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INVOKE THIS SKILL at the START of any LangChain/LangGraph/Deep Agents project, before writing any agent code.
Install Framework Selection
$ npx skills add langchain-ai/skills-benchmarks --skill framework-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install langchain-ai/skills-benchmarks framework-selection --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/framework-selection .claude/skills/framework-selection && 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 "framework-selection" agent skill from https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/framework-selection into .claude/skills/framework-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "framework-selection", 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/framework-selectionType 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 framework-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install langchain-ai/skills-benchmarks framework-selection --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/framework-selection .agents/skills/framework-selection && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "framework-selection" agent skill from https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/framework-selection into .agents/skills/framework-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "framework-selection", 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 framework-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install langchain-ai/skills-benchmarks framework-selection --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/framework-selection .cursor/skills/framework-selection && 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 "framework-selection" agent skill from https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/framework-selection into .cursor/skills/framework-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "framework-selection", 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/framework-selection--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 framework-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install langchain-ai/skills-benchmarks framework-selection --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/framework-selection .gemini/skills/framework-selection && 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 "framework-selection" agent skill from https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/framework-selection into .gemini/skills/framework-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "framework-selection", 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 framework-selectionInstalls 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 framework-selection -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/framework-selection .github/skills/framework-selection && 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 "framework-selection" agent skill from https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/framework-selection into .github/skills/framework-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "framework-selection", 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 framework-selection -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 framework-selection --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/framework-selection .opencode/skills/framework-selection && 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 "framework-selection" agent skill from https://github.com/langchain-ai/skills-benchmarks/tree/main/skills/benchmarks/framework-selection into .opencode/skills/framework-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "framework-selection", 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
framework-selection- GitHub stars
- 118
- Token cost
- ~1.8k tokens
- SKILL.md length
- 769 words
- Files
- 1
- Skills in repo
- 8
- Repo updated
- First seen
- Licence
- MIT
At a glance
INVOKE THIS SKILL at the START of any LangChain/LangGraph/Deep Agents project, before writing any agent code.
- Tasks that involve Building AI agents
- SKILL.md covers Decision Guide, Framework Profiles, Mixing Layers and Quick Reference
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Framework Selection is an agent skill from langchain-ai/skills-benchmarks, published by the product's own GitHub organization. INVOKE THIS SKILL at the START of any LangChain/LangGraph/Deep Agents project, before writing any agent code. Determines which framework layer is right for the task: LangChain, LangGraph, Deep Agents, or a combination. Must be consulted before other agent skills.
Its SKILL.md is about 1.8k 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
- “/framework-selection”
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.
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
Framework Selection loads about 1.8k tokens when it runs. Until then it costs about 71 tokens; SKILL.md has 769 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). 769 words, ~1,810 tokens.
Download SKILL.mdSave it as
.claude/skills/framework-selection/SKILL.md (or your agent's skills folder).- name
- framework-selection
- description
- INVOKE THIS SKILL at the START of any LangChain/LangGraph/Deep Agents project, before writing any agent code. Determines which framework layer is right for the task: LangChain, LangGraph, Deep Agents, or a combination. Must be consulted before other agent skills.
<overview>
LangChain, LangGraph, and Deep Agents are **layered**, not competing choices. Each builds on the one below it:┌─────────────────────────────────────────┐
│ Deep Agents │ ← highest level: batteries included
│ (planning, memory, skills, files) │
├─────────────────────────────────────────┤
│ LangGraph │ ← orchestration: graphs, loops, state
│ (nodes, edges, state, persistence) │
├─────────────────────────────────────────┤
│ LangChain │ ← foundation: models, tools, chains
│ (models, tools, prompts, RAG) │
└─────────────────────────────────────────┘Picking a higher layer does not cut you off from lower layers — you can use LangGraph graphs inside Deep Agents, and LangChain primitives inside both.
This skill should be loaded at the top of any project before selecting other skills or writing agent code. The framework you choose dictates which other skills to invoke next.
</overview>
Decision Guide
<decision-table>Answer these questions in order:
| Question | Yes → | No → |
|---|---|---|
| Does the task require breaking work into sub-tasks, managing files across a long session, persistent memory, or loading on-demand skills? | Deep Agents | ↓ |
| Does the task require complex control flow — loops, dynamic branching, parallel workers, human-in-the-loop, or custom state? | LangGraph | ↓ |
| Is this a single-purpose agent that takes input, runs tools, and returns a result? | LangChain (create_agent) | ↓ |
| Is this a pure model call, retrieval pipeline, or simple prompt chain with no agent loop? | LangChain (direct model / chain) | — |
</decision-table>Framework Profiles
<langchain-profile>LangChain — Use when the task is focused and self-contained
Best for:
- Single-purpose agents that use a fixed set of tools
- RAG pipelines and document Q&A
- Model calls, prompt templates, output parsing
- Quick prototypes where agent logic is simple
Not ideal when:
- The agent needs to plan across many steps
- State needs to persist across multiple sessions
- Control flow is conditional or iterative
Skills to invoke next: langchain-fundamentals, langchain-rag, langchain-middleware
</langchain-profile><langgraph-profile>LangGraph — Use when you need to own the control flow
Best for:
- Agents with branching logic or loops (e.g. retry-until-correct, reflection)
- Multi-step workflows where different paths depend on intermediate results
- Human-in-the-loop approval at specific steps
- Parallel fan-out / fan-in (map-reduce patterns)
- Persistent state across invocations within a session
Not ideal when:
- You want planning, file management, and subagent delegation handled for you (use Deep Agents instead)
- The workflow is straightforward enough for a simple agent
Skills to invoke next: langgraph-fundamentals, langgraph-human-in-the-loop, langgraph-persistence
</langgraph-profile><deep-agents-profile>Deep Agents — Use when the task is open-ended and multi-dimensional
Best for:
- Long-running tasks that require breaking work into a todo list
- Agents that need to read, write, and manage files across a session
- Delegating subtasks to specialized subagents
- Loading domain-specific skills on demand
- Persistent memory that survives across multiple sessions
Not ideal when:
- The task is simple enough for a single-purpose agent
- You need precise, hand-crafted control over every graph edge (use LangGraph directly)
Middleware — built-in and extensible:
Deep Agents ships with a built-in middleware layer out of the box — you configure it, you don't implement it. The following come pre-wired; you can also add your own on top:
| Middleware | What it provides | Always on? |
|---|---|---|
TodoListMiddleware | write_todos tool — agent plans and tracks multi-step tasks | ✓ |
FilesystemMiddleware | ls, read_file, write_file, edit_file, glob, grep tools | ✓ |
SubAgentMiddleware | task tool — delegate work to named subagents | ✓ |
SkillsMiddleware | Load SKILL.md files on demand from a skills directory | Opt-in |
MemoryMiddleware | Long-term memory across sessions via a Store instance | Opt-in |
HumanInTheLoopMiddleware | Interrupt and request human approval before sensitive tool calls | Opt-in |
Skills to invoke next: deep-agents-core, deep-agents-memory, deep-agents-orchestration
</deep-agents-profile>Show full SKILL.md (270 more words)Show less
Mixing Layers
<mixing-layers>
Because the frameworks are layered, they can be combined in the same project. The most common pattern is using Deep Agents as the top-level orchestrator while dropping down to LangGraph for specialized subagents.When to mix
| Scenario | Recommended pattern |
|---|---|
| Main agent needs planning + memory, but one subtask requires precise graph control | Deep Agents orchestrator → LangGraph subagent |
| Specialized pipeline (e.g. RAG, reflection loop) is called by a broader agent | LangGraph graph wrapped as a tool or subagent |
| High-level coordination but low-level graph for a specific domain | Deep Agents + LangGraph compiled graph as a subagent |
How it works in practice
A LangGraph compiled graph can be registered as a subagent inside Deep Agents. This means you can build a tightly-controlled LangGraph workflow (e.g. a retrieval-and-verify loop) and hand it off to the Deep Agents task tool as a named subagent — the Deep Agents orchestrator delegates to it without caring about its internal graph structure.
LangChain tools, chains, and retrievers can be used freely inside both LangGraph nodes and Deep Agents tools — they are the shared building blocks at every level.
</mixing-layers>Quick Reference
<quick-reference>| LangChain | LangGraph | Deep Agents | |
|---|---|---|---|
| Control flow | Fixed (tool loop) | Custom (graph) | Managed (middleware) |
| Middleware layer | 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 |
| Flexibility | Medium | High | Medium |
Middleware is a concept specific to LangChain (callbacks) and Deep Agents (explicit middleware layer). LangGraph has no middleware — you wire behavior directly into nodes and edges.
</quick-reference>© 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
Files
Just SKILL.md in skills/benchmarks/framework-selection of langchain-ai/skills-benchmarks.
Open the folder on GitHubat commit 9195f8c
Compare with similar skills
Framework Selection 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 |
|---|---|---|---|---|---|---|
| Framework Selection this skilllangchain-ai/skills-benchmarks | 118 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Mem0 Platform SDKmem0ai/mem0 | 67k | 1 repos | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| LangSmith Trace DebuggingComposioHQ/awesome-claude-skills | 77k | 9 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 Example Agent
GetBindu/Bindu
Add a new self-contained example agent under examples/. An agent skill from GetBindu/Bindu.
Failproof AI SDK Integration
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Related
Categories
Questions about Framework Selection
What does Framework Selection do?
INVOKE THIS SKILL at the START of any LangChain/LangGraph/Deep Agents project, before writing any agent code. Framework Selection is an agent skill from langchain-ai/skills-benchmarks, published by the product's own GitHub organization. INVOKE THIS SKILL at the START of any LangChain/LangGraph/Deep Agents project, before writing any agent code.
When should I use Framework Selection?
Framework Selection fits situations like: tasks that involve Building AI agents.
How do I install Framework Selection in Claude Code?
Run `npx skills add langchain-ai/skills-benchmarks --skill framework-selection -a claude-code`. Or copy the skill folder (skills/benchmarks/framework-selection in langchain-ai/skills-benchmarks) into .claude/skills/framework-selection in your project. Claude Code loads it when a task matches its description.
How do I install Framework Selection in Codex?
Run `npx skills add langchain-ai/skills-benchmarks --skill framework-selection -a codex`. Or copy the skill folder (skills/benchmarks/framework-selection in langchain-ai/skills-benchmarks) into .agents/skills/framework-selection in your project. Codex loads it when a task matches its description.
Can I use Framework Selection 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 framework-selection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/framework-selection, .gemini/skills/framework-selection, .github/skills/framework-selection and .opencode/skills/framework-selection in your project.
What does Framework Selection need to run?
SKILL.md names no scripts, command-line tools or credentials: Framework Selection is instructions for the agent only.
Does Framework Selection 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 Framework Selection 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 Framework Selection use?
Framework Selection 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 Framework Selection use?
About 1.8k tokens (SKILL.md is roughly 7.2k 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 Framework Selection?
Skills that share tags, products or a category with Framework Selection: 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 Framework Selection?
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.