Hey everyone, I’m a Computer Science undergrad, and lately, I’ve been obsessed with the idea of autonomous coding agents. The problem? I simply cannot afford the costs of running massive context windows for multi-step reasoning. I wanted to build a CLI tool that could utilize local models, API endpoints or/and the coolest part, it can utilize tools like Codex , Antigravity , Cursor , VS Code’s Copilot (All of these tools have free tiers and student plans), and Claude Code to orchestrate them into a capable swarm. But as most of you know, if you try to make multiple models/agents do complex engineering, they hallucinate dependencies, overwrite each other’s code, and immediately blow up their context limits trying to figure out what the new code that just appeared is. To fix this, I built Forge. It is a git-native terminal orchestrator designed specifically to make cheap models punch way above their weight class. I had to completely rethink how context is managed to make this work, here is a condensed description of how the basics of it work: The Cached Hypergraph (Zero-RAG Context): Instead of dumping raw files into the prompt (which burns tokens and confuses smaller models), Forge runs a local background indexer that maps the entire codebase into a Semantic AST Hypergraph. Agents are forced to use a query_graph tool to page in only the exact function signatures they need at that exact millisecond. It drops context size by 90%. Git-Swarm Isolation: The smartest tool available gets chosen to generate a plan before it gets reviewed and refined. Than the Orchestrator that breaks the task down and spins up git worktrees. It assigns as many agents as necessary to work in parallel, isolated sandboxes, no race conditions, and the Orchestrator only merges the code that passes tests. Temporal Memory (Git Notes): Weaker models have bad memory. Instead of passing chat transcripts, agents write highly condensed YAML “handoffs” to the git reflog. If an agent hits a constraint (e.g., “API requires OAuth”), it saves that signal so the rest of the swarm never makes the same mistake and saves tokens across the board. The Ask: I am polishing this up to make it open-source for the community later this week. I want to know from the engineers here: For those using existing AI coding tools, what is the exact moment you usually give up and just write the code yourself? When tracking multiple agents in a terminal UI, what information is actually critical for you to see at a glance to trust what they are doing, versus what is just visual noise? I know I’m just a student and this isn’t perfect, so I’d appreciate any brutal, honest feedback before I drop the repo. submitted by /u/EmperorSaiTheGod
Originally posted by u/EmperorSaiTheGod on r/ClaudeCode