LangMem

LangMem

LangChain
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+

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About

LangMem is a lightweight, flexible Python SDK from LangChain that equips AI agents with long-term memory capabilities, enabling them to extract, store, update, and retrieve meaningful information from past interactions to become smarter and more personalized over time. It supports three memory types and offers both hot-path tools for real-time memory management and background consolidation for efficient updates beyond active sessions. Through a storage-agnostic core API, LangMem integrates seamlessly with any backend and offers native compatibility with LangGraph’s long-term memory store, while also allowing type-safe memory consolidation using schemas defined in Pydantic. Developers can incorporate memory tools into agents using simple primitives to enable seamless memory creation, retrieval, and prompt optimization within conversational flows.

About

AI agents fail silently in production. Wrong answers, broken loops, cost spikes, behavior drift after a prompt change, and no stack trace to explain why. Netra is the single platform to trace, evaluate, simulate and security test your agents. Built for text, voice, image and video agents. SOC2 Type II certified. GDPR and HIPAA compliant. US and EU data residency. Integrates with: LangChain, LangGraph, CrewAI, LlamaIndex, OpenAI, Anthropic, Gemini, AWS Bedrock, and 30+ more.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Supported
Chromebook Not Supported

Audience

AI developers and data scientists who build LangChain-based agents and want to implement long-term, structured memory to enhance personalization, coherence, and conversational depth

Audience

Teams building AI agents and Agentic AI products

Support

Phone Support Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Pricing

$39/month
Free Version Not Supported
Free Trial Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 4.0 / 5
support 5.0 / 5

Pros & Cons from Real Users

Pros

  • ==Simulation mode lets you stress-test agents before production. You can spin up multi-turn conversations with defined personas and goals, auto-evaluated against fact-checking criteria you set. Instead of manual testing or hoping users find the edge cases, you run realistic scenarios and see where agents fail. Saves weeks of "what if" testing. Evaluation framework is actually repeatable. Not just pass/fail. You're measuring model quality, reasoning accuracy, tool correctness, output reliability—with metrics that stick across runs. That means you can compare versions, track improvements, and catch regressions before they hit users. Alerting catches problems you'd otherwise miss. Cost spikes, performance drops, safety violations, weird agent behavior—real-time notifications instead of discovering issues in production. That's table stakes for shipping anything at scale. Works across the whole ecosystem. OpenTelemetry standards, integrations with OpenAI, Claude, Anthropic, vector databases, LangChain, LlamaIndex. Not locked into one vendor. Multi-tenancy for SaaS applications. Voice traces for voice pipelines too, if that's your stack. Netra handles both text and voice traces end-to-end. Text captures LLM calls, tool executions, vector searches. Voice pipelines trace the full chain: speech-to-text, reasoning, text-to-speech. One platform for observing how your agents think and communicate, no matter the modality. That's rare.

Cons

  • SDK integration is non-negotiable overhead. You can't just drop this in and observe magically. Netra requires explicit instrumentation across your codebase. Multi-language support helps, but you're still looking at integration work across services. That's friction most teams don't have budget for on day one. Less public mindshare than competitors. Datadog, New Relic, LangSmith get more air time. Netra doesn't have the same community gravity or case study library. Fewer public comparisons, less blog coverage, smaller forum. That means fewer answers when you hit problems and harder time recruiting help from people who already know the tool. The documentation is excellent and so is the customer support

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

LangChain
Founded: 2022
United States
langchain-ai.github.io/langmem/

Company Information

Netra
Founded: 2025
getnetra.ai/

Alternatives

Alternatives

Membase

Membase

Unibase
EverOS

EverOS

EverMind
Traccia

Traccia

Algen AI
PRISM

PRISM

Block Convey

Categories

AI Memory Layers Supported

Categories

Integrations

CrewAI Not Supported
LangChain Not Supported
LangGraph Supported
LlamaIndex Not Supported
OpenTelemetry Not Supported
PydanticAI Supported
Python Supported

Integrations

CrewAI Supported
LangChain Supported
LangGraph Not Supported
LlamaIndex Supported
OpenTelemetry Supported
PydanticAI Not Supported
Python Not Supported
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