chore: Release 1.7.7 (#4056)

# Changelog

## New Features:

- **Morph Tools:** Morph’s Fast Apply model as a Tool, it intelligently
merges your original code with update snippets at 98% accuracy and 4500+
tokens/second.

## Improvements:

- **LiteLLM File & Image Understanding**: Added support for file and
image inputs for `LiteLLM`.

## Bug Fixes:

- **OpenAIEmbedder() wrong dimensions for text-embedding-3-large:**
Automatically handles default `dimensions` length for
`text-embedding-3-small` as 1536 and `text-embedding-3-large` as 3072.

Author: dirkbrnd

cookbook/observability/langwatch_op.py

@@ -6,18 +6,17 @@
 3. Set your LangWatch API key as an environment variables:
   - export LANGWATCH_API_KEY=<your-key>
 """
+
 import os
-import langwatch
 
+import langwatch
 from agno.agent import Agent
 from agno.models.openai import OpenAIChat
 from agno.tools.yfinance import YFinanceTools
 from openinference.instrumentation.agno import AgnoInstrumentor
 
 # Initialize LangWatch and instrument Agno
-langwatch.setup(
-    instrumentors=[AgnoInstrumentor()]
-)
+langwatch.setup(instrumentors=[AgnoInstrumentor()])
 
 # Create and configure your Agno agent
 agent = Agent(
@@ -28,4 +27,4 @@
     debug_mode=True,
 )
 
-agent.print_response("What is the current price of Tesla?")
+agent.print_response("What is the current price of Tesla?")

cookbook/workflows_2/sync/01_basic_workflows/basic_workflow.py

@@ -1,21 +1,25 @@
-from pydantic import BaseModel, Field
-
 from agno.agent import Agent
 from agno.models.openai import OpenAIChat
 from agno.storage.postgres import PostgresStorage
 from agno.workflow.v2.step import Step
 from agno.workflow.v2.workflow import Workflow
+from pydantic import BaseModel, Field
 
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
+
 # Define response format
 class Source(BaseModel):
     name: str = Field(description="The name of the source")
     page_number: int = Field(description="The page number of the source")
 
+
 class Response(BaseModel):
     response: str = Field(description="The response to the user's query")
-    sources: list[Source] = Field(description="The sources used to generate the response")
+    sources: list[Source] = Field(
+        description="The sources used to generate the response"
+    )
+
 
 # Define agents
 information_agent = Agent(

libs/agno/agno/agent/agent.py

@@ -5282,7 +5282,7 @@ def get_relevant_docs_from_knowledge(
         """
         from agno.document import Document
 
-        if num_documents is None:
+        if num_documents is None and self.knowledge is not None:
             num_documents = self.knowledge.num_documents
         # Validate the filters against known valid filter keys
         if self.knowledge is not None:
@@ -5343,7 +5343,7 @@ async def aget_relevant_docs_from_knowledge(
         """Get relevant documents from knowledge base asynchronously."""
         from agno.document import Document
 
-        if num_documents is None:
+        if num_documents is None and self.knowledge is not None:
             num_documents = self.knowledge.num_documents
 
         # Validate the filters against known valid filter keys

libs/agno/agno/embedder/openai.py

@@ -26,11 +26,9 @@ class OpenAIEmbedder(Embedder):
     client_params: Optional[Dict[str, Any]] = None
     openai_client: Optional[OpenAIClient] = None
 
-    @property
-    def _dimensions(self) -> int:
-        if self.dimensions is not None:
-            return self.dimensions
-        return 3072 if self.id == "text-embedding-3-large" else 1536
+    def __post_init__(self):
+        if self.dimensions is None:
+            self.dimensions = 3072 if self.id == "text-embedding-3-large" else 1536
 
     @property
     def client(self) -> OpenAIClient:
@@ -57,7 +55,7 @@ def response(self, text: str) -> CreateEmbeddingResponse:
         if self.user is not None:
             _request_params["user"] = self.user
         if self.id.startswith("text-embedding-3"):
-            _request_params["dimensions"] = self._dimensions
+            _request_params["dimensions"] = self.dimensions
         if self.request_params:
             _request_params.update(self.request_params)
         return self.client.embeddings.create(**_request_params)

libs/agno/agno/team/team.py

@@ -7372,7 +7372,7 @@ def get_relevant_docs_from_knowledge(
         """Return a list of references from the knowledge base"""
         from agno.document import Document
 
-        if num_documents is None:
+        if num_documents is None and self.knowledge is not None:
             num_documents = self.knowledge.num_documents
 
         # Validate the filters against known valid filter keys
@@ -7429,7 +7429,7 @@ async def aget_relevant_docs_from_knowledge(
         """Get relevant documents from knowledge base asynchronously."""
         from agno.document import Document
 
-        if num_documents is None:
+        if num_documents is None and self.knowledge is not None:
             num_documents = self.knowledge.num_documents
 
         # Validate the filters against known valid filter keys

libs/agno/agno/tools/jina.py

@@ -1,12 +1,11 @@
-import json
 from os import getenv
 from typing import Any, Dict, List, Optional
 
 import httpx
 from pydantic import BaseModel, Field, HttpUrl
 
 from agno.tools import Toolkit
-from agno.utils.log import log_info, logger
+from agno.utils.log import logger
 
 
 class JinaReaderToolsConfig(BaseModel):

libs/agno/agno/tools/zep.py

@@ -118,7 +118,7 @@ def initialize(self) -> bool:
 
             # Create session associated with the user
             try:
-                self.zep_client.memory.add_session(session_id=self.session_id, user_id=self.user_id)
+                self.zep_client.thread.create(thread_id=self.session_id, user_id=self.user_id)
                 log_debug(f"Created session {self.session_id} for user {self.user_id}")
             except Exception as e:
                 log_debug(f"Session may already exist: {e}")
@@ -148,16 +148,16 @@ def add_zep_message(self, role: str, content: str) -> str:
 
         try:
             zep_message = ZepMessage(
-                role_type=role,
+                role=role,
                 content=content,
             )
 
             # Prepare ignore_roles if needed
             ignore_roles_list = ["assistant"] if self.ignore_assistant_messages else None
 
             # Add message to Zep memory
-            self.zep_client.memory.add(  # type: ignore
-                session_id=self.session_id,
+            self.zep_client.thread.add_messages(  # type: ignore
+                thread_id=self.session_id,
                 messages=[zep_message],
                 ignore_roles=ignore_roles_list,
             )
@@ -171,7 +171,7 @@ def get_zep_memory(self, memory_type: str = "context") -> str:
         """
         Retrieves the memory for the current Zep session.
         Args:
-            memory_type: The type of memory to retrieve ('context', 'messages', 'relevant_facts').
+            memory_type: The type of memory to retrieve ('context', 'messages').
         Returns:
             The requested memory content as a string, or an error string.
         """
@@ -181,21 +181,16 @@ def get_zep_memory(self, memory_type: str = "context") -> str:
 
         try:
             log_debug(f"Getting Zep memory for session {self.session_id}")
-            memory_data = self.zep_client.memory.get(session_id=self.session_id)  # type: ignore
 
             if memory_type == "context":
                 # Ensure context is a string
-                return memory_data.context or "No context available."
+                user_context = self.zep_client.thread.get_user_context(thread_id=self.session_id, mode="basic")  # type: ignore
+                log_debug(f"Memory data: {user_context}")
+                return user_context.context or "No context available."
             elif memory_type == "messages":
+                messages_list = self.zep_client.thread.get(thread_id=self.session_id)  # type: ignore
                 # Ensure messages string representation is returned
-                return str(memory_data.messages) if memory_data.messages else "No messages available."
-            elif memory_type == "relevant_facts":
-                # Return all relevant facts from memory
-                if memory_data.relevant_facts:
-                    facts_str = "\n".join([f"- {fact.fact}" for fact in memory_data.relevant_facts])
-                    return f"Relevant facts:\n{facts_str}"
-                else:
-                    return "No relevant facts available."
+                return str(messages_list.messages) if messages_list.messages else "No messages available."
             else:
                 warning_msg = f"Unsupported memory_type requested: {memory_type}. Returning empty string."
                 log_warning(warning_msg)
@@ -328,7 +323,7 @@ async def initialize(self) -> bool:
 
             # Create session associated with the user
             try:
-                await self.zep_client.memory.add_session(session_id=self.session_id, user_id=self.user_id)  # type: ignore
+                await self.zep_client.thread.create(thread_id=self.session_id, user_id=self.user_id)  # type: ignore
                 log_debug(f"Created session {self.session_id} for user {self.user_id}")
             except Exception as e:
                 log_debug(f"Session may already exist: {e}")
@@ -361,16 +356,16 @@ async def add_zep_message(self, role: str, content: str) -> str:
 
         try:
             zep_message = ZepMessage(
-                role_type=role,
+                role=role,
                 content=content,
             )
 
             # Prepare ignore_roles if needed
             ignore_roles_list = ["assistant"] if self.ignore_assistant_messages else None
 
             # Add message to Zep memory
-            await self.zep_client.memory.add(  # type: ignore
-                session_id=self.session_id,
+            await self.zep_client.thread.add_messages(  # type: ignore
+                thread_id=self.session_id,
                 messages=[zep_message],
                 ignore_roles=ignore_roles_list,
             )
@@ -384,7 +379,7 @@ async def get_zep_memory(self, memory_type: str = "context") -> str:
         """
         Retrieves the memory for the current Zep session.
         Args:
-            memory_type: The type of memory to retrieve ('context', 'messages', 'relevant_facts').
+            memory_type: The type of memory to retrieve ('context', 'messages').
         Returns:
             The requested memory content as a string, or an error string.
         """
@@ -396,25 +391,19 @@ async def get_zep_memory(self, memory_type: str = "context") -> str:
             return "Error: Zep client/session not initialized."
 
         try:
-            memory_data = await self.zep_client.memory.get(session_id=self.session_id)  # type: ignore
-
             if memory_type == "context":
                 # Ensure context is a string
-                return memory_data.context or "No context available."
+                user_context = await self.zep_client.thread.get_user_context(thread_id=self.session_id, mode="basic")  # type: ignore
+                log_debug(f"Memory data: {user_context}")
+                return user_context.context or "No context available."
             elif memory_type == "messages":
                 # Ensure messages string representation is returned
-                return str(memory_data.messages) if memory_data.messages else "No messages available."
-            elif memory_type == "relevant_facts":
-                # Return relevant facts from memory
-                if memory_data.relevant_facts:
-                    facts_str = "\n".join([f"- {fact.fact}" for fact in memory_data.relevant_facts])
-                    return f"Relevant facts:\n{facts_str}"
-                else:
-                    return "No relevant facts available."
+                messages_list = await self.zep_client.thread.get(thread_id=self.session_id)  # type: ignore
+                return str(messages_list.messages) if messages_list.messages else "No messages available."
             else:
                 warning_msg = f"Unsupported memory_type requested: {memory_type}. Returning context."
                 log_warning(warning_msg)
-                return memory_data.context or "No context available."
+                return "No context available."
 
         except Exception as e:
             error_msg = f"Failed to get Zep memory for session {self.session_id}: {e}"

libs/agno/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "agno"
-version = "1.7.6"
+version = "1.7.7"
 description = "Agno: a lightweight library for building Multi-Agent Systems"
 requires-python = ">=3.7,<4"
 readme = "README.md"
@@ -107,7 +107,7 @@ mcp = ["mcp"]
 mem0 = ["mem0ai"]
 newspaper = ["newspaper4k", "lxml_html_clean"]
 opencv = ["opencv-python"]
-psycopg = ["psycopg-binary"]
+psycopg = ["psycopg-binary", "psycopg"]
 todoist = ["todoist-api-python"]
 valyu = ["valyu"]
 webex = ["webexpythonsdk"]

libs/agno/tests/unit/tools/test_jina.py

@@ -227,8 +227,7 @@ def test_read_url_with_truncation(mock_httpx_get):
 
 
 @patch("agno.tools.jina.httpx.post")
-@patch("agno.tools.jina.log_info")
-def test_search_query_successful(mock_log_info, mock_httpx_post, sample_search_query_response):
+def test_search_query_successful(mock_httpx_post, sample_search_query_response):
     """Test successful search query"""
     # Setup mock response
     mock_response = MagicMock()