2b-agent 1.1.2

A local-first coding agent that keeps small local models focused instead of hallucinating.

2B

A local-first coding agent for the terminal. It runs your own models over Ollama's native protocol, keeps them focused instead of hallucinating, and gives you a full-screen TUI — streaming replies, a live plan checklist, narrated tool actions — without ever routing your local model through a translation layer that would confuse it.

I built 2B to keep working when the power and the internet don't — I live somewhere the grid isn't a guarantee, and I wanted a coding agent that doesn't fall apart the moment I'm offline.

macOS only. 2B is built and tested for macOS — the installer is a shell script that leans on Homebrew, and the clipboard integration uses pbcopy. It hasn't been tested elsewhere.

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Why I built it

I kept being told that small models — Nemotron 3 Nano 4B, gpt-oss:20b, the Qwen family — were "good enough" for agentic coding. On paper they were. In practice, every off-the-shelf harness I tried broke them:

• opencode made gpt-oss invent tool names that didn't exist and emit fake <command> tags as plain text.
• Cline, Goose, Continue, OpenHands each failed in their own way — malformed tool schemas, reasoning collapse, the model narrating tool calls instead of making them.

The models weren't the problem. The harnesses were. Nearly all of them talk to a local model through a generic OpenAI-compatible /v1 shim and pile abstraction on top of it. That shim measurably degrades a small model's tool selection, and the extra complexity buries whatever capability the model actually has.

The one thing that worked cleanly was a ~350-line script I wrote that talked to Ollama's native /api/chat endpoint with a tiny, fixed set of five tools. So I grew that prototype into a real, shareable tool. That's 2B.

The core rule, and the whole point: all complexity lives on the host side. The model's world never changes — the same five tools, the same native wire format for whatever provider is active, no generic shim. Everything you see below — the TUI, the plan checklist, task management, model switching, auto-compaction — is something 2B renders around the tool loop, never a new thing the model has to understand.

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What it does

• Five tools, and only five. list_files, read_file, search_files, edit_file, write_file. That small, concrete surface is exactly what keeps a small model reliable. It explores before it edits — searching for where something lives instead of guessing paths — and prefers exact-snippet edits over rewriting whole files.
• Edits that survive small-model drift. edit_file resolves the target host-side in tiers — exact, then whitespace-tolerant, then indentation-agnostic (re-indenting your snippet to the file) — so a model that gets the whitespace slightly wrong still lands the edit instead of bouncing off an exact-match error. It never applies on an ambiguous match, and the tool the model sees is unchanged — all the tolerance lives on the host.
• Catches its own mistakes. After a successful edit, 2B runs the file's checker host-side (dart analyze, ruff or py_compile, …) and folds any new errors straight into the tool result — so a model that just broke the build sees it on the same turn, with no new tool to learn. Bounded so it can't flood a small window, skipped silently when there's no checker, and off with TWOB_NO_DIAGNOSTICS.
• Finds definitions, not just matches. search_files marks which hit is the definition of a symbol and floats it to the top, and read_file appends a compact symbol outline with line anchors — so "where is X defined?" is answered by the tools the model already calls, with no navigation tool to learn. When a language server is installed (dart language-server, pyright, gopls, …) it resolves symbols semantically over LSP, spoken as raw stdlib JSON-RPC; a curated MCP resolver is used if one's enabled; with neither, it falls back to a dependency-free regex map. Host-side, schema unchanged; off with TWOB_NO_LSP.
• Native protocols, never a shim. Local Ollama models get Ollama's own /api/chat with NDJSON streaming. Each cloud provider gets its own native format. Nothing is translated through a lowest-common-denominator layer.
• Streaming, full-screen TUI. A scrolling conversation, a framed input box, and a live status line with a spinner, elapsed time, a context-window meter (ctx N%, amber as it fills), and — for local models — a RAM/GPU readout pulled from Ollama.
• Narrated tool actions. Instead of a wall of raw read_file {...}, you see what it's doing in plain language, tied together with a tree gutter and a ✓/✗ per step:

  ├ ✓ Searching for "MemoryScopeLevel" in lib
  ├ ✓ Reading lib/memory/memory_store.dart
  └ ✓ Editing lib/memory/memory_store.dart
  

• A live plan checklist. The model writes a short numbered plan before its first tool call; 2B parses it and renders it as a checklist that advances (□ pending, ■ active, ✓ done) as the work progresses. Purely cosmetic — a wrong guess never breaks anything.
• Many providers, one conversation. Ollama (local and cloud), OpenAI, OpenRouter, Mistral, NVIDIA, Anthropic, and Google Gemini. 2B keeps history in a provider-agnostic form and re-serializes it fresh for whoever's active — so you can switch models mid-task with /model and keep every bit of context. Start a task on a local Qwen, hand it to Claude when it gets hard, keep going.
• Knows the project. /init scans the repo and writes a compact 2B.md — stack, layout, and a ranked symbol map — that's auto-loaded into context, so the model starts knowing where things are instead of hunting for files. /map shows a budget-bounded outline on demand. All bounded, so it never floods a small local window.
• Runs things — split by model. Local models get run_git (git only, never a raw shell — no chaining/injection); cloud models get a full run_command shell (tests, build, git). Read-only git runs freely; anything that mutates is confirmation-gated and refused in plan mode.
• Sandboxed, and wary of untrusted content. On the cloud path, run_command's writes are confined to the workspace by a host sandbox (macOS sandbox-exec, Linux bubblewrap) — on by default, TWOB_NO_SEATBELT to disable. Commands that reach the network or a known-secret path (~/.ssh, ~/.aws, .env) re-prompt even under an "allow-all" grant, and your ambient secrets (*_API_KEY, AWS_*, …) are scrubbed from the environment 2B hands a command. Tool output — file contents, command results — is fenced as untrusted so a poisoned file can't turn a read into an instruction the model obeys. TWOB_SEATBELT=strict additionally denies network and confines reads to the workspace, so a command can't read secrets elsewhere on disk. It's defense-in-depth for a personal tool, not a hardened multi-tenant jail — but it's on by default.
• Delegates read-only exploration, and edits, to sub-agents (cloud). On the cloud path the model can delegate one or more investigations to run in parallel, each in its own isolated context, and get back short findings — so a big search-and-read never bloats the main conversation. Each sub-agent can only list_files, read_file, and search_files. It can also delegate work sub-tasks that propose file edits in their own isolated context; the parent reviews the whole batch once and applies it (accept-edits auto-applies, plan mode keeps it read-only), and if two workers touch the same file that's refused as a conflict rather than applied. Workers can't run commands and the parent is still the only one that writes; local models keep their frozen five tools untouched, and delegation is cloud-only for now.
• Cheaper multi-turn cloud sessions. Anthropic requests mark the system prompt and tool definitions as cacheable, so a long conversation pays full price for that stable prefix once instead of on every turn.
• Leaner, sturdier cloud sessions. Old, bulky tool outputs are trimmed out of each request before it's sent — the stored conversation is untouched, and it's off with TWOB_NO_TRIM — transient provider errors (HTTP 429 / 5xx) are retried with backoff instead of killing the task, and TWOB_SUBAGENT_MODEL runs delegated sub-agents on a cheaper model than the parent.
• MCP tools, curated. Pull in tools from MCP servers (dart, mempalace, …) — but per tool, not wholesale, because flooding a small model with tools is exactly what breaks it. You enable a server and pick which of its tools the model sees (/mcp); local models are capped to a few so their context stays lean. See MCP servers.
• Operating modes, cycled with Shift+Tab or set with /mode:
  • normal — every write/edit asks first.
  • accept edits — writes apply automatically.
  • plan mode — read-only; edit_file/write_file and MCP tools are refused (they may change state), so the model investigates and returns a plan instead of touching anything.
• Auto-compaction. When a conversation nears the model's context window — which happens fast on small local windows — 2B folds the older turns into a summary and keeps going uninterrupted, instead of hitting the wall. It cuts on a safe boundary so nothing breaks, and shows you "Compacting conversation…" while it does it.
• Themes. /theme system (default — transparent, uses your terminal's own background), /theme light (a warm parchment palette), /theme dark (a dimmed version). Switches live.
• Copy that actually works. Drag to select any text and press Ctrl+C, or Ctrl+Y / /copy to grab the whole last reply. On macOS this goes through pbcopy, so it lands on your clipboard even in Terminal.app (which ignores the escape sequence most TUIs rely on).
• Multiple tasks. Queue tasks, background the running one with Ctrl+B, foreground it later with /fg. A backgrounded task pauses when it needs to write and waits for you.
• Undo. /undo reverts the last write or edit; /undo N the last N, /undo <path> the most recent edit to a given file.
• Sessions persist and resume. Each task's conversation is saved to ~/.config/2b/history.db (stdlib SQLite, no deps; TWOB_NO_HISTORY to disable). 2b --continue picks up your most recent session, 2b --resume <id> a specific one, and /sessions (or 2b --list-sessions) lists them.

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Install

One line — paste it in your terminal:

curl -fsSL https://raw.githubusercontent.com/dea6cat/2b-agent/main/install.sh | sh

It installs uv if you don't have it, installs the 2b command, then — on an interactive terminal — walks you through local model setup:

1. Optional clean install — offers to remove other agentic tools that proved unreliable with local models (opencode, Continue, Goose, Cline, OpenHands) and their configs. Off by default; it asks first.
2. Grades your machine — reads your RAM and chip and rates each candidate model (✓ fits well / ~ tight / ✗ needs NGB+), defaulting to the best one your hardware can run.
3. You pick one or several from the menu.
4. Installs Ollama and pulls what you chose, with a live progress bar.
5. Self-tests each model — tok/s + GPU residency, then a correctness check that runs a real one-line edit through 2B itself and verifies the result (✓ correct / ✗ wrong, ~20–90s per model). It only reports — it never removes a model — and --no-benchmark skips it. Then it prints how to launch 2B.

Already have Ollama and some models? It skips what you already have — it lists your installed models, offers to just use them (pulling nothing), and marks anything in the menu you've already got. Your existing setup is left untouched.

Prefer to do it by hand? Install the published package from PyPI, then run the same onboarding the installer uses:

pip install 2b-agent      # or: uv tool install 2b-agent
2b setup                  # grades your machine, installs Ollama, pulls a model, self-tests, fixes PATH

On macOS/Linux you can also use Homebrew (it puts 2b on your PATH automatically):

brew install dea6cat/2b/twob-agent   # the formula is twob-agent; it installs the `2b` command
2b setup

2b setup is the single source of truth for onboarding — the curl … | sh installer just installs uv + the 2b command and then runs it, so you get the exact same setup either way. (On first launch with no model configured, 2b offers to run it for you.)

The installer — and 2b setup — are scriptable: --yes (accept defaults, no prompts), --clean / --no-clean, --models "qwen3.5:9b qwen3:8b", --no-models, --no-benchmark (skip the correctness check), --fix-path / --no-fix-path (add uv's tool dir to your PATH for you via uv tool update-shell, or leave it — otherwise it asks, and never edits a profile without consent). Pass them through the pipe with ... | sh -s -- --yes --models "qwen3.5:9b".

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Use it

2b                       # start in the current directory, autodetects a local model
2b "add a docstring to lib/main.dart"   # run one task, then drop into the session
2b --model qwen3.5:9b    # pin a model
2b --continue            # resume your most recent session (--resume <id> for a specific one)
2b --list-sessions       # list saved sessions
2b --list-models         # what's available across configured providers
2b --doctor              # diagnose PATH, Ollama, and the default model, then exit
2b --test                # grade your installed local models (tok/s + a real coding test)
2b --test qwen3.5:9b     # grade just one model; 'auto' removes the ones that fail
2b --update              # upgrade to the latest release (uv tool upgrade)
2b --rm                  # uninstall 2B and delete its config (asks first); --rm --yes to skip

Then just type what you want done. Type / to see the commands.

Updating

One command, whatever you installed with — it detects the method and runs the right upgrade:

2b --update

That resolves to uv tool upgrade 2b-agent (the curl … | sh installer / uv), pipx upgrade 2b-agent (pipx), or pip install -U 2b-agent (pip). You can of course run the matching command yourself — e.g. if you installed with pip:

pip install -U 2b-agent

2B also checks for a newer release in the background (at most once a day, never blocking startup) and prints a one-line notice on the next launch when one is available — set TWOB_NO_UPDATE_CHECK=1 to turn that off. Releases are tagged vMAJOR.MINOR.PATCH.

Providers

Local Ollama needs nothing. For anything else, set the matching environment variable and it shows up automatically in /models:

Provider	Environment variable
Ollama	OLLAMA_API_BASE (or OLLAMA_HOST)
Ollama Cloud	OLLAMA_API_KEY
OpenAI	OPENAI_API_KEY
OpenRouter	OPENROUTER_API_KEY
Mistral	MISTRAL_API_KEY
NVIDIA	NVIDIA_API_KEY
Anthropic	ANTHROPIC_API_KEY
Google	GEMINI_API_KEY (or GOOGLE_API_KEY)

Or connect one from inside 2B — /connect <provider> prompts for the key with a hidden field and saves it to ~/.config/2b/keys.json (chmod 600) so it's there next time; /connect on its own shows what's connected, and /disconnect <provider> removes a saved key. A key exported in your shell always takes precedence over a saved one.

Switch models anytime with /model <name>. A bare name works when it's unambiguous; otherwise use provider:model (e.g. /model anthropic:claude-sonnet-5).

Commands

Command	What it does
/help	List commands
/model [name]	Show or switch model — context is preserved
/models [filter]	List available models, grouped by provider
/default [name]	Show or set the persisted default model (used when no --model is given)
/connect [provider] [key]	Connect a provider (hidden key prompt); bare shows status
/disconnect <provider>	Remove a saved provider key
/init	Scan the project → write 2B.md (a compact map auto-loaded into context on new tasks)
/map [subdir]	Show a budget-bounded symbol outline of the project
/mcp	MCP servers/tools: status, tools <server>, enable/disable <server> <tool…|all>
/mode [normal|accept|plan]	Set operating mode (or Shift+Tab to cycle)
/theme [system|light|dark]	Switch color theme
/context	Show estimated context usage (auto-compacts near the limit)
/copy	Copy the last reply to the clipboard (Ctrl+Y)
/task <desc>	Queue a task
/tasks	List tasks and their status
/fg <id>	Foreground a backgrounded task
/sessions	List saved sessions (resume with 2b --continue / --resume <id>)
/tool <name> <args>	Run a frozen tool directly, bypassing the model (e.g. /tool read_file path=a.dart)
/history search <q>	Search the scrollback; then n / N jump between matches
/yes	Toggle accept-edits mode
/undo	Revert the last write/edit
/diff	Re-show the last diff
/add <file>	Pre-load a file into the current task's context
/fetch <url>	Fetch a web page and pre-load its readable content into the current task's context
/clear	Reset the current task's history
/quit	Exit

Keyboard

Key	Action
Shift+Tab	Cycle operating mode
Ctrl+B	Background the running task
Ctrl+Y	Copy the last reply
Ctrl+C	Copy the current mouse selection
Esc	Stop the current stream/task immediately — back to idle
Ctrl+D	Quit
Tab	Accept the top /-command suggestion

MCP servers (extra tools)

2B can pull in tools from MCP servers (stdio) like dart or mempalace. But its whole reason for existing is that small local models break when you flood them with tools — so MCP tools are opt-in and curated per tool: you enable a server and pick exactly which of its tools reach the model. Nothing is exposed until you say so.

Declare servers the usual way — a Claude-Code-style mcpServers block in ~/.config/2b/mcp.json (or ./.mcp.json in a project, which wins):

{
  "mcpServers": {
    "dart": { "command": "dart", "args": ["mcp-server"] }
  }
}

Then curate from inside 2B:

/mcp                          # servers and how many tools each has enabled
/mcp tools dart               # list a server's tools ([x] = enabled)
/mcp enable dart hot_reload analyze_files
/mcp enable dart all          # expose everything (careful on small models)
/mcp disable dart hot_reload  # or /mcp disable dart  to turn the whole server off

Enabled tools appear to the model as server__tool (e.g. dart__hot_reload) and their results come straight back into the loop. Only the tools you enable are ever sent — the model's tool list stays as small as you keep it.

Configuration

• Context window (local) — sized to your machine. For a local model 2B works out the largest window your box can run comfortably and pins num_ctx to it on every request (Ollama otherwise defaults to ~4k regardless of the model). It reads the model's architecture and trained max from /api/show, computes the KV-cache cost per token, and fits it into the RAM left after the model weights plus a headroom reserve — so a 16 GB laptop, an 18 GB one, and a 64 GB workstation each get a different, appropriate window (e.g. qwen3.5:9b ≈ 13k on 18 GB), never more than the model was trained for. That number drives auto-compaction (~75%) and the read-a-section threshold. Set TWOB_CONTEXT_TOKENS to override (higher if you want to spend more RAM, lower to save it).

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Honest caveats

• It reads and writes wherever you point it — with guardrails. 2B resolves absolute paths and paths outside the working directory on purpose; it's a personal tool for your machine. Writes are still confirmed in normal mode, plan mode refuses them entirely, reads of known-secret paths (~/.ssh, ~/.aws, …) prompt first, and on the cloud path run_command's writes are sandbox-confined to the workspace by default (see the sandbox bullet above). The command classifier is defense-in-depth, not an unbypassable boundary — the sandbox is the boundary.
• Switching to a stronger model mid-task hands it a tool-call history it didn't make. For these five simple tools that's low-risk (the wire format is unambiguously the new provider's own; only the choices inside came from a weaker model), but you may see mild "why did I read that file" moments. It is not the shim-degradation failure that sank the other harnesses.
• It's a full-screen TUI. That means it lives in the terminal's alternate screen, so a single mouse drag selects what's on screen, not scrolled-off history. For a classic inline REPL, run 2b --classic.

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How it's built

• Python, standard library first. The only real dependencies are rich, prompt_toolkit, and textual. Every provider talks over urllib — no per-provider SDKs.
• A canonical conversation model that each provider serializes fresh on every request. That re-derivation is exactly what makes switching models mid-task safe.
• One worker thread per task, emitting events into a queue the UI thread drains and renders — so the tool code stays untouched off the main thread and one thread owns the terminal.
• The five-tool schema is frozen. It's what makes small models reliable, and it isn't up for redesign.

Built for local models, kept on task.