ZepMemory implementation for LlamaIndex

llama-index-integrations/memory/llama-index-memory-zep/.gitignore

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llama-index-integrations/memory/llama-index-memory-zep/BUILD

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+poetry_requirements(
+    name="poetry",
+)

llama-index-integrations/memory/llama-index-memory-zep/README.md

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+`Note`: Before running or testing the code in this notebook, ensure that you have set up the `Zep server`.
+
+# 🧠 Zep Memory Integration with LlamaIndex Agents
+
+This notebook demonstrates how to use [Zep Memory](https://docs.getzep.com/) with various agent types from [LlamaIndex](https://github.com/jerryjliu/llama_index), including:
+
+- `SimpleChatEngine`
+- `ReActAgent`
+- `FunctionCallingAgent`
+- `AgentWorkflow`
+
+Both **synchronous** and **asynchronous** memory clients are supported and demonstrated.
+
+---
+
+## 📦 Install Dependencies
+
+```bash
+# pip install llama_index_memory_zep
+# pip install llama-index zep-python openai
+```
+
+---
+
+## 🔐 Environment Setup
+
+```python
+import os
+
+os.environ["OPENAI_API_KEY"] = "sk-..."  # Replace with your actual OpenAI key
+```
+
+---
+
+## 📚 Import Required Packages
+
+```python
+import uuid
+from zep_python.client import Zep, AsyncZep
+from llamaindex.memory.zep import ZepMemory
+from llama_index.llms.openai import OpenAI
+```
+
+---
+
+## 🔁 Initialize Clients and IDs
+
+```python
+zep_client = Zep(api_key="mysupersecretkey", base_url="http://localhost:8000")
+azep_client = AsyncZep(
+    api_key="mysupersecretkey", base_url="http://localhost:8000"
+)
+
+user_id = uuid.uuid4().hex
+session_id = uuid.uuid4().hex
+
+# Register user
+zep_client.user.add(user_id=user_id)
+
+# Start memory session
+zep_client.memory.add_session(session_id=session_id, user_id=user_id)
+```
+
+---
+
+## 🧠 Initialize Zep Memory
+
+```python
+memory = ZepMemory.from_defaults(
+    zep_client=zep_client, session_id=session_id, user_id=user_id
+)
+amemory = ZepMemory.from_defaults(
+    zep_client=azep_client, session_id=session_id, user_id=user_id
+)
+```
+
+---
+
+## 🤖 LLM Setup
+
+```python
+llm = OpenAI(model="gpt-4o-mini")
+```
+
+---
+
+## 💬 SimpleChatEngine
+
+### ✅ Sync Example
+
+```python
+from llama_index.core.chat_engine.simple import SimpleChatEngine
+
+agent = SimpleChatEngine.from_defaults(llm=llm, memory=memory)
+
+agent.chat("Hi, my name is Younis")
+agent.chat("What was my name?")
+```
+
+### 🌀 Async Example
+
+```python
+agent = SimpleChatEngine.from_defaults(llm=llm, memory=amemory)
+
+agent.chat("Hi, my name is Younis")
+agent.chat("What was my name?")
+```
+
+---
+
+## 🔁 ReActAgent
+
+### ✅ Sync Example
+
+```python
+from llama_index.core.agent import ReActAgent
+
+agent = ReActAgent.from_tools(tools=[], llm=llm, memory=memory, verbose=True)
+
+agent.chat("What's the capital of France?")
+agent.chat("What was my previous question?")
+```
+
+### 🌀 Async Example
+
+```python
+agent = ReActAgent.from_tools(tools=[], llm=llm, memory=amemory, verbose=True)
+
+agent.chat("What's the capital of France?")
+agent.chat("What was my previous question?")
+```
+
+---
+
+## ⚙️ FunctionCallingAgent
+
+### ✅ Sync Example
+
+```python
+from llama_index.core.agent import FunctionCallingAgent
+
+agent = FunctionCallingAgent.from_tools(
+    [], llm=llm, memory=memory, verbose=True
+)
+
+agent.chat("Hi, my name is Younis")
+agent.chat("What was my name?")
+```
+
+### 🌀 Async Example
+
+```python
+agent = FunctionCallingAgent.from_tools(
+    [], llm=llm, memory=amemory, verbose=True
+)
+
+agent.chat("Hi, my name is Younis")
+agent.chat("What was my name?")
+```
+
+---
+
+## 🧩 AgentWorkflow
+
+```python
+from llama_index.core.agent.workflow import (
+    AgentWorkflow,
+    AgentStream,
+    FunctionAgent,
+)
+
+research_agent = FunctionAgent(
+    name="ResearchAgent",
+    description="Responsible for synthesizing structured responses.",
+    system_prompt="""
+    You are the ResearchAgent. Your task is to compile and synthesize information based on context.
+    Be systematic, transparent, and clear in your responses.
+    """,
+    llm=llm,
+    tools=[],
+    verbose=True,
+)
+
+agent_workflow = AgentWorkflow(
+    agents=[research_agent],
+    root_agent=research_agent.name,
+    initial_state={"answer_content": ""},
+)
+
+# Run with sync memory
+handler = agent_workflow.run(
+    user_msg="Explain the heuristic function in detail.", memory=memory
+)
+
+# Stream response
+current_agent = None
+async for event in handler.stream_events():
+    if (
+        hasattr(event, "current_agent_name")
+        and event.current_agent_name != current_agent
+    ):
+        current_agent = event.current_agent_name
+        print(f"\\n{'='*50}\\n🤖 Agent: {current_agent}\\n{'='*50}\\n")
+    if isinstance(event, AgentStream):
+        print(event.delta, end="", flush=True)
+```
+
+### 🌀 Async Memory?
+
+Just replace `memory=memory` with `memory=amemory`.
+
+---
+
+## ✅ Final Notes
+
+- This example assumes your Zep server is running locally at `http://localhost:8000`
+- All memory-aware agents should now retain previous conversation turns
+- Use this setup as a base for tool-enhanced agents, longer workflows, or integrations

llama-index-integrations/memory/llama-index-memory-zep/llama_index/memory/zep/BUILD

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+python_sources()

llama-index-integrations/memory/llama-index-memory-zep/llama_index/memory/zep/__init__.py

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+from llama_index.memory.zep.base import ZepMemory
+
+__all__ = ["ZepMemory"]