# memory-agent
[](https://github.com/gzileni/memory-agent)
[](https://github.com/gzileni/memory-agent/stargazers)
[](https://github.com/gzileni/memory-agent/network)
The library allows managing both [**persistence**](https://langchain-ai.github.io/langgraph/how-tos/persistence/) and [**memory**](https://langchain-ai.github.io/langgraph/concepts/memory/#what-is-memory) for a **LangGraph** agent.
**memory-agent** uses **Redis** as the backend for **short‑term memory** and **long‑term persistence** and **semantic search**.
---
## 🔑 Key Features
- **Dual-layer memory system**
- **Short-term memory with Redis** → fast, volatile storage with TTL for active sessions.
- **Long-term persistence with Qdrant** → semantic search, embeddings, and cross‑session retrieval.
- **Integration with LangGraph** → stateful LLM agents with checkpoints and memory tools.
- **Multi-LLM**
- OpenAI (via `AgentOpenAI`)
- Ollama (via `AgentOllama`) for local inference
- **Flexible embeddings**
- OpenAI embeddings (default)
- Ollama embeddings (e.g., `nomic-embed-text`)
- **Automatic memory management**
- Summarization and reflection to compress context
- **Observability**
- Structured logging, compatible with **Grafana/Loki**
- **Easy installation & deployment**
- `pip install`
- [Docker‑ready](./docker/README.md)
---
## 🧠 Memory vs 🗃️ Persistence
| Function | Database | Why |
|-----------------|----------|-----|
| **Memory** | Redis | Performance, TTL, fast session context |
| **Persistence** | Redis | Vector search, long‑term storage |
---
## 📦 Installation
```bash
pip install memory-agent
```
For local use with **Ollama** or local embeddings:
- Install Ollama: https://ollama.ai
---
## ▶️ Usage examples (repository root)
The examples show how to configure the agent, send messages (including **streaming**) and share memory between different agents.
### 1) [`demo.py`](./demo.py) — Quick start with Ollama + memory
What it does:
1. Saves to context: `"My name is Giuseppe. Remember that."`
2. Asks a factoid: `"What is the capital of France?"` (streaming)
3. Retrieves from **short‑term memory**: `"What is my name?"` (streaming)
Essential snippet (simplified):
```python
from memory_agent.agent.ollama import AgentOllama
from demo_config import thread_id, user_id, session_id, model_ollama, redis_config, model_embedding_vs_config, model_embedding_config, qdrant_config, collection_config
agent = AgentOllama(
thread_id=thread_id,
user_id=user_id,
session_id=session_id,
model_config=model_ollama,
redis_config=redis_config,
qdrant_config=qdrant_config,
collection_config=collection_config,
embedding_store_config=model_embedding_vs_config,
embedding_model_config=model_embedding_config,
)
# Non-streaming call
text = await agent.invoke("My name is Giuseppe. Remember that.")
# Streaming call
async for token in agent.invoke_stream("What is the capital of France?"):
print(token, end="")
# Retrieve from context
async for token in agent.invoke_stream("What is my name?"):
print(token, end="")
```
Run:
```bash
python demo.py
```
What to expect:
- On the first request the agent stores the information (“Giuseppe”).
- On the third request the agent should answer with the previously provided name.
---
### 2) [`demo_config.py`](./demo_config.py) — Centralized configuration
This file defines **all parameters** used by the examples:
- **Session identifiers**:
```python
thread_id = "thread_demo"
user_id = "user_demo"
session_id = "session_demo"
```
- **LLM model (Ollama)**:
```python
model_ollama = {
"model": "llama3.1",
"model_provider": "ollama",
"api_key": None,
"base_url": "http://localhost:11434",
"temperature": 0.5,
}
```
- **Qdrant**:
```python
qdrant_config = {
"url": "http://localhost:6333",
}
```
- **Embeddings (via Ollama)**:
```python
model_embedding_config = {
"name": "nomic-embed-text",
"url": "http://localhost:11434"
}
```
- **Vector Store / Collection** (example): COSINE distance with `qdrant_client.http.models.Distance.COSINE`.
- **Redis**: connection/TTL parameters for short‑term memory.
> Modify these values to point to your Redis/Qdrant/Ollama instances. Other examples import directly from `demo_config.py`.
---
### 3) [`demo_mem_shared.py`](./demo_mem_shared.py) — Shared memory between two agents (LangGraph)
This example shows how **two distinct agents** can **share the same memory**.
The idea is to create two `AgentOllama` instances (e.g., `agent_1` and `agent_2`) that use **the same backends** (Redis + Qdrant) and **the same relevant identifiers** (e.g., collection, user, thread), so that what the first agent stores is available to the second.
Flow:
1. `agent_1` receives: `"My name is Giuseppe. Remember that."` and stores it.
2. `agent_2` receives: `"What is my name?"` and retrieves the answer from shared memory.
Essential snippet (simplified):
```python
agent_1 = AgentOllama(... shared ...)
agent_2 = AgentOllama(... shared ...)
await agent_1.invoke("My name is Giuseppe. Remember that.")
# The other agent pulls from the same memory
answer = await agent_2.invoke("What is my name?")
print(answer) # → "Your name is Giuseppe" (expected)
```
Run:
```bash
python demo_mem_shared.py
```
> This pattern is useful when multiple services/workers collaborate on the same user or conversation, leveraging **Redis** for short‑term state and **Qdrant** for persistence/semantic search across sessions.
---
## ⚙️ Prerequisites
- **Redis** running (used for short‑term memory)
- **Ollama** running (LLM and optionally embeddings)
- **OpenAI** API KEY to make request to OpenAI API
- Correct variables/URLs in `demo_config.py`
---
## [Docker](./docker/README.md)
---
## 🧪 Tips
- For **multi‑worker** environments, ensure `thread_id`, `user_id` and `session_id` and collection keys are consistent across processes that need to share memory.
- To separate memories of different agents, use **distinct session/thread IDs** or different collections in Qdrant.
- Tune model `temperature` and pruning/summarization parameters to balance cost/quality/context.
---
## 🛠️ Troubleshooting
- **Doesn't retrieve memory** → check Redis reachability and that IDs (thread/user/session) are consistent between requests.
- **Semantic search not effective** → verify embeddings are enabled (e.g., `nomic-embed-text`) and that Qdrant has the correct collection.
- **Streaming prints nothing** → ensure you iterate the `invoke_stream(...)` generator and do `print(token, end="")`.
---
## [📄 License MIT](./LICENSE.md)
Raw data
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"description": "# memory-agent \n\n[](https://github.com/gzileni/memory-agent) \n[](https://github.com/gzileni/memory-agent/stargazers) \n[](https://github.com/gzileni/memory-agent/network) \n\nThe library allows managing both [**persistence**](https://langchain-ai.github.io/langgraph/how-tos/persistence/) and [**memory**](https://langchain-ai.github.io/langgraph/concepts/memory/#what-is-memory) for a **LangGraph** agent.\n\n**memory-agent** uses **Redis** as the backend for **short\u2011term memory** and **long\u2011term persistence** and **semantic search**.\n\n\n\n---\n\n## \ud83d\udd11 Key Features\n\n- **Dual-layer memory system**\n - **Short-term memory with Redis** \u2192 fast, volatile storage with TTL for active sessions.\n - **Long-term persistence with Qdrant** \u2192 semantic search, embeddings, and cross\u2011session retrieval.\n- **Integration with LangGraph** \u2192 stateful LLM agents with checkpoints and memory tools.\n- **Multi-LLM**\n - OpenAI (via `AgentOpenAI`)\n - Ollama (via `AgentOllama`) for local inference\n- **Flexible embeddings**\n - OpenAI embeddings (default)\n - Ollama embeddings (e.g., `nomic-embed-text`)\n- **Automatic memory management**\n - Summarization and reflection to compress context\n- **Observability**\n - Structured logging, compatible with **Grafana/Loki**\n- **Easy installation & deployment**\n - `pip install`\n - [Docker\u2011ready](./docker/README.md)\n\n---\n\n## \ud83e\udde0 Memory vs \ud83d\uddc3\ufe0f Persistence\n\n| Function | Database | Why |\n|-----------------|----------|-----|\n| **Memory** | Redis | Performance, TTL, fast session context |\n| **Persistence** | Redis | Vector search, long\u2011term storage |\n\n---\n\n## \ud83d\udce6 Installation\n\n```bash\npip install memory-agent\n```\n\nFor local use with **Ollama** or local embeddings:\n- Install Ollama: https://ollama.ai\n\n---\n\n## \u25b6\ufe0f Usage examples (repository root)\n\nThe examples show how to configure the agent, send messages (including **streaming**) and share memory between different agents.\n\n### 1) [`demo.py`](./demo.py) \u2014 Quick start with Ollama + memory\n\nWhat it does:\n1. Saves to context: `\"My name is Giuseppe. Remember that.\"` \n2. Asks a factoid: `\"What is the capital of France?\"` (streaming) \n3. Retrieves from **short\u2011term memory**: `\"What is my name?\"` (streaming)\n\nEssential snippet (simplified):\n```python\nfrom memory_agent.agent.ollama import AgentOllama\nfrom demo_config import thread_id, user_id, session_id, model_ollama, redis_config, model_embedding_vs_config, model_embedding_config, qdrant_config, collection_config\n\nagent = AgentOllama(\n thread_id=thread_id,\n user_id=user_id,\n session_id=session_id,\n model_config=model_ollama,\n redis_config=redis_config,\n qdrant_config=qdrant_config,\n collection_config=collection_config,\n embedding_store_config=model_embedding_vs_config,\n embedding_model_config=model_embedding_config,\n)\n\n# Non-streaming call\ntext = await agent.invoke(\"My name is Giuseppe. Remember that.\")\n\n# Streaming call\nasync for token in agent.invoke_stream(\"What is the capital of France?\"):\n print(token, end=\"\")\n\n# Retrieve from context\nasync for token in agent.invoke_stream(\"What is my name?\"):\n print(token, end=\"\")\n```\n\nRun:\n```bash\npython demo.py\n```\n\nWhat to expect:\n- On the first request the agent stores the information (\u201cGiuseppe\u201d). \n- On the third request the agent should answer with the previously provided name.\n\n---\n\n### 2) [`demo_config.py`](./demo_config.py) \u2014 Centralized configuration\n\nThis file defines **all parameters** used by the examples:\n\n- **Session identifiers**:\n ```python\n thread_id = \"thread_demo\"\n user_id = \"user_demo\"\n session_id = \"session_demo\"\n ```\n- **LLM model (Ollama)**:\n ```python\n model_ollama = {\n \"model\": \"llama3.1\",\n \"model_provider\": \"ollama\",\n \"api_key\": None,\n \"base_url\": \"http://localhost:11434\",\n \"temperature\": 0.5,\n }\n ```\n- **Qdrant**:\n ```python\n qdrant_config = {\n \"url\": \"http://localhost:6333\",\n }\n ```\n- **Embeddings (via Ollama)**:\n ```python\n model_embedding_config = {\n \"name\": \"nomic-embed-text\",\n \"url\": \"http://localhost:11434\"\n }\n ```\n- **Vector Store / Collection** (example): COSINE distance with `qdrant_client.http.models.Distance.COSINE`. \n- **Redis**: connection/TTL parameters for short\u2011term memory.\n\n> Modify these values to point to your Redis/Qdrant/Ollama instances. Other examples import directly from `demo_config.py`.\n\n---\n\n### 3) [`demo_mem_shared.py`](./demo_mem_shared.py) \u2014 Shared memory between two agents (LangGraph)\n\nThis example shows how **two distinct agents** can **share the same memory**. \nThe idea is to create two `AgentOllama` instances (e.g., `agent_1` and `agent_2`) that use **the same backends** (Redis + Qdrant) and **the same relevant identifiers** (e.g., collection, user, thread), so that what the first agent stores is available to the second.\n\nFlow:\n1. `agent_1` receives: `\"My name is Giuseppe. Remember that.\"` and stores it. \n2. `agent_2` receives: `\"What is my name?\"` and retrieves the answer from shared memory.\n\nEssential snippet (simplified):\n```python\nagent_1 = AgentOllama(... shared ...)\nagent_2 = AgentOllama(... shared ...)\n\nawait agent_1.invoke(\"My name is Giuseppe. Remember that.\")\n\n# The other agent pulls from the same memory\nanswer = await agent_2.invoke(\"What is my name?\")\nprint(answer) # \u2192 \"Your name is Giuseppe\" (expected)\n```\n\nRun:\n```bash\npython demo_mem_shared.py\n```\n\n> This pattern is useful when multiple services/workers collaborate on the same user or conversation, leveraging **Redis** for short\u2011term state and **Qdrant** for persistence/semantic search across sessions.\n\n---\n\n## \u2699\ufe0f Prerequisites\n\n- **Redis** running (used for short\u2011term memory)\n- **Ollama** running (LLM and optionally embeddings)\n- **OpenAI** API KEY to make request to OpenAI API\n- Correct variables/URLs in `demo_config.py`\n\n---\n\n## [Docker](./docker/README.md)\n\n---\n\n## \ud83e\uddea Tips\n\n- For **multi\u2011worker** environments, ensure `thread_id`, `user_id` and `session_id` and collection keys are consistent across processes that need to share memory. \n- To separate memories of different agents, use **distinct session/thread IDs** or different collections in Qdrant. \n- Tune model `temperature` and pruning/summarization parameters to balance cost/quality/context.\n\n---\n\n## \ud83d\udee0\ufe0f Troubleshooting\n\n- **Doesn't retrieve memory** \u2192 check Redis reachability and that IDs (thread/user/session) are consistent between requests. \n- **Semantic search not effective** \u2192 verify embeddings are enabled (e.g., `nomic-embed-text`) and that Qdrant has the correct collection. \n- **Streaming prints nothing** \u2192 ensure you iterate the `invoke_stream(...)` generator and do `print(token, end=\"\")`.\n\n---\n\n## [\ud83d\udcc4 License MIT](./LICENSE.md)\n",
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