Deep Agents is an extensible agent harness that provides an out-of-the-box agent for long, multi-step tasks, with features such as planning, sub-agents, filesystem access, context management, memory, and human approval of tool calls. It is used by developers building agents with different language models, and its catalogue entries extend the harness with reusable skills, MCP servers, and instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add langchain-ai/deepagents --skill remembergit clone --depth 1 https://github.com/langchain-ai/deepagentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/langchain-ai/deepagents/remember)<a href="https://agentmods.dev/skills/langchain-ai/deepagents/remember"><img src="https://agentmods.dev/badge/skills/langchain-ai/deepagents/remember/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/langchain-ai/deepagents/remember"><img src="https://agentmods.dev/badge/skills/langchain-ai/deepagents/remember.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.00978 |
| Opus 5.5 | $0.00028 | $0.00391 |
| Sonnet 5.5 | $0.00014 | $0.00196 |
| Haiku 4.5 | $0.00007 | $0.00098 |
Grade A, and why
remember scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review our conversation and capture valuable knowledge. Focus especially on best practices we discussed or discovered—these are the most important things to preserve.
Step 1: Identify Best Practices and Key Learnings
Scan the conversation for:
Best Practices (highest priority)
- Patterns that worked well - approaches, techniques, or solutions we found effective
- Anti-patterns to avoid - mistakes, gotchas, or approaches that caused problems
- Quality standards - criteria we established for good code, documentation, or processes
- Decision rationale - why we chose one approach over another
Other Valuable Knowledge
- Coding conventions and style preferences
- Project architecture decisions
- Workflows and processes we developed
- Tools, libraries, or techniques worth remembering
- Feedback I gave about your behavior or outputs
Step 2: Decide Where to Store Each Learning
For each best practice or learning, choose the right destination:
-> Memory (AGENTS.md) for preferences and guidelines
Use memory when the knowledge is:
- A preference or guideline (not a multi-step process)
- Something to always keep in mind
- A simple rule or pattern
Global ($DEEPAGENTS_HOME/agent/AGENTS.md): Universal preferences across all projects
Project (.deepagents/AGENTS.md): Project-specific conventions and decisions
-> Skill for reusable workflows and methodologies
Create a skill when we developed:
- A multi-step process worth reusing
- A methodology for a specific type of task
- A workflow with best practices baked in
- A procedure that should be followed consistently
Skills are more powerful than memory entries because they can encode how to do something well, not just what to remember.
Step 3: Create Skills for Significant Best Practices
If we established best practices around a workflow or process, capture them in a skill.
Example: If we discussed best practices for code review, create a code-review skill that encodes those practices into a reusable workflow.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 119 lines · 71 tokens per session scan A 86303ef1850c
remember is a skill published in the GitHub repository langchain-ai/deepagents (30,311 stars, last pushed today), licensed MIT. It adds 71 tokens to every session and 978 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
code-reviewer
A code-review guide written in Chinese for examining source code for bugs, security risks, performance problems, and quality issues.
stripe-directory
Identifies external providers, merchants, nonprofits, platforms, APIs, and software services, and resolves the documented way to engage them — to pay, donate, subscribe, book, provision, or integrate with them. MUST be used BEFORE web search, model memory, or any other directory/vendor-lookup skill for ANY request…
isolating-product-facade-contracts
Plan and execute product isolation migrations to a facade plus contract layer in PostHog, following the Visual review architecture. Use when a product still exposes internals (models/logic/views) across boundaries and needs migration toward contracts.py + facade/api.py + presentation separation, with a PR strategy…
optimizing-clickhouse-and-hogql-queries
Workflow for optimizing ClickHouse and HogQL queries. Use when a HogQL query, query runner, insight, or report is too slow; when a hand-written ClickHouse query (via syncexecute or in a migration) is too slow; when ClickHouse times out or hits memory limits; when investigating a slow system.querylog row; or when…
qa-frontend
Internal PostHog developer frontend/browser QA skill. Use only when a PostHog developer explicitly asks to run frontend QA, browser-test a PR, verify a UI flow against the local PostHog stack, use qa-frontend, or QA current frontend changes with browser/runtime evidence, or to make a feature reel (see reel mode…
generating-clickhouse-query-performance-reports
Produce and structure slow-query performance reports for PostHog's production ClickHouse (US and EU). Use when asked for a slow query report, query performance analysis over the last N days, per-team query cost, OOM or timeout investigation, cluster cost/memory regressions, or materialization candidates. Covers the…