agent6

A coding agent that jails model commands and uses editable state machines for long-running tasks.

The model can write code and ask to run commands, but those commands go through a jail with restricted filesystem and network access. Long-running workflows can be written, reviewed, edited, resumed, and replayed as declarative state machines instead of being left to an open-ended agent loop.

Full documentation: agent6.dev

the TUI the full agent, as a live dashboard

the CLI the full agent, in any terminal

the web UI the full agent, desktop or phone

Features

• Sandboxed execution for every LLM-chosen child process, jailed individually with Landlock + seccomp; the default strict isolation adds namespaces + pivot_root, rebinds .git read-only, and gives jailed commands no network
• Works with Anthropic and any OpenAI-compatible endpoint (OpenAI, OpenRouter, Ollama, vLLM, llama.cpp, LM Studio)
• Per-step git commits, snapshot-resumable runs, per-turn forkable checkpoints, a hard metered USD budget with a token fallback for calls the meter cannot price
• Plan, run, review, and ask modes; a live terminal dashboard, a zero-dependency browser UI (agent6 web, phone-friendly), and an editor-driven ACP agent (agent6 acp); persistent transcripts and a searchable run history
• Sessions build on each other: --from <session-id> seeds a new run or ask with another session's context (the source is untouched -- keeping a session's mode is fork), a session can read this project's other sessions, and /btw <question> asks a one-off question beside a live run without interrupting it -- the answer prints whole at the next turn boundary and stays resumable as the ask it is
• Long jobs do not hold a turn open: run_command with background: true starts a slow build or a watcher, read_background polls it, /shells lists what a run started and how each one ended, and nothing a run started outlives it
• Transparent, steerable context compaction: every surface shows what left the model's context, and the conversation view shows the summary a restart continued from; /compact [focus] compacts on demand, /pin <text> makes an instruction survive compaction verbatim, and agent6 memory pin keeps a load-bearing memory in every run's prompt (trimmed last, under the block's cap)
• State machines (agent6 machine) for long-running automated tasks: LLM-drafted, operator-reviewed, journaled, and replayable; they can pause for operator input, accept events, be steered from any front-end, and notify you when they need attention
• Skills: install standard SKILL.md packs (superpowers, caveman, any agentskills.io repo) with agent6 skills install <url>; they index into the system prompt, load on demand via a read-only tool, and fire as /name pause-menu commands or run --skill; nothing in a skill is ever executed. The format is shared with Claude Code and Pi, and [skills].extra_dirs loads an existing ~/.claude/skills-style collection in place. Repo instructions are read from AGENTS.md (a repo using CLAUDE.md can symlink it)
• Small, fixed LLM tool surface; the only extension point is operator-configured MCP servers, off by default
• Eight runtime dependencies, no telemetry, no auto-update
• Parallel fan-out (agent6 run --parallel N|model-a,model-b): N isolated clone-based lanes run independently, each an ordinary sandboxed run; results auto-compare (reviewer-model judge, else verify+cost) into a ranked report. Nothing auto-merges; agent6 sessions merge <id> picks a winner. agent6 sessions compare <id> <id> ... runs the same ranked comparison over any past runs. The web/TUI composer and a live-run steer share one grammar, /parallel [N|models] <task> (repeat the token to queue more tasks), to dispatch and join a sibling group mid-conversation

Install

From PyPI with uv or pipx:

uv tool install agent6        # or: pipx install agent6

agent6 needs Linux for the sandbox, Python 3.12+, and an API key for at least one provider. macOS runs unsandboxed behind a warning; on Windows use WSL. See installation for the full requirements and building from source.

Quick start

# Connect a provider once (stored in ~/.config/agent6/, key in a 0600 secrets file).
# Already connected on this machine? Skip both; `agent6 check` verifies it.
agent6 connect                # interactive: pick provider, paste API key
agent6 model worker anthropic claude-sonnet-4-6

# Run the agent on a task. agent6 infers a verify command if you haven't set one.
cd your-repo
agent6 run "add a --json output mode to the CLI"

# Watch and drive runs from a terminal, a full-screen TUI, a browser, or an editor.
agent6 attach <session-id>        # follow + answer a run live (default: conversation view; --raw for the event stream)
agent6 tui                    # full-screen dashboard hub
agent6 web                    # browser UI on http://127.0.0.1:7658 (phone-friendly)
agent6 acp                    # speak ACP on stdio; an editor spawns this

# Audit the effective config, pre-flight the sandbox, resume or fork a run.
agent6 config show
agent6 check
agent6 resume <session-id>
agent6 fork <session-id> --at-turn 7

That is the whole loop. See getting started for the full command tour, the web UI for driving runs from a phone, configuration for every field, and the security model for what the sandbox enforces.

Config is layered: built-in secure defaults, then the global ~/.config/agent6/config.toml, then the per-repo config (out of the workspace, per-machine, not committed), then an explicit --config FILE. Every field has a default; security-sensitive fields default to the safe value (network = "auto", run_commands = "ask", protect_git = true), and git_ops.py refuses push, --force, and history rewrites unconditionally.

Benchmarks

Reproducible harnesses live under bench/. The headline one is the cross-model sweep (bench/sweep): replicated runs on real-world tasks, scored out-of-band by each project's own test suite, reported with confidence intervals for success rate and cost plus a latency comparison. Also there: real-world SWE-bench-Lite-style tasks, head-to-head runs against Claude Code / opencode / aider, machine create validation, and a perf-optimization harness. The recorded numbers in those are mostly small-n exploratory runs; re-run before quoting.

Contributing

Read AGENTS.md first. The repo's verify command decides whether a change is landable:

uv run ruff check && uv run ruff format --check && \
  uv run pyright && uv run tach check && uv run pytest

Adding a tool, loosening a security default, dialling a new network destination, or changing the jail (src/agent6/jail/) requires a Security review note: paragraph in the commit message.

License

Apache-2.0.