memory-agent 2.0.14

A Python library for advanced memory management in AI agent applications

memory-agent

The library allows managing both persistence and memory for a LangGraph agent.

memory-agent uses Redis as the backend for short‑term memory and long‑term persistence and semantic search.

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🔑 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

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🧠 Memory vs 🗃️ Persistence

Function	Database	Why
Memory	Redis	Performance, TTL, fast session context
Persistence	Redis	Vector search, long‑term storage

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📦 Installation

pip install memory-agent

For local use with Ollama or local embeddings:

• Install Ollama: https://ollama.ai

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▶️ 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 — 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):

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:

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.

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2) demo_config.py — Centralized configuration

This file defines all parameters used by the examples:

• Session identifiers:

  thread_id = "thread_demo"
  user_id = "user_demo"
  session_id = "session_demo"
  

• LLM model (Ollama):

  model_ollama = {
      "model": "llama3.1",
      "model_provider": "ollama",
      "api_key": None,
      "base_url": "http://localhost:11434",
      "temperature": 0.5,
  }
  

• Qdrant:

  qdrant_config = {
      "url": "http://localhost:6333",
  }
  

• Embeddings (via Ollama):

  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.

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3) 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):

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:

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.

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⚙️ 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

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Docker

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🧪 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.

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🛠️ 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="").

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📄 License MIT