AI Agent OSS, curated
A maintained directory of open-source frameworks, tools and runtimes for building AI agents — with activity, licensing and trade-offs made explicit.
Featured projectsEditor-selected, reviewed within the last 30 days
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Browser UseComputer UseExtracts the interactive elements of a page and hands the model a structured list, instead of asking it to guess click coordinates from a screenshot. That makes it markedly more reliable for form flows and scraping. It degrades on canvas-heavy applications and on sites with aggressive bot protection, where there is no clean DOM to read.
DifyWorkflowA complete platform — prompt orchestration, retrieval, agent tools and an admin UI — that non-engineers can operate once a team has set it up. The licence is a modified Apache 2.0 with additional conditions on multi-tenant hosting and branding, so it is source-available rather than open source. Read it before building a product on top.
hermes-agentTools & IntegrationsHermes Agent is Nous Research's own agent: a terminal UI plus a single gateway process that carries the same conversation into Telegram, Discord, Slack, WhatsApp, Signal and Email, with a cron scheduler, isolated subagents, and seven terminal backends from local and Docker to Modal and Vercel Sandbox. Its distinguishing piece is a closed learning loop — the agent curates its own memory on periodic nudges, creates skills after complex tasks, and searches past sessions through FTS5 with LLM summarization — which means the state that makes it useful accumulates under `~/.hermes`, outside your version control, and needs occasional pruning. It is an assistant you run and configure rather than a library you build on: the README documents `hermes` subcommands and slash commands, not an embedding API, so it is a poor fit if you wanted an agent loop to call from your own code. Putting a shell-capable agent behind chat platforms also makes the command-approval and DM-pairing settings load-bearing rather than optional.
LangfuseObservabilityRecords what an agent actually did — every call, tool invocation and cost — and turns those traces into evaluation datasets. The managed service runs the same codebase you can self-host with Docker Compose, so migrating in either direction stays cheap. Some enterprise features sit outside the MIT core; check which tier you need before committing.
LangGraphFrameworksLangGraph has you define the agent's flow as a graph. Nodes are the units of work — an LLM call, a tool run — edges decide which node runs next, and every node reads and writes one shared state. Edges can branch on that state to form loops, and the state is checkpointed at each node, so a run can stop for a person's approval and resume later, or restart from the last checkpoint after a crash. The cost is code: a simple tool-calling agent takes noticeably more setup here than elsewhere.
n8nWorkflowConnects hundreds of services as workflows you draw on screen — each step in the flow is a node — and increasingly puts an AI agent node inside those workflows. The licence is the thing to check first: it is source-available under the Sustainable Use License, not OSI-approved open source. Internal use and self-hosting are fine; reselling it as a hosted service is not.
OllamaModel ServingPulls a quantised model — one compressed to run in less memory — and starts a local HTTP API in front of it, so running a model is a single command. The obvious starting point for building against a model on your own hardware. For serving many people at once, a server built for throughput will do considerably better.
openclawTools & IntegrationsOpenClaw installs a Gateway on your own machine and makes it the local control plane for sessions, tools, events and channel connections, so the Control UI, CLI, TUI and every connected messaging channel drive the same assistant. Models can be hosted or local, and the project expects new capabilities to arrive as plugins built on the plugin SDK rather than as changes to the core. The constraints are stated plainly in the README: it is designed for a single operator, tools run on the host for the main session unless you configure sandboxing, and DM-capable channels leave the assistant reachable by unknown senders until you approve a pairing with `openclaw pairing approve <channel> <code>`. The README tells you to read the security, exposure and sandboxing guides before connecting other users or exposing the Gateway remotely, which is a fair signal that shared or multi-user deployment is not the case it was built for.
opencodeCoding AgentsThe distinguishing idea is that everything is declared rather than hard-wired: you define named agents in JSON or a markdown file, give each one its own model and its own per-tool permissions — allow, ask or deny for reading, editing and running commands — and switch between them with a keystroke. Two built-in primary agents ship this way, one with full access and one that asks before editing or running anything. It is MIT and provider-agnostic, but it moves fast and it recently changed hands, so old links and pinned versions go stale quickly.
OpenHandsCoding AgentsEdits files, runs tests and browses documentation inside an isolated container, so a failed run cannot damage the host. Competitive on SWE-bench style benchmarks, but budget carefully before pointing it at a real repository — long autonomous runs consume a great deal of tokens and still need review before anything is merged.
