Add Example Agent
GetBindu/Bindu
Add a new self-contained example agent under examples/. An agent skill from GetBindu/Bindu.
Explains how to build agents with the Deep Agents framework: create_deep_agent, the built-in middleware, the harness, SKILL.md format and configuration options.
$ npx skills add langchain-ai/langchain-skills --skill deep-agents-core -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install langchain-ai/langchain-skills deep-agents-core --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/langchain-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/config/skills/deep-agents-core .claude/skills/deep-agents-core && 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 "deep-agents-core" agent skill from https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/deep-agents-core into .claude/skills/deep-agents-core/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-agents-core", 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/langchain-skills/tree/main/config/skills/deep-agents-coreType 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/langchain-skills --skill deep-agents-core -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install langchain-ai/langchain-skills deep-agents-core --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/config/skills/deep-agents-core .agents/skills/deep-agents-core && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "deep-agents-core" agent skill from https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/deep-agents-core into .agents/skills/deep-agents-core/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-agents-core", 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/langchain-skills --skill deep-agents-core -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install langchain-ai/langchain-skills deep-agents-core --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/config/skills/deep-agents-core .cursor/skills/deep-agents-core && 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 "deep-agents-core" agent skill from https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/deep-agents-core into .cursor/skills/deep-agents-core/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-agents-core", 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/langchain-skills.git --path config/skills/deep-agents-core--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/langchain-skills --skill deep-agents-core -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install langchain-ai/langchain-skills deep-agents-core --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/config/skills/deep-agents-core .gemini/skills/deep-agents-core && 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 "deep-agents-core" agent skill from https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/deep-agents-core into .gemini/skills/deep-agents-core/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-agents-core", 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/langchain-skills deep-agents-coreInstalls 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/langchain-skills --skill deep-agents-core -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/config/skills/deep-agents-core .github/skills/deep-agents-core && 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 "deep-agents-core" agent skill from https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/deep-agents-core into .github/skills/deep-agents-core/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-agents-core", 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/langchain-skills --skill deep-agents-core -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/langchain-skills deep-agents-core --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/langchain-ai/langchain-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/config/skills/deep-agents-core .opencode/skills/deep-agents-core && 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 "deep-agents-core" agent skill from https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/deep-agents-core into .opencode/skills/deep-agents-core/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-agents-core", 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.
deep-agents-coreExplains how to build agents with the Deep Agents framework: create_deep_agent, the built-in middleware, the harness, SKILL.md format and configuration options.
Deep Agents is described as an opinionated agent framework on top of LangChain and LangGraph that ships with middleware: task planning through TodoListMiddleware, file-based context management with pluggable backends, delegation to specialized subagents, long-term memory across threads via a Store, human approval for sensitive operations, and on-demand skills. The harness supplies these, so you configure rather than implement them.
A comparison table helps choose between Deep Agents and LangChain's create_agent: Deep Agents for multi-step planning, large context needing file management, specialized subagents or persistent memory, and create_agent for simple single-purpose or single-session tasks. A middleware table maps each need to its middleware and notes, for example that HumanInTheLoopMiddleware requires a checkpointer and MemoryMiddleware requires a Store instance.
Examples in both Python and TypeScript show a basic agent with a custom tool and a full configuration with subagents, skills and persistence, using FilesystemBackend and MemorySaver. The excerpt is truncated, so further sections on the SKILL.md format are not covered here.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 16a992f. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python, typescript and markdown).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Deep Agents Core loads about 3.1k tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 505 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.
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.
The full file from langchain-ai/langchain-skills at commit 16a992f, republished under its MIT licence (© langchain-ai). 505 words, ~3,066 tokens.
.claude/skills/deep-agents-core/SKILL.md (or your agent's skills folder).<overview>
Deep Agents are an opinionated agent framework built on LangChain/LangGraph with built-in middleware:
The agent harness provides these capabilities automatically - you configure, not implement.
</overview>
<when-to-use>
| Use Deep Agents When | Use LangChain's create_agent When |
|---|---|
| Multi-step tasks requiring planning | Simple, single-purpose tasks |
| Large context requiring file management | Context fits in a single prompt |
| Need for specialized subagents | Single agent is sufficient |
| Persistent memory across sessions | Ephemeral, single-session work |
</when-to-use>
<middleware-selection>
| If you need to... | Middleware | Notes |
|---|---|---|
| Track complex tasks | TodoListMiddleware | Default enabled |
| Manage file context | FilesystemMiddleware | Configure backend |
| Delegate work | SubAgentMiddleware | Add custom subagents |
| Add human approval | HumanInTheLoopMiddleware | Requires checkpointer |
| Load skills | SkillsMiddleware | Provide skill directories |
| Access memory | MemoryMiddleware | Requires Store instance |
</middleware-selection>
<ex-basic-agent>
<python>
Create a basic deep agent with a custom tool and invoke it with a user message.
from deepagents import create_deep_agent
from langchain.tools import tool
@tool
def get_weather(city: str) -> str:
"""Get the weather for a given city."""
return f"It is always sunny in {city}"
agent = create_deep_agent(
model="claude-sonnet-4-5-20250929",
tools=[get_weather],
system_prompt="You are a helpful assistant"
)
config = {"configurable": {"thread_id": "user-123"}}
result = agent.invoke({
"messages": [{"role": "user", "content": "What's the weather in Tokyo?"}]
}, config=config)</python>
<typescript>
Create a basic deep agent with a custom tool and invoke it with a user message.
import { createDeepAgent } from "deepagents";
import { tool } from "@langchain/core/tools";
import { z } from "zod";
const getWeather = tool(
async ({ city }) => `It is always sunny in ${city}`,
{ name: "get_weather", description: "Get weather for a city", schema: z.object({ city: z.string() }) }
);
const agent = await createDeepAgent({
model: "claude-sonnet-4-5-20250929",
tools: [getWeather],
systemPrompt: "You are a helpful assistant"
});
const config = { configurable: { thread_id: "user-123" } };
const result = await agent.invoke({
messages: [{ role: "user", content: "What's the weather in Tokyo?" }]
}, config);</typescript>
</ex-basic-agent>
<ex-full-configuration>
<python>
Configure a deep agent with all available options including subagents, skills, and persistence.
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver
from langgraph.store.memory import InMemoryStore
agent = create_deep_agent(
name="my-assistant",
model="claude-sonnet-4-5-20250929",
tools=[custom_tool1, custom_tool2],
system_prompt="Custom instructions",
subagents=[research_agent, code_agent],
backend=FilesystemBackend(root_dir=".", virtual_mode=True),
interrupt_on={"write_file": True},
skills=["./skills/"],
checkpointer=MemorySaver(),
store=InMemoryStore()
)</python>
<typescript>
Configure a deep agent with all available options including subagents, skills, and persistence.
import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver, InMemoryStore } from "@langchain/langgraph";
const agent = await createDeepAgent({
name: "my-assistant",
model: "claude-sonnet-4-5-20250929",
tools: [customTool1, customTool2],
systemPrompt: "Custom instructions",
subagents: [researchAgent, codeAgent],
backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
interruptOn: { write_file: true },
skills: ["./skills/"],
checkpointer: new MemorySaver(),
store: new InMemoryStore()
});</typescript>
</ex-full-configuration>
<built-in-tools>
Every deep agent has access to:
write_todos - Track multi-step tasksls, read_file, write_file, edit_file, glob, greptask - Spawn specialized subagents</built-in-tools>
<skill-md-format>
Skills use **progressive disclosure** - agents only load content when relevant.
skills/
└── my-skill/
├── SKILL.md # Required: main skill file
├── examples.py # Optional: supporting files
└── templates/ # Optional: templates---
name: my-skill
description: Clear, specific description of what this skill does
---
# Skill Name
## Overview
Brief explanation of the skill's purpose.
## When to Use
Conditions when this skill applies.
## Instructions
Step-by-step guidance for the agent.</skill-md-format>
<skills-vs-memory>
| Skills | Memory (AGENTS.md) |
|---|---|
| On-demand loading | Always loaded at startup |
| Task-specific instructions | General preferences |
| Large documentation | Compact context |
| SKILL.md in directories | Single AGENTS.md file |
</skills-vs-memory>
<ex-skills-with-filesystem-backend>
<python>
Set up an agent with skills directory and filesystem backend for on-demand skill loading.
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver
agent = create_deep_agent(
backend=FilesystemBackend(root_dir=".", virtual_mode=True),
skills=["./skills/"],
checkpointer=MemorySaver()
)
result = agent.invoke({
"messages": [{"role": "user", "content": "Use the python-testing skill"}]
}, config={"configurable": {"thread_id": "session-1"}})</python>
<typescript>
Set up an agent with skills directory and filesystem backend for on-demand skill loading.
import { createDeepAgent, FilesystemBackend } from "deepagents";
import { MemorySaver } from "@langchain/langgraph";
const agent = await createDeepAgent({
backend: new FilesystemBackend({ rootDir: ".", virtualMode: true }),
skills: ["./skills/"],
checkpointer: new MemorySaver()
});
const result = await agent.invoke({
messages: [{ role: "user", content: "Use the python-testing skill" }]
}, { configurable: { thread_id: "session-1" } });</typescript>
</ex-skills-with-filesystem-backend>
<ex-skills-with-store-backend>
<python>
Load skill content into a Store backend for environments without filesystem access.
from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from deepagents.backends.utils import create_file_data
from langgraph.store.memory import InMemoryStore
store = InMemoryStore()
# Load skill content into store
skill_content = """---
name: python-testing
description: Best practices for Python testing with pytest
---
# Python Testing Skill
..."""
store.put(
namespace=("filesystem",),
key="/skills/python-testing/SKILL.md",
value=create_file_data(skill_content)
)
agent = create_deep_agent(
backend=lambda rt: StoreBackend(rt),
store=store,
skills=["/skills/"]
)</python>
</ex-skills-with-store-backend>
<boundaries>
</boundaries>
<fix-checkpointer-for-interrupts>
<python>
Interrupts require a checkpointer.
# WRONG
agent = create_deep_agent(interrupt_on={"write_file": True})
# CORRECT
agent = create_deep_agent(interrupt_on={"write_file": True}, checkpointer=MemorySaver())</python>
<typescript>
Interrupts require a checkpointer.
// WRONG
const agent = await createDeepAgent({ interruptOn: { write_file: true } });
// CORRECT
const agent = await createDeepAgent({ interruptOn: { write_file: true }, checkpointer: new MemorySaver() });</typescript>
</fix-checkpointer-for-interrupts>
<fix-store-for-memory>
<python>
StoreBackend requires a Store instance for persistent memory across threads.
# WRONG
agent = create_deep_agent(backend=lambda rt: StoreBackend(rt))
# CORRECT
agent = create_deep_agent(backend=lambda rt: StoreBackend(rt), store=InMemoryStore())</python>
<typescript>
StoreBackend requires a Store instance for persistent memory across threads.
// WRONG
const agent = await createDeepAgent({ backend: (config) => new StoreBackend(config) });
// CORRECT
const agent = await createDeepAgent({ backend: (config) => new StoreBackend(config), store: new InMemoryStore() });</typescript>
</fix-store-for-memory>
<fix-thread-id-for-conversations>
<python>
Use consistent thread_id to maintain conversation context across invocations.
# WRONG: Each invocation is isolated
agent.invoke({"messages": [{"role": "user", "content": "Hi"}]})
agent.invoke({"messages": [{"role": "user", "content": "What did I say?"}]})
# CORRECT
config = {"configurable": {"thread_id": "user-123"}}
agent.invoke({"messages": [...]}, config=config)
agent.invoke({"messages": [...]}, config=config)</python>
<typescript>
Use consistent thread_id to maintain conversation context across invocations.
// WRONG: Each invocation is isolated
await agent.invoke({ messages: [{ role: "user", content: "Hi" }] });
await agent.invoke({ messages: [{ role: "user", content: "What did I say?" }] });
// CORRECT
const config = { configurable: { thread_id: "user-123" } };
await agent.invoke({ messages: [...] }, config);
await agent.invoke({ messages: [...] }, config);</typescript>
</fix-thread-id-for-conversations>
<fix-frontmatter-required>
# WRONG: Missing frontmatter in SKILL.md
# My Skill
This is my skill...
# CORRECT: Include YAML frontmatter
---
name: my-skill
description: Python testing best practices with pytest fixtures and mocking
---
# My Skill
This is my skill...</fix-frontmatter-required>
<fix-backend-for-skills>
<python>
Skills require a proper backend to load from the filesystem.
# WRONG: Skills won't load without proper backend
agent = create_deep_agent(skills=["./skills/"])
# CORRECT: Use FilesystemBackend for local skills
agent = create_deep_agent(
backend=FilesystemBackend(root_dir=".", virtual_mode=True),
skills=["./skills/"]
)</python>
</fix-backend-for-skills>
<fix-specific-skill-descriptions>
Use specific descriptions to help agents decide when to use a skill.
# WRONG: Vague description
---
name: helper
description: Helpful skill
---
# CORRECT: Specific description
---
name: python-testing
description: Python testing best practices with pytest fixtures, mocking, and async patterns
---</fix-specific-skill-descriptions>
<fix-subagent-skills>
<python>
Skills are not inherited by subagents - provide them explicitly.
# WRONG: Custom subagents don't inherit skills
agent = create_deep_agent(
skills=["/main-skills/"],
subagents=[{"name": "helper", ...}] # No skills
)
# CORRECT: Provide skills explicitly
agent = create_deep_agent(
skills=["/main-skills/"],
subagents=[{"name": "helper", "skills": ["/helper-skills/"], ...}]
)</python>
</fix-subagent-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
Just SKILL.md in config/skills/deep-agents-core of langchain-ai/langchain-skills.
Open the folder on GitHubat commit 16a992f
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in langchain-ai/langchain-skills, which our catalogue first saw on October 7, 2026.
Deep Agents Core 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 |
|---|---|---|---|---|---|---|
| Deep Agents Core this skilllangchain-ai/langchain-skills | 1.3k | — | ~3.1k | Automated safety check: Pass | MIT | |
| 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 | |
| Dive Into LangGraphluochang212/dive-into-langgraph | 457 | — | ~837 | Automated safety check: Notes | Custom licence | |
| Tool Designagentailor/fullstack-langgraph-nextjs-agent | 132 | — | ~3.2k | Automated safety check: Pass | MIT | |
| Langgraph Testing Evaluationsoba-labs/langchain-agent-skills | 107 | — | ~2.3k | Automated safety check: Pass | MIT |
GetBindu/Bindu
Add a new self-contained example agent under examples/. An agent skill from GetBindu/Bindu.
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.
luochang212/dive-into-langgraph
A Chinese-language guide and reference for building agents with LangGraph 1.0, from a first ReAct agent through middleware, memory, MCP, RAG and web search.
agentailor/fullstack-langgraph-nextjs-agent
Design and verify tools that AI agents can actually use — for any framework or language (MCP servers, LangChain/LangGraph, function-calling, raw JSON schema; TypeScript, Python, or otherwise).
soba-labs/langchain-agent-skills
A skill your agent uses when you need to test or evaluate LangGraph/LangChain agents: writing unit or integration tests, generating test scaffolds, mocking LLM/tool behavior, running trajectory…
TencentCloudBase/CloudBase-AI-Toolkit
Build and deploy AI agents with CloudBase Agent SDK (TypeScript & Python).
langchain-ai/langchain-skills
Builds agent evaluations in stages: inspect the repository and traces, agree a Task Spec with you, then build, audit and run a Harbor task with an independent verifier.
langchain-ai/langchain-skills
Fans a list of independent items out to subagents in parallel, merges the results back into a table and supports retrying only the rows that failed.
langchain-ai/langchain-skills
INVOKE THIS SKILL when implementing human-in-the-loop patterns, pausing for approval, or handling errors in LangGraph.
langchain-ai/langchain-skills
Routes LangGraph agents with typed decision models that return probabilities, and finds LLM calls that only exist to produce a routing decision.
langchain-ai/langchain-skills
INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping.
langchain-ai/langchain-skills
INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents.
Works with
Categories
Explains how to build agents with the Deep Agents framework: create_deep_agent, the built-in middleware, the harness, SKILL.md format and configuration options. Deep Agents is described as an opinionated agent framework on top of LangChain and LangGraph that ships with middleware: task planning through TodoListMiddleware, file-based context management with pluggable backends, delegation to specialized subagents, long-term memory across threads via a Store, human approval for sensitive operations, and on-demand skills. The harness supplies these, so you configure rather than implement them.
Deep Agents Core fits situations like: building an agent that must plan multi-step work and manage files; choosing between Deep Agents and a plain LangChain create_agent; adding subagents, memory or human approval to a Deep Agents application.
Run `npx skills add langchain-ai/langchain-skills --skill deep-agents-core -a claude-code`. Or copy the skill folder (config/skills/deep-agents-core in langchain-ai/langchain-skills) into .claude/skills/deep-agents-core in your project. Claude Code loads it when a task matches its description.
Run `npx skills add langchain-ai/langchain-skills --skill deep-agents-core -a codex`. Or copy the skill folder (config/skills/deep-agents-core in langchain-ai/langchain-skills) into .agents/skills/deep-agents-core in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add langchain-ai/langchain-skills --skill deep-agents-core -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deep-agents-core, .gemini/skills/deep-agents-core, .github/skills/deep-agents-core and .opencode/skills/deep-agents-core in your project.
SKILL.md names no scripts, command-line tools or credentials: Deep Agents Core is instructions for the agent only. Our summary lists: The deepagents package for Python or TypeScript.
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.
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.
Deep Agents Core is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Deep Agents Core: Add Example Agent (GetBindu/Bindu, 10k stars), Failproof AI SDK Integration (FailproofAI/failproofai, 5.3k stars), Dive Into LangGraph (luochang212/dive-into-langgraph, 457 stars) and Tool Design (agentailor/fullstack-langgraph-nextjs-agent, 132 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
langchain-ai (a GitHub organization, an official publisher) maintains it in langchain-ai/langchain-skills, which has 1,274 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 8, 2026.
Source: langchain-ai/langchain-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.