Workflow & Low-code

hermes-agentTools & Integrations

Hermes 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.

n8nWorkflow

Connects 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.

AutoGPTWorkflow

An agent here is a graph of blocks — one block calls a service, transforms data, prompts a model or branches — assembled on a canvas rather than written in code, then run on demand, on a schedule or from a trigger. One repository holds both the free self-hosted platform and the code behind the paid hosted one, and that is the catch: everything under the platform directory is PolyForm Shield, which permits use but forbids offering it as a competing product.

LangflowWorkflow

Useful for sketching an agent visually and for showing non-engineers what a pipeline actually does, then exporting it as an API. Visual editors get unwieldy as branching grows, so treat it as a design and demo surface rather than the final home for complex logic.

DifyWorkflow

A 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.

ragflowRAG & Retrieval

RAGFlow is a server you run rather than a library you import: documents go into a dataset, it splits them into chunks, and you can read every chunk on screen and hand-correct the ones that came out wrong before any of it reaches a model, with chat answers citing the chunk they came from. Around that sit an agent canvas and an ingestion pipeline you can rebuild yourself, so retrieval, tools and MCP servers can be wired together without writing code. The optical character recognition, table-structure and layout models that make sense of scans run on PDFs and images only — Word, Excel and PowerPoint files are read structurally, and the docs tell you to convert a DOCX to PDF if you want the visual parser on it. The price of all this is weight: a Docker Compose stack with Elasticsearch, MySQL, MinIO and Redis behind it, 4 cores and 16 GB of RAM as the stated minimum, and parsing that the project's own FAQ concedes is slower than LangChain's. If your documents are already clean text and you want retrieval inside your own Python application, LlamaIndex or Haystack will fit better.

AnythingLLMRAG & Retrieval

AnythingLLM ships the whole retrieval stack as one installable application: a document collector, an embedding model, a vector database and the chat interface arrive together, so you drop files into a workspace and start asking questions without wiring services to each other. Documents attached to a chat go into the model's context in full by default, and only when they overflow the context window does the app offer to chunk and embed them, after which answers are assembled from a few retrieved snippets. New workspaces run in agent mode, so the same chat window can browse the web, query a SQL database, call MCP servers or run a flow you drew in the visual editor. The trade-off is that this is an application rather than a library — you extend it through its settings, its custom-skill plugin format and its REST API, never by importing it into a program of your own — and the three ways to run it are not the same product: the desktop build has no accounts, the Docker build has no bundled model, and the hosted tier has neither MCP nor custom skills.

LibreChatWorkflow

One deployment puts OpenAI, Anthropic, Google, Bedrock and any OpenAI-compatible endpoint behind a single login, and its agent builder lets non-programmers assemble assistants — instructions, tools, file search, code execution, MCP servers — then share them with access controls by user, group or role, with LDAP, OIDC and SAML covering enterprise sign-on. The cost is operational: a default install already runs six containers, and code execution and web search each add further services or external keys. AnythingLLM remains the simpler pick for one person wanting one container; LibreChat is built for the multi-user case. ClickHouse acquired the project in November 2025, stating the licence stays MIT.

ChatDevOrchestration

ChatDev 2.0 builds a multi-agent system as a graph you draw in the browser or write as YAML: agent, python, human and subgraph nodes joined by edges that can branch, loop and fan out. You start a run from the Launch tab, watch each node move from pending to running to success or failed, answer a human node mid-run, and the same file runs from Python through the chatdev package on PyPI. The virtual software company from the 2023 paper is still here, rebuilt as one of the shipped workflows with nine agents from Chief Executive Officer to Software Test Engineer, but the 1.x code behind that paper now sits on a separate branch. The platform itself is young, and its own guides already disagree with the code on field names, so expect to learn it from the sample YAML files rather than the prose.

conductorWorkflow

Conductor runs workflows you write as JSON — a list of tasks wired together by reference name — on a server you host, and it saves the result of every task, so a run that dies resumes at the task it stopped on instead of from the top. The code that does the real work sits outside the engine in workers: ordinary programs that poll the server over HTTP for one type of task and report the result, so any language that can call an HTTP API can host one and nothing in them has to be written a special way. Recent versions pull LLM calls, MCP tool discovery and vector search into the workflow as built-in task types, which makes an agent loop resumable without any framework code. The price is operational: this is a JVM server with a database, a distributed lock and an index behind it, and the open-source build ships no authentication of its own.

OnyxRAG & Retrieval

Formerly Danswer, this is the whole surface a company usually assembles by hand — a chat interface, indexing from more than fifty sources, custom agents with their own instructions and actions, a multi-step research mode that returns a report, web search, code execution and artifacts — deployable by one command and pointed at any model provider, self-hosted or proprietary. Two things to weigh: the full deployment is a stack of index, workers, inference servers, cache and blob store, and directories named ee carry a separate enterprise licence rather than MIT.

MastraFrameworks

Agents are declared with instructions and a model, tools are validated on both ends by a schema, and workflows are chained from steps that can suspend mid-run and resume later because their execution state is written to storage. The breadth is the appeal — retrieval, memory, evaluation and tracing are in the same package rather than assembled from four — but breadth also means a large surface to learn, and the Apache-2.0 grant stops short of a few directories the project reserves under a separate enterprise licence.