openloops compared with LangGraph and CrewAI
Where each framework gives you the work done and where it leaves it to you, from persistence to MCP.
01 / Side by side
Feature by feature
| openloops | LangGraph (OSS) | CrewAI (OSS) | |
|---|---|---|---|
| Persistence | Built in, zero config (MongoDB) | You choose and configure a checkpointer: in-memory, SQLite or Postgres, the last two in separate packages | Memory is opt-in and stored as local files (ChromaDB and SQLite, or LanceDB in newer versions), tied to the machine |
| Multi-user scoping | Built in: chats, tasks, context, and MCP servers are per-user from the start | Conversations are scoped by a thread_id. Mapping them to users, and scoping memory and tools per user, is up to you | Not a concept of the framework, you build it |
| Tool execution and retries | Validation, confirmation of destructive tools, duplicate and invented-id protection, and LLM and MCP retries are built into the harness | Per-node RetryPolicy and error handling that you configure | Retry limits are configured per agent or task |
| MCP support | Native: servers are a per-user record, tools load on demand, and every server goes through an approval flow | A separate package (langchain-mcp-adapters) with a client that you wire into the graph | Native mcps field on agents, or MCPServerAdapter. Servers are declared in code, not loaded per user |
| User management | Included (openloops/users, openloops/authentication) | Not included, you build your own | Not included, you build your own |
| LLM usage and cost tracking | Included (openloops/llmcalls): per answer, per chat, and per-user statistics | Not included in OSS, available via paid LangSmith | Third-party integrations such as OpenLIT report token usage and cost |
| Observability | Sentry and Langfuse via env vars, plus traces stored per chat | Your own tracing setup, or paid LangSmith | Third-party integrations (Langfuse, OpenLIT, Arize Phoenix, Opik, Weave) that you set up |
| Pre-built agents | Growing marketplace of ready-to-use loops | You build every agent from primitives | No built-in agent marketplace |
| What you write | Node functions (plain async functions) | Nodes and the graph's edges, explicitly | Agents, tasks, and crew orchestration, declared per role |
| Mental model | A state machine your loop drives itself | A graph you declare and compile ahead of time | A crew of role-based agents coordinated for you |
02 / In depth
One article per alternative
Openloops vs. LangGraph: An Honest Comparison for 2026
Why two years of building with LangGraph and CrewAI led me to open-source openloops, and how the two frameworks actually compare, feature by feature.
Openloops vs. CrewAI: An Honest Comparison for 2026
A grounded, feature-by-feature look at how openloops and CrewAI differ: multi-agent coordination, memory and persistence, multi-tenant isolation, and MCP support.
Openloops vs. OpenClaw: An Honest Comparison for 2026
OpenClaw is a complete personal AI assistant for one person. openloops is a framework for building a product other people will use. Here's what that actually means in practice.
Openloops vs. ZeroClaw: An Honest Comparison for 2026
ZeroClaw is a lean, single-binary personal AI assistant written in Rust. openloops is a framework for building a product other people will use. Here's what that difference actually means.
Openloops vs. Hermes Agent: An Honest Comparison for 2026
Hermes Agent is a self-improving personal assistant built by Nous Research, with a closed learning loop and user modeling for one person. openloops is a framework for a product many people will use. Here's what that difference means.
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