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37 changes: 37 additions & 0 deletions GSoC-Ideas/2026/CodingAgent.md
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# CodingAgent: Open-Source CLI AI Coding Assistant

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⚠️ Potential issue | 🟡 Minor

🧩 Analysis chain

🏁 Script executed:

#!/bin/bash
# Check if CodingAgent.md is referenced in index.md
rg -i "codingagent" GSoC-Ideas/2026/index.md -C 2

Repository: AOSSIE-Org/Info

Length of output: 41


Add a link to this file in the 2026 GSoC Ideas index.

CodingAgent.md is not currently linked in GSoC-Ideas/2026/index.md. Please add a link to this idea in the index file so it's discoverable alongside other 2026 GSoC ideas.

🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.

In `@GSoC-Ideas/2026/CodingAgent.md` at line 2, Add a markdown link to the new
idea file so it's discoverable: update GSoC-Ideas/2026/index.md and insert a
link to CodingAgent.md (e.g., a bullet or list entry referencing
"CodingAgent.md" with a readable title) in the same style/section used for other
2026 ideas; ensure the link path points to GSoC-Ideas/2026/CodingAgent.md and
that formatting matches existing entries in index.md.

## Background and Motivation
Our motivation for building CodingAgent is that there is still no truly good open-source coding agent that works like the best closed-source tools such as Codex, Antigravity, or Claude Code.
We’ve used all of these extensively and we are frustrated that We can’t plug my favorite open-source models into a comparable, powerful agent experience.
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⚠️ Potential issue | 🟡 Minor

Fix pronoun consistency: "my favorite" → "our favorite".

Line 5 still contains "my favorite" which conflicts with the maintainer's feedback to replace first-person singular pronouns with plural or eliminate them entirely.

📝 Proposed fix
-We've used all of these extensively and we are frustrated that We can't plug my favorite open-source models into a comparable, powerful agent experience.
+We've used all of these extensively and we are frustrated that We can't plug our favorite open-source models into a comparable, powerful agent experience.
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
Weve used all of these extensively and we are frustrated that We cant plug my favorite open-source models into a comparable, powerful agent experience.
We've used all of these extensively and we are frustrated that We can't plug our favorite open-source models into a comparable, powerful agent experience.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.

In `@GSoC-Ideas/2026/CodingAgent.md` at line 5, Replace the singular first-person
phrase "my favorite" with the plural "our favorite" in the sentence that
currently reads "We’ve used all of these extensively and we are frustrated that
We can’t plug my favorite open-source models into a comparable, powerful agent
experience." — locate the exact phrase "my favorite" and change it to "our
favorite" to maintain pronoun consistency with the maintainer's guidance.

Key capabilities like robust persistent memory, solid MCP integration, and local vector-based storage for long-term context are either missing or poorly implemented in current open-source options.
We want to change that by creating an agent that treats memory and context as first-class citizens, not afterthoughts.
My goal is to build something that operates at the level of serious engineering workflows, not just toy demos.
In essence, We want to create the “clawdbot for coding” — a scalable, developer-first AI coding agent that is fully open, extensible, and production-ready.

## Overview of Tasks
* Setup TypeScript CLI skeleton with commander + multi-LLM config
* Build agent loop: goal → plan → file/shell tools → memory compaction
* Test end-to-end: codingagi "create hello.ts"

Candidates are expected to refine these tasks in their GSoC proposals.

## Requirements

* Model Agnostic - Works seamlessly with any LLM (Claude, OpenAI, Ollama, Kimi) via simple config, not locked to one provider
* Local vector store + structured notes that survive sessions and context limits (beats all open source agents)
* Reliable goal→plan→execute→observe cycle with MCP tools, file editing, and shell execution at Claude Code/Antigravity level (This point is very important to be explained in detail in the proposal, there are many complex plans in the market which are actually not suitable for this project, i want something straight forward, amazing and simple to use for the end user.)

## Resources

* Learn how memory layer is used here: https://github.com/opencode-ai/opencode
* excellent for CLI structure and local model integration: https://github.com/google-gemini/gemini-cli
* Full video tutorial building CLI agents with MCP, memory, and multi-agent workflows: https://www.youtube.com/watch?v=eNbza7cBL0U
* Loves Antigravity's autonomy/browser integration but warns of destructive ops, security gaps, and enterprise compliance risks vs Claude's safer design: https://blog.getbind.co/antigravity-vs-claude-code-which-is-better-for-developers/

## Mentors

* GitHub: [coderwithsense](https://github.com/coderwithsense/); Discord: rmvfsx

## Communication Channel

Join our Discord servers (https://discord.gg/xnmAPS7zqB and https://discord.gg/fuuWX4AbJt) and discuss this idea in [general](https://discord.com/channels/1022871757289422898/1180497744666837003).