Mem0 Platform SDK
mem0ai/mem0
Adds persistent memory to AI apps with the Mem0 Python and TypeScript SDKs: store, search, update and delete user memories, with framework integrations.
Build LLM applications with LangChain. An agent skill from magnus919/agent-skills.
Install Langchain
$ npx skills add magnus919/agent-skills --skill langchain -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install magnus919/agent-skills langchain --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/langchain .claude/skills/langchain && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "langchain" agent skill from https://github.com/magnus919/agent-skills/tree/main/langchain into .claude/skills/langchain/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langchain", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/magnus919/agent-skills/tree/main/langchainType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add magnus919/agent-skills --skill langchain -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install magnus919/agent-skills langchain --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/langchain .agents/skills/langchain && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "langchain" agent skill from https://github.com/magnus919/agent-skills/tree/main/langchain into .agents/skills/langchain/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langchain", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add magnus919/agent-skills --skill langchain -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install magnus919/agent-skills langchain --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/langchain .cursor/skills/langchain && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "langchain" agent skill from https://github.com/magnus919/agent-skills/tree/main/langchain into .cursor/skills/langchain/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langchain", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/magnus919/agent-skills.git --path langchain--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add magnus919/agent-skills --skill langchain -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install magnus919/agent-skills langchain --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/langchain .gemini/skills/langchain && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "langchain" agent skill from https://github.com/magnus919/agent-skills/tree/main/langchain into .gemini/skills/langchain/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langchain", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install magnus919/agent-skills langchainInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add magnus919/agent-skills --skill langchain -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/langchain .github/skills/langchain && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "langchain" agent skill from https://github.com/magnus919/agent-skills/tree/main/langchain into .github/skills/langchain/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langchain", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add magnus919/agent-skills --skill langchain -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install magnus919/agent-skills langchain --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/langchain .opencode/skills/langchain && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "langchain" agent skill from https://github.com/magnus919/agent-skills/tree/main/langchain into .opencode/skills/langchain/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langchain", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
langchain- GitHub stars
- 115
- Token cost
- ~2.3k tokens
- SKILL.md length
- 938 words
- Files
- 17 (incl. scripts, references)
- Skills in repo
- 131
- Repo updated
- First seen
- Licence
- MIT
At a glance
Build LLM applications with LangChain. An agent skill from magnus919/agent-skills.
- Works in 5 steps: LCEL is the composition primitive. The… → Agents run on LangGraph. Since v1.0,… → RAG is a chain, not a framework.… → …
- Working with LangChain
- SKILL.md covers Core Principles, Where to Start, Pipeline Mode and Quick Reference, plus 7 more sections
- Runs Python scripts from its folder
What it does
Langchain is an agent skill from magnus919/agent-skills. Build LLM applications with LangChain. Use when working with LangChain or comparing LLM application frameworks. Do not use this skill for unrelated requests; route to the nearest named specialist.
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 20 other files, including scripts and reference files (for example `README.md`, `evals/evals.json` and `references/agent-patterns.md`).
It sits in AI & LLM Engineering, covering Building AI agents. It works with LangChain and LangGraph. The repository describes itself as: Curated collection of AI agent skills for Hermes and other agent frameworks. The licence is MIT.
When your agent uses it
- Working with LangChain
- Comparing LLM application frameworks
- Unrelated requests
- Route to the nearest named specialist
Example prompts
- “/langchain”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- LCEL is the composition primitive. The pipe operator (|) chains Runnables. Every component — prompt, model, parser, retriever — implements…
- Agents run on LangGraph. Since v1.0, create_agent generates a LangGraph state machine underneath. You get streaming, persistence, and…
- RAG is a chain, not a framework. retriever | prompt | model | parser is the canonical RAG pattern. Document loaders, splitters, and vector…
- LangSmith is production observability. Enable tracing at startup. 89% of production teams use observability — without it, debugging agent…
- The ecosystem is the moat. 1000+ integrations mean model providers, vector stores, and tools are swappable with one line. Build against…
What it can do on your machine
Read from SKILL.md and the folder at commit 22b4723. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Langchain loads about 2.3k tokens when it runs, and up to ~9.4k if it reads all its reference files. Until then it costs about 52 tokens; SKILL.md has 938 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from magnus919/agent-skills at commit 22b4723, republished under its MIT licence (© magnus919). 938 words, ~2,277 tokens.
Download SKILL.mdSave it as
.claude/skills/langchain/SKILL.md (or your agent's skills folder). This skill also uses 16 other files; get the full folder from GitHub.- name
- langchain
- description
- Build LLM applications with LangChain. Use when working with LangChain or comparing LLM application frameworks. Do not use this skill for unrelated requests; route to the nearest named specialist.
- license
- MIT
- metadata.author
- Magnus Hedemark
- metadata.version
- 1.1.0
- metadata.source
- https://github.com/langchain-ai/langchain
LangChain Expert Skill
LangChain is an MIT-licensed Python framework for building LLM-powered applications. Since v1.0 (October 2025), it provides a layered architecture: high-level chain composition via LCEL (LangChain Expression Language), agent creation via create_agent (running on the LangGraph runtime underneath), and production observability via LangSmith. With 1000+ integrations and 100K+ GitHub stars, it is the most widely adopted LLM orchestration framework.
Key v1.0 change: All new LangChain agents run on the LangGraph runtime. AgentExecutor is in maintenance mode until December 2026. Use create_agent for new agents. Drop to LangGraph directly when you need full state-machine control.
⚠️ CRITICAL: Do NOT use AgentExecutor for new code. It is in maintenance mode until December 2026. Use
create_agent(model, tools, prompt)instead — it generates a LangGraph state machine with streaming, persistence, and observability out of the box.
Core Principles
These principles govern every decision when building with LangChain. Read them before proceeding to the reference guides.
- LCEL is the composition primitive. The pipe operator (
|) chains Runnables. Every component — prompt, model, parser, retriever — implements the Runnable interface. Build everything in LCEL. - Agents run on LangGraph. Since v1.0,
create_agentgenerates a LangGraph state machine underneath. You get streaming, persistence, and observability without writing graph code. Drop to LangGraph when you need branching, cycles, or human-in-the-loop. - RAG is a chain, not a framework.
retriever | prompt | model | parseris the canonical RAG pattern. Document loaders, splitters, and vector stores are all interchangeable components. - LangSmith is production observability. Enable tracing at startup. 89% of production teams use observability — without it, debugging agent behavior is guesswork.
- The ecosystem is the moat. 1000+ integrations mean model providers, vector stores, and tools are swappable with one line. Build against the interface, not the implementation.
Where to Start
| You already have... | Start here |
|---|---|
| Nothing — blank project | Install LangChain, build a basic LCEL chain |
| Documents to query | Build a RAG chain (load, split, embed, retrieve, generate) |
| A need for agentic behavior | Use create_agent with tools |
| Existing AgentExecutor code | Migrate to create_agent — see references/agent-patterns.md |
| A production deployment | Add LangSmith tracing + LangServe deployment |
| Comparing frameworks | See the Framework Routing Guide |
Pipeline Mode
| Mode | When | Phases to run | Skip |
|---|---|---|---|
| Quick | Single chain, exploration | prompt → model → parser | Retrieval, agents, production hardening |
| RAG | Document Q&A | load → split → embed → retrieve → generate | Agent orchestration, deployment |
| Agent | Tool-using agents | create_agent + tools + LangGraph runtime | If simple chain suffices |
| Production | Shipping to users | RAG/Agent + LangSmith + LangServe | Nothing |
Quick Reference
| Task | Approach | Reference |
|---|---|---|
| Basic chain | prompt | model | parser | references/lcel-reference.md |
| RAG pipeline | retriever | prompt | model | parser | references/rag-strategies.md |
| Create agent | create_agent(model, tools, prompt) | references/agent-patterns.md |
| Tool definition | @tool decorator | references/agent-patterns.md |
| Multi-agent | LangGraph supervisor pattern | references/agent-patterns.md |
| Observability | Set LANGCHAIN_TRACING_V2=true | references/production-deployment.md |
| Deployment | LangServe or LangSmith Deployment | references/production-deployment.md |
| Vector store | One-line swap (Chroma, Pinecone, pgvector) | references/integration-ecosystem.md |
When to Use This Skill
Load this skill any time you are:
- Building LCEL chains for LLM-powered applications
- Implementing RAG pipelines over enterprise or personal data
- Creating agents with tool-calling and multi-step reasoning
- Deploying LLM applications to production with observability
- Comparing LangChain with LlamaIndex, Haystack, or raw API calls
Show full SKILL.md (440 more words)Show less
Framework Routing Guide
This skill is part of a portfolio of framework skills. When deciding which fits:
| Scenario | Reach for | Why |
|---|---|---|
| I have chains to compose | LangChain | LCEL is the cleanest pipe-based composition model |
| I have documents to query | LlamaIndex | Data ingestion and retrieval are first-class primitives |
| I have agents to orchestrate | LangGraph | State-machine semantics, subgraphs, human-in-the-loop |
| I have a tool to wrap as an agent | PydanticAI | Type-safe agent definitions with dependency injection |
| I have search pipelines | Haystack | Pipeline model is more mature for search workloads |
| Fast prototype of any kind | LangChain | Fastest path from zero to working chain |
| A typed, bounded decision inside an agent workflow | System One | System One owns question/model behavior and calibration; use LangChain middleware or outer conditional orchestration as the implementation seam, and harness-engineering for placement and whole-task outcomes |
Reference Files
| Reference | Load when | File |
|---|---|---|
| LCEL Reference | Building chains with the pipe operator | references/lcel-reference.md |
| Architecture | Understanding package structure, Runnable, v1.0 | references/architecture.md |
| RAG Strategies | Building RAG pipelines | references/rag-strategies.md |
| Agent Patterns | Creating agents with tools and multi-agent | references/agent-patterns.md |
| Typed System One decision in an agent | Route the adapter seam, unknown lane, and handoff between model semantics and harness outcomes | references/agent-patterns.md — see “Keep typed decisions outside the chat model”; continue to harness-engineering for placement and whole-task evidence |
| Production & Deployment | LangServe, LangSmith, deployment | references/production-deployment.md |
| Integration Ecosystem | Model providers, vector stores, tools | references/integration-ecosystem.md |
| FAQ & Troubleshooting | Common errors and fixes | references/faq-and-troubleshooting.md |
| Callbacks System | Custom logging, monitoring, agent auditing | references/callbacks.md |
| Validation Audit | Research validation of all API claims | references/validation-audit.md |
Template Files
| Template | When to use | File |
|---|---|---|
| Basic Chain | Single prompt→model→parser chain | templates/basic-chain.py |
| RAG Pipeline | Document Q&A with retrieval | templates/rag-pipeline.py |
| Agent with Tools | Tool-using agent with LangGraph runtime | templates/agent-with-tools.py |
| Production Deploy | LangServe deployment with LangSmith | templates/production-deploy.py |
Scripts
| Script | Purpose | File |
|---|---|---|
| check-setup | Verify LangChain installation | scripts/check-setup.py |
Troubleshooting Guide
| Symptom | Likely cause | Fix | Reference |
|---|---|---|---|
| Chain returns nothing | Output parser not connected | Add .pipe(StrOutputParser()) or equivalent | references/lcel-reference.md |
| Agent not calling tools | Tool schema mismatch | Check tool has docstring and type hints | references/agent-patterns.md |
| LangSmith traces missing | LANGCHAIN_TRACING_V2 not set | Set env var before any chain execution | references/production-deployment.md |
| Deprecation warning | Using AgentExecutor | Migrate to create_agent (LangGraph runtime) | references/agent-patterns.md |
| Model not found | Integration package missing | Install langchain-openai, langchain-anthropic, etc. | references/integration-ecosystem.md |
| Streaming not working | LCEL chain not streaming-native | Ensure all components implement stream() | references/lcel-reference.md |
| Vector store connection fails | Wrong credentials or missing package | Install langchain-community + provider package | references/integration-ecosystem.md |
When NOT to Use LangChain
- Single-model, single-prompt application — raw API calls are simpler and more debuggable
- Maximum transparency needed — LangGraph (which LangChain uses underneath) provides more visibility
- Pure multi-agent state machines — LangGraph directly is the correct tool, not the high-level API
- Stateless microservice with no LLM orchestration — LangChain adds overhead without benefit
© magnus919, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 16 other files (scripts, references) in langchain of magnus919/agent-skills.
- SKILL.md
- README.md
- evals/evals.json
- references/agent-patterns.md
- references/architecture.md
- references/callbacks.md
- references/faq-and-troubleshooting.md
- references/integration-ecosystem.md
- references/lcel-reference.md
- references/production-deployment.md
- references/rag-strategies.md
- references/validation-audit.md
- scripts/check-setup.py
- templates/agent-with-tools.py
- templates/basic-chain.py
- templates/production-deploy.py
- templates/rag-pipeline.py
Open the folder on GitHubat commit 22b4723
Compare with similar skills
Langchain next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Langchain this skillmagnus919/agent-skills | 115 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Mem0 Platform SDKmem0ai/mem0 | 67k | 1 repos | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| LangSmith Trace DebuggingComposioHQ/awesome-claude-skills | 77k | 8 repos | ~2.7k | Automated safety check: Pass | None | |
| Add Example AgentGetBindu/Bindu | 10k | — | ~1.1k | Automated safety check: Notes | Custom licence | |
| Failproof AI SDK IntegrationFailproofAI/failproofai | 5.3k | — | ~6k | Automated safety check: Pass | Custom licence | |
| Edgeone Makers MigrationTencentEdgeOne/edgeone-makers-tools | 1.9k | 1 repos | ~4.1k | Automated safety check: Pass | MIT |
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Add a new self-contained example agent under examples/. An agent skill from GetBindu/Bindu.
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Categories
Questions about Langchain
What does Langchain do?
Build LLM applications with LangChain. An agent skill from magnus919/agent-skills. Langchain is an agent skill from magnus919/agent-skills. Build LLM applications with LangChain.
When should I use Langchain?
Langchain fits situations like: working with LangChain; comparing LLM application frameworks; unrelated requests; route to the nearest named specialist.
How do I install Langchain in Claude Code?
Run `npx skills add magnus919/agent-skills --skill langchain -a claude-code`. Or copy the skill folder (langchain in magnus919/agent-skills) into .claude/skills/langchain in your project. Claude Code loads it when a task matches its description.
How do I install Langchain in Codex?
Run `npx skills add magnus919/agent-skills --skill langchain -a codex`. Or copy the skill folder (langchain in magnus919/agent-skills) into .agents/skills/langchain in your project. Codex loads it when a task matches its description.
Can I use Langchain in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add magnus919/agent-skills --skill langchain -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/langchain, .gemini/skills/langchain, .github/skills/langchain and .opencode/skills/langchain in your project.
What does Langchain need to run?
Going by SKILL.md and its folder, Langchain needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Langchain access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Langchain safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Langchain use?
Langchain is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Langchain use?
About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.1k tokens, read only when the agent opens those files.
What are the alternatives to Langchain?
Skills that share tags, products or a category with Langchain: Mem0 Platform SDK (mem0ai/mem0, 67k stars), LangSmith Trace Debugging (ComposioHQ/awesome-claude-skills, 77k stars), Add Example Agent (GetBindu/Bindu, 10k stars) and Failproof AI SDK Integration (FailproofAI/failproofai, 5.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Langchain?
magnus919 (a GitHub user) maintains it in magnus919/agent-skills, which has 115 GitHub stars. The repository holds 131 skills in this directory. The repository was last updated on October 10, 2026.
Source: magnus919/agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.