feat: implemented caching of agent

src/backend/langflow/cache/utils.py

@@ -0,0 +1,49 @@
+import contextlib
+import hashlib
+import json
+import os
+from pathlib import Path
+import tempfile
+import dill
+
+PREFIX = "langflow_cache"
+
+
+def clear_old_cache_files(max_cache_size: int = 10):
+    cache_dir = Path(tempfile.gettempdir())
+    cache_files = list(cache_dir.glob(f"{PREFIX}_*.dill"))
+
+    if len(cache_files) > max_cache_size:
+        cache_files_sorted_by_mtime = sorted(
+            cache_files, key=lambda x: x.stat().st_mtime, reverse=True
+        )
+
+        for cache_file in cache_files_sorted_by_mtime[max_cache_size:]:
+            with contextlib.suppress(OSError):
+                os.remove(cache_file)
+
+
+def remove_position_info(node):
+    node.pop("position", None)
+
+
+def compute_hash(graph_data):
+    for node in graph_data["nodes"]:
+        remove_position_info(node)
+
+    cleaned_graph_json = json.dumps(graph_data, sort_keys=True)
+    return hashlib.sha256(cleaned_graph_json.encode("utf-8")).hexdigest()
+
+
+def save_cache(hash_val, chat_data):
+    cache_path = Path(tempfile.gettempdir()) / f"{PREFIX}_{hash_val}.dill"
+    with cache_path.open("wb") as cache_file:
+        dill.dump(chat_data, cache_file)
+
+
+def load_cache(hash_val):
+    cache_path = Path(tempfile.gettempdir()) / f"{PREFIX}_{hash_val}.dill"
+    if cache_path.exists():
+        with cache_path.open("rb") as cache_file:
+            return dill.load(cache_file)
+    return None

src/backend/langflow/interface/run.py

@@ -2,30 +2,48 @@ import contextlib
 import io
 import re
 from typing import Any, Dict
+from langflow.cache.utils import compute_hash, load_cache, save_cache
 
 from langflow.graph.graph import Graph
 from langflow.interface import loading
 from langflow.utils import payload
 
 
+def load_langchain_object(data_graph):
+    computed_hash = compute_hash(data_graph)
+
+    # Load langchain_object from cache if it exists
+    langchain_object = load_cache(computed_hash)
+    if langchain_object is None:
+        nodes = data_graph["nodes"]
+        # Add input variables
+        nodes = payload.extract_input_variables(nodes)
+        # Nodes, edges and root node
+        edges = data_graph["edges"]
+        graph = Graph(nodes, edges)
+
+        langchain_object = graph.build()
+
+    return computed_hash, langchain_object
+
+
 def process_graph(data_graph: Dict[str, Any]):
     """
     Process graph by extracting input variables and replacing ZeroShotPrompt
     with PromptTemplate,then run the graph and return the result and thought.
     """
-    nodes = data_graph["nodes"]
-    # Add input variables
-    # ? Is this necessary?
-    nodes = payload.extract_input_variables(nodes)
-    # Nodes, edges and root node
-    edges = data_graph["edges"]
-    graph = Graph(nodes, edges)
-
-    langchain_object = graph.build()
+    # Load langchain object
+    computed_hash, langchain_object = load_langchain_object(data_graph)
     message = data_graph["message"]
-    # Process json
+
+    # Generate result and thought
     result, thought = get_result_and_thought_using_graph(langchain_object, message)
 
+    # Save langchain_object to cache
+    # We have to save it here because if the
+    # memory is updated we need to keep the new values
+    save_cache(computed_hash, langchain_object)
+
     return {
         "result": result,
         "thought": re.sub(