Bantam is a minimalist, dependency-free AI agent specification with reference implementations in **Go** (`main.go` + `term_*.go`, module `code.luxferre.top/luxferre/bantam`) and **Perl 5** as **MicroBantam** (`mb`, under 100 SLOC). It provides an agentic loop capable of autonomous tool execution, shell interaction, real-time response streaming, markdown terminal rendering with box-drawing tables, Fibonacci backoff network resilience, and subagent delegation using any OpenAI-compatible completions API.
The entire philosophy of Bantam is built upon two principles:
1. The structure must be as simple as possible for anyone to be able to reimplement the agent from a plain algorithm description.
2. The agent only needs to provide two tools: a tool to call shell commands and a tool to call itself. In theory, this should be sufficient to give LLMs the ability to handle tasks of any complexity.
Because of the second principle, Bantam itself was named after Victorinox Bantam Alox, a small and lightweight Swiss army knife with only two tools.
The Go port is a single `main.go` plus four platform files (`term_linux.go`, `term_bsd.go`, `term_windows.go`, `term_other.go`) for the built-in raw-terminal line editor — zero external dependencies.
In interactive mode, prompts can span multiple lines: press **Ctrl+J** to insert a real line break (the cursor moves to the next line), then **Enter** to submit the whole multi-line prompt. The Go port ships its own raw-mode line editor (arrow keys move the cursor, Up/Down browse history, Backspace edits, Ctrl+C/Ctrl+D exit), so this works everywhere without dependencies.
- `/clear` — reset the conversation to just the system prompt
- `/quit` — exit
The current conversation is also **auto-saved** to `~/.bantam/sessions/autosave.json` after every turn, on `/clear`, `/load`, `/compact`, and on exit — so you can always `/load autosave` to resume where you left off.
Using these rules, everyone can build their own copy of Bantam from scratch in little time in any language that supports file access, shell and HTTP(S) calls.
### High-Level Overview
1. **Initialization**: Read `system.txt` and `model.cfg`. Prepare an array of messages starting with the system prompt `{"role": "system", "content": system_prompt}`.
2. **Input Processing**: Take user prompt (via command-line file parameter or interactive stdin), append `{"role": "user", "content": prompt}`, and invoke `AL(cfg, messages)`.
3. **Agentic Loop (`AL`)**:
- Send `messages` and tool definitions to the OpenAI-compatible `/chat/completions` API endpoint with custom `User-Agent` headers.
- If `stream=true`, parse SSE data chunks (`data: {...}`) in real-time to stream reasoning content (`reasoning_content`) and response text directly to stdout, bracketing the reasoning block with `--- reasoning start ---` / `--- reasoning end ---` markers.
- Reconstruct the assistant message. If `tool_calls` exist, trace the call (`[tool call: name(args)]`), validate JSON arguments, execute the requested tool (`shell_exec` or `run_subagent`), trace the result (`[tool result: name]`), append the tool response `{"role": "tool", "tool_call_id": id, "content": result}`, and repeat the loop.
- If no tool calls remain or `max_al_iterations` is reached, return the updated messages list.
### Main program
1. Read system prompt from `system.txt` (default if missing).
2. Read model parameters from `model.cfg` (`key=value` format).
3. Prepare a new message list with the system prompt (`role: "system"`).
4. Read the first command-line parameter. If non-empty, read user prompt from the specified file, append to `messages` (`role: "user"`), run `AL(cfg, messages)`, and exit.
5. Read user prompt from standard input (with `readline` line editing and history in `~/.bantam_history`; **Ctrl+J** inserts a real newline into the line being edited). If equal to `/quit` or EOF, exit. If equal to `/clear`, reset `messages` to step 3 and return to step 5. If equal to `/save`, write the whole `messages` array to `~/.bantam/sessions/<id>.json` (with an auto-generated summary) and return to step 5. If equal to `/list`, print saved sessions and their summaries and return to step 5. If starting with `/load`, replace `messages` with the saved session's messages (by exact id or unique prefix) and return to step 5. If equal to `/compact`, ask the LLM to summarize the conversation, replace `messages` with `[system, summary-user-message]`, and return to step 5. If starting with `/cfg`, display the current value (`/cfg <param>`) or update `model.cfg` live (`/cfg <param> <val>`) and return to step 5. If equal to `/help`, print the command list and return to step 5. After every user turn and on exit, auto-save `messages` to `~/.bantam/sessions/autosave.json`.
- If `stream=true`, parse SSE stream (`data: {...}`) for real-time reasoning and text output, bracketing reasoning with `--- reasoning start ---` / `--- reasoning end ---` markers.
2. Append the assistant's response message object to `messages`. If non-streaming and response has reasoning tokens (`reasoning_content` or `reasoning`), output them wrapped in `--- reasoning start ---` / `--- reasoning end ---` markers.
3. If there are pending `tool_calls` in the assistant response:
- For each tool call, output a trace log (`[tool call: name(args)]`).
- Validate JSON arguments. If invalid, format a tool-error response so the LLM can self-correct.
- Execute tool action (`shell_exec` or `run_subagent`).
- Output a trace log of the result (`[tool result: name]`).
- Append tool result message (`role: "tool"`, `tool_call_id`, `content`: result string) to `messages`.
- Loop back to step 1.
4. If no pending tool calls (or if `max_al_iterations` is reached), stop and return `messages`.
If the API rejects the request with an `Invalid assistant message: content or tool_calls must be set` error (usually caused by a previously cut-off stream that left an empty assistant message in the session), all implementations strip the last `assistant`-role message from the session and retry the call.
- `timeout` (HTTP timeout in seconds for LLM API calls, default 300; in the Go port it bounds connection setup and time-to-first-byte, so long streaming responses are not cut off mid-stream)
The Go port's built-in editor tracks the cursor with its own column math (terminal auto-wrap aware) and redraws from the first line of the buffer, so wrapped input stays clean at any terminal width.
When enabled, the interactive console uses a subtle ANSI palette: the pending-request status `...requesting...` is darkened bold, reasoning markers are cyan, reasoning text is dim, `[tool call: ...]` traces are yellow, `[tool result: ...]` headers are green, tool result bodies are dim (red for tool errors/unknown tools), and errors/network retries are red. Tool result payloads fed back to the LLM are never colored.
- Action: run `AL(cfg, [{"role": "system", "content": system_prompt + "\n\nImportant: this is a child agent"}, {"role": "user", "content": prompt}])` subject to recursion depth limit (`MAX_DEPTH = 5`) and return the text content of the last `assistant`-role message.
MicroBantam (`mb`) is a compressed Perl 5 reference implementation of the same agent in **under 100 SLOC**, written to stay readable while keeping the full agentic core. It reads the same `model.cfg` and `system.txt` from the current working directory.
- Full agentic loop: LLM calls, `shell_exec` / `run_subagent` tool execution with JSON argument validation (invalid args are fed back so the model can self-correct), and the 5-level subagent recursion depth limit
- A `...requesting...` in-flight indicator: in-place on a TTY (`\r` overwrite, erased on completion), a plain line when output is piped
- Session management: `/save`, `/list`, `/load <id>` (exact id only, no prefix matching), `/cfg <param> [val]`, and auto-save to `~/.bantam/sessions/autosave.json` after every turn and on exit; session ids get `-1`, `-2`, ... suffixes on same-second collisions
The default system prompt instructs the agent to respect `AGENTS.md`/`GEMINI.md`/`CLAUDE.md` files.
### Is there any common config place for Bantam?
No, loading `model.cfg` and `system.txt` is deliberately only supported from the current working directory. This allows natural separation of configs and system prompts per project. In case there's no `system.txt` inside the project, the concise and sensible default system prompt will be loaded. In case there's no `model.cfg` inside the project, Bantam will use the free Big Pickle model from OpenCode Zen with the temperature 0.7. Big Pickle has been chosen as the default because it has no set expiration date, unlike other OpenCode's keyless tiers.
On Android, Bantam (Go) and MicroBantam (`mb`) are easily runnable within the Termux environment. The Go implementation is preferred for performance reasons.
On iOS/iPadOS, the easiest way to use Bantam is to run MicroBantam (`mb`) inside iSH. Some terminal features may not be available (run with `rlwrap` to bring them back), but the agent itself is fully functional.
