Mem0 Platform SDK
mem0ai/mem0
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ALWAYS START HERE for any LangChain, Deep Agents, or LangGraph agent building project.
Install Langchain Oss Primer
$ npx skills add langchain-ai/skills-benchmarks --skill langchain-oss-primer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install langchain-ai/skills-benchmarks langchain-oss-primer --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/langchain-ai/skills-benchmarks.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/benchmarks/langchain-oss-primer .claude/skills/langchain-oss-primer && 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-oss-primer" agent skill from https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/langchain-oss-primer into .claude/skills/langchain-oss-primer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langchain-oss-primer", 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/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/langchain-oss-primerType 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 langchain-ai/skills-benchmarks --skill langchain-oss-primer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install langchain-ai/skills-benchmarks langchain-oss-primer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/langchain-ai/skills-benchmarks.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/benchmarks/langchain-oss-primer .agents/skills/langchain-oss-primer && 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-oss-primer" agent skill from https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/langchain-oss-primer into .agents/skills/langchain-oss-primer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langchain-oss-primer", 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 langchain-ai/skills-benchmarks --skill langchain-oss-primer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install langchain-ai/skills-benchmarks langchain-oss-primer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/langchain-ai/skills-benchmarks.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/benchmarks/langchain-oss-primer .cursor/skills/langchain-oss-primer && 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-oss-primer" agent skill from https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/langchain-oss-primer into .cursor/skills/langchain-oss-primer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langchain-oss-primer", 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/langchain-ai/skills-benchmarks.git --path skills/benchmarks/langchain-oss-primer--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 langchain-ai/skills-benchmarks --skill langchain-oss-primer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install langchain-ai/skills-benchmarks langchain-oss-primer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/langchain-ai/skills-benchmarks.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/benchmarks/langchain-oss-primer .gemini/skills/langchain-oss-primer && 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-oss-primer" agent skill from https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/langchain-oss-primer into .gemini/skills/langchain-oss-primer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langchain-oss-primer", 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 langchain-ai/skills-benchmarks langchain-oss-primerInstalls 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 langchain-ai/skills-benchmarks --skill langchain-oss-primer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/langchain-ai/skills-benchmarks.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/benchmarks/langchain-oss-primer .github/skills/langchain-oss-primer && 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-oss-primer" agent skill from https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/langchain-oss-primer into .github/skills/langchain-oss-primer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langchain-oss-primer", 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 langchain-ai/skills-benchmarks --skill langchain-oss-primer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install langchain-ai/skills-benchmarks langchain-oss-primer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/langchain-ai/skills-benchmarks.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/benchmarks/langchain-oss-primer .opencode/skills/langchain-oss-primer && 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-oss-primer" agent skill from https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/langchain-oss-primer into .opencode/skills/langchain-oss-primer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "langchain-oss-primer", 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-oss-primer- GitHub stars
- 118
- Token cost
- ~4.1k tokens
- SKILL.md length
- 1,382 words
- Files
- 1
- Skills in repo
- 8
- Repo updated
- First seen
- Licence
- MIT
At a glance
ALWAYS START HERE for any LangChain, Deep Agents, or LangGraph agent building project.
- Works in 5 steps: Pick Your Framework → Pick Your Agent Archetype → Set Up Your Dependencies → …
- Tasks that involve Building AI agents
- SKILL.md covers Step 1 — Pick Your Framework, Step 2 — Pick Your Agent…, Step 3 — Set Up Your… and Step 4 — Set Your Environment…, plus 1 more section
- Needs LANGSMITH_API_KEY and OPENAI_API_KEY
What it does
Langchain Oss Primer is an agent skill from langchain-ai/skills-benchmarks, published by the product's own GitHub organization. ALWAYS START HERE for any LangChain, Deep Agents, or LangGraph agent building project. Required starting point before choosing other skills or writing any code. Covers framework selection (LangChain vs LangGraph vs Deep Agents), agent archetypes, dependency setup, and which skills to load next based on your decisions.
Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Building AI agents. It works with LangChain and LangGraph. The licence is MIT.
When your agent uses it
- Tasks that involve Building AI agents
Example prompts
- “Use the langchain-oss-primer skill to alway START HERE for any LangChain, Deep Agents, or LangGraph agent building project”
- “/langchain-oss-primer”
Requirements
- Python 3
- Node.js
Workflow steps
5 steps, taken from the step headings in SKILL.md.
- Pick Your Framework
- Pick Your Agent Archetype
- Set Up Your Dependencies
- Set Your Environment Variables
- Load the Right Skill Next
What it can do on your machine
Read from SKILL.md and the folder at commit 9195f8c. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are json and bash).
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 these keys or tokens, usually read from environment variables:
LANGSMITH_API_KEYOPENAI_API_KEYANTHROPIC_API_KEYGOOGLE_API_KEYMISTRAL_API_KEYGROQ_API_KEYCOHERE_API_KEYFIREWORKS_API_KEYTOGETHER_API_KEYHUGGINGFACEHUB_API_TOKENTAVILY_API_KEYPINECONE_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Langchain Oss Primer loads about 4.1k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 1,382 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from langchain-ai/skills-benchmarks at commit 9195f8c, republished under its MIT licence (© langchain-ai). 1,382 words, ~4,063 tokens.
Download SKILL.mdSave it as
.claude/skills/langchain-oss-primer/SKILL.md (or your agent's skills folder).- name
- langchain-oss-primer
- description
- ALWAYS START HERE for any LangChain, Deep Agents, or LangGraph agent building project. Required starting point before choosing other skills or writing any code. Covers framework selection (LangChain vs LangGraph vs Deep Agents), agent archetypes, dependency setup, and which skills to load next based on your decisions.
<overview>
**Always load this skill first.** This is the required starting point for any LangChain open source agent project — before choosing other skills, before writing code, before installing packages.It answers three questions every project must resolve upfront:
- Which framework? — LangChain, LangGraph, or Deep Agents
- Which agent archetype? — maps your use case to the right API and patterns
- What to install and which skills to load next
Load this skill first. Once you've decided on a framework and agent type, follow the "Next Skills" section at the bottom — it tells you exactly which skills to invoke next based on your choices.
</overview>
Step 1 — Pick Your Framework
The three frameworks are layered, not competing. Each builds on the one below:
┌─────────────────────────────────────────┐
│ Deep Agents │ ← batteries included
│ (planning, memory, skills, files) │
├─────────────────────────────────────────┤
│ LangGraph │ ← custom orchestration
│ (nodes, edges, state, persistence) │
├─────────────────────────────────────────┤
│ LangChain │ ← foundation
│ (models, tools, prompts, RAG) │
└─────────────────────────────────────────┘<framework-decision>Answer these questions in order:
| Question | Yes → | No → |
|---|---|---|
| User needs or wants planning, persistent memory, complex task management, long-running tasks, out-of-the-box file management, on-demand skills, or built-in middleware, subagents, easy expansion capabilities? | Deep Agents | ↓ |
| Needs custom control flow — specified loops, branching, deterministic parallel workers, or manually instrumented human-in-the-loop? | LangGraph | ↓ |
| Single-purpose agent with a fixed set of tools? | LangChain (create_agent) | ↓ |
| Simple prompt pipeline or retrieval chain with no agent loop? | LangChain (direct model / chain) | — |
Higher layers depend on lower ones only when necessary — you can mix them. A LangGraph graph can be a subagent inside Deep Agents; LangChain tools work inside both.
</framework-decision><framework-profiles>| LangChain | LangGraph | Deep Agents | |
|---|---|---|---|
| Control flow | Fixed (tool loop) | Custom (graph) | Managed (middleware) |
| Middleware | Callbacks only | ✗ None | ✓ Explicit, configurable |
| Planning | ✗ | Manual | ✓ TodoListMiddleware |
| File management | ✗ | Manual | ✓ FilesystemMiddleware |
| Persistent memory | ✗ | With checkpointer | ✓ MemoryMiddleware |
| Subagent delegation | ✗ | Manual | ✓ SubAgentMiddleware |
| On-demand skills | ✗ | ✗ | ✓ SkillsMiddleware |
| Human-in-the-loop | ✗ | Manual interrupt | ✓ HumanInTheLoopMiddleware |
| Custom graph edges | ✗ | ✓ Full control | Limited |
| Setup complexity | Low | Medium | Low |
Middleware is a concept specific to Deep Agents (explicit middleware layer). LangGraph has no middleware — behavior is wired directly into nodes and edges. If a user asks for built-in hooks or automatic middleware, route to Deep Agents.
</framework-profiles>Step 2 — Pick Your Agent Archetype
Once you've chosen a framework, match your use case to the right API and pattern.
<langchain-archetypes>LangChain — use create_agent()
Best for single-purpose agents in a ReACT style with a fixed tool set. No built-in planning, memory management, or delegation.
| Archetype | Description | Key tools |
|---|---|---|
| QA / Chatbot | Answer questions, summarise, classify. One job, done well. | LLM + optional retrieval |
| SQL Agent | Query a database, return structured results | SQLDatabase, create_agent |
| Search Agent | Look up information, return findings | TavilySearchResults, DuckDuckGoSearch |
| RAG Agent | Retrieve from a vector store, ground answers in documents | retriever tool + create_agent |
| Data Analysis Agent | Load, transform, and summarise structured data | PythonREPL, pandas tools |
| Tool-calling Agent | Call APIs, run code, or chain arbitrary tools | custom @tool functions |
All LangChain agents use create_agent(model, tools=[...]). Next skill: langchain-fundamentals.
</langchain-archetypes><langgraph-archetypes>LangGraph — use StateGraph
Best when you need explicit, deterministic control flow.
| Archetype | Description | Key pattern |
|---|---|---|
| Deterministic Parallel Workflows | Fan out to multiple nodes, collect results, merge | parallel edges → aggregation node |
| Multi-stage Pipeline | Extract → Transform → Load with typed state | TypedDict state + sequential nodes |
| Branching Classifier | Route inputs to different handlers based on content | conditional edges + classifer node |
| Reflection Loop | Generate → Critique → Revise cycle with explicit exit | cycle edges + iteration counter |
| Custom HITL | Complex human-in-the-loop with structured review and conditional edges | interrupt_before/interrupt_after + Command resume |
LangGraph agents use StateGraph(State) with explicit add_node, add_edge, add_conditional_edges. Next skill: langgraph-fundamentals.
</langgraph-archetypes><deep-agents-archetypes>Deep Agents — use create_deep_agent()
Best when the agent needs to manage its own work: planning tasks, remembering users across sessions, delegating to specialists, or managing files autonomously.
| Archetype | Description | Why Deep Agents |
|---|---|---|
| Research Assistant | Receives an open-ended research brief, breaks it into subtasks, delegates to specialist subagents, writes up findings | Needs SubAgentMiddleware for delegation + TodoListMiddleware for planning |
| Personal Assistant | Remembers user preferences, ongoing projects, and context across multiple sessions | Needs MemoryMiddleware (Store) for cross-session persistence |
| Coding Assistant | Reads codebases, writes files, plans refactors across many steps, optionally asks for approval before writes | Needs FilesystemMiddleware + TodoListMiddleware + optional HITL |
| Orchestrator | Top-level agent that routes work to 2+ specialized subagents (researcher, coder, writer…) | Needs SubAgentMiddleware with custom subagent configs |
| Long-running Task Agent | Multi-hour or multi-day workflows where state must survive restarts | Needs checkpointer + MemoryMiddleware |
| On-demand Skills Agent | Agent that loads different skill sets depending on what the user asks | Needs SkillsMiddleware + FilesystemBackend |
| Multi Agent Architecture | Agent that spawns or has access to subagents for isolated tasks |
All Deep Agents use create_deep_agent(model, tools=[...], ...). Next skill: deep-agents-core — load it immediately after deciding on Deep Agents.
<deep-agents-middleware>Show full SKILL.md (512 more words)Show less
Deep Agents built-in middleware
Six components pre-wired out of the box. First three are always active; the rest are opt-in:
| Middleware | Always on? | What it gives the agent |
|---|---|---|
TodoListMiddleware | ✓ | write_todos tool — tracks multi-step task plans |
FilesystemMiddleware | ✓ | ls, read_file, write_file, edit_file, glob, grep |
SubAgentMiddleware | ✓ | task tool — delegates subtasks to named subagents |
SkillsMiddleware | Opt-in | Loads SKILL.md files on demand from a skills directory |
MemoryMiddleware | Opt-in | Long-term memory across sessions via a Store instance |
HumanInTheLoopMiddleware | Opt-in | Pauses execution and requests human approval before specified tool calls |
You configure middleware — you don't implement it.
</deep-agents-middleware><mixing-note>
You can combine layers in the same project. The most common pattern: Deep Agents as the top-level orchestrator, with a compiled LangGraph graph registered as a specialized subagent. LangChain tools and chains are usable at every level.
</mixing-note></deep-agents-archetypes>Step 3 — Set Up Your Dependencies
Environment requirements
| Python | TypeScript / Node | |
|---|---|---|
| Runtime | Python 3.10+ | Node.js 20+ |
| LangChain | 1.0+ (LTS) | 1.0+ (LTS) |
| LangSmith SDK | >= 0.3.0 | >= 0.3.0 |
Always use LangChain 1.0+. LangChain 0.3 is maintenance-only until December 2026 — do not start new projects on it.
Core packages — always required
<python-core>
**Python**| Package | Role | Version |
|---|---|---|
langchain | Agents, chains, retrieval | >=1.0,<2.0 |
langchain-core | Base types & interfaces | >=1.0,<2.0 |
langsmith | Tracing, evaluation, datasets | >=0.3.0 |
</python-core> |
<typescript-core>
**TypeScript**| Package | Role | Version |
|---|---|---|
@langchain/core | Base types & interfaces (peer dep — install explicitly) | ^1.0.0 |
langchain | Agents, chains, retrieval | ^1.0.0 |
langsmith | Tracing, evaluation, datasets | ^0.3.0 |
</typescript-core> |
Orchestration — add based on your framework choice
<orchestration-packages>| Framework | Python | TypeScript |
|---|---|---|
| LangGraph | langgraph>=1.0,<2.0 | @langchain/langgraph ^1.0.0 |
| Deep Agents | deepagents (depends on LangGraph; installs it as a transitive dep) | deepagents |
</orchestration-packages>Model providers — pick the one(s) you use
<provider-packages>| Provider | Python | TypeScript |
|---|---|---|
| OpenAI | langchain-openai | @langchain/openai |
| Anthropic | langchain-anthropic | @langchain/anthropic |
| Google Gemini | langchain-google-genai | @langchain/google-genai |
| Mistral | langchain-mistralai | @langchain/mistralai |
| Groq | langchain-groq | @langchain/groq |
| Cohere | langchain-cohere | @langchain/cohere |
| AWS Bedrock | langchain-aws | @langchain/aws |
| Azure AI | langchain-azure-ai | @langchain/azure-openai |
| Ollama (local) | langchain-ollama | @langchain/ollama |
| Hugging Face | langchain-huggingface | — |
| Fireworks AI | langchain-fireworks | — |
| Together AI | langchain-together | — |
</provider-packages>Common tools & retrieval — add as needed
<tool-packages>| Package | Adds | Notes |
|---|---|---|
langchain-tavily / @langchain/tavily | Tavily web search | Keep at latest; frequently updated for compatibility |
langchain-text-splitters | Text chunking | Semver; keep current |
langchain-chroma / @langchain/community | Chroma vector store | Dedicated integration package; keep at latest |
langchain-pinecone / @langchain/pinecone | Pinecone vector store | Dedicated integration package; keep at latest |
langchain-qdrant / @langchain/qdrant | Qdrant vector store | Dedicated integration package; keep at latest |
faiss-cpu | FAISS vector store (Python only, local) | Via langchain-community |
langchain-community / @langchain/community | 1000+ integrations fallback | Python: NOT semver — pin to minor series |
langsmith[pytest] | pytest plugin | Requires langsmith>=0.3.4 |
Prefer dedicated integration packages over
langchain-communitywhen one exists — they are independently versioned and more stable.
</tool-packages>Dependency templates
<ex-langchain-python>
<python>
LangChain agent — provider-agnostic starting point.
```
# requirements.txt
langchain>=1.0,<2.0
langchain-core>=1.0,<2.0
langsmith>=0.3.0Add your model provider:
langchain-openai | langchain-anthropic | langchain-google-genai | ...
Add tools/retrieval as needed:
langchain-tavily | langchain-chroma | langchain-text-splitters | ...
</python>
</ex-langchain-python>
<ex-langgraph-python>
<python>
LangGraph project — provider-agnostic starting point.requirements.txt
langchain>=1.0,<2.0 langchain-core>=1.0,<2.0 langgraph>=1.0,<2.0 langsmith>=0.3.0
Add your model provider:
langchain-openai | langchain-anthropic | langchain-google-genai | ...
</python>
</ex-langgraph-python>
<ex-langgraph-typescript>
<typescript>
LangGraph project — provider-agnostic starting point.
```json
{
"dependencies": {
"@langchain/core": "^1.0.0",
"langchain": "^1.0.0",
"@langchain/langgraph": "^1.0.0",
"langsmith": "^0.3.0"
}
}</typescript>
</ex-langgraph-typescript><ex-deepagents-python>
<python>
Deep Agents project — provider-agnostic starting point.
```
# requirements.txt
deepagents
langchain>=1.0,<2.0
langchain-core>=1.0,<2.0
langsmith>=0.3.0Add your model provider:
langchain-openai | langchain-anthropic | langchain-google-genai | ...
</python>
</ex-deepagents-python>
<ex-deepagents-typescript>
<typescript>
Deep Agents project — provider-agnostic starting point.
```json
{
"dependencies": {
"deepagents": "latest",
"@langchain/core": "^1.0.0",
"langchain": "^1.0.0",
"langsmith": "^0.3.0"
}
}</typescript>
</ex-deepagents-typescript>Step 4 — Set Your Environment Variables
<environment-variables>
```bash
# LangSmith — always recommended for observability
LANGSMITH_API_KEY=<your-key>
LANGSMITH_PROJECT=<project-name> # optional, defaults to "default"Model provider — set the one(s) you use
OPENAI_API_KEY=<your-key>
ANTHROPIC_API_KEY=<your-key>
GOOGLE_API_KEY=<your-key>
MISTRAL_API_KEY=<your-key>
GROQ_API_KEY=<your-key>
COHERE_API_KEY=<your-key>
FIREWORKS_API_KEY=<your-key>
TOGETHER_API_KEY=<your-key>
HUGGINGFACEHUB_API_TOKEN=<your-key>
Common tool/retrieval services
TAVILY_API_KEY=<your-key>
PINECONE_API_KEY=<your-key>
</environment-variables>
---
## Step 5 — Load the Right Skill Next
Based on the framework and archetype you chose above, invoke these skills **now** before writing any code:
<next-skills>
### If you chose LangChain
| Your archetype | Load next |
|----------------|-----------|
| Any LangChain agent (QA bot, SQL, search, RAG, tool-calling) | **`langchain-fundamentals`** — always |
| Adding external tools/packages (Tavily, Pinecone, etc.) | **`langchain-dependencies`** — package patterns and version guidance |
| Need streaming or async responses | **`langchain-fundamentals`** then `langgraph-fundamentals` |
### If you chose LangGraph
| Your archetype | Load next |
|----------------|-----------|
| Any LangGraph graph | **`langgraph-fundamentals`** — always |
| Approval pipeline, HITL, or pause/resume | **`langgraph-fundamentals`** + `langgraph-human-in-the-loop` |
| State that must survive restarts or cross-thread memory | **`langgraph-persistence`** |
| Streaming output token by token | **`langgraph-fundamentals`** |
### If you chose Deep Agents
**Always load `deep-agents-core` first — it is the mandatory starting point for any Deep Agents project.**
| Your archetype | Load next (after `deep-agents-core`) |
|----------------|--------------------------------------|
| Research Assistant — delegates to specialist subagents | **`deep-agents-orchestration`** — subagent config, TodoList, HITL |
| Personal Assistant — remembers users across sessions | **`deep-agents-memory`** — MemoryMiddleware, Store backends |
| Coding Assistant — reads/writes files, plans refactors | `deep-agents-core` is sufficient; add `deep-agents-orchestration` if using HITL |
| Orchestrator — routes work across multiple named subagents | **`deep-agents-orchestration`** — SubAgentMiddleware patterns |
| Long-running task agent — survives restarts | **`deep-agents-memory`** + `deep-agents-orchestration` |
| On-demand skills agent | `deep-agents-core` covers SkillsMiddleware setup |
</next-skills>© langchain-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
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Just SKILL.md in skills/benchmarks/langchain-oss-primer of langchain-ai/skills-benchmarks.
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Compare with similar skills
Langchain Oss Primer 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 Oss Primer this skilllangchain-ai/skills-benchmarks | 118 | — | ~4.1k | 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 |
Similar skills
LangSmith Trace Debugging
ComposioHQ/awesome-claude-skills
Debugs LangChain and LangGraph agents by pulling recent execution traces with the langsmith-fetch CLI and reporting errors, tool calls, timings and token use.
Add Example Agent
GetBindu/Bindu
Add a new self-contained example agent under examples/. An agent skill from GetBindu/Bindu.
Failproof AI SDK Integration
FailproofAI/failproofai
Helps instrument a custom Python or TypeScript agent to record events for Failproof AI, verify what gets written, and run an evaluator worker that scores the runs.
Edgeone Makers Migration
TencentEdgeOne/edgeone-makers-tools
Migrate existing AI agent projects (LangChain, LangGraph, OpenAI Agents SDK, Claude Agent SDK, CrewAI) to EdgeOne Makers platform conventions.
Omnigent Framework Detection
omnigent-ai/omnigent
Scans Python agent code for framework imports and recommends the matching Omnigent executor type, or says when the framework is not natively supported yet.
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OpenAPI documentation and REST API design patterns. An agent skill from langchain-ai/skills-benchmarks.
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Questions about Langchain Oss Primer
What does Langchain Oss Primer do?
ALWAYS START HERE for any LangChain, Deep Agents, or LangGraph agent building project. Langchain Oss Primer is an agent skill from langchain-ai/skills-benchmarks, published by the product's own GitHub organization. ALWAYS START HERE for any LangChain, Deep Agents, or LangGraph agent building project.
When should I use Langchain Oss Primer?
Langchain Oss Primer fits situations like: tasks that involve Building AI agents.
How do I install Langchain Oss Primer in Claude Code?
Run `npx skills add langchain-ai/skills-benchmarks --skill langchain-oss-primer -a claude-code`. Or copy the skill folder (skills/benchmarks/langchain-oss-primer in langchain-ai/skills-benchmarks) into .claude/skills/langchain-oss-primer in your project. Claude Code loads it when a task matches its description.
How do I install Langchain Oss Primer in Codex?
Run `npx skills add langchain-ai/skills-benchmarks --skill langchain-oss-primer -a codex`. Or copy the skill folder (skills/benchmarks/langchain-oss-primer in langchain-ai/skills-benchmarks) into .agents/skills/langchain-oss-primer in your project. Codex loads it when a task matches its description.
Can I use Langchain Oss Primer 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 langchain-ai/skills-benchmarks --skill langchain-oss-primer -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-oss-primer, .gemini/skills/langchain-oss-primer, .github/skills/langchain-oss-primer and .opencode/skills/langchain-oss-primer in your project.
What does Langchain Oss Primer need to run?
Going by SKILL.md and its folder, Langchain Oss Primer needs credentials named LANGSMITH_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY and GOOGLE_API_KEY. Our summary lists: Python 3; Node.js.
Does Langchain Oss Primer 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 Oss Primer 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. Review the folder before installing.
What licence does Langchain Oss Primer use?
Langchain Oss Primer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Langchain Oss Primer use?
About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Langchain Oss Primer?
Skills that share tags, products or a category with Langchain Oss Primer: 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 Oss Primer?
langchain-ai (a GitHub organization, an official publisher) maintains it in langchain-ai/skills-benchmarks, which has 118 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 21, 2026.
Source: langchain-ai/skills-benchmarks on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.