🧵 pi-fabric

A programmable tool and agent runtime for Pi

One program for tools, MCP, agents, workflows, actors, mesh, councils, and recursion.

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Fabric gives Pi one programmable tool called fabric_exec, which composes core tools and MCP servers with captured extension tools. The default kernel runs checked TypeScript in isolated QuickJS; select sandboxed Python (Monty) with executor.kernel: "python". CPython is an explicit native escape hatch via executor.pythonRuntime: "cpython". The configured kernel is exclusive: there is no per-call language selector. Trusted TypeScript workloads can also use unsafe Node/Bun processes. Programs call host providers for agents, actors, and durable coordination, then return the result of their branches, loops, fan-out, and data flow. See execution kernels for Python usage, security boundaries, and guest-helper limitations.

Why Fabric?

	Capability	What it unlocks
⚡	Code mode	One flat tool schema; branching, loops, fan-out, and data flow live in TypeScript or configured Python.
🧰	Capability routing	Call Pi core tools, MCP servers, captured extension tools, or Fabric providers through one runtime.
🧑‍🤝‍🧑	Agent runtime	One-shot workers, durable resident agents, persistent event-driven actors, councils, and bounded recursive queries.
🕸️	Workflows + mesh	Phased progress plus durable topics, shared tasks, and compare-and-swap state.
🛡️	Guardrails	Approvals, isolation, timeouts, concurrency, recursion depth, and shared cost budgets.
🎛️	Native TUI	Live activity, an interactive dashboard, and settings without leaving Pi.

How it works

1. You ask in plain language.
2. Pi writes one program that calls the required tools and agents.
3. TypeScript is statically checked before execution; both kernels use the same authoritative host-call schema validation.
4. The result returns to your conversation. Intermediate work stays in the sandbox and appears in the activity panel and dashboard.

With the default TypeScript kernel, the model can write this program (TypeScript only):

const [manifest, sources] = await Promise.all([
  pi.read({ path: "package.json" }),
  pi.find({ pattern: "**/*.ts", path: "src" }),
]);
return {
  package: JSON.parse(manifest).name,
  sourceCount: sources.split("\n").filter(Boolean).length,
};

Independent calls run in parallel, and the returned object enters the model context. Known providers support concise direct calls such as mcp.fal_ai.get_model_schema(...), memory.recall(...), state.get(), schema.status(), and compact.status(). Refs found or computed at runtime use tools.call({ ref, args }) (TypeScript notation). Python uses await tools.call({"ref": ref, "args": args}), native dictionary results, and asyncio.gather for independent calls.

To select Python, put this in ~/.pi/agent/fabric.json or a trusted project's .pi/fabric.json, or use /fabric settings → Executor → Kernel:

{ "executor": { "kernel": "python" } }

Python defaults to Monty, a sandboxed Python subset with VM resource limits and no ambient filesystem, network, or process access. It is not CPython: arbitrary imports, third-party packages, and some Python features are unavailable. Full CPython 3.10+ requires explicit executor.pythonRuntime: "cpython" and runs trusted native code with full OS privileges outside schema enforce, like TypeScript's Node/Bun escape hatches. CPython enforcement additionally requires macOS sandbox-exec or Linux bwrap, failing closed without isolation. Missing Monty dependencies never trigger a native fallback. executor.runtime only affects TypeScript. See the kernel guide.

Install

Requires Node.js 24+ and Pi 0.80.6+. Monty's optional native package installs on supported platforms; only the explicit CPython escape hatch requires CPython 3.10+. Fabric also checks a detectable Pi host version at startup and warns when an older host may ignore continuation APIs such as actor triggerTurn.

pi install npm:pi-fabric

From GitHub:

pi install git:github.com/monotykamary/pi-fabric

From a local checkout:

pnpm install
pnpm build
pi install /absolute/path/to/pi-fabric

For one development run:

pi -e /absolute/path/to/pi-fabric

What you can ask for

Pi loads advanced patterns after direct user invocation. Run /skill:fabric-guide for one recommendation, or invoke the exact /skill:<name> yourself. An ordinary coding task keeps Pi on the core fabric-exec path.

You want	Run
Help choosing the smallest advanced mechanism	/skill:fabric-guide Choose a mechanism to audit every auth file and verify the findings.
Parallel audits, migrations, or research with verification	/skill:fabric-workflow Audit every auth file and synthesize verified findings.
Work too big for one context window	/skill:fabric-rlm Produce a compact architecture map of this repo.
A persistent watcher for one measurable goal	/skill:fabric-supervisor Watch this migration until it is complete and tested.
A strict auditor for one feature design spec	/skill:fabric-spec Implement docs/specs/checkout.md to the tee; nothing missing, nothing extra.
A quiet decision-point reviewer	/skill:fabric-advisor Focus on migration correctness.
Same-model independent reviewers and one decision	/skill:fabric-council Review this design for correctness, security, and operability.
Multi-model compare-not-merge deliberation or act mode	/skill:fabric-fusion Deliberate this design across models.
One command that chooses advisor or supervisor	/skill:fabric-ambient advisor Focus on migration correctness.
A durable team coordinating through versioned tasks	/skill:fabric-swarm Coordinate this migration across owned task partitions.
Evidence-gated edits with postconditions	/skill:fabric-schema Make this parser change only if focused tests stay green.

The foundation is the fabric-exec reference skill: the model loads it before its first fabric_exec call and again when a call errors on argument shape.

Agent conversations

Press ctrl+shift+a or run /fabric chat <agent-id-or-name> to open a live, full-screen child conversation with a multiline editor. Send steering or follow-ups directly, switch between nested agents, and return to Main without stopping its work. Drafts and scroll positions stay with each conversation. Completed one-shot agents are read-only; persistent actors accept further messages. Drag to select transcript text, use /copy or /copy selection, and type /help for the small set of preview-local commands with slash completion. See focused conversations for controls and current limitations.

The dashboard

Fabric includes a live activity surface in Pi:

• A compact widget above the chat (like pi-supervisor) whose header follows the current phase while its rows show active/completed agents, active actors, and their recent nested tool or code-change activity.
• /fabric (or /fabric dashboard): opens the Activity and Topology views. The user-facing Pi session appears as Main. You can queue or steer participants and inspect the project topology.
• /fabric settings: mirrors Pi's /settings and writes changes to fabric.json. TUI hosts get the searchable settings component; RPC hosts get the same nested sections, value/input/model pickers, list editors, and project/global save scopes through native dialog primitives.
• Tool display (compact by default, or full) is configured under /fabric settings → UI; compact elevates the declared display intent, hides the outer program, and applies to the current transcript immediately. Pi's tool-expand keybinding (ctrl+o by default) expands a compact card to the full transcript.

See the interface & commands reference for every view, keybinding, and slash command.

Reference

• Configuration: fabric.json, code modes, tool capture, approvals, and budgets.
• Execution kernels: exclusive TypeScript/Python selection, Monty sandboxing, CPython escape hatch, agent inheritance, and examples.
• Memory & recall: compact ranked hits, uniform follow calls, lossless expansion, and guest-local memory.walk computation.
• Interface & commands: dashboard, settings, keybindings, slash commands, and headless runs.
• Agents, actors & mesh: model handoff, /fabric prewalk, runners, transports, actors, councils, recursive queries, and durable coordination.
• Durable residency through Pi: background host lifecycle and the Pi-runtime launcher boundary.
• Components & committed capabilities: supervised effects, exact requirements, external per-model guidance and execution-profile replacement, rolling provider generations, actor commitments, and both formal calculi.
• External providers: the versioned provider protocol for extensions.
• Architecture & security: the host bridge, sandboxing, tool-call robustness, and limits.
• Catalog repairs: unique extra keys and unknown actions promoted into silent schema maps.
• Tool entropy: the deterministic entropy meter, on-demand session measurement, reduction proposals, the autonomous compile loop with its ratchet gate, and certify:entropy.
• Speculative PTC: pre-launching literal read calls while the program streams, with epoch + freshness guarantees.
• Skills: the core-first invocation policy and user-invoked advanced patterns.

Development

pnpm install
pnpm typecheck
pnpm test
pnpm build

The test suite covers:

• configuration and schema validation
• provider dispatch, registered-tool execution, QuickJS isolation, and Pi built-in calls
• agent fixtures for Claude and Veda
• workflows, durable mesh state, actor mailboxes, subscriptions, and actor restoration

Claude and Veda fixtures use local test processes with zero billable requests.

Acknowledgments

• Thanks to @hazrid93, whose request for a token-efficient LLM advisor pattern led to Fabric's advisor.
• Thanks to Chad Gibson at Neuralwatt, who supported extended tests of long MCR sessions and the related debugging work.

License

MIT