Match the project to your task before installing it.
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 checksReview the portable capability path.
Before continuingVerify in a sandboxDo not treat a preview pack as a proven local install.
GitHub snapshot1.5k stars170 forks · 12 contributors
Doramagic.ai Last verification date: 2026-07-08 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.
Publication status · 2026-07-08
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: May increase setup, validation, or first-run risk for the user.
- Evidence base: https://github.com/langchain-ai/langmem, https://github.com/langchain-ai/langmem#readme, Human Manual, Pitfall Log
01
Quick decision
Use this section to decide whether the project is worth a deeper read.LLM application framework for checking model, prompt, tool, retrieval, and chain integration boundaries.
1.5k stars · 170 forks
02
What it can do
Translate the upstream project into concrete capabilities the user can judge before installing.LangMem Overview and Architecture
Related topics: Long-Term Memory: Extraction, Tools, and Store Managers, Prompt Optimization and Learning, Short-Term Memory, Reflection, and Graph Workflows
Source: https://github.com/langchain-ai/langmem / Human Manual
Long-Term Memory: Extraction, Tools, and Store Managers
Related topics: LangMem Overview and Architecture, Short-Term Memory, Reflection, and Graph Workflows
Source: https://github.com/langchain-ai/langmem / Human Manual
Prompt Optimization and Learning
Related topics: LangMem Overview and Architecture, Short-Term Memory, Reflection, and Graph Workflows
Source: https://github.com/langchain-ai/langmem / Human Manual
Short-Term Memory, Reflection, and Graph Workflows
Related topics: LangMem Overview and Architecture, Long-Term Memory: Extraction, Tools, and Store Managers, Prompt Optimization and Learning
Source: https://github.com/langchain-ai/langmem / Human Manual
Doramagic Pitfall Log
Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.
Source: Doramagic discovery, validation, and Project Pack records
Sources: https://github.com/langchain-ai/langmem, Human Manual, Project Pack evidence, and downstream validation signals.
03
Community Discussion Evidence
Project-level external discussion stays visible on the detail page, not only inside the manual.Community Discussion Evidence
5 source-linked itemsReview 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.
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01
Persistence?
github / github_issue
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02
Security: OWASP Agent Memory Guard for memory poisoning defense (ASI06)
github / github_issue
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03
Security: OWASP Agent Memory Guard for memory poisoning defense (ASI06)
github / github_issue
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04
Enhance error message when summarization fails due to missing HumanMessa
github / github_issue
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05
Configuration risk requires verification
GitHub / issue
04
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.
previewRead the Human Manual
Understand inputs, outputs, limits, and failure modes.
manualTake context to your AI host
Use the compiled assets in your preferred AI environment.
contextRun sandbox verification
Confirm install commands and rollback before using a primary environment.
verifypip install -U langmemOfficial start command · https://github.com/langchain-ai/langmem#readme · verified: yes
05
Human Manual
The English page must expose the real manual, not a short placeholder.8+ sections · Human Manual
langmem Manual
LangMem helps agents learn and adapt from their interactions over time.
Open the full manual- https://github.com/langchain-ai/langmem Project Manual
- Table of Contents
- LangMem Overview and Architecture
- Related Pages
- Purpose and Scope
- Module Layout and Public API
- High-Level Architecture
- Knowledge Extraction and Storage
LangMem Overview and Architecture
Related topics: Long-Term Memory: Extraction, Tools, and Store Managers, Prompt Optimization and Learning, Short-Term Memory, Reflection, and Graph Workflows
Source: https://github.com/langchain-ai/langmem / Human Manual
Long-Term Memory: Extraction, Tools, and Store Managers
Related topics: LangMem Overview and Architecture, Short-Term Memory, Reflection, and Graph Workflows
Source: https://github.com/langchain-ai/langmem / Human Manual
Prompt Optimization and Learning
Related topics: LangMem Overview and Architecture, Short-Term Memory, Reflection, and Graph Workflows
Source: https://github.com/langchain-ai/langmem / Human Manual
Short-Term Memory, Reflection, and Graph Workflows
Related topics: LangMem Overview and Architecture, Long-Term Memory: Extraction, Tools, and Store Managers, Prompt Optimization and Learning
Source: https://github.com/langchain-ai/langmem / Human Manual
Doramagic Pitfall Log
Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.
Source: Doramagic discovery, validation, and Project Pack records
06
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.
07
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.
08
Pitfall Log and verification risks
Doramagic surfaces high-risk items before users treat a candidate capability as verified.Installation risk requires verification
May increase setup, validation, or first-run risk for the user.
Security or permission risk requires verification
May increase setup, validation, or first-run risk for the user.
Configuration risk requires verification
May increase setup, validation, or first-run risk for the user.
Capability evidence risk requires verification
May increase setup, validation, or first-run risk for the user.
Maintenance risk requires verification
May increase setup, validation, or first-run risk for the user.
Security or permission risk requires verification
May increase setup, validation, or first-run risk for the user.
Security or permission risk requires verification
May increase setup, validation, or first-run risk for the user.
Security or permission risk requires verification
May increase setup, validation, or first-run risk for the user.