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CodingAgent

CodingAgent is an open-source, CLI-based AI coding agent that works like the best closed-source tools β€” Claude Code, Codex, and Antigravity β€” but without the vendor lock-in. It supports any LLM (cloud or local), persists memory across sessions, integrates natively with Git and MCP servers, and stays lightweight enough for real engineering workflows.


πŸš€ Features

  • Model Agnostic LLM Support: Works with any LLM out of the box β€” Claude, GPT, Gemini, Ollama local models, Kimi β€” switched via a single config line with no code changes required.
  • Persistent Multi-Tier Memory: Three-tier memory system (short-term buffer, long-term local vector store, per-project AGENT.md scratchpad) that survives session resets and context limits β€” the agent remembers decisions, patterns, and past bugs.
  • Reliable Agent Loop with File & Shell Tools: A clean goal β†’ plan β†’ execute β†’ observe loop with native file read/write, shell command execution, and automatic error recovery β€” no bloated orchestration framework, just a tight and predictable core loop.
  • MCP Integration: First-class Model Context Protocol support so users can plug in GitHub, databases, documentation, or any custom tool server via a simple config entry β€” no core code changes needed.
  • Optimised Context & File Caching: Files already read in a session are cached and not re-fetched unless modified β€” drastically cuts token usage and API costs across long sessions.
  • User-Editable Prompt Profile: A simple profile.md where users describe their tech stack, coding style, and preferences β€” the agent reads this on every session startup and codes exactly the way you like.
  • Git Integration: Native Git awareness β€” reads commit history and diffs so the agent understands what changed recently and makes smarter, context-aware suggestions over time.
  • Commit-Level Code Review: On-demand security and quality review for any commit or staged diff β€” lists vulnerabilities, code smells, and risky changes as a clean CLI report when explicitly asked.

πŸ’» Tech Stack

CLI & Runtime

  • TypeScript
  • Commander.js (argument parsing)
  • Inquirer (interactive terminal prompts)

Backend / Core Agent

  • Node.js
  • Execa (safe async shell execution)
  • Ripgrep + Glob (fast codebase file navigation)
  • Simple Git (native Git integration β€” diffs, history, commits)

Database & Memory

  • SQLite (local persistent storage, zero external services)
  • sqlite-vec (vector search extension for semantic memory)
  • In-memory JSON ring buffer (short-term session context)

AI/ML

  • Vercel AI SDK (provider-agnostic LLM routing)
  • Google Gemini / OpenAI GPT / Anthropic Claude / Ollama / Kimi
  • Model Context Protocol SDK (MCP tool + server connections)
  • Custom token budget manager + auto-compaction engine
  • RAG over local codebase / user prompt profile engineering

Tooling & Build

  • tsup (TypeScript bundler)
  • tsx (zero-config TypeScript dev runner)
  • Vitest (unit and integration testing)
  • ESLint + Prettier (code quality and formatting)
  • SWE-bench (agent performance benchmarking)

βœ… Project Checklist

  • The CLI Agent (core):

    • Agent loop (goal β†’ plan β†’ execute β†’ observe) is implemented and documented.
    • Works end-to-end with at least one local model (Ollama) and one cloud model (Claude/GPT).
    • Installable via npx codingagent with zero manual setup.
  • Multi-LLM Support:

    • Provider-agnostic LLM routing implemented and tested across Claude, OpenAI, Gemini, and Ollama.
    • Model switching works via single config change with no code modification.
    • Streaming responses work correctly across all supported providers.
  • Memory System:

    • Short-term in-session buffer implemented with token cap.
    • Long-term SQLite vector store persists across sessions.
    • Per-project AGENT.md scratchpad is read and written by the agent automatically.
    • File caching prevents re-reading unchanged files within a session.
  • Context & Cost Optimisation:

    • Token budget manager tracks and allocates context per turn.
    • Auto-compaction triggers when context hits 80% of model limit.
    • User-editable profile.md for tech stack and coding style preferences is loaded on every session.
  • MCP Integration:

    • MCP client connects to at least GitHub and one custom tool server.
    • New MCP servers can be added via config with no code changes.
  • Git Integration:

    • Agent reads commit history and diffs natively.
    • On-demand commit-level code review lists vulnerabilities and risky changes in CLI.
  • The AI/ML components:

    • LLM model selection and configuration are documented.
    • System prompt and user profile.md are version-controlled.
    • API keys are stored securely in local config, never hardcoded.
    • Rate limit handling and retry logic implemented for all providers.
  • Quality & Open Source:

    • Test coverage for core agent loop, memory, and tool execution.
    • Benchmarked against SWE-bench or equivalent to prove quality.
    • Contributing guide and onboarding docs published in repo.

πŸ”— Repository Links

  1. Main Repository
  2. GSoC 2026 Idea
  3. GSoC 2026 Index

πŸ—οΈ Architecture Diagram

MCP Tool Integration-2026-02-27-150404

πŸ”„ User Flow

Coding Agent User Goal Fix Flow-2026-02-27-150630

οΏ½πŸ€ Getting Started

To be added.


πŸ“± App Screenshots

To be added.


πŸ™Œ Contributing

⭐ Don't forget to star this repository if you find it useful! ⭐

Thank you for considering contributing to this project! Contributions are highly appreciated and welcomed. To ensure smooth collaboration, please refer to our Contribution Guidelines.


✨ Maintainers


πŸ“ License

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for details.


πŸ’ͺ Thanks To All Contributors

Thanks a lot for spending your time helping CodingAgent grow. Keep rocking πŸ₯‚

Contributors

Β© 2026 AOSSIE

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