LangChain Apps Can Now Remember: Introducing Scoped Memory with Recallio

Recallio is now available as a native integration inside LangChain.

A plug-and-play memory API that gives your app scoped, queryable, persistent memory - without managing infra.

Why LangChain + Recallio?

LangChain gives you the building blocks for intelligent agents and workflows.
But “memory” in LangChain has traditionally been:

• Session-based
• Stored in local vars or ephemeral buffers
• Not built for scale, compliance, or user-specific scope

Recallio changes that.

Now, you can give your LangChain agent:

• Per-user scoped memory that persists across sessions
• Time-based TTL (e.g., forget after 30 days)
• Semantic recall (search memory like a mini RAG)
• Audit logs, exportability, and compliance hooks for enterprise needs
• Optional summarization for long memory histories

All via one API call:

from langchain_recallio.memory import RecallioMemory

Why Vector DBs Don’t Solve Memory

A vector DB is not memory. It’s storage.

If you’ve tried Pinecone, Qdrant, or Weaviate to “remember” things, you’ve likely had to:

• Build custom embedding pipelines
• Manually handle user/session scoping
• Layer on TTL logic
• Implement access controls yourself

You’re not building a vector store.
You’re trying to give your app a brain.

Recallio is memory-first, not vector-first.
It abstracts away the infra and gives you:

POST /memory/write
POST /memory/recall

Why OpenAI Memory Isn’t the Answer Either

OpenAI has memory—but only for their interface.

• It’s not accessible via API
• It’s tied to a single user-chat
• It doesn’t support TTL, export, scoping, or role-specific recall

Recallio is vendor-neutral, model-agnostic, and API-native.

Use GPT, Claude, LLaMA—Recallio stores and retrieves context across all of them.

Use Cases You Can Now Build in LangChain (with memory that lasts)

• AI project manager → remembers decisions per team, per project
• GPT-powered tutor → remembers each student’s learning gaps
• Legal assistant agent → recalls case facts across weeks
• Customer support bot → knows previous tickets per user
• Research agent → retrieves facts and context scoped to a topic

All of these require memory that is:

• Scoped
• Persistent
• Compliant
• Queryable

Now you don’t have to build it yourself.

Try It Now: LangChain + Recallio

1. Install

pip install langchain-recallio

2. Import:

from langchain_recallio.memory import RecallioMemory

3. Configure scoped memory:

memory = RecallioMemory(user_id="user123", ttl_days=30)

4. Store or recall:

memory.save_context({ "input": "What's the plan for Q3?" }, { "output": "Prepare roadmap." })

Ready to plug memory into your LangChain app?

• Try it on LangChain
• API
• Try now – no credit card, free tier available