Merge remote-tracking branch 'origin/dev' into streaming

.github/workflows/test.yml

@@ -30,4 +30,4 @@ jobs:
         run: poetry install
       - name: Run unit tests
         run: |
-          make test
+          make tests

Makefile

@@ -1,4 +1,4 @@
-.PHONY: all format lint build
+.PHONY: all format lint build build_frontend install_frontend run_frontend run_backend dev help tests coverage
 
 all: help
 
@@ -8,7 +8,7 @@ coverage:
 		--cov-report xml \
 		--cov-report term-missing:skip-covered
 
-test:
+tests:
 	poetry run pytest tests
 
 format:
@@ -71,3 +71,6 @@ help:
 	@echo 'build               - build the frontend static files and package the project'
 	@echo 'publish             - build the frontend static files and package the project and publish it to PyPI'
 	@echo 'dev                 - run the project in development mode with docker compose'
+	@echo 'tests               - run the tests'
+	@echo 'coverage            - run the tests and generate a coverage report'
+	@echo '----'

poetry.lock

@@ -1708,14 +1708,14 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "langchain"
-version = "0.0.158"
+version = "0.0.160"
 description = "Building applications with LLMs through composability"
 category = "main"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain-0.0.158-py3-none-any.whl", hash = "sha256:2121874e3c72db8987467e77c1890d53e146e5f90ab249e82194187568285e43"},
+    {file = "langchain-0.0.160-py3-none-any.whl", hash = "sha256:e09310cc07c38a5e6777bd3d30c51227e2da775d4267d3fb72697a4de3931da3"},
-    {file = "langchain-0.0.158.tar.gz", hash = "sha256:29b578a6c3ccb97b63ffa93451c590e03aa55c19a834c0a2d2f8573139ca5e90"},
+    {file = "langchain-0.0.160.tar.gz", hash = "sha256:427c142e2fdb9f9ef9f2352a2c82db28db4d11c61f02b20dd33e77d850fe81cc"},
 ]
 
 [package.dependencies]
@@ -1728,12 +1728,12 @@ openapi-schema-pydantic = ">=1.2,<2.0"
 pydantic = ">=1,<2"
 PyYAML = ">=5.4.1"
 requests = ">=2,<3"
-SQLAlchemy = ">=1.3,<3"
+SQLAlchemy = ">=1.4,<3"
 tenacity = ">=8.1.0,<9.0.0"
 tqdm = ">=4.48.0"
 
 [package.extras]
-all = ["O365 (>=2.0.26,<3.0.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.2.6,<0.3.0)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=3,<4)", "deeplake (>=3.3.0,<4.0.0)", "duckduckgo-search (>=2.8.6,<3.0.0)", "elasticsearch (>=8,<9)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jina (>=3.14,<4.0)", "jinja2 (>=3,<4)", "lancedb (>=0.1,<0.2)", "lark (>=1.1.5,<2.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "networkx (>=2.6.3,<3.0.0)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "openai (>=0,<1)", "opensearch-py (>=2.0.0,<3.0.0)", "pexpect (>=4.8.0,<5.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "pyvespa (>=0.33.0,<0.34.0)", "qdrant-client (>=1.1.2,<2.0.0)", "redis (>=4,<5)", "sentence-transformers (>=2,<3)", "spacy (>=3,<4)", "tensorflow-text (>=2.11.0,<3.0.0)", "tiktoken (>=0.3.2,<0.4.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)"]
+all = ["O365 (>=2.0.26,<3.0.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.2.6,<0.3.0)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=3,<4)", "deeplake (>=3.3.0,<4.0.0)", "duckduckgo-search (>=2.8.6,<3.0.0)", "elasticsearch (>=8,<9)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jina (>=3.14,<4.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lancedb (>=0.1,<0.2)", "lark (>=1.1.5,<2.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "networkx (>=2.6.3,<3.0.0)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "openai (>=0,<1)", "opensearch-py (>=2.0.0,<3.0.0)", "pexpect (>=4.8.0,<5.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "pyvespa (>=0.33.0,<0.34.0)", "qdrant-client (>=1.1.2,<2.0.0)", "redis (>=4,<5)", "sentence-transformers (>=2,<3)", "spacy (>=3,<4)", "tensorflow-text (>=2.11.0,<3.0.0)", "tiktoken (>=0.3.2,<0.4.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)"]
 azure = ["azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "openai (>=0,<1)"]
 cohere = ["cohere (>=3,<4)"]
 embeddings = ["sentence-transformers (>=2,<3)"]
@@ -1743,13 +1743,13 @@ qdrant = ["qdrant-client (>=1.1.2,<2.0.0)"]
 
 [[package]]
 name = "lit"
-version = "16.0.2"
+version = "16.0.3"
 description = "A Software Testing Tool"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "lit-16.0.2.tar.gz", hash = "sha256:d743ef55cb58764bba85768c502e2d68d87aeb4303d508a18abaa8a35077ab25"},
+    {file = "lit-16.0.3.tar.gz", hash = "sha256:25524fe51fa3261212cfd86a8891429ed0460e247384c5a2001612d08e362e00"},
 ]
 
 [[package]]
@@ -4555,18 +4555,18 @@ files = [
 
 [[package]]
 name = "types-requests"
-version = "2.29.0.0"
+version = "2.30.0.0"
 description = "Typing stubs for requests"
 category = "dev"
 optional = false
 python-versions = "*"
 files = [
-    {file = "types-requests-2.29.0.0.tar.gz", hash = "sha256:c86f4a955d943d2457120dbe719df24ef0924e11177164d10a0373cf311d7b4d"},
+    {file = "types-requests-2.30.0.0.tar.gz", hash = "sha256:dec781054324a70ba64430ae9e62e7e9c8e4618c185a5cb3f87a6738251b5a31"},
-    {file = "types_requests-2.29.0.0-py3-none-any.whl", hash = "sha256:4cf6e323e856c779fbe8815bb977a5bf5d6c5034713e4c17ff2a9a20610f5b27"},
+    {file = "types_requests-2.30.0.0-py3-none-any.whl", hash = "sha256:c6cf08e120ca9f0dc4fa4e32c3f953c3fba222bcc1db6b97695bce8da1ba9864"},
 ]
 
 [package.dependencies]
-types-urllib3 = "<1.27"
+types-urllib3 = "*"
 
 [[package]]
 name = "types-urllib3"
@@ -4891,6 +4891,21 @@ files = [
 [package.extras]
 test = ["pytest (>=6.0.0)"]
 
+[[package]]
+name = "wikipedia"
+version = "1.4.0"
+description = "Wikipedia API for Python"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
+    {file = "wikipedia-1.4.0.tar.gz", hash = "sha256:db0fad1829fdd441b1852306e9856398204dc0786d2996dd2e0c8bb8e26133b2"},
+]
+
+[package.dependencies]
+beautifulsoup4 = "*"
+requests = ">=2.0.0,<3.0.0"
+
 [[package]]
 name = "xlsxwriter"
 version = "3.1.0"
@@ -5069,4 +5084,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9"
-content-hash = "5e7c877648cffc95b5312d87c82afa7bb6a9ce331492769c93efcb8f99252d9a"
+content-hash = "5aca7cac07e5b678d4b01d994227da5ef06f4e8da6c2a854bab7e7e6cd17ccb5"

pyproject.toml

@@ -49,6 +49,7 @@ psycopg2-binary = "^2.9.6"
 pyarrow = "^11.0.0"
 websockets = "^11.0.2"
 tiktoken = "^0.3.3"
+wikipedia = "^1.4.0"
 
 [tool.poetry.group.dev.dependencies]
 black = "^23.1.0"

src/backend/langflow/api/endpoints.py

@@ -5,6 +5,12 @@ from fastapi import APIRouter, HTTPException
 
 from langflow.interface.run import process_graph_cached
 from langflow.interface.types import build_langchain_types_dict
+from langflow.api.schemas import (
+    ExportedFlow,
+    GraphData,
+    PredictRequest,
+    PredictResponse,
+)
 
 # build router
 router = APIRouter()
@@ -16,10 +22,14 @@ def get_all():
     return build_langchain_types_dict()
 
 
-@router.post("/predict")
+@router.post("/predict", response_model=PredictResponse)
-def get_load(data: Dict[str, Any]):
+async def get_load(predict_request: PredictRequest):
     try:
-        return process_graph_cached(data)
+        exported_flow: ExportedFlow = predict_request.exported_flow
+        graph_data: GraphData = exported_flow.data
+        data = graph_data.dict()
+        response = process_graph_cached(data, predict_request.message)
+        return PredictResponse(result=response.get("result", ""))
     except Exception as e:
         # Log stack trace
         logger.exception(e)

src/backend/langflow/api/schemas.py

@@ -1,8 +1,37 @@
-from typing import Any, Union
+from typing import Any, Union, Dict, List
 
 from pydantic import BaseModel, validator
 
 
+class GraphData(BaseModel):
+    """Data inside the exported flow."""
+
+    nodes: List[Dict[str, Any]]
+    edges: List[Dict[str, Any]]
+
+
+class ExportedFlow(BaseModel):
+    """Exported flow from LangFlow."""
+
+    description: str
+    name: str
+    id: str
+    data: GraphData
+
+
+class PredictRequest(BaseModel):
+    """Predict request schema."""
+
+    message: str
+    exported_flow: ExportedFlow
+
+
+class PredictResponse(BaseModel):
+    """Predict response schema."""
+
+    result: str
+
+
 class ChatMessage(BaseModel):
     """Chat message schema."""
 

src/backend/langflow/api/validate.py

@@ -49,5 +49,5 @@ def post_validate_node(node_id: str, data: dict):
             return str(node.params)
         raise Exception(f"Node {node_id} not found")
     except Exception as e:
-        logger.exception(e)
+        logger.error(e)
         raise HTTPException(status_code=500, detail=str(e)) from e

src/backend/langflow/cache/base.py

@@ -48,6 +48,7 @@ def memoize_dict(maxsize=128):
             cache.clear()
 
         wrapper.clear_cache = clear_cache  # type: ignore
+        wrapper.cache = cache  # type: ignore
         return wrapper
 
     return decorator

src/backend/langflow/config.yaml

@@ -51,6 +51,7 @@ tools:
   - BingSearchRun
   - GoogleSearchRun
   - GoogleSearchResults
+  - GoogleSerperRun
   - JsonListKeysTool
   - JsonGetValueTool
   - PythonREPLTool

src/backend/langflow/interface/agents/custom.py

@@ -27,7 +27,7 @@ from langchain.agents.agent_toolkits.vectorstore.prompt import (
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS as SQL_FORMAT_INSTRUCTIONS
 from langchain.base_language import BaseLanguageModel
-from langchain.llms.base import BaseLLM
+
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.sql_database import SQLDatabase
 from langchain.tools.python.tool import PythonAstREPLTool
@@ -63,7 +63,7 @@ class JsonAgent(AgentExecutor):
             llm=llm,
             prompt=prompt,
         )
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)  # type: ignore
         return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
 
     def run(self, *args, **kwargs):
@@ -110,7 +110,7 @@ class CSVAgent(AgentExecutor):
             prompt=partial_prompt,
         )
         tool_names = {tool.name for tool in tools}
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)  # type: ignore
 
         return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
 
@@ -134,7 +134,7 @@ class VectorStoreAgent(AgentExecutor):
 
     @classmethod
     def from_toolkit_and_llm(
-        cls, llm: BaseLLM, vectorstoreinfo: VectorStoreInfo, **kwargs: Any
+        cls, llm: BaseLanguageModel, vectorstoreinfo: VectorStoreInfo, **kwargs: Any
     ):
         """Construct a vectorstore agent from an LLM and tools."""
 
@@ -147,7 +147,7 @@ class VectorStoreAgent(AgentExecutor):
             prompt=prompt,
         )
         tool_names = {tool.name for tool in tools}
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)  # type: ignore
         return AgentExecutor.from_agent_and_tools(
             agent=agent, tools=tools, verbose=True
         )
@@ -171,7 +171,9 @@ class SQLAgent(AgentExecutor):
         super().__init__(*args, **kwargs)
 
     @classmethod
-    def from_toolkit_and_llm(cls, llm: BaseLLM, database_uri: str, **kwargs: Any):
+    def from_toolkit_and_llm(
+        cls, llm: BaseLanguageModel, database_uri: str, **kwargs: Any
+    ):
         """Construct a sql agent from an LLM and tools."""
         db = SQLDatabase.from_uri(database_uri)
         toolkit = SQLDatabaseToolkit(db=db, llm=llm)
@@ -213,7 +215,7 @@ class SQLAgent(AgentExecutor):
             prompt=prompt,
         )
         tool_names = {tool.name for tool in tools}  # type: ignore
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)  # type: ignore
         return AgentExecutor.from_agent_and_tools(
             agent=agent,
             tools=tools,  # type: ignore
@@ -256,7 +258,7 @@ class VectorStoreRouterAgent(AgentExecutor):
             prompt=prompt,
         )
         tool_names = {tool.name for tool in tools}
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)  # type: ignore
         return AgentExecutor.from_agent_and_tools(
             agent=agent, tools=tools, verbose=True
         )
@@ -275,7 +277,7 @@ class InitializeAgent(AgentExecutor):
     @classmethod
     def initialize(
         cls,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         tools: List[Tool],
         agent: str,
         memory: Optional[BaseChatMemory] = None,

src/backend/langflow/interface/agents/prebuilt.py

@@ -33,7 +33,7 @@ class MalfoyAgent(AgentExecutor):
             llm=llm,
             prompt=prompt,
         )
-        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)  # type: ignore
         return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
 
     def run(self, *args, **kwargs):

src/backend/langflow/interface/importing/utils.py

@@ -7,11 +7,9 @@ from langchain import PromptTemplate
 from langchain.agents import Agent
 from langchain.chains.base import Chain
 from langchain.chat_models.base import BaseChatModel
-from langchain.llms.base import BaseLLM
+from langchain.base_language import BaseLanguageModel
 from langchain.tools import BaseTool
 
-from langflow.interface.tools.base import tool_creator
-
 
 def import_module(module_path: str) -> Any:
     """Import module from module path"""
@@ -100,15 +98,19 @@ def import_agent(agent: str) -> Agent:
     return import_class(f"langchain.agents.{agent}")
 
 
-def import_llm(llm: str) -> BaseLLM:
+def import_llm(llm: str) -> BaseLanguageModel:
     """Import llm from llm name"""
     return import_class(f"langchain.llms.{llm}")
 
 
 def import_tool(tool: str) -> BaseTool:
     """Import tool from tool name"""
+    from langflow.interface.tools.base import tool_creator
 
-    return tool_creator.type_to_loader_dict[tool]["fcn"]
+    if tool in tool_creator.type_to_loader_dict:
+        return tool_creator.type_to_loader_dict[tool]["fcn"]
+
+    return import_class(f"langchain.tools.{tool}")
 
 
 def import_chain(chain: str) -> Type[Chain]:

src/backend/langflow/interface/loading.py

@@ -15,7 +15,7 @@ from langchain.agents.loading import load_agent_from_config
 from langchain.agents.tools import Tool
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.loading import load_chain_from_config
-from langchain.llms.base import BaseLLM
+from langchain.base_language import BaseLanguageModel
 from langchain.llms.loading import load_llm_from_config
 from pydantic import ValidationError
 
@@ -74,12 +74,10 @@ def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
         return loaded_toolkit
     elif base_type == "embeddings":
         # ? Why remove model from params?
-
         try:
             params.pop("model")
         except KeyError:
             pass
-
         # remove all params that are not in class_object.__fields__
         try:
             return class_object(**params)
@@ -188,7 +186,7 @@ def load_langchain_type_from_config(config: Dict[str, Any]):
 
 def load_agent_executor_from_config(
     config: dict,
-    llm: Optional[BaseLLM] = None,
+    llm: Optional[BaseLanguageModel] = None,
     tools: Optional[list[Tool]] = None,
     callback_manager: Optional[BaseCallbackManager] = None,
     **kwargs: Any,

src/backend/langflow/interface/run.py

@@ -101,13 +101,12 @@ def process_graph(data_graph: Dict[str, Any]):
     return {"result": str(result), "thought": thought.strip()}
 
 
-def process_graph_cached(data_graph: Dict[str, Any]):
+def process_graph_cached(data_graph: Dict[str, Any], message: str):
     """
     Process graph by extracting input variables and replacing ZeroShotPrompt
     with PromptTemplate,then run the graph and return the result and thought.
     """
     # Load langchain object
-    message = data_graph.pop("message", "")
     is_first_message = len(data_graph.get("chatHistory", [])) == 0
     langchain_object = load_or_build_langchain_object(data_graph, is_first_message)
     logger.debug("Loaded langchain object")
@@ -120,7 +119,7 @@ def process_graph_cached(data_graph: Dict[str, Any]):
 
     # Generate result and thought
     logger.debug("Generating result and thought")
-    result, thought = get_result_and_steps(langchain_object, message)
+    result, thought = get_result_and_thought(langchain_object, message)
     logger.debug("Generated result and thought")
     return {"result": str(result), "thought": thought.strip()}
 
@@ -247,7 +246,7 @@ async def get_result_and_steps(langchain_object, message: str, **kwargs):
     return result, thought
 
 
-def async_get_result_and_steps(langchain_object, message: str):
+def get_result_and_thought(langchain_object, message: str):
     """Get result and thought from extracted json"""
     try:
         if hasattr(langchain_object, "verbose"):
@@ -302,34 +301,6 @@ def async_get_result_and_steps(langchain_object, message: str):
     return result, thought
 
 
-def get_result_and_thought(extracted_json: Dict[str, Any], message: str):
-    """Get result and thought from extracted json"""
-    try:
-        langchain_object = loading.load_langchain_type_from_config(
-            config=extracted_json
-        )
-        with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer):
-            output = langchain_object(message)
-            intermediate_steps = (
-                output.get("intermediate_steps", []) if isinstance(output, dict) else []
-            )
-            result = (
-                output.get(langchain_object.output_keys[0])
-                if isinstance(output, dict)
-                else output
-            )
-
-            if intermediate_steps:
-                thought = format_intermediate_steps(intermediate_steps)
-            else:
-                thought = output_buffer.getvalue()
-
-    except Exception as e:
-        result = f"Error: {str(e)}"
-        thought = ""
-    return result, thought
-
-
 def format_intermediate_steps(intermediate_steps):
     formatted_chain = "> Entering new AgentExecutor chain...\n"
     for step in intermediate_steps:

src/backend/langflow/interface/tools/base.py

@@ -29,7 +29,9 @@ TOOL_INPUTS = {
         placeholder="",
         value="",
     ),
-    "llm": TemplateField(field_type="BaseLLM", required=True, is_list=False, show=True),
+    "llm": TemplateField(
+        field_type="BaseLanguageModel", required=True, is_list=False, show=True
+    ),
     "func": TemplateField(
         field_type="function",
         required=True,
@@ -65,6 +67,7 @@ class ToolCreator(LangChainTypeCreator):
     def type_to_loader_dict(self) -> Dict:
         if self.tools_dict is None:
             all_tools = {}
+
             for tool, tool_fcn in ALL_TOOLS_NAMES.items():
                 tool_params = get_tool_params(tool_fcn)
                 tool_name = tool_params.get("name", tool)

src/backend/langflow/interface/tools/constants.py

@@ -1,3 +1,4 @@
+from langchain import tools
 from langchain.agents import Tool
 from langchain.agents.load_tools import (
     _BASE_TOOLS,
@@ -5,50 +6,16 @@ from langchain.agents.load_tools import (
     _EXTRA_OPTIONAL_TOOLS,
     _LLM_TOOLS,
 )
-from langchain.tools.bing_search.tool import BingSearchRun
+from langchain.tools.json.tool import JsonSpec
-from langchain.tools.google_search.tool import GoogleSearchResults, GoogleSearchRun
-from langchain.tools.json.tool import JsonGetValueTool, JsonListKeysTool, JsonSpec
-from langchain.tools.python.tool import PythonAstREPLTool, PythonREPLTool
-from langchain.tools.requests.tool import (
-    RequestsDeleteTool,
-    RequestsGetTool,
-    RequestsPatchTool,
-    RequestsPostTool,
-    RequestsPutTool,
-)
-from langchain.tools.sql_database.tool import (
-    InfoSQLDatabaseTool,
-    ListSQLDatabaseTool,
-    QueryCheckerTool,
-    QuerySQLDataBaseTool,
-)
-from langchain.tools.wikipedia.tool import WikipediaQueryRun
-from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
 
+from langflow.interface.importing.utils import import_class
 from langflow.interface.tools.custom import PythonFunction
 
 FILE_TOOLS = {"JsonSpec": JsonSpec}
 CUSTOM_TOOLS = {"Tool": Tool, "PythonFunction": PythonFunction}
-OTHER_TOOLS = {
+
-    "QuerySQLDataBaseTool": QuerySQLDataBaseTool,
+OTHER_TOOLS = {tool: import_class(f"langchain.tools.{tool}") for tool in tools.__all__}
-    "InfoSQLDatabaseTool": InfoSQLDatabaseTool,
+
-    "ListSQLDatabaseTool": ListSQLDatabaseTool,
-    "QueryCheckerTool": QueryCheckerTool,
-    "BingSearchRun": BingSearchRun,
-    "GoogleSearchRun": GoogleSearchRun,
-    "GoogleSearchResults": GoogleSearchResults,
-    "JsonListKeysTool": JsonListKeysTool,
-    "JsonGetValueTool": JsonGetValueTool,
-    "PythonREPLTool": PythonREPLTool,
-    "PythonAstREPLTool": PythonAstREPLTool,
-    "RequestsGetTool": RequestsGetTool,
-    "RequestsPostTool": RequestsPostTool,
-    "RequestsPatchTool": RequestsPatchTool,
-    "RequestsPutTool": RequestsPutTool,
-    "RequestsDeleteTool": RequestsDeleteTool,
-    "WikipediaQueryRun": WikipediaQueryRun,
-    "WolframAlphaQueryRun": WolframAlphaQueryRun,
-}
 ALL_TOOLS_NAMES = {
     **_BASE_TOOLS,
     **_LLM_TOOLS,  # type: ignore

src/frontend/dev.Dockerfile

@@ -15,7 +15,10 @@ WORKDIR /home/node/app
 COPY --chown=node:node . ./
 
 COPY ./set_proxy.sh .
-RUN chmod +x set_proxy.sh && ./set_proxy.sh
+RUN chmod +x set_proxy.sh && \
+    cat set_proxy.sh | tr -d '\r' > set_proxy_unix.sh && \
+    chmod +x set_proxy_unix.sh && \
+    ./set_proxy_unix.sh
 
 USER node
 

src/frontend/set_proxy.sh

@@ -7,4 +7,4 @@ packagejson=$(cat package.json)
 
 packagejson=$(echo "$packagejson" | jq ".proxy = \"$backend_url\"")
 
-echo "$packagejson" > package.json
+echo "$packagejson" > package.json

src/frontend/src/contexts/tabsContext.tsx

@@ -1,10 +1,10 @@
 import {
-	createContext,
+  createContext,
-	useEffect,
+  useEffect,
-	useState,
+  useState,
-	useRef,
+  useRef,
-	ReactNode,
+  ReactNode,
-	useContext,
+  useContext,
 } from "react";
 import { FlowType } from "../types/flow";
 import { LangFlowState, TabsContextType } from "../types/tabs";
@@ -15,221 +15,221 @@ import { APITemplateType, TemplateVariableType } from "../types/api";
 const { v4: uuidv4 } = require("uuid");
 
 const TabsContextInitialValue: TabsContextType = {
-	save: () => {},
+  save: () => {},
-	tabIndex: 0,
+  tabIndex: 0,
-	setTabIndex: (index: number) => {},
+  setTabIndex: (index: number) => {},
-	flows: [],
+  flows: [],
-	removeFlow: (id: string) => {},
+  removeFlow: (id: string) => {},
-	addFlow: (flowData?: any) => {},
+  addFlow: (flowData?: any) => {},
-	updateFlow: (newFlow: FlowType) => {},
+  updateFlow: (newFlow: FlowType) => {},
-	incrementNodeId: () => 0,
+  incrementNodeId: () => 0,
-	downloadFlow: (flow: FlowType) => {},
+  downloadFlow: (flow: FlowType) => {},
-	uploadFlow: () => {},
+  uploadFlow: () => {},
-	hardReset: () => {},
+  hardReset: () => {},
 };
 
 export const TabsContext = createContext<TabsContextType>(
-	TabsContextInitialValue
+  TabsContextInitialValue
 );
 
 export function TabsProvider({ children }: { children: ReactNode }) {
-	const { setNoticeData } = useContext(alertContext);
+  const { setNoticeData } = useContext(alertContext);
-	const [tabIndex, setTabIndex] = useState(0);
+  const [tabIndex, setTabIndex] = useState(0);
-	const [flows, setFlows] = useState<Array<FlowType>>([]);
+  const [flows, setFlows] = useState<Array<FlowType>>([]);
-	const [id, setId] = useState("");
+  const [id, setId] = useState("");
-	const { templates } = useContext(typesContext);
+  const { templates } = useContext(typesContext);
 
-	const newNodeId = useRef(0);
+  const newNodeId = useRef(0);
-	function incrementNodeId() {
+  function incrementNodeId() {
-		newNodeId.current = newNodeId.current + 1;
+    newNodeId.current = newNodeId.current + 1;
-		return newNodeId.current;
+    return newNodeId.current;
-	}
+  }
-	function save() {
+  function save() {
-		if (flows.length !== 0)
+    if (flows.length !== 0)
-			window.localStorage.setItem(
+      window.localStorage.setItem(
-				"tabsData",
+        "tabsData",
-				JSON.stringify({ tabIndex, flows, id, nodeId: newNodeId.current })
+        JSON.stringify({ tabIndex, flows, id, nodeId: newNodeId.current })
-			);
+      );
-	}
+  }
-	useEffect(() => {
+  useEffect(() => {
-		//save tabs locally
+    //save tabs locally
-		save();
+    save();
-	}, [flows, id, tabIndex, newNodeId]);
+  }, [flows, id, tabIndex, newNodeId]);
 
-	useEffect(() => {
+  useEffect(() => {
-		//get tabs locally saved
+    //get tabs locally saved
-		let cookie = window.localStorage.getItem("tabsData");
+    let cookie = window.localStorage.getItem("tabsData");
-		if (cookie && Object.keys(templates).length > 0) {
+    if (cookie && Object.keys(templates).length > 0) {
-			let cookieObject: LangFlowState = JSON.parse(cookie);
+      let cookieObject: LangFlowState = JSON.parse(cookie);
-			cookieObject.flows.forEach((flow) => {
+      cookieObject.flows.forEach((flow) => {
-				flow.data.nodes.forEach((node) => {
+        flow.data.nodes.forEach((node) => {
-					if (Object.keys(templates[node.data.type]["template"]).length > 0) {
+          if (Object.keys(templates[node.data.type]["template"]).length > 0) {
-						node.data.node.template = updateTemplate(
+            node.data.node.template = updateTemplate(
-							templates[node.data.type][
+              templates[node.data.type][
-								"template"
+                "template"
-							] as unknown as APITemplateType,
+              ] as unknown as APITemplateType,
 
-							node.data.node.template as APITemplateType
+              node.data.node.template as APITemplateType
-						);
+            );
-					}
+          }
-				});
+        });
-			});
+      });
-			setTabIndex(cookieObject.tabIndex);
+      setTabIndex(cookieObject.tabIndex);
-			setFlows(cookieObject.flows);
+      setFlows(cookieObject.flows);
-			setId(cookieObject.id);
+      setId(cookieObject.id);
-			newNodeId.current = cookieObject.nodeId;
+      newNodeId.current = cookieObject.nodeId;
-		}
+    }
-	}, [templates]);
+  }, [templates]);
-	function hardReset() {
+  function hardReset() {
-		newNodeId.current = 0;
+    newNodeId.current = 0;
-		setTabIndex(0);
+    setTabIndex(0);
-		setFlows([]);
+    setFlows([]);
-		setId("");
+    setId("");
-	}
+  }
 
-	/**
+  /**
-	 * Downloads the current flow as a JSON file
+   * Downloads the current flow as a JSON file
-	 */
+   */
-	function downloadFlow(flow: FlowType) {
+  function downloadFlow(flow: FlowType) {
-		// create a data URI with the current flow data
+    // create a data URI with the current flow data
-		const jsonString = `data:text/json;chatset=utf-8,${encodeURIComponent(
+    const jsonString = `data:text/json;chatset=utf-8,${encodeURIComponent(
-			JSON.stringify(flow)
+      JSON.stringify(flow)
-		)}`;
+    )}`;
 
-		// create a link element and set its properties
+    // create a link element and set its properties
-		const link = document.createElement("a");
+    const link = document.createElement("a");
-		link.href = jsonString;
+    link.href = jsonString;
-		link.download = `${normalCaseToSnakeCase(flows[tabIndex].name)}.json`;
+    link.download = `${flows[tabIndex].name}.json`;
 
-		// simulate a click on the link element to trigger the download
+    // simulate a click on the link element to trigger the download
-		link.click();
+    link.click();
-		setNoticeData({
+    setNoticeData({
-			title: "Warning: Critical data,JSON file may including API keys.",
+      title: "Warning: Critical data,JSON file may including API keys.",
-		});
+    });
-	}
+  }
 
-	/**
+  /**
-	 * Creates a file input and listens to a change event to upload a JSON flow file.
+   * Creates a file input and listens to a change event to upload a JSON flow file.
-	 * If the file type is application/json, the file is read and parsed into a JSON object.
+   * If the file type is application/json, the file is read and parsed into a JSON object.
-	 * The resulting JSON object is passed to the addFlow function.
+   * The resulting JSON object is passed to the addFlow function.
-	 */
+   */
-	function uploadFlow() {
+  function uploadFlow() {
-		// create a file input
+    // create a file input
-		const input = document.createElement("input");
+    const input = document.createElement("input");
-		input.type = "file";
+    input.type = "file";
-		// add a change event listener to the file input
+    // add a change event listener to the file input
-		input.onchange = (e: Event) => {
+    input.onchange = (e: Event) => {
-			// check if the file type is application/json
+      // check if the file type is application/json
-			if ((e.target as HTMLInputElement).files[0].type === "application/json") {
+      if ((e.target as HTMLInputElement).files[0].type === "application/json") {
-				// get the file from the file input
+        // get the file from the file input
-				const file = (e.target as HTMLInputElement).files[0];
+        const file = (e.target as HTMLInputElement).files[0];
-				// read the file as text
+        // read the file as text
-				file.text().then((text) => {
+        file.text().then((text) => {
-					// parse the text into a JSON object
+          // parse the text into a JSON object
-					let flow: FlowType = JSON.parse(text);
+          let flow: FlowType = JSON.parse(text);
 
-					addFlow(flow);
+          addFlow(flow);
-				});
+        });
-			}
+      }
-		};
+    };
-		// trigger the file input click event to open the file dialog
+    // trigger the file input click event to open the file dialog
-		input.click();
+    input.click();
-	}
+  }
-	/**
+  /**
-	 * Removes a flow from an array of flows based on its id.
+   * Removes a flow from an array of flows based on its id.
-	 * Updates the state of flows and tabIndex using setFlows and setTabIndex hooks.
+   * Updates the state of flows and tabIndex using setFlows and setTabIndex hooks.
-	 * @param {string} id - The id of the flow to remove.
+   * @param {string} id - The id of the flow to remove.
-	 */
+   */
-	function removeFlow(id: string) {
+  function removeFlow(id: string) {
-		setFlows((prevState) => {
+    setFlows((prevState) => {
-			const newFlows = [...prevState];
+      const newFlows = [...prevState];
-			const index = newFlows.findIndex((flow) => flow.id === id);
+      const index = newFlows.findIndex((flow) => flow.id === id);
-			if (index >= 0) {
+      if (index >= 0) {
-				if (index === tabIndex) {
+        if (index === tabIndex) {
-					setTabIndex(flows.length - 2);
+          setTabIndex(flows.length - 2);
-					newFlows.splice(index, 1);
+          newFlows.splice(index, 1);
-				} else {
+        } else {
-					let flowId = flows[tabIndex].id;
+          let flowId = flows[tabIndex].id;
-					newFlows.splice(index, 1);
+          newFlows.splice(index, 1);
-					setTabIndex(newFlows.findIndex((flow) => flow.id === flowId));
+          setTabIndex(newFlows.findIndex((flow) => flow.id === flowId));
-				}
+        }
-			}
+      }
-			return newFlows;
+      return newFlows;
-		});
+    });
-	}
+  }
-	/**
+  /**
-	 * Add a new flow to the list of flows.
+   * Add a new flow to the list of flows.
-	 * @param flow Optional flow to add.
+   * @param flow Optional flow to add.
-	 */
+   */
-	function addFlow(flow?: FlowType) {
+  function addFlow(flow?: FlowType) {
-		// Get data from the flow or set it to null if there's no flow provided.
+    // Get data from the flow or set it to null if there's no flow provided.
-		const data = flow?.data ? flow.data : null;
+    const data = flow?.data ? flow.data : null;
-		const description = flow?.description ? flow.description : "";
+    const description = flow?.description ? flow.description : "";
 
-		if (data) {
+    if (data) {
-			data.nodes.forEach((node) => {
+      data.nodes.forEach((node) => {
-				if (Object.keys(templates[node.data.type]["template"]).length > 0) {
+        if (Object.keys(templates[node.data.type]["template"]).length > 0) {
-					node.data.node.template = updateTemplate(
+          node.data.node.template = updateTemplate(
-						templates[node.data.type]["template"] as unknown as APITemplateType,
+            templates[node.data.type]["template"] as unknown as APITemplateType,
-						node.data.node.template as APITemplateType
+            node.data.node.template as APITemplateType
-					);
+          );
-				}
+        }
-			});
+      });
-		}
+    }
-		// Create a new flow with a default name if no flow is provided.
+    // Create a new flow with a default name if no flow is provided.
-		let newFlow: FlowType = {
+    let newFlow: FlowType = {
-			description,
+      description,
-			name: flow?.name ?? "New Flow",
+      name: flow?.name ?? "New Flow",
-			id: id.toString(),
+      id: id.toString(),
-			data,
+      data,
-		};
+    };
 
-		// Increment the ID counter.
+    // Increment the ID counter.
-		setId(uuidv4());
+    setId(uuidv4());
 
-		// Add the new flow to the list of flows.
+    // Add the new flow to the list of flows.
-		setFlows((prevState) => {
+    setFlows((prevState) => {
-			const newFlows = [...prevState, newFlow];
+      const newFlows = [...prevState, newFlow];
-			return newFlows;
+      return newFlows;
-		});
+    });
 
-		// Set the tab index to the new flow.
+    // Set the tab index to the new flow.
-		setTabIndex(flows.length);
+    setTabIndex(flows.length);
-	}
+  }
-	/**
+  /**
-	 * Updates an existing flow with new data
+   * Updates an existing flow with new data
-	 * @param newFlow - The new flow object containing the updated data
+   * @param newFlow - The new flow object containing the updated data
-	 */
+   */
-	function updateFlow(newFlow: FlowType) {
+  function updateFlow(newFlow: FlowType) {
-		setFlows((prevState) => {
+    setFlows((prevState) => {
-			const newFlows = [...prevState];
+      const newFlows = [...prevState];
-			const index = newFlows.findIndex((flow) => flow.id === newFlow.id);
+      const index = newFlows.findIndex((flow) => flow.id === newFlow.id);
-			if (index !== -1) {
+      if (index !== -1) {
-				newFlows[index].description = newFlow.description ?? "";
+        newFlows[index].description = newFlow.description ?? "";
-				newFlows[index].data = newFlow.data;
+        newFlows[index].data = newFlow.data;
-				newFlows[index].name = newFlow.name;
+        newFlows[index].name = newFlow.name;
-			}
+      }
-			return newFlows;
+      return newFlows;
-		});
+    });
-	}
+  }
 
-	return (
+  return (
-		<TabsContext.Provider
+    <TabsContext.Provider
-			value={{
+      value={{
-				save,
+        save,
-				hardReset,
+        hardReset,
-				tabIndex,
+        tabIndex,
-				setTabIndex,
+        setTabIndex,
-				flows,
+        flows,
-				incrementNodeId,
+        incrementNodeId,
-				removeFlow,
+        removeFlow,
-				addFlow,
+        addFlow,
-				updateFlow,
+        updateFlow,
-				downloadFlow,
+        downloadFlow,
-				uploadFlow,
+        uploadFlow,
-			}}
+      }}
-		>
+    >
-			{children}
+      {children}
-		</TabsContext.Provider>
+    </TabsContext.Provider>
-	);
+  );
 }

tests/test_agents_template.py

@@ -48,7 +48,6 @@ def test_zero_shot_agent(client: TestClient):
         "type": "Tool",
         "list": True,
         "advanced": False,
-        "value": [],
     }
 
 

tests/test_cache.py

@@ -1,10 +1,11 @@
 import json
-import tempfile
-from pathlib import Path
 
 import pytest
-from langflow.cache.base import PREFIX, save_cache
+from langflow.interface.run import (
-from langflow.interface.run import load_langchain_object
+    build_graph,
+    build_langchain_object_with_caching,
+    load_or_build_langchain_object,
+)
 
 
 def get_graph(_type="basic"):
@@ -40,26 +41,44 @@ def langchain_objects_are_equal(obj1, obj2):
     return str(obj1) == str(obj2)
 
 
-def test_cache_creation(basic_data_graph):
+# Test load_or_build_langchain_object
-    # Compute hash for the input data_graph
+def test_load_or_build_langchain_object_first_message_true(basic_data_graph):
-    # Call process_graph function to build and cache the langchain_object
+    build_langchain_object_with_caching.clear_cache()
-    is_first_message = True
+    graph = load_or_build_langchain_object(basic_data_graph, is_first_message=True)
-    computed_hash, langchain_object = load_langchain_object(
+    assert graph is not None
-        basic_data_graph, is_first_message=is_first_message
-    )
-    save_cache(computed_hash, langchain_object, is_first_message)
-    # Check if the cache file exists
-    cache_file = Path(tempfile.gettempdir()) / f"{PREFIX}/{computed_hash}.dill"
-
-    assert cache_file.exists()
 
 
-def test_cache_reuse(basic_data_graph):
+def test_load_or_build_langchain_object_first_message_false(basic_data_graph):
-    # Call process_graph function to build and cache the langchain_object
+    graph = load_or_build_langchain_object(basic_data_graph, is_first_message=False)
-    result1 = load_langchain_object(basic_data_graph)
+    assert graph is not None
 
-    # Call process_graph function again to use the cached langchain_object
-    result2 = load_langchain_object(basic_data_graph)
 
-    # Compare the results to ensure the same langchain_object was used
+# Test build_langchain_object_with_caching
-    assert langchain_objects_are_equal(result1, result2)
+def test_build_langchain_object_with_caching(basic_data_graph):
+    build_langchain_object_with_caching.clear_cache()
+    graph = build_langchain_object_with_caching(basic_data_graph)
+    assert graph is not None
+
+
+# Test build_graph
+def test_build_graph(basic_data_graph):
+    graph = build_graph(basic_data_graph)
+    assert graph is not None
+    assert len(graph.nodes) == len(basic_data_graph["nodes"])
+    assert len(graph.edges) == len(basic_data_graph["edges"])
+
+
+# Test cache size limit
+def test_cache_size_limit(basic_data_graph):
+    build_langchain_object_with_caching.clear_cache()
+    for i in range(11):
+        modified_data_graph = basic_data_graph.copy()
+        nodes = modified_data_graph["nodes"]
+        node_id = nodes[0]["id"]
+        # Now we replace all instances ode node_id with a new id in the json
+        json_string = json.dumps(modified_data_graph)
+        modified_json_string = json_string.replace(node_id, f"{node_id}_{i}")
+        modified_data_graph_new_id = json.loads(modified_json_string)
+        build_langchain_object_with_caching(modified_data_graph_new_id)
+
+    assert len(build_langchain_object_with_caching.cache) == 10

tests/test_chains_template.py

@@ -191,7 +191,7 @@ def test_llm_checker_chain(client: TestClient):
         "multiline": False,
         "password": False,
         "name": "llm",
-        "type": "BaseLLM",
+        "type": "BaseLanguageModel",
         "list": False,
         "advanced": False,
     }

tests/test_graph.py

@@ -1,7 +1,7 @@
 from typing import Type, Union
 
 import pytest
-from langchain.agents import AgentExecutor
+from langchain.chains.base import Chain
 from langchain.llms.fake import FakeListLLM
 from langflow.graph import Edge, Graph, Node
 from langflow.graph.nodes import (
@@ -15,7 +15,7 @@ from langflow.graph.nodes import (
     WrapperNode,
 )
 from langflow.interface.run import get_result_and_steps
-from langflow.utils.payload import build_json, get_root_node
+from langflow.utils.payload import get_root_node
 
 # Test cases for the graph module
 
@@ -102,32 +102,13 @@ def test_get_node_neighbors_basic(basic_graph):
     # We need to check if there is a Chain in the one of the neighbors'
     # data attribute in the type key
     assert any(
-        "Chain" in neighbor.data["type"] for neighbor, val in neighbors.items() if val
+        "ConversationBufferMemory" in neighbor.data["type"]
-    )
-    # assert Serper Search is in the neighbors
-    assert any(
-        "Serper" in neighbor.data["type"] for neighbor, val in neighbors.items() if val
-    )
-    # Now on to the Chain's neighbors
-    chain = next(
-        neighbor
         for neighbor, val in neighbors.items()
-        if "Chain" in neighbor.data["type"] and val
-    )
-    chain_neighbors = basic_graph.get_node_neighbors(chain)
-    assert chain_neighbors is not None
-    assert isinstance(chain_neighbors, dict)
-    # Check if there is a LLM in the chain's neighbors
-    assert any(
-        "OpenAI" in neighbor.data["type"]
-        for neighbor, val in chain_neighbors.items()
         if val
     )
-    # Chain should have a Prompt as a neighbor
+
     assert any(
-        "Prompt" in neighbor.data["type"]
+        "OpenAI" in neighbor.data["type"] for neighbor, val in neighbors.items() if val
-        for neighbor, val in chain_neighbors.items()
-        if val
     )
 
 
@@ -209,7 +190,7 @@ def test_get_root_node(basic_graph, complex_graph):
     root = get_root_node(basic_graph)
     assert root is not None
     assert isinstance(root, Node)
-    assert root.data["type"] == "ZeroShotAgent"
+    assert root.data["type"] == "TimeTravelGuideChain"
     # For complex example, the root node is a ZeroShotAgent too
     assert isinstance(complex_graph, Graph)
     root = get_root_node(complex_graph)
@@ -218,26 +199,6 @@ def test_get_root_node(basic_graph, complex_graph):
     assert root.data["type"] == "ZeroShotAgent"
 
 
-def test_build_json(basic_graph):
-    """Test building JSON from graph"""
-    assert isinstance(basic_graph, Graph)
-    root = get_root_node(basic_graph)
-    json_data = build_json(root, basic_graph)
-    assert isinstance(json_data, dict)
-    assert json_data["_type"] == "zero-shot-react-description"
-    assert isinstance(json_data["llm_chain"], dict)
-    assert json_data["llm_chain"]["_type"] == "llm_chain"
-    assert json_data["llm_chain"]["memory"] is None
-    assert json_data["llm_chain"]["verbose"] is False
-    assert isinstance(json_data["llm_chain"]["prompt"], dict)
-    assert isinstance(json_data["llm_chain"]["llm"], dict)
-    assert json_data["llm_chain"]["output_key"] == "text"
-    assert isinstance(json_data["allowed_tools"], list)
-    assert all(isinstance(tool, dict) for tool in json_data["allowed_tools"])
-    assert isinstance(json_data["return_values"], list)
-    assert all(isinstance(val, str) for val in json_data["return_values"])
-
-
 def test_validate_edges(basic_graph):
     """Test validating edges"""
 
@@ -269,45 +230,11 @@ def test_build_params(basic_graph):
     assert all(edge.matched_type in edge.source_types for edge in basic_graph.edges)
     # Get the root node
     root = get_root_node(basic_graph)
-    # Root node is a ZeroShotAgent
+    # Root node is a TimeTravelGuideChain
-    # which requires an llm_chain, allowed_tools and return_values
+    # which requires an llm and memory
     assert isinstance(root.params, dict)
-    assert "llm_chain" in root.params
+    assert "llm" in root.params
-    assert "allowed_tools" in root.params
+    assert "memory" in root.params
-    assert "return_values" in root.params
-    # The llm_chain should be a Node
-    assert isinstance(root.params["llm_chain"], Node)
-    # The allowed_tools should be a list of Nodes
-    assert isinstance(root.params["allowed_tools"], list)
-    assert all(isinstance(tool, Node) for tool in root.params["allowed_tools"])
-    # The return_values is of type str so it should be a list of strings
-    assert isinstance(root.params["return_values"], list)
-    assert all(isinstance(val, str) for val in root.params["return_values"])
-    # The llm_chain should have a prompt and llm
-    llm_chain_node = root.params["llm_chain"]
-    assert isinstance(llm_chain_node.params, dict)
-    assert "prompt" in llm_chain_node.params
-    assert "llm" in llm_chain_node.params
-    # The prompt should be a Node
-    assert isinstance(llm_chain_node.params["prompt"], Node)
-    # The llm should be a Node
-    assert isinstance(llm_chain_node.params["llm"], Node)
-    # The prompt should have format_insctructions, suffix, prefix
-    prompt_node = llm_chain_node.params["prompt"]
-    assert isinstance(prompt_node.params, dict)
-    assert "format_instructions" in prompt_node.params
-    assert "suffix" in prompt_node.params
-    assert "prefix" in prompt_node.params
-    # All of them should be of type str
-    assert isinstance(prompt_node.params["format_instructions"], str)
-    assert isinstance(prompt_node.params["suffix"], str)
-    assert isinstance(prompt_node.params["prefix"], str)
-    # The llm should have a model
-    llm_node = llm_chain_node.params["llm"]
-    assert isinstance(llm_node.params, dict)
-    assert "model_name" in llm_node.params
-    # The model should be a str
-    assert isinstance(llm_node.params["model_name"], str)
 
 
 def test_build(basic_graph, complex_graph, openapi_graph):
@@ -324,18 +251,18 @@ def assert_agent_was_built(graph):
     # Build the Agent
     result = graph.build()
     # The agent should be a AgentExecutor
-    assert isinstance(result, AgentExecutor)
+    assert isinstance(result, Chain)
 
 
-def test_agent_node_build(basic_graph):
+def test_agent_node_build(complex_graph):
-    agent_node = get_node_by_type(basic_graph, AgentNode)
+    agent_node = get_node_by_type(complex_graph, AgentNode)
     assert agent_node is not None
     built_object = agent_node.build()
     assert built_object is not None
 
 
-def test_tool_node_build(basic_graph):
+def test_tool_node_build(complex_graph):
-    tool_node = get_node_by_type(basic_graph, ToolNode)
+    tool_node = get_node_by_type(complex_graph, ToolNode)
     assert tool_node is not None
     built_object = tool_node.build()
     assert built_object is not None

tests/test_loading.py

@@ -1,7 +1,7 @@
 import json
 
 import pytest
-from langchain.agents import AgentExecutor
+from langchain.chains.base import Chain
 from langflow import load_flow_from_json
 from langflow.graph import Graph
 from langflow.utils.payload import get_root_node
@@ -11,7 +11,7 @@ def test_load_flow_from_json():
     """Test loading a flow from a json file"""
     loaded = load_flow_from_json(pytest.BASIC_EXAMPLE_PATH)
     assert loaded is not None
-    assert isinstance(loaded, AgentExecutor)
+    assert isinstance(loaded, Chain)
 
 
 def test_get_root_node():