema.properties); - } - if (!graphSchema.input_schema?.properties || !graphSchema.output_schema?.properties) { - return { config: [], input: CONSTANT_KEYS, output: CONSTANT_KEYS }; - } - const inputSchema = Object.keys(graphSchema.input_schema.properties); - const outputSchema = Object.keys(graphSchema.output_schema.properties); - - return { - input: inputSchema && inputSchema.length ? [...inputSchema, ...CONSTANT_KEYS] : null, - output: outputSchema && outputSchema.length ? [...outputSchema, ...CONSTANT_KEYS] : null, - config: configSchema, - }; - } catch (e) { - return { config: [], input: CONSTANT_KEYS, output: CONSTANT_KEYS }; - } - } - - langGraphDefaultMergeState(state: State, messages: LangGraphMessage[], tools: any): State { - if (messages.length > 0 && "role" in messages[0] && messages[0].role === "system") { - // remove system message - messages = messages.slice(1); - } - - // merge with existing messages - const existingMessages: LangGraphPlatformMessage[] = state.messages || []; - const existingMessageIds = new Set(existingMessages.map((message) => message.id)); - const messageIds = new Set(messages.map((message) => message.id)); - - let removedMessages: RemoveMessage[] = []; - if (messages.length < existingMessages.length) { - // Messages were removed - removedMessages = existingMessages - .filter((m) => !messageIds.has(m.id)) - .map((m) => new RemoveMessage({ id: m.id! })); - } - - const newMessages = messages.filter((message) => !existingMessageIds.has(message.id)); - - return { - ...state, - messages: [...removedMessages, ...newMessages], - tools: [...(state.tools ?? []), ...tools], - }; - } - - startStep(nodeName: string) { - this.dispatchEvent({ - type: EventType.STEP_STARTED, - stepName: nodeName, - }); - this.activeRun!.nodeName = nodeName; - } - - endStep() { - if (!this.activeRun!.nodeName) { - throw new Error("No active step to end"); - } - this.dispatchEvent({ - type: EventType.STEP_FINISHED, - stepName: this.activeRun!.nodeName!, - }); - this.activeRun!.nodeName = undefined; - } -} - -export * from "./types"; +export * from './agent' +export class LangGraphHttpAgent extends HttpAgent {} \ No newline at end of file