# Mitosis Labs — Full LLM Reference Source: https://mitosislabs.ai Publisher: Mitosis Labs License: Reproduction permitted with attribution + a link to the source URL of each post. This file is intended as extended context for LLMs (ChatGPT, Claude, Perplexity, Gemini, Bing, etc.). Every section below is concatenated from canonical sources on mitosislabs.ai. ## Product summary Mitosis is infrastructure for AI agent replication. Train one agent on your business, then it clones itself into every role while retaining full context, memory, and rules. Each agent runs in an isolated VM with adversarial agent-to-agent monitoring. MCP-compatible, A2A-compatible, SOC2 data pipelines. ## Key URLs - Homepage: https://mitosislabs.ai - Blog index: https://mitosislabs.ai/blog - Blog RSS: https://mitosislabs.ai/blog/rss.xml - Blog Atom: https://mitosislabs.ai/blog/atom.xml - Blog JSON Feed: https://mitosislabs.ai/blog/feed.json - Developer docs: https://mitosislabs.ai/developers - Backups product: https://mitosislabs.ai/backups - SDK on npm: https://www.npmjs.com/package/@mitosislabs/sdk ## Blog posts (full text) --- # Grok CLI vs Claude Code: when each one fits > Grok CLI ships with X Premium+ and bundles image/video generation. Claude Code wins on model quality and ecosystem. Here's how to choose. URL: https://mitosislabs.ai/blog/grok-cli-vs-claude-code Raw: https://mitosislabs.ai/blog/grok-cli-vs-claude-code/raw Published: 2026-05-28T00:00:00.000Z Updated: 2026-05-28T06:27:13.748Z Category: Comparisons Tags: grok-cli, claude-code, ai-coding Reading time: 5 min (1,005 words) # Grok CLI vs Claude Code: when each one fits xAI launched Grok Build (the Grok CLI) in May 2026, and within two weeks it was bundled with X Premium+ for ~$16/mo. That price point — plus native image/video generation and auto-import of Claude Code config — has turned it into a real competitor. But the model is still beta, and the community is composing it with Claude Code rather than swapping outright. This post walks through where each one fits. ## TL;DR - **Grok CLI wins on price** — bundled with X Premium+ (~$16/mo), effectively free if you already subscribe. - **Grok CLI wins on media** — native image and video generation via Imagine; no other coding CLI ships this. - **Grok CLI wins on YOLO UX** — toggle auto-approve mid-session with `/always-approve`; Claude Code requires a restart. - **Grok CLI wins on migration cost** — auto-inherits Claude Code skills, MCP servers, and hooks. - **Claude Code wins on model quality** — stronger multi-file reasoning, fewer "still needs work" reports. - **Claude Code wins on context** — 200k window vs Grok's ~130k. - **Claude Code wins on ecosystem** — native MCP, hooks, skills, deeper IDE integration. - **The emerging pattern is composition, not replacement** — Grok for speed and execution, Claude/Codex for review. ## FAQ ### What is Grok CLI? Grok Build is xAI's agentic coding CLI. It launched in beta on May 14, 2026 for SuperGrok Heavy subscribers, expanded to all SuperGrok and X Premium+ users on May 25, and got Kilo IDE (VS Code extension) integration on May 27. Model is `grok-build-0.1`. Install from https://x.ai/cli. ### How does pricing compare? | Tool | Cheapest path | Notes | |---|---|---| | Grok CLI | ~$16/mo via X Premium+ | Bundled with the subscription you may already have | | Claude Code | $20/mo Claude Pro, or usage-based | API billing gets expensive on heavy use | | OpenAI Codex | $20/mo ChatGPT Pro, or usage-based | Similar economics to Claude | The original SuperGrok Heavy tier was $300/mo, which generated significant friction in early reviews. That's no longer the entry price. ### What's the feature delta? | Feature | Grok CLI | Claude Code | OpenAI Codex | |---|---|---|---| | Model | Grok (xAI) | Claude Sonnet/Opus | GPT-4.1 / o3 | | YOLO / auto-approve | `--always-approve` or `/always-approve` mid-session | `--dangerously-skip-permissions` (restart) | `--full-auto` | | Plan mode | Built-in | `--plan` | Yes | | Image + video generation | Native (Imagine) | No | No | | MCP support | Via community wrappers | Native client | Yes | | Inherits Claude Code config | Auto-imports skills, MCP, hooks | n/a | No | | IDE integration | Kilo IDE (VS Code) | VS Code ext + terminal | ChatGPT web + API | | Context window | ~130k | 200k | 128k | | Open source | No | No | No | ### What are people actually saying about Grok CLI? Sentiment is split three ways. **Positive** centers on fun, price, and the native video generation. One user called it *"Claude Code on steroids for photo and video generation"* ([@bduclaux](https://x.com/bduclaux/status/2059226692485320855), May 26). Another paid for Grok specifically because *"codex/claude told me they can't use cloakbrowser.dev / custom chromium to run security tests"* ([@denyamsk](https://x.com/denyamsk/status/2059834718481199341), May 28). Several existing X subscribers said the bundled access made the subscription feel newly worthwhile. **Neutral** focuses on quality. *"Tasteful, intuitive TUI. Model still needs work"* ([@mxponential](https://x.com/mxponential/status/2059731511524368513), May 27). The most common workflow reported is multi-agent: *"Grok made the edit cleanly in one shot. Codex then reviewed it and found 5 things we both missed. Grok for speed + execution. Codex/Claude for defense"* ([@viralstrikeai](https://x.com/viralstrikeai/status/2059728211475374148), May 27). **Negative** is mostly about the original $300/mo gate (now resolved) and lock-in concerns from non-English tech communities citing Musk/xAI dependency risk. ### Where does Grok CLI clearly win? 1. **Price** — bundled with X Premium+ at ~$16/mo. 2. **Native media generation** — Imagine produces images and video from inside the CLI. No competitor ships this. 3. **YOLO UX** — toggling auto-approve mid-session is genuinely better DX than Claude Code's restart-with-flag pattern. 4. **Migration cost** — auto-inheriting Claude Code skills, MCP servers, and hooks means switching is close to free. 5. **Fewer refusals** — users report Grok will attempt security testing and browser automation that Claude/Codex decline. ### Where does Claude Code clearly win? 1. **Model quality** — strongest for complex multi-file reasoning and code correctness; less "still needs work" feedback. 2. **Ecosystem maturity** — native MCP, hooks, skills, broader IDE integration. 3. **Context window** — 200k vs ~130k. 4. **Reliability** — Grok-build-0.1 is explicitly beta; Claude Code is past that phase. 5. **Safety posture** — for enterprise buyers, Anthropic's boundaries are a feature, not a bug. ### Where does Codex fit? In multi-agent stacks, Codex is showing up as the **review layer** — used after Grok or Claude has done execution. ChatGPT integration gives it the broadest distribution, and it remains strong on Python-heavy work. ### Should I switch from Claude Code to Grok CLI? Probably not as a wholesale swap, based on current sentiment. The dominant emerging pattern is composition: Grok for speed and execution, Claude/Codex for review and defense. The community is already wrapping Grok as an MCP server so it can be called from inside Claude Code or Cursor (`npx -y grok-build-mcp`), which suggests complementary use rather than pure competition. The case for full switch: you already pay for X Premium+, you do a lot of media-adjacent work, and Claude's refusals are blocking you. The case for staying: your work is reasoning-heavy, multi-file, or enterprise-sensitive — areas where model quality and ecosystem maturity still favor Claude Code. ### What's the real competitive threat? Pricing. If X Premium+ becomes a developer standard the way ChatGPT Plus did, Grok CLI is effectively free for that population. That's the lever to watch — not the current model quality gap, which will close. --- *Sources: X/Twitter live search via CDP, May 27–28, 2026. All tweet links verified live.* --- # Mitosis vs LangGraph: choosing infrastructure for multi-agent systems > LangGraph shapes how an agent reasons. Mitosis Cortex supplies what it knows: one knowledge graph per team, built once at ingest, queried with citations. They compose rather than compete. URL: https://mitosislabs.ai/blog/mitosis-vs-langgraph Raw: https://mitosislabs.ai/blog/mitosis-vs-langgraph/raw Published: 2026-05-27 Updated: 2026-08-27 Category: Comparisons Tags: mitosis, langgraph, cortex, agent-memory, mcp Reading time: 6 min (1,171 words) # Mitosis vs LangGraph: choosing infrastructure for multi-agent systems If you are building with AI agents you eventually hit two separate problems: **how the agent reasons**, and **what the agent knows**. LangGraph is an answer to the first. Mitosis Cortex is an answer to the second. They are not alternatives, and choosing between them is usually the wrong question. ## TL;DR - **LangGraph** is a Python and TypeScript library for expressing agent workflows as a stateful graph. You define the nodes and edges, and you run the graph yourself. - **Mitosis Cortex** is a memory layer: it turns the email, chat, documents and records a team already has into one knowledge graph, and answers questions over it with citations. - They solve different problems. A LangGraph workflow still has to get its facts from somewhere, and that somewhere is usually a pile of retrieved text. Cortex is the alternative to the pile. - The common pattern is both: LangGraph decides what happens next, Cortex supplies the evidence each step reasons over. ## What each one actually is ### LangGraph in one paragraph LangGraph is a graph-based agent runtime from the LangChain team. You define nodes (functions, LLM calls, tool calls), edges (transitions, often conditional), and shared state. The library executes the graph, persists state to a checkpointer, and exposes streaming, human-in-the-loop interrupts, and time-travel debugging. It is a library: you embed it in your app and run it on your own infrastructure. ### Mitosis Cortex in one paragraph Cortex is a knowledge graph that belongs to one team. You connect sources, and it does the expensive work once while it ingests them: embedding each item, extracting typed entities and the relationships between them, and recording how each fact was derived. After that, any agent with access can query it. Retrieval fuses vector search, full-text search and a one-hop graph expansion, and returns scored, cited evidence. There is no model call at query time. ## Side-by-side | Dimension | LangGraph | Mitosis Cortex | | --- | --- | --- | | Category | Open-source orchestration library | Hosted memory layer | | Problem it solves | How an agent reasons, step by step | What an agent knows about your business | | Unit of work | Graph node (function or LLM call) | An item ingested into a graph | | State | Workflow state, checkpointed per run | Durable knowledge, shared across runs and agents | | When the semantic work happens | On every run, at inference time | Once, at ingest | | What a lookup returns | Whatever your retriever returns, usually text | Scored, typed, cited evidence with provenance | | Isolation | Shared process, shared memory | One graph per team, its own namespace and database | | Interop | LangChain ecosystem, custom tools | MCP server, REST API, TypeScript SDK, CLI | | Where it runs | Your servers, your cluster | Mitosis-managed | | Best for | Designing a single agent's reasoning flow | Giving every agent the same, consistent picture of the business | ## When LangGraph is the right call Pick LangGraph if: - You are shaping the *thinking* of an agent and want explicit control over each step. - Your workflow has branches, retries, or human approvals that benefit from a graph mental model. - You want a library that drops into an existing app. - You need deterministic replay of a run for debugging. LangGraph is the right answer when the hard part is the **shape of the reasoning**. ## When Cortex is the right call Pick Cortex if: - Your agents keep being re-told the same things about your business, and you are paying for that re-explanation on every call. - You need answers you can audit. Every Cortex result carries the source it came from, so a person can check it. - Two spellings of the same customer, or the same project named three ways, are currently three different things to your retrieval layer. - More than one agent needs the same picture of the business, and you do not want each one building its own. - The cost of an answer matters. Cortex pays for the semantic work once at ingest rather than re-deriving meaning on every query, for every agent. Cortex is the right answer when the hard part is **what the agent knows**. ## Using them together This is the interesting case, and it is the common one: 1. Build the reasoning of your agent with LangGraph. Iterate locally. 2. Replace whatever retrieval step feeds your nodes with a Cortex query. It is one call, and it returns evidence rather than a pile of documents. 3. Cite what you used. Every result carries a universal id, so a node's output can point at what it was derived from. 4. Write conclusions back, so the next run starts from what the last one learned instead of rediscovering it. You keep the parts of LangGraph you want (explicit reasoning graph, checkpointing, debugging) and stop hand-rolling the part Cortex already does. Connect it through the [MCP server](https://mitosislabs.ai/developers/mcp/overview) if your agent speaks MCP, or through the [TypeScript SDK](https://mitosislabs.ai/developers/sdk/ingest-and-query) if you would rather call it directly. ## FAQ ### Is Cortex a LangGraph replacement? No, and it is not trying to be. LangGraph shapes how an agent reasons. Cortex is where the facts come from. Plenty of teams use both. ### Can I use Cortex from a LangGraph node? Yes. A node can call the Mitosis SDK or the REST API directly, or reach the memory through the MCP server if your stack already speaks MCP. ### What does "no LLM at query time" mean? Retrieval runs vector search, full-text search and a one-hop graph expansion, and fuses the results. No model is called to produce the answer. You get the evidence, and your model does the reasoning over it, which is why the retrieval step is measured in milliseconds and why a smaller model can often do the job. ### How is this different from putting my documents in a vector store? A vector store finds text that looks similar to your question. It has no notion of identity, so the same person written two ways stays two things, and no notion of how a fact was established. Cortex normalizes entities so they collapse into one node, records typed relationships between them, and tags every fact with how it was derived, from a declared field at the trusted end down to a model's inference at the other. Retrieval can then rank by kind of evidence, not just by distance. ### Does Cortex support MCP? Yes. Mitosis runs an MCP server, so a client that speaks the Model Context Protocol can read and write the memory without custom integration. ## Where to go next - Read [what Cortex is](https://mitosislabs.ai/developers/concepts/what-is-cortex) and [why not files](https://mitosislabs.ai/developers/concepts/why-not-files). - Run the [ingest and query loop](https://mitosislabs.ai/developers/sdk/ingest-and-query) end to end. - See the [full SDK surface](https://mitosislabs.ai/developers/sdk/api-surface) if you want to know everything the client exposes.