An AI agent that lives in every messaging app you use, plus 3 more open source standouts

The Lens

Hermes is Nous Research's open-source autonomous agent. It builds skills from experience, remembers them across sessions, and connects to Telegram, Discord, Slack, WhatsApp, Signal, and email out of the box. It drives more than 300 models through OpenRouter, OpenAI, Anthropic, Hugging Face endpoints, or Nous Research's own portal. MIT licensed. Install is one curl command on Linux, macOS, WSL2, or Termux. After hermes setup, point it at any provider; switching models is a single CLI flag with no code changes. It runs on a $5 VPS or a GPU cluster, and releases land several times a month. Nous now sells subscriptions alongside the free agent, which is the change worth knowing about. There is still a free tier, and the software itself has no paid edition or feature gate. The paid plans are model access: Plus at $20 a month, Super at $100, and Ultra at $200, each bundling 300+ models and the hosted tools Hermes calls into one bill instead of four provider accounts. Each tier carries a monthly credit allowance in the neighbourhood of what you pay for it. Solo builders can ignore all of it and bring an OpenRouter key. Anyone tired of managing separate provider keys has a reason to look. The catch is that it is research-y. Nous is an AI research lab, not a SaaS company. Docs are dense, support is community-driven, and self-improving claims always come with caveats. Treat it as an experiment you are running, not a production-grade agent you are deploying.

The Lens

Cc-switch wraps them into a single Tauri-based GUI. Cross-platform, open source, and free. Consider it a launcher that lets you switch between agents without context-switching between terminals. Setup is straightforward: download the app, configure your API keys, and pick which agents you want active. It doesn't add intelligence on top of the agents themselves. It's a convenience layer. The value is entirely in the unified interface and the ability to compare agent outputs side-by-side. Solo developers who already use multiple coding agents will get the most out of this. Teams probably don't need it since most teams standardize on one agent. If you only use one coding agent, there's nothing here for you. The catch: it's a wrapper, not a product. If the underlying agents change their CLI interfaces (which they do, frequently), cc-switch breaks until someone updates the integration. You're adding a dependency on a third-party GUI for tools that already work fine in a terminal.

The Lens

opencode is an open-source AI coding agent that lives in your terminal. It is built by Anomaly, the team behind SST, and it is the project now hosted at opencode.ai (the old sst/opencode repo redirects here). Bring any model provider, Anthropic, OpenAI, Google, GitHub Copilot, or a local model, and it reads, writes, and edits files and runs shell commands from a clean TUI. MIT licensed. The design choice that sets it apart is a two-agent flow: a plan agent that only reads and analyzes, and a build agent that actually makes changes, so you review the plan before anything touches your code. It pulls in LSP diagnostics as it works. Install is trivial through npm, Homebrew, or a one-line script, with no Docker and no GPU. Supply an API key for a hosted model, or point it at a local one and run for nothing. The agent is still free and there are still no feature gates, but Anomaly now sells model access too. Zen is their gateway of models they have benchmarked for coding agents: pay per token with no subscription, several models free for now, GPT 5.6 Luna at $0.20 in and $1.20 out per million tokens under 272K of context, Claude Opus 5 at $5.00 and $25.00, GPT 5.5 Pro at $30.00 and $180.00. Balances auto-reload $20 when they drop below $5, and card processing of 4.4% plus $0.30 is passed through at cost. There is an enterprise track as well. Solo developers with existing API access should keep using it exactly as before. The catch is the model bill and the terminal-first workflow. opencode is free, but the models it drives are not, and heavy use adds up the same way a subscription would. For a graphical editor experience this is not it. It is a TUI first, with a desktop app still in beta.

The Lens

LLM Wiki builds a persistent, interlinked wiki out of your documents instead of re-answering the same questions from scratch. Most retrieval tools reason again on every query; this one reasons once, writes wiki pages with source traceability and cross-references, then maintains them as you add material. Feed it PDFs, Word files, or web clips. GPL-3.0, desktop app, macOS/Windows/Linux. It needs an LLM provider configured before it does anything: OpenAI, Anthropic, Google, Ollama, or any OpenAI-compatible endpoint. Point it at Ollama and the whole thing runs local and costs nothing. Point it at a hosted API and ingest is where the money goes, because the two-step pass reasons over content before generating pages. The build has grown well past a document indexer: a Rust backend agent with tool use, multimodal ingest that pulls images out of PDFs, a Deep Research mode running multi-query web searches, a Chrome clipper, and a knowledge graph view with community detection. Solo researchers and anyone sitting on a document pile get real value. Teams get less, since there is no shared instance. The catch is that GPL-3.0 is a deliberate choice with consequences. Build a product on this and your product inherits the license. For personal use, irrelevant; for anything commercial, it is the first thing to check. The quieter catch: a wiki generated by an LLM is a wiki full of LLM claims, and traceability tells you where a sentence came from, not whether it is correct.