Pi vs OpenCode vs Hermes: Which Works Best With a Local Model?

Compare Pi, OpenCode, and Hermes for local coding agents: prompt overhead, setup, error handling, and undo strategies on your own machine. Pi, OpenCode, and Hermes Agent all run on your local machine, but each fills your model's context window differently before touching your code. Pi starts with about twelve hundred tokens of built-in prompt, leaving roughly thirty-one thousand tokens on a thirty-two thousand token window and processing in under three seconds on an M1 Pro. OpenCode delivers about seven thousand tokens upfront, its bash tool instructions alone exceed Pi's entire prompt, leaving about twenty-six thousand tokens and taking roughly sixteen seconds to process. Hermes Agent loads roughly fifteen thousand tokens of tooling designed for a complete personal assistant with memory and messaging, refuses to start below sixty-four thousand tokens, and takes roughly forty seconds on the same M1 Pro. Each handles your local model's mistakes differently: Pi rejects edits with exact string mismatches, OpenCode cycles through nine matching strategies to forgive indentation errors, and Hermes uses fuzzy matching plus automated syntax checks. For safety, Pi offers no built-in guardrails and requires git branches or optional extensions; OpenCode ships with a dedicated Plan agent and snapshots that auto-undo file changes; Hermes executes immediately by default but repairs mangled tool calls. This breakdown is for builders running inference locally who need to choose between minimal overhead (Pi), integrated safety rails (OpenCode), or an always-on assistant that codes (Hermes). Chapters: 0:00 Intro 1:18 Pi 5:37 OpenCode 9:34 Hermes 13:44 Verdict Tools & resources mentioned: Pi: https://github.com/earendil-works/pi OpenCode: https://github.com/anomalyco/opencode Hermes Agent: https://github.com/NousResearch/herme... Ollama: https://docs.ollama.com/integrations llama.cpp: https://github.com/ggml-org/llama.cpp About The Stack The Stack helps you build with AI. Each video takes one tool, model, or workflow and shows how it works in a few focused minutes, with the real benchmarks and real costs. We go deep on Claude Code and Cursor for AI coding, AI agents and MCP servers, the open-source AI tools and GitHub repos most people miss, RAG and vector search, fine-tuning, and running local LLMs on your own machine with Ollama and LM Studio. We compare models like ChatGPT and Claude, test AI automation with Zapier, Make, and n8n, and flag the tools that actually ship. Subscribe for new breakdowns: https://www.youtube.com/@the-stack-ai... #local coding agent #pi coding agent #opencode #hermes agent #local models