LLM Application Framework · Public

langmem

LLM application framework for checking model, prompt, tool, retrieval, and chain integration boundaries.

LLM appsRAGTool callingPythonFramework migration

Best fitDevelopers building Python LLM apps, RAG workflows, tool calling, or agent prototypes that need a shared abstraction layer.

Check whether this project matches your task before installing it.

What it can doStructured LLM app starting paths, RAG/tool-calling checks, migration reminders, permission boundaries, and acceptance checks

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot1.5k stars

167 forks · 12 contributors

Official first step Read manual preview Source repository

Publication status · 2026-05-24

What is langmem?

langmem is an LLM application framework for model, prompt, tool, retrieval, chain, and callback interfaces.
Best fit: Developers building Python LLM apps, RAG workflows, tool calling, or agent prototypes that need a shared abstraction layer.
Not for: Not for one-off model API calls, simple prompting, or stateful agent orchestration work that should first compare LangGraph-style options.
Capability added to an AI workflow: Structured LLM app starting paths, RAG/tool-calling checks, migration reminders, permission boundaries, and acceptance checks
First safe verification step: Verify install, import, and one minimal task path in a temporary Python environment before using a primary project.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: The main risk is carrying unverified abstractions, legacy APIs, or provider integrations into production code.
Evidence base: https://github.com/langchain-ai/langmem, https://github.com/langchain-ai/langmem#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forDevelopers building Python LLM apps, RAG workflows, tool calling, or agent prototypes that need a shared abstraction layer.

Match the project to your task before installing it.

CapabilityStructured LLM app starting paths, RAG/tool-calling checks, migration reminders, permission boundaries, and acceptance checks

LLM application framework for checking model, prompt, tool, retrieval, and chain integration boundaries.

Repositorylangchain-ai/langmem

1.5k stars · 167 forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Home - LangMem Overview

Related topics: System Architecture, Core Concepts, Installation and Setup

Source: https://github.com/langchain-ai/langmem / Human Manual

Installation and Setup

Related topics: Home - LangMem Overview, LangGraph Integration

Source: https://github.com/langchain-ai/langmem / Human Manual

System Architecture

Related topics: Core Concepts, Memory Tools - Hot Path Management, Prompt Optimization

Source: https://github.com/langchain-ai/langmem / Human Manual

Core Concepts

Related topics: System Architecture, Memory Tools - Hot Path Management, Background Memory Manager

Source: https://github.com/langchain-ai/langmem / Human Manual

Memory Tools - Hot Path Management

Related topics: Core Concepts, Background Memory Manager, LangGraph Integration

Source: https://github.com/langchain-ai/langmem / Human Manual

Sources: https://github.com/langchain-ai/langmem, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars1.5k stars

Forks167 forks

Contributors12 contributors

Licenseunknown

Community Discussion Evidence

6 source-linked items

Review these external discussions before using langmem with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
Persistence?
github / github_issue
02
Security: OWASP Agent Memory Guard for memory poisoning defense (ASI06)
github / github_issue
03
Security: OWASP Agent Memory Guard for memory poisoning defense (ASI06)
github / github_issue
04
Enhance error message when summarization fails due to missing HumanMessa
github / github_issue
05
GRAPH_RECURSION_LIMIT
github / github_issue
06
Configuration risk needs validation
GitHub / issue

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install -U langmem

Official start command · https://github.com/langchain-ai/langmem#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

langmem Manual

Related topics: System Architecture, Core Concepts, Installation and Setup

Open the full manual

https://github.com/langchain-ai/langmem Project Manual
Table of Contents
Home - LangMem Overview
Related Pages
Core Concepts
Memory Architecture Overview
Components
Memory Types

Home - LangMem Overview

Related topics: System Architecture, Core Concepts, Installation and Setup

Source: https://github.com/langchain-ai/langmem / Human Manual

Installation and Setup

Related topics: Home - LangMem Overview, LangGraph Integration

Source: https://github.com/langchain-ai/langmem / Human Manual

System Architecture

Related topics: Core Concepts, Memory Tools - Hot Path Management, Prompt Optimization

Source: https://github.com/langchain-ai/langmem / Human Manual

Core Concepts

Related topics: System Architecture, Memory Tools - Hot Path Management, Background Memory Manager

Source: https://github.com/langchain-ai/langmem / Human Manual

Memory Tools - Hot Path Management

Related topics: Core Concepts, Background Memory Manager, LangGraph Integration

Source: https://github.com/langchain-ai/langmem / Human Manual

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start ManifestPROJECT_PACK.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.

Pitfall Log and verification risks

Doramagic surfaces high-risk items before users treat a candidate capability as verified.

high

Review upstream issue

The source signal needs review before production use.

high

Review upstream issue

The source signal needs review before production use.

high

Review upstream issue

The source signal needs review before production use.

medium

Review upstream issue

The source signal needs review before production use.

medium

Review upstream issue

README/documentation is current enough for a first validation pass.

medium

Review upstream issue

The source signal needs review before production use.

medium

Review upstream issue

no_demo

medium

Review upstream issue

no_demo