Introduction

ServFlow is an agent builder. You describe an AI agent: what it should do, which models and tools it can use, and how the outside world reaches it. ServFlow runs it for you. There’s no glue code to write and no framework to learn: the agent is the product, and a compiled Go engine serves it.

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How ServFlow works

ServFlow is the only system you need to run and manage agents. The plumbing you would otherwise assemble yourself (webhook servers, cron jobs, sandboxed file storage, credential handling) is built in, and everything an agent depends on is created and managed in one place:

• Agents: the top-level object. An agent has a name, a model, and instructions, and everything below belongs to one.
• Workflows: the work an agent performs, either as HTTP endpoints the world can call (webhooks) or as scheduled and internal jobs (tasks).
• Tools: what the agent can decide to do while it works, such as query a database, hit an API, transform data, or run a workflow. The same building blocks compose workflows.
• Context retrieval: the agent pulls in what it needs to know before it responds, whether by searching a vector store, looking up records in a database, or reading files from its workspace.
• A workspace: files the agent can read and write, so it has somewhere to keep memories, state, and output.
• Integrations and secrets: the AI models, databases, and APIs the agent is allowed to use, with credentials stored encrypted and out of your configs.
• The dashboard and the CLI: two ways to build the same objects. Design visually on the canvas, or declare agents as JSON or YAML files, deploy them from the CLI, and check them into git.

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Example agents

ServFlow doesn’t box you into a single shape of agent. Here are some examples of agents you can create:

• A conversational agent that answers questions in a chat widget or on Telegram
• An autonomous agent that reads pull requests and comments on them
• A scheduled agent that watches data sources and reports what changed
• A plain HTTP API. The same engine serves ordinary endpoints, so ServFlow works even when no AI is involved

A workflow is a graph of tools: call a model, query a database, hit an API, branch, transform. Whatever shape your agent takes, it’s built from the same pieces.

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What makes it different

Agents are first-class, deployable objects. One agent bundles its prompts, tools, workflows, workspace, and credentials under a single name, so you can look at it, version it, move it, and delete it as one thing. It bends to your systems, not the other way around. Integrations connect agents to the infrastructure you already run: your PostgreSQL, your MongoDB, your vector store, your internal APIs. And because every agent is a plain JSON or YAML config underneath, you can customize any part of it and manage it like the rest of your code. It’s self-contained. A single binary serves your agents, the API, and the dashboard, with SQLite for storage. There’s no cluster to stand up and no fleet of services to keep alive. It’s light and fast. The engine is compiled Go: one process, a low memory footprint, and near-instant startup. It stays fast under real traffic, so your agents’ latency comes from the models they call, not from the platform running them. The same binary is comfortable on a laptop, in a container, or on a small VM. Local models are first-class. A provider is just an endpoint and a credential, so you can point your agents at any OpenAI-compatible server: Ollama or LM Studio on your machine, vLLM or LiteLLM on your own hardware. Pair a local model with the lightweight engine and the whole stack runs on one box, with nothing leaving it.

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Monitoring

Every run is traced end to end: which model was called, how many tokens it used, which tools it invoked, where the time went, and what failed. Analysing those traces tells you your agents’ success rate, not just their uptime: how often runs complete, where they go wrong, and what each one costs. Traces follow the OpenTelemetry standard, so they work with the observability stack you already have. ServFlow Cloud takes this further: it hosts your agents on managed infrastructure and turns those traces into insight you can use to improve them.

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Next steps