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# Bantam: tiny, powerful, DIY AI agent
## About
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, direct shell interaction, file writing/editing, real-time response streaming, markdown terminal rendering with box-drawing tables, Fibonacci backoff network resilience, context window auto-discovery, token usage tracking with prompt cache breakdowns, and conversation compaction 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 (`shell_exec`) and a tool to write/edit files (`write_file`). 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.
## Usage
### Prerequisites
- **Go 1.21+** or **Perl 5.14+** (standard library / core modules only)
- An OpenAI-compatible API endpoint (or OpenAI API key)
### Installation (Go)
```bash
go install code.luxferre.top/luxferre/bantam@latest
```
This installs the `bantam` binary into `$(go env GOPATH)/bin` (make sure it is on your `PATH`). To build from a local checkout instead:
```bash
go build ./... # produces ./bantam
# or run without building:
go run . prompt.txt
```
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.
### Running Bantam
All implementations read `model.cfg` (or `.bantam.cfg`, which takes priority if present) from the current working directory.
1. Configure `model.cfg` (or `.bantam.cfg`, which takes priority) with your API settings:
```ini
endpoint=https://api.kilo.ai/api/openrouter
model=kilo-auto/free
temperature=0.7
api_key=your_api_key_here
stream=true
context_window=200000
```
2. Interactive mode:
```bash
bantam # Go (or: go run .)
./mb # MicroBantam (Perl 5)
```
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 clears line / interrupts in-flight run, Ctrl+D exits), working everywhere without third-party dependencies.
After every interaction, Bantam displays token usage (prompt tokens, cached/uncached breakdown when supported by the provider, completion tokens, and context window utilization):
```text
[tokens: 1420 prompt (1000 cached, 420 uncached) + 85 completion | context: 1420/200000 (0.7%)]
```
Sessions are saved under `~/.bantam/sessions/` and can be managed with these commands:
- `/save [name]` — save the entire conversation to a session file (named `name`, or defaulting to the MD5 hash of the current project directory) and generate its summary
- `/continue` (alias `/cont`) — continue/autoload the session corresponding to the current project directory
- `/list` — list saved sessions (newest first) with their ids, timestamps, message counts and summaries (marking the current project's session)
- `/load <id>` — load a saved session (exact id or unique prefix) and continue from there
- `/compact` — compact context down to the system message and a concise summary using the LLM; the compaction prompt is appended to the conversation to derive the summary, then the conversation is reset to `[system, summary-user-message]` (a fresh prefix, so downstream prompt-cache hits depend on the provider and are not guaranteed)
- `/cfg <param> [val]` — inspect or update a configuration parameter live (writes to `.bantam.cfg`)
- `/models` — query the `/models` path on the current inference endpoint and print a plain list of supported model IDs, marking the currently configured model with a leading `* ` (Go port)
- `!<cmd>` — execute a shell command directly through `shell_exec` without adding the result to the conversation context (Go port)
- `/help` — show all supported commands
- `/clear` — reset the conversation to just the system prompt
- `/quit` — exit
When context window usage reaches **60% or higher**, Bantam automatically offers to compact the conversation:
```text
Context usage is at 62.4% (124800 / 200000 tokens). Compact conversation? [Y/n]:
```
Pressing **Ctrl+C** during an active inference run or long-running shell execution cleanly interrupts the turn without appending partial or malformed responses to the conversation context.
The current conversation is also **auto-saved** to `~/.bantam/sessions/<project-md5>.json` after every turn, on `/clear`, `/load`, `/compact`, `/continue`, and on exit — so you can always run `/continue` (or `/cont`) to resume where you left off.
3. File input mode:
```bash
bantam prompt.txt # Go
./mb prompt.txt # MicroBantam (Perl 5)
```
## Rules of Bantam (The Algorithm)
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 the config file (`.bantam.cfg` if present, else `model.cfg`). Discover context window size from the `/models` endpoint (or fallback to `context_window` from `model.cfg` or 200,000 tokens). Prepare an array of messages starting with the built-in system prompt `{"role": "system", "content": system_prompt}`.
2. **Input Processing**: Take user prompt (via command-line file parameter or interactive stdin). If prefixed with `!`, execute the command directly via `shell_exec` without appending to conversation context. Otherwise, append `{"role": "user", "content": prompt}`, and invoke `AL(cfg, messages)`.
3. **Agentic Loop (`AL`)**:
- Send `messages` and tool definitions (`shell_exec`, `write_file`) to the OpenAI-compatible `/chat/completions` API endpoint with custom `User-Agent` headers and `stream_options: {"include_usage": true}`.
- Support context cancellation (e.g. on `SIGINT` / Ctrl+C) to cleanly abort in-flight requests without appending incomplete messages.
- On network or HTTP failure, retry using Fibonacci backoff delays (`1s, 1s, 2s, 3s, 5s, 8s, 13s, 21s, 34s`).
- 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, rendering Markdown and tables constrained to terminal width.
- Reconstruct the assistant message and track usage tokens (`prompt_tokens`, `completion_tokens`, cached tokens). If `tool_calls` exist, trace the call (`[tool call: name(args)]`), validate JSON arguments, execute the requested tool (`shell_exec` or `write_file`), 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 and turn usage stats.
4. **Post-Turn Reporting & Compaction**:
- Display token usage and context window percentage.
- If context usage is >= 60%, prompt user to compact.
- Compaction appends `"You are now acting as a compaction engine. Summarize the preceding conversation concisely but completely..."` as a user message to the conversation, invokes the LLM, and then resets the conversation to the system prompt plus a `user` message carrying the resulting summary. (The compaction prompt is not retained; the post-compaction conversation is a new prefix, so prompt-cache hits are provider-dependent.)
### Main program
1. Initialize system prompt from built-in default.
2. Read model parameters from the config file (`.bantam.cfg` if present, else `model.cfg`) in `key=value` format and discover context window size.
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. If prefixed with `!`, execute the shell command directly via `shell_exec` and exit. Otherwise, append to `messages` (`role: "user"`), run `AL(cfg, messages)`, display token usage, 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 starting with `/save`, write the whole `messages` array to `~/.bantam/sessions/<name>.json` (or `<project-md5>.json` if no name is given) and return to step 5. If equal to `/continue` or `/cont`, load the session corresponding to the current project's MD5 hash and return to step 5. If equal to `/list`, print saved sessions and their summaries (marking current project session) 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 by appending the compaction prompt to derive the summary, replace `messages` with `[system, summary-user-message]`, and return to step 5. If starting with `/cfg`, display the current value (`/cfg <param>`) or update the config live by writing to `.bantam.cfg` (`/cfg <param> <val>`) and return to step 5. If equal to `/models`, query the `/models` path on the current inference endpoint and print a plain list of supported model IDs (the currently configured model marked with a leading `* `), then return to step 5. If starting with `!`, execute the command directly via `shell_exec` without adding the result to `messages` 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/<project-md5>.json`.
6. Append user prompt to `messages` (`role: "user"`), run `AL(cfg, messages)`, display token usage, check 60% context threshold for auto-compaction, and go to step 5.
### Agentic loop (`AL(cfg, messages)`) function
1. Call OpenAI-compatible Completions API (`POST {endpoint}/chat/completions`) forwarding relevant parameters from `cfg` (`model`, `temperature`, `stream`, `reasoning_effort`, etc., excluding internal agent configs like `endpoint`, `api_key`, `timeout`, `shell_timeout`, `max_al_iterations`, `color`, `context_window`) and optional `api_key` bearer header.
- Set custom `User-Agent` header (`Mozilla/5.0 (compatible; Bantam/1.0)`) to avoid gateway 403 blocks.
- Retry network/HTTP errors with Fibonacci backoff delays (`1s, 1s, 2s, 3s, 5s, 8s, 13s, 21s, 34s`).
- 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)]`).
- Sanitize tool arguments to filter out non-printable and space-like Unicode characters (protecting against indirect prompt injection), and validate JSON arguments. If invalid, format a tool-error response so the LLM can self-correct.
- Execute tool action (`shell_exec` or `write_file`).
- Sanitize the tool result output to strip any non-printable and space-like Unicode characters (leaving only ASCII space, tab, newline, and printable Unicode characters).
- 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` and accumulated usage.
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.
### Model configuration parameters
(shared by all implementations; the config file — `.bantam.cfg` takes priority over `model.cfg` when both exist — is plain `key=value` with `#` comments)
- `endpoint` (base OpenAI-compatible API URL, default `https://api.kilo.ai/api/openrouter`)
- `model` (model name, default `kilo-auto/free`)
- `temperature` (model temperature, default 0.7)
- `api_key` (API key / Bearer token, optional; fall back to `OPENAI_API_KEY` env var)
- `stream` (stream response tokens in real-time, default `true`)
- `color` (ANSI coloring: `auto` (TTY-detected, default), `always`, or `never`; also disabled by `NO_COLOR`/`BANTAM_NO_COLOR` env vars)
- `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)
- `shell_timeout` (timeout in seconds for `shell_exec` commands, default 120)
- `max_al_iterations` (max tool-call loop iterations per `AL()` invocation, default 1000)
- `context_window` (context window size in tokens, auto-discovered from `/models` API if available, fallback to this setting, default 200000)
- `bantam_tools_dir` (optional path to a directory of extra shell tools; the Go port appends `"Extra shell tools can be found at <dir>"` to the system prompt at startup when set. The `BANTAM_TOOLS_DIR` environment variable overrides this and is checked first; if neither is set, nothing is appended. Note: this is an agent-internal hint, not forwarded to the API.)
The Go port also supports SOCKS5 proxying via the `SOCKS_PROXY` (or `socks_proxy`) environment variable (e.g. `SOCKS_PROXY=socks5://127.0.0.1:1080` or `SOCKS_PROXY=127.0.0.1:1080`), falling back to standard `HTTP_PROXY` / `HTTPS_PROXY` environment variables.
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.
### Tool call definitions
#### `write_file` tool
- Parameters:
- `path` (string, required): JSON-escaped file path to write to (must be created unless existing).
- `offset` (integer, optional, default 0): byte offset to start writing from.
- `del_bytes` (integer, optional, default 0): bytes to delete starting from the `offset` prior to writing.
- `content` (string, required, may be empty): JSON-escaped content to write to the file.
- Return value: string
- Action: write `content` into the file at `path` starting at `offset` after deleting `del_bytes` bytes (creating the file and any necessary parent directories).
#### `shell_exec` tool
- Parameters: `command` (string)
- Return value: string
- Action: run shell command specified in `command` subject to `shell_timeout` (default 120s) and return `output + '\n\nexit: ' + exit_code` string.
## MicroBantam
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 config file (`.bantam.cfg` if present, else `model.cfg`) from the current working directory.
### Features
- Full agentic loop: LLM calls, `shell_exec` / `write_file` tool execution with JSON argument validation (invalid args are fed back so the model can self-correct)
- Indirect prompt injection defense: sanitizes tool parameters and tool outputs by filtering non-printable and space-like Unicode characters, preserving standard space, tab, newline, and printable Unicode characters
- 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
- Interactive mode (`/quit`, `/clear`, `/save`, `/list`, `/load <id>`, `/cfg`, `/help`) and file input mode
- Same built-in default system prompt and `OPENAI_API_KEY` fallback as the Go implementation
- Automatic recovery from `Invalid assistant message: content or tool_calls must be set` API errors: strips the last assistant message and retries (matches the Go port; note that a 4xx response other than this specific error aborts the run with an `API error` message, unlike the Go port which retries only on 5xx/408/429)
### What it drops
- Streaming (requests are non-streaming; `stream` is ignored)
- ANSI coloring/styling (`color` is ignored)
- Line editing, Ctrl+J multi-line prompts and history (plain single-line prompts)
- Fibonacci backoff network retries (a failed request aborts with an `API error` message)
- `/compact` context summarization
### Running MicroBantam
```bash
./mb # interactive (or: perl mb)
./mb prompt.txt # file input mode
```
## Repository layout
- `main.go`, `term_linux.go`, `term_bsd.go`, `term_windows.go`, `term_other.go` — Go implementation (stdlib only, module `code.luxferre.top/luxferre/bantam`)
- `mb` — MicroBantam, compressed Perl 5 implementation (core modules only, under 100 SLOC)
- `model.cfg` (or `.bantam.cfg`, which takes priority) — configuration file
- `README.md` — this document
## Extra tools
The `extras/` directory contains small, dependency-light shell scripts that extend Bantam without changing its core. Because Bantam's built-in tools are `shell_exec` and `write_file`, these helpers can be invoked directly by the agent through `shell_exec` to give it real-world capabilities (web search, live weather) that the base model alone does not have. They are plain `/bin/sh` scripts depending only on `curl` (and `jq` where noted), so the agent can discover and run them just like any other command.
If you keep your own collection of helper scripts, point Bantam at them with the `BANTAM_TOOLS_DIR` environment variable or the `bantam_tools_dir` key in the config file (`.bantam.cfg` if present, else `model.cfg`). When either is defined (environment variable taking precedence over the config key), the Go port appends the line `Extra shell tools can be found at <dir>` to the system prompt at startup, so the agent is aware of where to look for them. The `extras/` scripts shipped here are just examples of what such a directory can contain.
### `extras/websearch`
A simple web search tool for Bantam built on [Exa's MCP server](https://mcp.exa.ai/mcp)
(Streamable HTTP). It speaks the JSON-RPC MCP protocol (`initialize` ->
`notifications/initialized` -> `tools/call` with `web_search_exa`) and prints
the formatted result text. Usage:
```sh
extras/websearch "your query" # default 5 results
extras/websearch "your query" 10 # custom number of results
```
Environment:
- `EXA_MCP_ENDPOINT` — endpoint URL (default `https://mcp.exa.ai/mcp`).
- `EXA_API_KEY` — optional; when set, it is passed as an `?api_key=` query
parameter so no extra HTTP headers are added beyond `Content-Type`.
Dependencies: `curl`, `jq`.
### `extras/weather`
A wrapper around the [wttr.in](https://github.com/chubin/wttr.in) weather API
that queries current conditions and forecasts for any location. It supports the
graphical ANSI terminal view (default), one-line presets (`1`-`4`), custom
`%`-notation formats, and the rich JSON document (`?format=j1`). Usage:
```sh
extras/weather # auto-detect location from request IP
extras/weather London # graphical report
extras/weather -f 3 "New York" # one-line preset
extras/weather -f "%l: %c %t" Paris # custom one-line format
extras/weather -u u -L de Berlin # USCS units, German output
extras/weather -j Tokyo # raw JSON (pretty-printed via jq)
```
Options include `-l/--location`, `-u/--units` (`m`/`u`/`M`), `-L/--lang`,
`-f/--format`, `-j/--json`, `-0`/`-1`/`-2` (view depth), `-q`/`--quiet`,
`-A` (force ANSI) and `-h/--help`. Environment overrides: `WTTRAPI` (default
`https://wttr.in`) and `WEATHER_TIMEOUT` (default `20`s).
Dependencies: `curl`, `jq` (only required for the JSON format).
### `extras/context7`
A documentation lookup tool for Bantam backed by the
[Context7 public MCP server](https://mcp.context7.com/mcp). It speaks the same
JSON-RPC MCP protocol as `websearch` (`initialize` ->
`notifications/initialized` -> `tools/call`) and prints the returned text.
Context7 keeps up-to-date docs and code examples for thousands of libraries and
exposes two tools, both wrapped here:
- `resolve-library-id` — maps a library name to a Context7 ID
(`/org/project`).
- `query-docs` — fetches documentation and code examples for a resolved ID.
Usage:
```sh
extras/context7 resolve "React" "hooks" # list candidate library IDs
extras/context7 query "/reactjs/react.dev" "useEffect cleanup" # fetch docs
extras/context7 docs "Express" "middleware error handling" # resolve + query
extras/context7 --help
```
The `docs` subcommand is a convenience that resolves the library, auto-selects
the top-ranked match, and immediately queries it (the chosen ID is printed to
stderr so stdout stays clean for piping). Environment overrides:
`CONTEXT7_MCP_ENDPOINT` (default `https://mcp.context7.com/mcp`) and
`CONTEXT7_API_KEY` (optional; sent as the `X-Context7-API-Key` header for
higher rate limits / private docs).
Dependencies: `curl`, `jq`.
## FAQ
### Does Bantam support `AGENTS.md`?
The default system prompt instructs the agent to respect `AGENTS.md` contents in the project.
### How do I tell Bantam about extra shell tools?
Set the `BANTAM_TOOLS_DIR` environment variable (or the `bantam_tools_dir` key in the config file — `.bantam.cfg` if present, else `model.cfg`) to a directory containing your helper scripts. When defined, the Go port appends `Extra shell tools can be found at <dir>` to the system prompt at startup, making the agent aware of them. The environment variable takes precedence over the config key; if neither is set, nothing is appended.
### Is there any common config place for Bantam?
No, loading the config file (`.bantam.cfg` if present, else `model.cfg`) is deliberately only supported from the current working directory. This allows natural separation of configs per project. In case there's no config file inside the project, Bantam will use the `kilo/auto-free` model from Kilo Code with the temperature 0.7.
### Why no MCP support?
If you need MCP server tools, there's nothing a simple shell wrapper cannot solve in this case.
### Why no sandboxing?
Same philosophy as the Pi agent: there's nothing a simple chroot environment cannot solve in case sandboxing is really necessary.
### Any advanced authentication schemes or header injection?
You can pair Bantam with the [Dynagate](https://code.luxferre.top/luxferre/dynagate) LLM gateway to achieve all that.
### How to run on mobiles?
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.
## Credits
Created by Luxferre in 2026, released into the public domain with no warranties.
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