LangMem vs. Graphlit: LangChain Memory Framework vs. Full-Stack Semantic Platform

LangChain won the framework wars. Now comes the hard part: making agents that actually remember.

If you've built agents with LangChain, you've hit the memory wall. Context windows run out. Conversations reset. Your agent forgets who the user is, what they discussed yesterday, and why they prefer TypeScript over Python.

Enter LangMem, LangChain's answer to persistent agent memory. It gives LangChain developers primitives for storing, enriching, and retrieving memories across conversations. If you're already deep in the LangChain ecosystem, LangMem provides native integration with LangGraph's storage layer.

But there's a different approach: Graphlit, a standalone semantic memory platform that works with any agent framework — LangChain included. Instead of memory primitives you assemble yourself, Graphlit provides comprehensive infrastructure: multimodal ingestion, entity extraction, knowledge graphs, and semantic search in a managed platform.

If you're building agents with LangChain and evaluating memory options, this comparison will help you understand when LangMem's framework-native approach makes sense — and when a full-stack memory platform is the better choice.

TL;DR — Quick Feature Comparison

Understanding the Platforms

What is LangMem?

LangMem is LangChain's memory framework — a set of primitives for giving agents long-term memory that persists across conversations. It's designed to integrate seamlessly with LangGraph, LangChain's orchestration layer.

LangMem provides three key components:

1. Core Memory API: Storage-agnostic interface that works with any backend (Postgres, Redis, in-memory, custom stores)
2. Memory Tools: Agent-accessible tools for recording and searching memories during conversations ("hot path")
3. Background Memory Manager: Async service that extracts, consolidates, and enriches memories outside the conversation flow

The philosophy: give developers building blocks for memory. You control storage, define memory schemas, and decide how memory gets formed and retrieved.

Example usage:

from langmem import create_manage_memory_tool, create_search_memory_tool
from langgraph.store.memory import InMemoryStore

store = InMemoryStore(index={"dims": 1536, "embed": "openai:text-embedding-3-small"})

agent = create_react_agent(
    "anthropic:claude-3-5-sonnet-latest",
    tools=[
        create_manage_memory_tool(namespace=("memories",)),
        create_search_memory_tool(namespace=("memories",))
    ],
    store=store
)

LangMem is powerful for developers who want full control over memory architecture and already have LangChain expertise.

What is Graphlit?

Graphlit is a semantic memory platform — managed infrastructure that handles the entire memory stack for AI agents. We're framework-agnostic: whether you use LangChain, LlamaIndex, AutoGPT, or build custom agents, Graphlit provides the same unified API.

Instead of primitives you assemble, Graphlit provides:

• Multimodal ingestion via 30+ connectors (Slack, GitHub, documents, audio, video)
• Automated entity extraction using Schema.org standards
• Knowledge graphs that connect ideas across all content
• Hybrid semantic search (vector + keyword + graph traversal)
• Managed infrastructure (no databases to operate)

Graphlit works with LangChain agents via our SDKs or through MCP (Model Context Protocol), but we're not LangChain-specific. This independence gives you flexibility: switch agent frameworks without rebuilding your memory layer.

Where LangMem gives you memory primitives, Graphlit gives you memory infrastructure.

Memory Model: Primitives vs. Comprehensive Infrastructure

LangMem provides a storage-agnostic core API that lets you define memory schemas:

# Define memory types
memories = [
    {"type": "semantic", "content": "User prefers TypeScript"},
    {"type": "episodic", "content": "Discussed API redesign on 10/15"},
    {"type": "procedural", "content": "Always run tests before deployment"}
]

You decide:

• What content to store as memories
• When memories should be formed (during conversations or in background)
• Where to store them (Postgres, Redis, vector DB, etc.)
• How to structure memory namespaces

This flexibility is powerful but requires architectural decisions. You're building a memory system, not just using one.

Graphlit provides comprehensive semantic infrastructure where memory emerges from content understanding:

Ingest: Slack thread about API redesign
Extract: Entities [Person: Alice], [Project: API v2], [Date: Oct 15]
Graph: Alice → (discussed) → API v2 → (scheduled) → Oct 15
Index: Vector embeddings + keyword index + graph edges

You don't define memory schemas — we extract structured knowledge automatically. Agents query this rich semantic layer without managing storage, schemas, or enrichment pipelines.

Trade-off: LangMem gives more control, Graphlit gives faster time-to-value.

Integration: Framework-Native vs. Framework-Agnostic

LangMem is built specifically for LangChain and LangGraph:

• Native integration with BaseStore interface
• Memory tools work with create_react_agent
• Background manager uses LangGraph primitives
• Checkpointing and state management leverage LangGraph features

If you're already invested in LangChain, this integration is seamless. But if you want to use other frameworks or switch frameworks later, you'll need to adapt LangMem's abstractions.

Graphlit is framework-agnostic:

• REST/GraphQL API works with any agent framework
• SDKs for Python, JavaScript/TypeScript, C#
• MCP (Model Context Protocol) support for universal agent integration
• No lock-in to specific orchestration frameworks

This means you can:

• Use Graphlit with LangChain today, switch to another framework tomorrow
• Mix agent frameworks in the same system
• Build custom agents without framework dependencies

If framework flexibility matters, Graphlit's independence is valuable.

Data Ingestion: Build vs. Buy

LangMem focuses on conversation memory — storing messages and structured data from agent interactions. For other data sources, you build ingestion logic:

# You write code to ingest documents, emails, Slack, etc.
def ingest_slack_messages(channel_id):
    messages = fetch_slack_messages(channel_id)
    for msg in messages:
        # Extract relevant info
        # Format as memory
        # Store via LangMem API

This gives you control but requires engineering effort for each data source.

Graphlit provides 30+ pre-built connectors:

• Communication: Slack, Discord, email, RSS feeds
• Development: GitHub, Jira, Linear, GitLab
• Documents: PDF, DOCX, PPTX, Markdown, HTML
• Media: Audio (transcription), video (scene analysis), images (OCR)
• Web: URL ingestion, sitemaps, continuous monitoring

Connect a data source once, and Graphlit continuously syncs, processes, and enriches content. No pipeline code needed.

If you need custom data sources, LangMem gives more flexibility. If you want standard sources working immediately, Graphlit eliminates months of integration work.

Memory Management: Hot Path vs. Background

LangMem introduces two patterns for memory formation:

Hot Path Memory Tools

Agents actively manage memory during conversations:

tools = [
    create_manage_memory_tool(),  # Agent decides what to remember
    create_search_memory_tool()   # Agent searches when relevant
]

The agent calls these tools explicitly, giving transparency and control.

Background Memory Manager

Async processing that extracts memories outside conversations:

manager = BackgroundMemoryManager()
manager.enrich_memories(conversation_id)  # Runs after conversation

This prevents memory operations from slowing down agent responses.

Graphlit uses continuous background processing by default:

• All ingestion happens asynchronously
• Entity extraction, graph building, and embedding occur automatically
• No explicit memory tools needed (though available via MCP)
• Agents query enriched semantic memory without triggering processing

LangMem's dual approach (hot path + background) gives more control over when memory forms. Graphlit's always-on enrichment is simpler but less transparent.

Search and Retrieval Architecture

LangMem provides semantic search over memories:

• Vector similarity search using your chosen embedding model
• Namespace-based memory organization
• Filter by memory type (semantic, episodic, procedural)
• Custom retrieval logic through BaseStore interface

You control the retrieval strategy, which is powerful for specialized use cases but requires more implementation work.

Graphlit offers unified hybrid search:

• Vector semantic search (conceptual similarity)
• Keyword search with BM25 ranking
• Graph-aware context expansion (traverse entity relationships)
• Entity filters ("content where Alice discussed pricing")
• Temporal filters (date ranges, recency)
• Metadata filters (type, source, user, collection)

Our search is more opinionated and comprehensive — we've pre-built retrieval patterns that work for most agent use cases.

Storage and Infrastructure

LangMem is storage-agnostic:

• Use InMemoryStore for development
• Use AsyncPostgresStore for production
• Bring your own vector database
• Integrate custom storage backends via BaseStore

This flexibility means you can:

• Use existing database infrastructure
• Optimize for specific performance requirements
• Keep data on-premise if needed

But you manage the infrastructure: scaling, backups, performance tuning, and operational monitoring are your responsibility.

Graphlit provides managed infrastructure:

• We handle all storage (vector, graph, document stores)
• Automatic scaling based on usage
• No database operations needed

This means you can't choose specific database technologies. If infrastructure control matters (for integration or security reasons), LangMem wins. If you want zero operational overhead, Graphlit eliminates toil.

Developer Experience and Learning Curve

LangMem requires:

• Deep LangChain/LangGraph knowledge
• Understanding of memory schemas and enrichment patterns
• Storage backend setup and configuration
• Writing ingestion logic for diverse data sources

For teams already expert in LangChain, this fits naturally. For teams new to agent frameworks, it's another learning curve.

Graphlit requires:

• Basic API usage (REST/GraphQL)
• SDK familiarity (Python, JS/TS, or C#)
• No framework-specific knowledge needed

Graphlit's learning curve is shallower because we abstract framework complexity. Connect data sources, search content, build agents — all through one API.

Use Cases: When to Choose Each

Choose LangMem if:

• You're deeply invested in the LangChain/LangGraph ecosystem
• You want fine-grained control over memory architecture
• You need custom memory schemas and retrieval patterns
• You have engineering resources to build ingestion pipelines
• You prefer open-source with self-hosted deployment
• Your memory needs are primarily conversation-focused

Choose Graphlit if:

• You want comprehensive memory infrastructure without operational overhead
• You need multimodal content support (docs, audio, video, feeds)
• You want 30+ connectors working out of the box
• You prefer framework-agnostic tools (not locked into LangChain)
• Your agents operate across diverse content types and sources
• You're building production systems and need to ship fast

Final Verdict: Primitives vs. Platform

LangMem and Graphlit represent two philosophies for agent memory:

LangMem gives you memory primitives. It's perfect for teams that want control, already understand LangChain deeply, and are comfortable building memory systems from components. The native LangGraph integration makes it seamless for LangChain users.

Graphlit gives you memory infrastructure. We handle multimodal ingestion, entity extraction, knowledge graphs, semantic search, and scaling. You focus on agent logic, not memory operations.

For LangChain experts building custom memory architectures, LangMem is an excellent framework. For teams that want production-ready semantic memory without becoming infrastructure experts, Graphlit provides comprehensive, framework-agnostic infrastructure.

Both approaches are valid. The choice comes down to: do you want to build your memory system (LangMem) or use a memory platform (Graphlit)?

Explore more:

• Graphlit Docs
• LangMem GitHub
• LangMem Docs
• Schedule a chat
• Join our Discord

Some teams build their memory stack. Others ship products. Know which team you want to be.