claudectl

Orchestrate a swarm of Claude Code agents with a local-LLM brain that learns from you.

_{~6 MB binary (full features, Homebrew bottle). Sub-50ms startup. Zero config required.}

Website | Demo | Blog: Why a local brain? | Releases

What it does for you

Run claudectl --brain-stats impact to see your numbers:

  ╔════════════════════════════════════════════════╗
  ║              IMPACT SCORECARD                  ║
  ║             1200 decisions tracked             ║
  ╠════════════════════════════════════════════════╣
  ║  Auto-handled                             71%  ║
  ║  ████████████████████░░░░░░░░  847/1200        ║
  ║                                                ║
  ║  Brain accuracy                          96.2%  ║
  ║  ███████████████████████████░  1154/1200       ║
  ║                                                ║
  ║  Coverage vs static rules               2.9x  ║
  ║  brain ████████████████████████████  100%      ║
  ║  rules █████████░░░░░░░░░░░░░░░░░░░  34%      ║
  ║                                                ║
  ║  Dangerous ops blocked      12  Time saved 42m  ║
  ║  2 critical | 10 high-risk | 847 auto x 3s    ║
  ║                                                ║
  ║  Learning: correction rate 8.4% ↓ 2.1% (-6pp) ║
  ╚════════════════════════════════════════════════╝

Install

brew install mercurialsolo/tap/claudectl     # Homebrew (macOS / Linux)
cargo install claudectl                       # Cargo (any platform)

Both produce the same ~6 MB binary with bus/coord/relay/hive enabled — claudectl bus, coord, relay, and hive work out of the box. For the minimal ~3.5 MB sync-only build, opt out with cargo install claudectl --no-default-features --features hive.

Building from source (Cargo) requires rustc 1.88+. Older toolchains fail with an opaque transitive-dependency error before the build starts — run rustup update stable first. The Homebrew bottle ships prebuilt and has no toolchain requirement.

curl -fsSL https://raw.githubusercontent.com/mercurialsolo/claudectl/main/install.sh | sh
nix run github:mercurialsolo/claudectl
git clone https://github.com/mercurialsolo/claudectl.git && cd claudectl && cargo install --path .

Get started

claudectl demo                # Guided first-value tour — no live sessions needed
claudectl init                # Onboarding wizard (budget, brain, hooks, bus, skills)
claudectl doctor              # Verify install + runtime health (✓ checklist)
claudectl                     # Live dashboard — see all sessions at a glance
claudectl --brain             # Enable local LLM auto-pilot

After brew upgrade claudectl, run claudectl init --upgrade to re-sync hook entries, plugin files, and DB migrations to the new binary. claudectl doctor's plugin version row will tell you when this is needed.

The init wizard walks five phases — weekly budget, local-LLM brain detection, Claude Code hook install, agent-bus role, and curated skill suggestions. Plugin files (slash commands, supervisor agent, bus MCP server registration) are embedded in the binary and written to ~/.claude/plugins/claudectl/ automatically — no repo clone. Run claudectl doctor to verify every piece is wired up, or claudectl init --check for the drift report against the onboarding marker.

Why claudectl

• Local LLM auto-pilot — a brain that learns your preferences and auto-approves/denies tool calls. No cloud API.
• Hive mind — knowledge distillation, archiving, and curriculum generation. Connect instances to share learnings across machines.
• Self-improving — detects friction patterns, suggests rules, and gets smarter with every correction.
• Multi-session orchestration — run parallel tasks with dependency ordering and cross-session context routing.
• Health monitoring — catches cognitive decay, cost spikes, error loops, and context saturation before they waste money.
• Works everywhere — Claude Code plugin for inline use, TUI dashboard for oversight, CLI for automation.

Full feature comparison

Local LLM Brain

The brain observes all your sessions and makes real-time decisions:

• Approve safe tool calls automatically (reads, greps, test runs)
• Deny dangerous operations before they execute (force pushes, destructive commands)
• Terminate sessions that are looping, stalled, or burning money
• Route summarized output between sessions so they share context
• Spawn new sessions when the brain detects parallelizable work

ollama pull gemma4:e4b && ollama serve    # One-time setup
claudectl --brain                         # Advisory mode (default)
claudectl --brain --auto-run              # Auto mode: brain executes without asking
claudectl --mode auto                     # Or toggle mid-session (Ctrl+b in TUI)

Works with any OpenAI-compatible endpoint: ollama, llama.cpp, vLLM, LM Studio.

How the brain learns

The brain learns from everything you do — not just brain-involved decisions, but every manual approve, reject, rule execution, and conflict resolution. All data stays on your machine.

Explain & audit every decision

To leave the brain unattended you need to trust and audit it. Every decision now carries a "why" — the source (rule, few-shot, or LLM), the confidence, and the past examples that informed it — surfaced inline in --brain-query output and the Brain Review panel.

claudectl --brain-review           # Triage decisions; press [c] to correct-and-learn in one keystroke
claudectl --brain-export           # Export the decision timeline as Markdown (paste into a PR)
claudectl --brain-export json --project acme   # …or JSON, filtered to one project (or --pid <n>)

A one-key correct-and-learn in the review view records the right answer as canonical training material, so the next similar decision improves.

Self-improving sessions

The brain automatically detects friction patterns and suggests workflow improvements:

claudectl --brain --insights on     # Enable auto-generation (every 10 decisions)
claudectl --brain --insights        # View current insights

Detects: friction patterns, error loops, context blowouts, missing rules, accuracy gaps, cost trends. Only new insights are surfaced — the system tracks what you've already seen. Use /auto-insights in the Claude Code plugin.

Claude Code Plugin

Integrates the brain directly into Claude Code sessions — no TUI required.

Headless Mode

Run the full autonomous stack without a TUI. Attach a dashboard from another terminal.

claudectl --headless --brain --auto-run           # Human-readable events
claudectl --headless --brain --auto-run --json    # Structured JSON events

What runs in headless mode:

• Brain inference (approve/deny/route/spawn with adaptive confidence)
• Coordination layer (leases, interrupts, handoffs, memory)
• Context rot prevention (auto-raises compact/stop interrupts when decay detected)
• Rule evaluation and health monitoring

The TUI dashboard can run alongside -- both share state via the coordination SQLite store, brain decision logs, and session discovery.

# Background daemon
nohup claudectl --headless --brain --auto-run > ~/.claudectl/autopilot.jsonl 2>&1 &

# Attach dashboard in another terminal
claudectl

Coordination Layer

Multi-agent coordination for parallel coding sessions. Prevents duplicate work, manages ownership, and routes context between agents.

Enabled by default. For the minimal sync-only build, use cargo build --no-default-features --features hive.

# Ownership leases — prevent two agents from editing the same file
claudectl coord claim --session sess_1 --path src/app.rs --mode exclusive
claudectl coord release lease_123

# Handoffs — structured context transfer between sessions
claudectl coord handoff --from sess_1 --to sess_2 --task task_1 --summary 'Fix path normalization'

# Interrupts — typed cross-agent signals with delivery modes
claudectl coord raise --type pause --target sess_1 --reason 'lease conflict'
claudectl coord ack intr_123

# Memory — validated patterns promoted from brain decisions
claudectl coord promote --project myproject
claudectl coord context --session sess_1           # Preview injected context

# Inspection
claudectl coord leases                             # Active ownership leases
claudectl coord interrupts                         # Pending interrupts
claudectl coord events                             # Event audit log
claudectl coord metrics                            # Coordination health metrics
claudectl coord eval                               # Run 10 eval scenarios
claudectl coord adapters                           # Registered agent adapters

The coordination layer stores state in a local SQLite database (~/.claudectl/coord/coord.db) and injects compact context into the brain's prompt before every decision.

Agent Bus (preview)

A durable directory + mailbox that exposes the running swarm as an MCP server. Agents discover each other (list_agents), look up their own role (whoami), publish directed messages, and drain their inbox at turn boundaries. Phases 1–4 of the design spec are shipped.

Enabled by default. Pulls in rmcp + a current-thread Tokio runtime, which is why the default binary is ~6 MB; the no-async-runtime invariant is deliberately relaxed for the bus feature path. For the minimal sync-only build (~3.5 MB), use cargo build --no-default-features --features hive.

# Bind durable role addresses to working directories
claudectl bus role bind planner ~/work/proj-plan
claudectl bus role bind impl    ~/work/proj-impl
claudectl bus role list

# Directed send + drain
claudectl bus send impl "review the auth diff" --from planner --priority high
( cd ~/work/proj-impl && claudectl bus inbox )

# Resolve which role this cwd is
claudectl bus whoami

# Run the MCP server on stdio (this is what the Claude Code plugin invokes)
claudectl bus stdio

The Claude Code plugin registers the bus as an MCP server (claude-plugin/.mcp.json) and ships two slash commands: /inbox (drain mailbox) and /role <name> (e.g. /role frontend, /role tester — set this session's role). Bindings are PID-keyed when made through the TUI's Ctrl+R or /role; cwd-keyed for the legacy claudectl bus role bind <name> <cwd> flow. The resolver prefers a PID match over cwd-inference, which disambiguates "two sessions in one worktree".

Mailboxes live in ~/.claudectl/bus/bus.db (SQLite WAL). Message bodies are sanitized at the boundary — a leading / is neutralized so a queued message cannot smuggle a slash command into the recipient.

Full guide: Agent Bus — wire-up, role binding, sending and receiving messages, worked planner→implementer example, where state lives, uninstall.

Flow guards (hop limit, per-role rate limit, reserved-role ACL) and the supervisor for long-horizon role persistence are shipped. Pub/sub subscribe + claim protocol and the TUI bus view are still in flight. See AGENT_BUS.md for the per-phase status table.

Supervisor

Durable, verified task lifecycles on top of the bus. Where the agent bus answers "how do I talk to the swarm?", the supervisor answers "how do I trust the swarm to run unattended overnight?" Submit a task, it lands in a SQLite ledger; the reconciler assigns it via mailbox (or spawns a session if no role is set); declared verifiers (run / brain / agent) gate the move to DONE; a dead session triggers Resume with a recovery prompt that carries autopsy findings forward; health-check signals (stalled, retry loop) become first-class transitions instead of dashboard warnings.

# One-shot inline submission
claudectl supervisor submit \
  --name "auth-middleware" \
  --cwd ~/work/services \
  --prompt "Add JWT middleware to all API routes" \
  --role backend --budget-usd 3.00

# Batch submission from a TOML file
claudectl supervisor run tasks.toml --dry-run    # preview
claudectl supervisor run tasks.toml              # commit

# Inspect fleet state
claudectl supervisor status
claudectl supervisor status --state RUNNING
claudectl supervisor logs <task_id>              # full transition log + verifier history

# Lifecycle controls
claudectl supervisor cancel <task_id>            # idempotent move to CANCELLED
claudectl supervisor drain                       # stop issuing new assignments
claudectl supervisor undrain                     # resume

A tasks.toml block carrying every supported field — including the three verifier kinds:

[[task]]
name        = "auth-middleware"
role        = "backend"
cwd         = "./services"
prompt      = "Add JWT auth middleware to all API routes"
model       = "sonnet"
budget_usd  = 3.00
max_retries = 2
timeout_min = 45

  [[task.verify]]
  run  = "cargo test --all-targets"            # exit code is the verdict

  [[task.verify]]
  brain = "Review the diff for auth-coverage gaps. PASS or FAIL with reasons."
                                                # routed to the local brain — free

  [[task.verify]]
  agent = "Adversarial review: find a request that bypasses the middleware."
  model = "haiku"                               # headless claude -p, own budget
  budget_usd = 0.25

Tasks are submittable three ways: this CLI, a task.created bus message addressed to the supervisor role, or the submit_task MCP tool from inside a Claude Code session. All three land as the same SQLite row.

Three load-bearing properties from the design spec:

• Cattle vs. pets — a task survives the death of the session executing it; the next attempt is a fresh session that reads the recovery context (original prompt + autopsy + prior verifier failures + tree-state drift warning).
• Crash-safe by construction — kill the headless daemon mid-tick, restart, and observed state re-converges from ~/.claudectl/coord/coord.db. The ledger is the source of truth.
• Fail-closed verifiers — a brain/agent verifier whose reply lacks the leading PASS / FAIL: marker is treated as FAIL. RFC §5 calls this the verifier-is-the-gradient principle: every FAIL output becomes the retry prompt the next attempt sees.

Metrics (team observability): claudectl supervisor metrics 0.0.0.0:9464 serves a Prometheus /metrics endpoint (claudectl_tasks_by_state, claudectl_fleet_cost_usd_total, claudectl_retries_total, claudectl_verifier_pass_rate) that Grafana or Datadog scrape with no claudectl-specific glue. Each scrape reads the coord DB fresh (WAL), so it runs safely alongside the reconciler. A ready dashboard and the full recipe live in docs/team-observability.md.

Policy as code (team guardrails): commit a .claudectl/policy.toml to the repo and its [deny] commands/tools are enforced by the brain gate at the highest precedence — a developer can't soften them by editing local config. Scaffold with claudectl --policy init, inspect with claudectl --policy. See docs/policy-as-code.md.

Hive Mind & Relay

The brain distills your decisions into shareable knowledge. Connect instances across machines to build a convergent hive mind.

# Hive knowledge is built-in — view what the brain has learned
claudectl hive status
claudectl hive knowledge
claudectl hive distill                # Condense archive into curriculum

# Relay for cross-machine networking is enabled by default
claudectl relay invite                # Generate an invite code
claudectl relay join YEK-AGA-YHK-QAA-BM       # Join from another machine
claudectl relay discover              # Scan LAN for nearby instances

# Start coordinator with HTTP API for multi-machine dashboard
claudectl relay serve --http-port 9876 --auth-token secret
# Remote sessions appear in the TUI as [worker-id] project-name
# GET /api/sessions returns the unified view across all workers

Knowledge categories (best practices, techniques, workflow patterns) propagate automatically. Personal patterns (time-of-day habits, cost tolerance) stay local. You control what's shared:

[hive]
share_categories = ["best_practice", "technique"]
exclude_tools = ["Write"]
max_units = 500
max_prompt_units = 20

See the full Relay & Hive Mind guide.

Orchestrate Sessions

Run coordinated tasks with dependency ordering, retries, and cross-session data routing:

{
  "tasks": [
    { "name": "auth", "cwd": "./backend", "prompt": "Add JWT auth middleware" },
    { "name": "tests", "cwd": "./backend", "prompt": "Update API tests. Previous: {{auth.stdout}}", "depends_on": ["auth"] },
    { "name": "docs", "cwd": "./docs", "prompt": "Document the new auth flow", "depends_on": ["auth"] }
  ]
}

claudectl --run tasks.json --parallel
claudectl --decompose "Add auth, write tests, update docs"   # Auto-split into parallel tasks

Session Health Monitoring

Continuously checks each session and surfaces problems in the dashboard:

• Cognitive decay — composite 0-100 score tracking degradation over time
• Proactive compaction — suggests /compact at 50% context, before the 80/90% thresholds
• Cost spikes — flags when burn rate exceeds the session average
• Loop detection — catches tools failing repeatedly in retry loops
• Stall detection — sessions spending money but producing no edits
• File conflicts — detects when multiple sessions edit the same file

Spend Control

claudectl --budget 5 --kill-on-budget     # Auto-kill at $5
claudectl --notify                        # Desktop notifications on blocks
claudectl --stats --since 24h            # Aggregated cost statistics

Auto-Rules

[[rules]]
name = "approve-cargo"
match_tool = ["Bash"]
match_command = ["cargo"]
action = "approve"

[[rules]]
name = "deny-rm-rf"
match_command = ["rm -rf"]
action = "deny"

[[rules]]
name = "kill-runaway"
match_cost_above = 20.0
action = "terminate"

Rules support matching by tool, command, project, cost, and error state. Deny rules always take precedence.

Docs

Community

• Questions or ideas? Start a Discussion
• Found a bug? Open an issue
• Share your setup in Show & Tell

License

MIT