# 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 - Docs: https://mitosislabs.ai/docs - Backups product: https://mitosislabs.ai/backups - SDK on npm: https://www.npmjs.com/package/@mitosislabs/sdk ## Blog posts (full text) --- # Mitosis vs LangGraph: choosing infrastructure for multi-agent systems > LangGraph gives you a graph runtime for orchestrating agent workflows. Mitosis gives you isolated VMs, agent replication, and adversarial monitoring. Here's when each one is the right call. URL: https://mitosislabs.ai/blog/mitosis-vs-langgraph Raw: https://mitosislabs.ai/blog/mitosis-vs-langgraph/raw Published: 2026-05-27 Category: Comparisons Tags: mitosis, langgraph, multi-agent, agent-orchestration, mcp Reading time: 6 min (1,199 words) # Mitosis vs LangGraph: choosing infrastructure for multi-agent systems If you're building a system with more than one AI agent, you eventually hit the same fork in the road: **who owns the orchestration, and who owns the runtime?** LangGraph and Mitosis sit on opposite sides of that fork. This post explains the difference, who each one is for, and how to combine them when neither alone is enough. ## TL;DR - **LangGraph** is a Python/TypeScript library for expressing agent workflows as a stateful graph. You run the graph yourself. - **Mitosis** is infrastructure for running agents at scale: isolated VMs per agent, agent-to-agent monitoring, replication ("mitosis"), and MCP/A2A-compatible interop. You don't run the graph — you ship agents. - They are not direct competitors. LangGraph is great inside a single agent or for prototyping a fixed flow; Mitosis is the substrate when you need many agents to live, work, and replicate as a colony. - If you're already on LangGraph and outgrowing it, the upgrade path is "wrap each LangGraph node as a Mitosis agent and let the platform handle isolation, scaling, and oversight." ## 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's a library — you embed it in your app and run it on your own infrastructure. ### Mitosis in one paragraph Mitosis is a platform: each agent gets a dedicated, isolated VM (in practice, a Kubernetes pod) with its own credentials, memory, and toolset. Agents talk to each other through encrypted channels with adversarial monitoring (other agents independently verify each output). A single agent can self-replicate ("undergo mitosis") into a specialized clone that inherits context but evolves toward its role. Mitosis exposes MCP and A2A so existing agents — including LangGraph-powered ones — can plug in. ## Side-by-side | Dimension | LangGraph | Mitosis | | --- | --- | --- | | Category | Open-source orchestration library | Hosted infrastructure platform | | Unit of work | Graph node (function or LLM call) | Agent (isolated VM with identity, memory, credentials) | | Coordination | In-process graph execution, shared state | Encrypted A2A messaging between independent agents | | Isolation | Shared process, shared memory | Dedicated VM per agent, no cross-contamination | | Oversight | Human-in-the-loop interrupts, time-travel | Agent-to-agent adversarial verification + audit logs | | Replication | Spawn new graph runs (stateless by default) | First-class: train one agent, clone it into many roles | | Interop | LangChain ecosystem, custom tools | MCP, A2A, bring your own agents (Salesforce, custom, etc.) | | Where it runs | Your servers, your container, your cluster | Mitosis-managed cluster (or bring your own K8s) | | Best for | Designing a single agent's reasoning flow | Operating a fleet of agents as a business | ## When LangGraph is the right call Pick LangGraph if: - You're shaping the *thinking* of a single agent and want explicit control over each step. - Your workflow has clear branches, retries, or human approvals that benefit from a graph mental model. - You want a library that drops into an existing app without paying for a platform. - The number of "agents" in your system is small (1–3) and they live in the same process. LangGraph is the right answer when the hard part is the **shape of the reasoning**, not the operational reality of running many agents safely. ## When Mitosis is the right call Pick Mitosis if: - You need to go from one agent to many — not just spinning up more graph runs, but running genuinely independent agents with their own identities, credentials, and memory. - You can't have agents share a process (compliance, blast-radius, multi-tenant security). - You want oversight that scales: not "a human reviews each step" but "other agents adversarially verify each step in real time." - You want the same agent to clone itself into specialized roles without retraining each one from scratch. - You're integrating with MCP-compatible tools or A2A-speaking agents from other vendors. Mitosis is the right answer when the hard part is the **operational reality of a fleet of agents**, not the reasoning of one. ## Using them together The two are complementary more often than competitive. A common pattern: 1. Build the reasoning of a single agent with LangGraph. Iterate locally. 2. Wrap that agent as a Mitosis agent (expose its main entry point as the agent's task handler). 3. Let Mitosis handle isolation, scaling, replication, A2A messaging, and oversight. 4. When you need a second agent — say, a verifier — write it the same way and let Mitosis orchestrate the dialogue. You keep the parts of LangGraph you love (explicit reasoning graph, debugging, checkpointing) and stop reinventing the parts Mitosis already gives you (VM isolation, identity, monitoring, replication). ## Migration checklist If you're moving an existing LangGraph workflow onto Mitosis: - [ ] Identify each "agent-like" node — anything that owns credentials, calls external services, or holds long-lived state. These become Mitosis agents. - [ ] Pull node-local secrets into the agent's environment instead of the graph state. - [ ] Replace in-process state-sharing with A2A messages between agents. - [ ] Move human-in-the-loop interrupts to the Mitosis approvals primitive. - [ ] Let Mitosis handle the "spawn N parallel workers" case via replication instead of fan-out edges. ## FAQ ### Is Mitosis a LangGraph replacement? No. LangGraph is the right tool for shaping how a single agent reasons. Mitosis is the right tool for running many agents safely in production. Many teams use both — LangGraph inside the agent, Mitosis around the agent. ### Can I run LangGraph agents on Mitosis today? Yes. Mitosis agents are containers that speak MCP and A2A. Wrap your LangGraph executor as the agent's task handler and ship the image to a Mitosis pod. Existing tools and checkpointers continue to work. ### Why isolated VMs per agent instead of a shared process? Three reasons. **Security:** a compromised agent can't read another agent's memory. **Compliance:** customer data routes through a single agent's environment, not a shared one. **Blast radius:** a runaway agent burns its own VM, not the whole fleet. ### What does "agent replication" mean in practice? Train one agent on your business — its tools, credentials, memory, constraints. When you need another agent in a new role (say, support after building sales), you don't start over. The original agent clones itself; the clone inherits the training and then differentiates toward its new role. We call this mitosis. ### Does Mitosis support MCP? Yes — every Mitosis agent can speak the Model Context Protocol natively. That means tools written for Claude, Cursor, or any MCP-compatible client work without modification. ## Where to go next - Start an [agent on Mitosis](https://mitosislabs.ai/signup) and try the replication primitive. - Read the [SDK docs](https://mitosislabs.ai/docs) to wrap an existing agent. - Browse the [other comparisons](https://mitosislabs.ai/blog/category/comparisons) — including Mitosis vs CrewAI, AutoGen, and OpenAI Swarm.