Every engineering team building with LLMs hits the same fork in the road. Do you use a platform, or do you build your own agent framework? The answer is not obvious, and anyone who tells you it is probably selling something.
We built Swrly because we hit the limits of DIY agent orchestration ourselves. But we are not going to pretend it is the right choice for every team. Here is an honest breakdown.
The Build-vs-Buy Question
The decision is not really about cost. It is about what your team should be spending its time on.
If your core product is an AI agent system -- if orchestration logic is your competitive advantage -- then building your own framework makes sense. You need full control over execution, memory, and tool dispatch because those are the things that differentiate you.
If your team uses AI agents to automate internal workflows, review PRs, triage incidents, or augment existing processes, then building orchestration infrastructure from scratch is a distraction. You would not build your own CI/CD system either.
What You Get with Swrly
Swrly is an opinionated platform. That means you trade some flexibility for a lot of velocity. Here is what you get out of the box:
Visual builder with 8 node types. Agent nodes, integration nodes, condition nodes, triggers, loops, approvals, and joins. You drag them onto a canvas, connect them, and you have a directed workflow. No Python glue code. No YAML files. The topology is the source of truth.
Trigger system. Five trigger types -- manual, webhook, cron, API, and event-based. Your workflows can fire on a GitHub push, a Slack message, a cron schedule, or an API call from your existing CI/CD pipeline. This is plumbing that takes weeks to build correctly and months to make reliable.
Observability from day one. Every agent run streams logs in real time. You can see which node is executing, what it produced, how long it took, and where it failed. When a 6-node workflow produces bad output, you click on the node that went wrong instead of reading through a 500-line trace.
47 integrations, 350+ MCP tools. GitHub, Slack, Linear, Jira, PagerDuty, Datadog, Sentry, databases, HTTP endpoints. Each integration is a set of pre-built tools that agents can call. No writing API wrapper code.
Zero infrastructure. No Redis clusters to manage, no BullMQ queues to tune, no worker processes to scale. You configure your agents, connect your API keys via BYOK, and run.
Plan enforcement and RBAC. Usage limits, role-based access, API key management, workspace isolation. The kind of boring operational stuff that takes a surprising amount of time to build.
What You Get Rolling Your Own
There are real advantages to building a custom framework. We would be dishonest to ignore them.
Full control over the execution engine. Swrly runs agents through the Claude Code SDK. If you need to use a different model provider, or you need custom execution semantics like speculative branching or dynamic agent spawning, you need your own runtime.
Custom memory and state management. Swrly provides context accumulation and scratchpad-based state. If your use case requires long-term vector memory, retrieval-augmented generation, or custom knowledge graphs, a platform's state model may not be enough.
Deep framework integration. If your team already has a LangChain or CrewAI codebase with months of investment, migrating to a platform has real costs. Sometimes the right move is to keep building on what you have.
Niche execution patterns. Multi-turn conversations, adversarial agent debates, agent-as-judge evaluation loops -- these patterns do not map cleanly to a DAG-based workflow builder. Custom code gives you the freedom to model these directly.
No vendor dependency. Your orchestration logic lives in your repo, runs on your infra, and does not depend on a third-party service being available.
Decision Framework
Here is a table we use internally when talking to teams evaluating Swrly:
| Factor | Swrly | DIY Framework |
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