u/0xchamin

I wanted to understand how coding agents actually work at the lowest level, so I studied a few open-source implementations and built one in Python.

It's called hummcode (the Hummingbird Coding Agent).

pip install hummcode

GitHub: https://github.com/0xchamin/hummcode
PyPI: https://pypi.org/project/hummcode/

Hummcode is a coding agent with a clean async event loop, tree-based session memory, and a small set of composable primitive tools. No framework. No magic. The entire architecture is based on fundamentals.

Here are the tools

• read_file— reads any file
• list_files — recursive directory listing, skips node_modules/.git etc.
• edit_file — surgical search-and-replace (validates uniqueness before writing — refuses if the string appears 0 or 2+ times)
• execute_bash — runs shell commands, 120s timeout, returns stdout + stderr
• ask_oracle — delegates isolated questions to a cheaper secondary model

Here's the memory system overview:

Most agents use a flat messages = []list. The problem is every failed attempt stays in context forever and eventually you hit the token limit and get a 400. hummcode uses a tree instead — every message is a node with a parent pointer. If the agent goes down a bad path, `/rewind` moves the pointer back and the dead branch is excluded from all future LLM calls. When tokens get too high, a sliding window + LLM summarisation compacts the old nodes cleanly.

Hummcode is model agnostic:

Powered by LiteLLM. Swap between Claude, GPT-4o, Mistral, or Ollama by changing one env variable. You can also switch mid-session with `/model`.

The UI:

Ships with a Textual TUI — dual pane, chat on the left, tool execution log on the right. Also runs headless with `hummcode --cli`.

This work is hugely inspired from following notable work:

• Geoffry Huntley's coding agent post (ghuntley.com/agent) and his Go implementation (link to Git repo)
• Pi coding agent by Earendil (pi.dev) — the tree memory idea came from here
• TeenyCode by Yang Shun — proved you don't need much code for a working agent

This is v0.1.1. Rough in places. Happy to answer questions about the architecture or the design decisions.

If you like my work, please do star my Git repo. thanks!

GitHub: https://github.com/0xchamin/hummcode
PyPI: https://pypi.org/project/hummcode/

I'd like to share an MCP Server + PyPI package + AI web app. The app is called VoyageIntel. You can download and install VoyageIntel via:
pip install voyageintel && voyageintel serve.

For your quick reference, there are the link:

• AI web app: https://voyage.skyintel.dev/
• PyPI: https://pypi.org/project/voyageintel/
• GitHub: https://github.com/0xchamin/skyintel

VoyageIntel provides real time flight (commercial, military, private), satellite, International Space Station (ISS), and also vessel (cargo, passenger, fast++) tracking.

It is also an MCP server, fully compatible with Claude Code, Claude Desktop, VS Code - CoPilot, Cursor, Gemini CLI, Codex etc. MCP server works in both stdio and streamableHTTP modes. MCP server utilizes the underlying LLM natively. This also comes with a CLI. For CLI, you've the option to bring your own key (BYOK), and leverage LLMs. The web app also has a chat interface where you can ask questions in natural language- powered by LLMs. Chat also implements BYOK (the keys are stored in user's local browser storage and not shared). The chat also implements guardrails (check GitHub repo `railway-guardrail` branch).

This project is based on fully open source data. I leveraged FastMCP, LiteLLM, LLMGuard, and LangFuse (I wanted to deep dive into what happens in LLM/ API, tool calls, so thought of integrating LangFuse).

I highly recommend you to read through the README.md file of voyageintel branch of this repo (this is the most uptodate branch). It's very deep and comprehensive.

I'd like to hear your feedback. Pull-requests/ feature requests are also welcome. Please do star the GitHub repo if you find this interesting.

PS: VoyageIntel is an extension of SkyIntel*- which I built previously (also shared with the community, a couple of weeks ago)*.