Linchpin vs Devin

Devin is a hosted, closed-source autonomous software engineer built by Cognition Labs. You sign up, give it a task in natural language, and it plans, codes, runs, and reports back. The agent loop, the sandbox, the model choice, the planner — all of it runs on Cognition's infrastructure. You do not run Devin yourself; you use it via web or Slack. Pricing is subscription-based and per-usage.

Linchpin is an open-source, self-hostable runtime for managed AI agents. It is a platform — three services and Postgres — that runs agents as long-lived sessions on your VM. You define agents, environments, tools, and policies; Linchpin handles sandboxing, the event log, model calls, and streaming. Linchpin itself is not a coding agent. It is the runtime layer on which you build (or run) one. License: Apache-2.0.

So they are not direct substitutes. Devin is a product you use. Linchpin is the platform you build on. The question "Linchpin vs Devin" usually really means "is there a self-hosted, open-source way to get Devin-shaped behavior?" — and Linchpin is the runtime piece of that answer.

• You need to run agents on your own infrastructure. Compliance, customer data, air-gapped envs, EU residency — anything that rules out a hosted U.S. SaaS that sees every prompt. Linchpin runs entirely on your VM with no outbound control-plane call.
• You want to control the model and switch providers freely. Linchpin uses OpenRouter for cloud (Claude, GPT, Gemini, Llama, DeepSeek, ~200 more) and Ollama for local. Pick a different model per agent; switch any time.
• You want to read the agent runtime end-to-end. Linchpin is ~3k LOC of Python plus a React console. You can audit every code path, every prompt, every tool call. Devin is a black box.
• You are building a product, not buying one. Linchpin is the platform layer. You write the agent prompts, the policies, the UX. Linchpin handles sessions, sandboxes, events.
• You want a long-term cost line that scales with VMs and tokens, not seats. Apache-2.0 license; no per-user fee.

• You want a coding agent today, fully managed. Devin ships as a product. You don't build infrastructure; you submit tasks and get pull requests.
• You don't want to think about model choice. Cognition picks the model and the planner. You don't tune it; they do.
• You are okay with prompts and code going through a U.S. SaaS. No compliance constraints, no data-residency requirements, no air-gap needs.
• You want polished UX out of the box. Slack integration, web UI, conversation thread — all there day one.

If your goal is "open-source self-hosted equivalent of Devin," the practical recipe is to combine Linchpin with an open-source coding agent and a code sandbox image:

• Runtime layer: Linchpin — sessions, event log, sandboxed Docker containers, tool execution, model adapter.
• Coding agent definition: system prompt, allowed tools (bash, read, write, edit, glob, grep, web_fetch, web_search), and policies (always_ask vs always_allow).
• Sandbox image: Linchpin spawns a Docker container per session with Python, Node, git, ripgrep preinstalled — close to what Devin runs.
• Model: OpenRouter routes to the same frontier models Devin uses (Claude, GPT, etc.). Or run a local Llama / Qwen via Ollama if you want zero outbound calls.
• UX layer: the Linchpin console, or write your own thin web UI against the HTTP API.

This is not a drop-in replacement. Devin's planner and tool selection are tuned for end-to-end software engineering tasks; you would tune yours. But all the runtime pieces — sessions, sandboxes, event log, streaming, vaults — are already in Linchpin. See the docs for the quickstart.

• Linchpin vs OpenHands — open-source coding agent vs runtime
• Linchpin vs Anthropic Managed Agents — open vs hosted control plane
• Linchpin vs Manus — open self-host vs general-purpose hosted
• Use case: self-hosted Devin alternative
• Use case: self-hosted AI agents
• What is a managed agent runtime?