pi-subagents

pi-subagents lets Pi delegate work to focused child agents. Use it for code review, scouting, implementation, parallel audits, saved workflows, background jobs, and anything else that benefits from a second or third set of model eyes.

https://github.com/user-attachments/assets/702554ec-faaf-4635-80aa-fb5d6e292fd1

Installation

pi install npm:pi-subagents

That is the only required step. You can add optional pieces later.

Try this first

You do not need to create agents, write config, or learn slash commands. After installing, ask Pi for delegation in plain language:

Use reviewer to review this diff.

Ask oracle for a second opinion on my current plan.

Use scout to understand this code based on our discussion then ask me clarification questions.

Run parallel reviewers: one for correctness, one for tests, and one for unnecessary complexity.

That is enough to start.

What happens

Pi is the parent session. A subagent is a focused child Pi session with its own job.

When you ask for a subagent, Pi starts the child, gives it the task, and brings the result back. Foreground runs stream in the conversation. Background runs keep working and can be checked later.

Installing the extension does not start an automatic reviewer in the background. It gives Pi a delegation tool. If you want every implementation reviewed, say that in your prompt or put it in your project instructions:

When you finish implementing, run a reviewer subagent before summarizing.

Good first prompts

These cover most day-to-day use:

Ask oracle for a second opinion on my current plan. Challenge assumptions and tell me what I might be missing.

Use oracle to help solve this hard bug. Have it inspect the code and propose the best next move before we edit anything.

Run parallel reviewers on this diff. I want one focused on correctness, one on tests, and one on unnecessary complexity.

Have worker implement this approved plan. Afterward, run parallel reviewers, summarize their feedback, and apply the fixes that make sense.

Use scout to understand the auth flow, then have planner turn that into an implementation plan.

Those are ordinary Pi requests. Pi decides whether to call subagent, which agent to use, and whether a chain or parallel run makes sense.

Common workflows

Want	Ask naturally
Get a second opinion	“Ask oracle to review this plan and challenge assumptions.”
Solve a hard problem	“Use oracle to investigate this bug before we edit.”
Review a diff	“Use reviewer to review this diff.”
Run parallel reviewers	“Run reviewers for correctness, tests, and cleanup.”
Implement then review	“Implement this, then review it.”
Execute a plan carefully	“Have worker implement this approved plan, then run reviewers and apply the feedback.”
Scout before planning	“Use scout to inspect the auth flow before planning.”
Run in the background	“Run this in the background.”
Browse agents	“Show me the available subagents.”
Use a saved workflow	“Run the review chain on this branch.”
See running work	“Show active async runs.”
Check setup	“Check whether subagents are configured correctly.”

The extension ships with builtin agents you can use immediately.

Builtin agents in plain English

Agent	Use it when you want...
scout	Fast local codebase recon: relevant files, entry points, data flow, risks, and where another agent should start.
researcher	Web/docs research with sources: official docs, specs, benchmarks, recent changes, and a concise research brief.
planner	A concrete implementation plan from existing context. It should read and plan, not edit code.
worker	Implementation work, including approved oracle handoffs. It edits files, validates, and escalates unapproved decisions instead of guessing.
reviewer	Code review and small fixes. It checks the implementation against the task/plan, tests, edge cases, and simplicity.
context-builder	A stronger setup pass before planning: gathers code context and writes handoff material such as context.md and meta-prompt.md.
oracle	A second opinion before acting. It challenges assumptions, catches drift, and recommends the safest next move without editing.
delegate	A lightweight general delegate when you want a child agent that behaves close to the parent session.

A simple rule of thumb: use scout before you understand the code, researcher before you trust external facts, planner before a bigger change, worker to implement, reviewer to check, and oracle when the decision itself feels risky.

Changing a builtin agent's model

Builtin agents inherit your current Pi default model by default. This keeps new installs from depending on a provider you may not have configured. If you want a role to use a specific model, set an override instead of copying the bundled agent file.

For one run, put the override in the command:

/run reviewer[model=anthropic/claude-sonnet-4:high] "Review this diff"

For a persistent override, edit settings. This example pins the reviewer everywhere, adds a backup model for provider failures, and keeps the other builtins on your normal default model:

{
  "subagents": {
    "agentOverrides": {
      "reviewer": {
        "model": "anthropic/claude-sonnet-4",
        "thinking": "high",
        "fallbackModels": ["openai/gpt-5-mini"]
      }
    }
  }
}

Use ~/.pi/agent/settings.json for a user override or .pi/settings.json for a project override. The same agentOverrides block can change tools, skills, inherited context, prompt text, or disable a builtin. If you want a totally different agent, create a user or project agent with the same name; for normal tweaks, prefer overrides.

Where running subagents show up

Foreground runs stream progress in the conversation while they run.

Background runs keep working after control returns to you. Inspect active runs with subagent({ action: "status" }), or a specific run with subagent({ action: "status", id: "..." }).

They also show a compact async widget and send completion notifications. Parallel background runs show per-agent progress instead of fake chain steps. Chains with parallel groups keep their grouped shape in progress and results, so failed or paused agents stay visible next to completed ones.

You can also ask naturally:

Show me the current async runs.

If something feels misconfigured, run:

/subagents-doctor

or ask:

Check whether subagents and intercom are set up correctly.

Recommended orchestration pattern (scaffolding)

Use orchestration as parent-agent guidance, not as a runtime workflow mode. For implementation work, the recommended loop is:

clarify → planner → worker → fresh reviewers → worker

Use the optional prompt shortcuts below when you want the pattern to be repeatable.

Packaged planner, worker, and oracle default to forked context when a launch omits context; pass context: "fresh" when you intentionally want a fresh child run.

Child-safety boundaries are enforced at runtime. Spawned child sessions do not register the subagent tool, do not receive the bundled pi-subagents skill, and receive explicit boundary instructions that they are not the parent orchestrator and must not propose or run subagents. Forked child context filtering also removes parent-only subagent artifacts (including old hidden orchestration-instruction messages, slash/status/control messages, and prior parent subagent tool-call/tool-result history) while preserving ordinary prose and unrelated tool calls/results.

Optional shortcuts

The package includes reusable prompt templates for common workflows. You do not need them, but they are handy when you want the same shape every time:

Prompt	Use it for
/parallel-review	Launch fresh-context reviewers with distinct angles, then synthesize what to fix.
/parallel-research	Combine researcher and scout for external evidence, local code context, and practical tradeoffs.
/parallel-context-build	Run context-builder agents in parallel to produce planning handoff context and meta-prompts.
/parallel-handoff-plan	Combine external research and context-builder passes into an implementation handoff plan and meta-prompt.
/gather-context-and-clarify	Scout/research first, then ask the user the clarification questions that matter.
/parallel-cleanup	Run review-only cleanup passes after implementation.

Add autofix to /parallel-review or /parallel-cleanup to apply only the synthesized fixes worth doing now after reviewers return.

Optional pi-intercom companion

pi-subagents works without pi-intercom. Install pi-intercom only if you want child agents to talk back to the parent Pi session while they are running.

pi install npm:pi-intercom

Most users do not call intercom directly. After pi-intercom is installed, pi-subagents can automatically give child agents a private coordination channel back to the parent session. The bridge recognizes the normal pi install npm:pi-intercom package install as well as legacy local extension checkouts.

Use it for work where the child might need a decision instead of guessing:

Run this implementation in the background. If the worker gets blocked or needs a product decision, have it ask me through intercom.

Ask oracle to review this plan. If it sees a decision I need to make, have it ask me instead of assuming.

The child can use one dedicated coordination tool:

• contact_supervisor: the child contacts the parent/supervisor session that delegated the task. Use reason: "need_decision" for blocking decisions or clarification, and reason: "progress_update" for short non-blocking updates when a discovery changes the plan. Do not ask for clarification when the only conflict is review-only/no-edit versus progress-writing or artifact-writing instructions; no-edit wins.

Child-side routine completion handoffs are still not expected. With the intercom bridge active, parent-side pi-subagents sends grouped completion results through pi-intercom: one grouped message per foreground parent subagent run and one per completed async result file. Acknowledged foreground delivery returns a compact receipt with artifact/session paths; if unacknowledged, the normal full output is preserved. Grouped messages include child intercom targets and full child summaries.

If a child appears stalled, needs-attention notices can show up in the parent session with useful next actions, such as checking subagent({ action: "status" }), interrupting the run, or nudging the child.

If messages do not show up, run:

/subagents-doctor

For normal use, you do not need to configure anything. Advanced users can tune the bridge with intercomBridge in the configuration section below.

At this point, you know enough to use the plugin. The rest of this README is reference material for exact command syntax, custom agents, saved chains, worktrees, and configuration.

Direct commands

Skip this section until you want exact syntax.

Command	Description
/run <agent> [task]	Run one agent; omit the task for self-contained agents
/chain agent1 "task1" -> agent2 "task2"	Run agents in sequence
/parallel agent1 "task1" -> agent2 "task2"	Run agents in parallel
/run-chain <chainName> -- <task>	Launch a saved .chain.md workflow
/subagents-doctor	Show read-only setup diagnostics

Commands validate agent names locally, support tab completion, and send results back into the conversation.

Per-step tasks

Use -> to separate steps and give each step its own task:

/chain scout "scan the codebase" -> planner "create an implementation plan"
/parallel scanner "find security issues" -> reviewer "check code style"

Both double and single quotes work. You can also use -- as a delimiter:

/chain scout -- scan code -> planner -- analyze auth

Steps without a task inherit behavior from the execution mode. Chain steps get {previous}, the prior step’s output. Parallel steps use the first available task as a fallback.

/chain scout "analyze auth" -> planner -> worker
# scout gets "analyze auth"; planner gets scout output; worker gets planner output

For a shared task, list agents and place one -- before the task:

/chain scout planner -- analyze the auth system
/parallel scout reviewer -- check for security issues

Inline per-step config

Append [key=value,...] to an agent name to override defaults for that step:

/chain scout[output=context.md] "scan code" -> planner[reads=context.md] "analyze auth"
/run scout[model=anthropic/claude-sonnet-4] summarize this codebase
/parallel reviewer[skills=code-review+security] "review backend" -> reviewer[model=openai/gpt-5-mini] "review frontend"

Key	Example	Description
output	output=context.md	Write results to a file. For /chain and /parallel, relative paths live under the chain directory; for /run, relative paths resolve against cwd.
outputMode	outputMode=file-only	Return only a concise file reference for saved output instead of the full saved content. Requires output; default is inline.
reads	reads=a.md+b.md	Read files before executing. + separates multiple paths.
model	model=anthropic/claude-sonnet-4	Override model for this step.
skills	skills=planning+review	Override injected skills. + separates multiple skills.
progress	progress	Enable progress tracking.

Set output=false, reads=false, or skills=false to disable that behavior explicitly. Do not use output=false for file-only returns; use outputMode=file-only with an output path.

Background and forked runs

Add --bg to run in the background:

/run scout "audit the codebase" --bg
/chain scout "analyze auth" -> planner "design refactor" -> worker --bg
/parallel scout "scan frontend" -> scout "scan backend" --bg

Add --fork to start each child from a real branched session created from the parent’s current leaf:

/run reviewer "review this diff" --fork
/chain scout "analyze this branch" -> planner "plan next steps" --fork
/parallel scout "audit frontend" -> reviewer "audit backend" --fork

You can combine them in either order:

/run reviewer "review this diff" --fork --bg
/run reviewer "review this diff" --bg --fork

Background runs are detached. If the parent agent has other independent work, it should keep working. If it has nothing useful to do until the background result arrives, it should end the turn instead of running sleep or status-polling loops. Pi will deliver the completion when the run finishes.

The oracle and worker builtins are designed for an explicit decision loop. A typical pattern is to ask oracle for diagnosis and a recommended execution prompt, then only run worker after the main agent approves that direction.

Clarify and launch UI

Chains open a clarify UI by default so you can preview and edit the workflow before it runs. Single and parallel tool calls can opt into the same flow with clarify: true; slash commands launch directly.

Common clarify keys:

• Enter runs in the foreground, or in the background if background is toggled on
• Esc cancels or backs out
• ↑↓ moves between steps or tasks
• e edits the task/template
• m selects a model
• t selects thinking level
• s selects skills
• b toggles background execution
• w edits output/write behavior where supported
• r edits reads where supported
• p toggles progress tracking where supported Picker screens use ↑↓, Enter, Esc, and type-to-filter. The full-screen editor supports word wrapping, paste, Esc to save, and Ctrl+C to discard.

Agents and chains

Agents are markdown files with YAML frontmatter and a system prompt body. They define the specialist that will run in the child Pi process.

Agent locations, lowest to highest priority:

Scope	Path
Builtin	~/.pi/agent/extensions/subagent/agents/
User	~/.pi/agent/agents/**/*.md
Project	.pi/agents/**/*.md

Project discovery also reads legacy .agents/**/*.md files. Nested subdirectories are discovered recursively. .chain.md files do not define agents. If both .agents/ and .pi/agents/ define the same parsed runtime agent name, .pi/agents/ wins. Use agentScope: "user" | "project" | "both" to control discovery; both is the default and project definitions win runtime-name collisions.

Builtin agents load at the lowest priority, so a user or project agent with the same name overrides them. They do not pin a provider model; they inherit your current Pi default model unless you set subagents.agentOverrides.<name>.model. oracle is an advisory reviewer that critiques direction and proposes an execution prompt without editing files. worker is the implementation agent for normal tasks and approved oracle handoffs.

The researcher builtin uses web_search, fetch_content, and get_search_content; those require pi-web-access:

pi install npm:pi-web-access

Builtin overrides

You can override selected builtin fields without copying the whole agent. Overrides live in settings:

• User: ~/.pi/agent/settings.json
• Project: .pi/settings.json

Example:

{
  "subagents": {
    "agentOverrides": {
      "reviewer": {
        "inheritProjectContext": false
      }
    }
  }
}

Supported override fields are model, fallbackModels, thinking, systemPromptMode, inheritProjectContext, inheritSkills, defaultContext, disabled, skills, tools, and systemPrompt. Use defaultContext: false in builtin overrides to clear an inherited context default. Project overrides beat user overrides.

Set disabled: true to hide a builtin from runtime discovery and agent-facing subagent({ action: "list" }) output. For bulk control, set subagents.disableBuiltins: true in settings.

Prompt assembly

Subagents are designed to be narrow by default. Custom agents start with a clean system prompt and only the context you intentionally give them. They do not automatically inherit Pi’s whole base prompt, project instruction files, or discovered skills catalog.

Use these fields when an agent should see more:

Field	Effect
systemPromptMode: append	Append the agent prompt to Pi’s normal base prompt.
inheritProjectContext: true	Keep inherited project instructions from files like AGENTS.md and CLAUDE.md.
inheritSkills: true	Let the child see Pi’s discovered skills catalog.
defaultContext: fork	Use forked session context when a launch omits context; explicit context: "fresh" still wins.

Builtin agents opt into project instruction inheritance by default so they follow repo-specific rules out of the box. delegate also uses append mode because its job is orchestration inside the parent workflow.

Agent frontmatter

A typical agent looks like this:

---
name: scout
# Optional: registers this as code-analysis.scout while preserving name: scout
package: code-analysis
description: Fast codebase recon
tools: read, grep, find, ls, bash, mcp:chrome-devtools
extensions:
model: claude-haiku-4-5
fallbackModels: openai/gpt-5-mini, anthropic/claude-sonnet-4
thinking: high
systemPromptMode: replace
inheritProjectContext: false
inheritSkills: false
skills: safe-bash, chrome-devtools
output: context.md
defaultReads: context.md
defaultProgress: true
interactive: true
maxSubagentDepth: 1
---

Your system prompt goes here.

Important fields:

Field	Notes
package	Optional package identifier. A file with name: scout and package: code-analysis registers as code-analysis.scout; serialization keeps name and package separate.
tools	Builtin tool allowlist. mcp: entries select direct MCP tools when pi-mcp-adapter is installed.
extensions	Omitted means normal extensions; empty means no extensions; comma-separated values allowlist specific extensions.
model	Default model. Bare ids prefer the current provider when possible, then unique registry matches.
fallbackModels	Ordered backup models for provider/model failures such as quota, auth, timeout, or unavailable model. Ordinary task failures do not trigger fallback.
thinking	Appended as a :level suffix at runtime unless a suffix is already present.
systemPromptMode	replace by default; append keeps Pi’s base prompt.
inheritProjectContext	Keeps or strips inherited project instruction blocks.
inheritSkills	Keeps or strips Pi’s discovered skills catalog.
defaultContext	Optional fresh or fork launch context default for this agent.
skills	Injects specific skills directly, regardless of inheritSkills.
output	Default single-agent output file.
defaultReads	Files to read before running in chain/parallel behavior.
defaultProgress	Maintain progress.md.
interactive	Parsed for compatibility but not enforced in v1.
maxSubagentDepth	Tightens nested delegation for this agent’s children.

Tool and extension selection

If tools is omitted, pi-subagents does not pass --tools, so the child gets Pi’s normal builtin tools. If tools is present, regular tool names become an explicit allowlist. mcp: entries are split out and forwarded as direct MCP selections. Path-like tools entries, such as extension paths or .ts/.js files, are treated as tool-extension paths rather than builtin tool names.

Examples:

• tools omitted and extensions omitted: normal builtins and normal extensions.
• tools: mcp:chrome-devtools: normal builtins plus direct Chrome DevTools MCP tools.
• tools: read, bash, mcp:chrome-devtools: only read and bash as builtins, plus direct Chrome DevTools MCP tools.

Direct MCP tools require pi-mcp-adapter. Subagents only receive direct MCP tools when mcp: entries are listed in their frontmatter; global directTools: true in mcp.json is not enough by itself. The generic mcp proxy tool can still be used for discovery when available. The adapter caches tool metadata at startup, so after connecting a new MCP server for the first time, restart Pi before relying on direct tools.

extensions controls child extension loading:

# Omitted: all normal extensions load

# Empty: no extensions
extensions:

# Allowlist
extensions: /abs/path/to/ext-a.ts, /abs/path/to/ext-b.ts

When extensions is present, it takes precedence over extension paths implied by tools entries.

Chain files

Chains are reusable .chain.md workflows stored separately from agent files.

Scope	Path
User	~/.pi/agent/chains/**/*.chain.md
Project	.pi/chains/**/*.chain.md

Nested subdirectories are discovered recursively. If user and project scopes define the same parsed runtime chain name, the project chain wins. Chains support the same optional package frontmatter as agents; name: review-flow plus package: code-analysis runs as code-analysis.review-flow.

Example:

---
name: scout-planner
description: Gather context then plan implementation
---

## scout
output: context.md

Analyze the codebase for {task}

## planner
reads: context.md
model: anthropic/claude-sonnet-4-5:high
progress: true

Create an implementation plan based on {previous}

Each ## agent-name section is a step. Config lines such as output, outputMode, reads, model, skills, and progress go immediately after the header. A blank line separates config from task text.

For output, reads, skills, and progress, chain behavior is three-state: omitted inherits from the agent, a value overrides, and false disables.

Create chains by writing .chain.md files directly or with the subagent({ action: "create", config: ... }) management action. Run them with natural language or:

/run-chain scout-planner -- refactor authentication

Chain variables

Task templates support:

Variable	Description
{task}	Original task from the first step.
{previous}	Output from the prior step, or aggregated output from a parallel step.
{chain_dir}	Path to the chain artifact directory.

Parallel outputs are aggregated with clear separators before being passed to the next step:

=== Parallel Task 1 (worker) ===
...

=== Parallel Task 2 (worker) ===
...

Skills

Skills are SKILL.md files injected into an agent’s system prompt.

Discovery uses project-first precedence:

1. .pi/skills/{name}/SKILL.md
2. Project packages and project settings packages via package.json -> pi.skills
3. Current task cwd package via package.json -> pi.skills
4. .pi/settings.json -> skills
5. ~/.pi/agent/skills/{name}/SKILL.md
6. User packages and user settings packages via package.json -> pi.skills
7. ~/.pi/agent/settings.json -> skills

Use agent defaults, override them at runtime, or disable them:

{ agent: "scout", task: "..." }
{ agent: "scout", task: "...", skill: "tmux, safe-bash" }
{ agent: "scout", task: "...", skill: false }

For chains, skill at the top level is additive. A step-level skill overrides that step; false disables skills for that step.

Injected skills use this shape:

<skill name="safe-bash">
[skill content from SKILL.md, frontmatter stripped]
</skill>

Missing skills do not fail execution. The result summary shows a warning.

Bundled skill

The package bundles a pi-subagents skill that is automatically available to the parent agent when the extension is installed. It is for the orchestrating parent only: child subagents never receive it, and their context is explicitly filtered to strip parent-only orchestration instructions.

What the bundled skill covers:

• Delegation patterns: when to launch which agent, whether to use single, parallel, chain, or async mode, and whether to use fresh or forked context
• Prompt workflow recipes: how to apply the packaged techniques directly with subagent(...) when the user describes the workflow in natural language instead of invoking a slash command. This includes parallel review, parallel research, parallel context-build, parallel handoff-plan, gather-context-and-clarify, and parallel cleanup
• Role-agent prompting guidance: compact contract prompts instead of long scripts, what to include in role-specific meta prompts, and retrieval budgets for researchers
• Safety boundaries: child agents must not run subagents, must not invent intercom targets, and must escalate unapproved decisions
• Intercom conventions: when to ask vs send, and how parent-side result delivery works with pi-intercom
• Control and diagnostics: attention signals, soft interrupts, status, and the doctor action

If you are writing an agent that orchestrates subagents, the bundled skill helps it behave correctly without guessing the patterns. If you are a human user, you do not need to read it directly; the README and prompt shortcuts encode the same workflows in user-facing form.

Programmatic tool usage

These are the parameters the LLM passes when it calls the subagent tool. Most users ask naturally or use slash commands instead.

Execution examples

// Single agent
{ agent: "worker", task: "refactor auth" }
{ agent: "scout", task: "find todos", maxOutput: { lines: 1000 } }
{ agent: "scout", task: "investigate", output: false }
{ agent: "scout", task: "write a large report", output: "reports/scout.md", outputMode: "file-only" }

// Forked context
{ agent: "worker", task: "continue this thread", context: "fork" }

// Parallel
{ tasks: [{ agent: "scout", task: "a" }, { agent: "reviewer", task: "b" }] }
{ tasks: [{ agent: "scout", task: "audit auth", count: 3 }] }
{ tasks: [{ agent: "scout", task: "audit frontend" }, { agent: "reviewer", task: "audit backend" }], context: "fork" }

// Chain
{ chain: [
  { agent: "scout", task: "Gather context for auth refactor" },
  { agent: "planner" },
  { agent: "worker" },
  { agent: "reviewer" }
]}

// Chain without TUI, suitable for background execution
{ chain: [...], clarify: false, async: true }

// Chain with fan-out/fan-in
{ chain: [
  { agent: "scout", task: "Gather context" },
  { parallel: [
    { agent: "worker", task: "Implement feature A from {previous}" },
    { agent: "worker", task: "Implement feature B from {previous}" }
  ], concurrency: 2, failFast: true },
  { agent: "reviewer", task: "Review all changes from {previous}" }
]}

// Worktree isolation
{ tasks: [
  { agent: "worker", task: "Implement auth" },
  { agent: "worker", task: "Implement API" }
], worktree: true }

Management actions

Agent definitions are not loaded into context by default. Management actions let the LLM discover, inspect, create, update, and delete agents and chains at runtime.

{ action: "list" }
{ action: "list", agentScope: "project" }
{ action: "get", agent: "scout" }
{ action: "get", agent: "code-analysis.scout" }
{ action: "get", chainName: "review-pipeline" }

{ action: "create", config: {
  name: "Code Scout",
  package: "code-analysis",
  description: "Scans codebases for patterns and issues",
  scope: "user",
  systemPrompt: "You are a code scout...",
  systemPromptMode: "replace",
  inheritProjectContext: false,
  inheritSkills: false,
  model: "anthropic/claude-sonnet-4",
  fallbackModels: ["openai/gpt-5-mini", "anthropic/claude-haiku-4-5"],
  tools: "read, bash, mcp:github/search_repositories",
  extensions: "",
  skills: "parallel-scout",
  thinking: "high",
  output: "context.md",
  reads: "shared-context.md",
  progress: true
}}

{ action: "create", config: {
  name: "review-pipeline",
  description: "Scout then review",
  scope: "project",
  steps: [
    { agent: "scout", task: "Scan {task}", output: "context.md" },
    { agent: "reviewer", task: "Review {previous}", reads: ["context.md"] }
  ]
}}

{ action: "update", agent: "code-analysis.scout", config: { model: "openai/gpt-4o" } }
{ action: "update", chainName: "review-pipeline", config: { steps: [...] } }
{ action: "delete", agent: "scout" }
{ action: "delete", chainName: "review-pipeline" }

create uses config.scope, not agentScope. config.name is the local frontmatter name; optional config.package registers the runtime name as {package}.{name} and is saved as separate name and package frontmatter. update and delete use the runtime name and agentScope only when the same runtime name exists in multiple scopes. To clear optional string fields, including package, set them to false or "".

Parameter reference

Param	Type	Default	Description
agent	string	-	Agent name for single mode, or target for management actions.
task	string	-	Task string for single mode.
action	string	-	list, get, create, update, delete, status, interrupt, resume, or doctor.
chainName	string	-	Chain name for management actions.
config	object/string	-	Agent or chain config for create/update.
output	string | false	agent default	Override single-agent output file.
outputMode	"inline" | "file-only"	inline	Return saved output inline or as a concise saved-file reference. file-only requires an output path.
skill	string | string[] | false	agent default	Override skills or disable all.
model	string	agent default	Override model.
tasks	array	-	Top-level parallel tasks. Supports agent, task, cwd, count, output, outputMode, reads, progress, skill, and model.
concurrency	number	config or 4	Top-level parallel concurrency.
worktree	boolean	false	Create isolated git worktrees for parallel tasks.
chain	array	-	Sequential and parallel chain steps.
context	fresh | fork	agent default or fresh	fork creates real branched sessions from the parent leaf. Packaged planner, worker, and oracle default to fork.
chainDir	string	temp chain dir	Persistent directory for chain artifacts.
clarify	boolean	true for chains	Show TUI preview/edit flow.
agentScope	user | project | both	both	Agent discovery scope. Project wins on collisions.
async	boolean	false	Background execution. Chains require clarify: false.
cwd	string	runtime cwd	Override working directory.
maxOutput	object	200KB, 5000 lines	Final output truncation limits.
artifacts	boolean	true	Write debug artifacts.
includeProgress	boolean	false	Include full progress in result.
share	boolean	false	Upload session export to GitHub Gist.
sessionDir	string	derived	Override session log directory.

context: "fork" fails fast when the parent session is not persisted, the current leaf is missing, or the branched child session cannot be created. It never silently downgrades to fresh. In multi-agent runs, if any requested agent has defaultContext: fork and the launch omits context, the whole invocation uses forked context; pass context: "fresh" when you intentionally want a fresh run.

Use outputMode: "file-only" when a saved output may be large and the parent only needs a pointer. The returned text is a compact reference like Output saved to: /abs/report.md (48.2 KB, 2847 lines). Read this file if needed. Failed runs and save errors still return normal inline output for debugging. In chains, later {previous} steps receive the same compact reference when the prior step used file-only mode.

Sequential and parallel chain tasks accept agent, task, cwd, output, outputMode, reads, progress, skill, and model. Parallel tasks also accept count. Parallel step groups accept parallel, concurrency, failFast, and worktree.

Status and control actions:

subagent({ action: "status" })
subagent({ action: "status", id: "<run-id>" })
subagent({ action: "interrupt", id: "<run-id>" })
subagent({ action: "resume", id: "<run-id>", message: "follow-up question" })
subagent({ action: "resume", id: "<run-id>", index: 1, message: "follow-up for child 2" })
subagent({ action: "doctor" })

resume sends the follow-up directly when an async child is still reachable over intercom. After completion, it revives the child by starting a new async child from the stored child session file. Multi-child async runs and remembered foreground single, parallel, or chain runs can be revived by passing index to choose the child. Revive starts a new child process from the old session context; it does not restart the same OS process, and it requires the chosen child to have a persisted .jsonl session file.

Worktree isolation

Parallel agents can clobber each other if they edit the same checkout. worktree: true gives each parallel child its own git worktree branched from HEAD.

{ tasks: [
  { agent: "worker", task: "Implement auth", count: 2 },
  { agent: "worker", task: "Implement API" }
], worktree: true }

{ chain: [
  { agent: "scout", task: "Gather context" },
  { parallel: [
    { agent: "worker", task: "Implement feature A from {previous}" },
    { agent: "worker", task: "Implement feature B from {previous}" }
  ], worktree: true },
  { agent: "reviewer", task: "Review all changes from {previous}" }
]}

Requirements:

• run inside a git repo
• working tree must be clean
• node_modules/ is symlinked into each worktree when present
• task-level cwd overrides must be omitted or match the shared cwd
• configured worktreeSetupHook must return valid JSON before timeout

After a worktree parallel step completes, per-agent diff stats are appended to the output and full patch files are written to artifacts. Worktrees and temp branches are cleaned up in finally blocks.

Configuration

pi-subagents reads optional JSON config from ~/.pi/agent/extensions/subagent/config.json.

asyncByDefault

{ "asyncByDefault": true }

Makes top-level calls use background execution when the request does not explicitly set async. Callers can still force foreground with async: false unless forceTopLevelAsync is enabled.

forceTopLevelAsync

{ "forceTopLevelAsync": true }

Forces depth-0 single, parallel, and chain runs into background mode and bypasses clarify UI by forcing clarify: false. Nested calls keep their own inherited settings.

parallel

{
  "parallel": {
    "maxTasks": 12,
    "concurrency": 6
  }
}

maxTasks defaults to 8; concurrency defaults to 4. Per-call concurrency takes precedence.

defaultSessionDir

{ "defaultSessionDir": "~/.pi/agent/sessions/subagent/" }

Session directory precedence is: params.sessionDir, then config.defaultSessionDir, then a directory derived from the parent session. Sessions are always enabled.

maxSubagentDepth

{ "maxSubagentDepth": 1 }

Controls nested delegation when no inherited PI_SUBAGENT_MAX_DEPTH is already in effect. Per-agent maxSubagentDepth can tighten the limit for that agent’s child runs, but cannot relax an inherited stricter limit.

intercomBridge

{
  "intercomBridge": {
    "mode": "always",
    "instructionFile": "./intercom-bridge.md"
  }
}

Controls whether subagents receive runtime intercom coordination instructions and whether intercom and contact_supervisor are auto-added to their tool allowlist when needed.

Fields:

• mode: default always; use fork-only to inject only for forked runs, or off to disable the bridge.
• instructionFile: optional Markdown template replacing the default bridge instructions. {orchestratorTarget} is interpolated. Relative paths resolve from ~/.pi/agent/extensions/subagent/.

Bridge activation also requires pi-intercom to be installed and enabled through pi install npm:pi-intercom or a legacy local extension checkout, a targetable current session name or fallback alias, and pi-intercom in any explicit agent extensions allowlist.

The default injected guidance tells children to use contact_supervisor with reason: "need_decision" when blocked or needing a decision, reason: "progress_update" only for meaningful blocked/progress updates, generic intercom as fallback plumbing, and avoid routine completion handoffs.

worktreeSetupHook

{
  "worktreeSetupHook": "./scripts/setup-worktree.mjs",
  "worktreeSetupHookTimeoutMs": 45000
}

The hook runs once per created worktree. Paths must be absolute, ~/..., or repo-relative; bare command names are rejected.

stdin is a JSON object with repoRoot, worktreePath, agentCwd, branch, index, runId, and baseCommit. stdout must be one JSON object, for example:

{ "syntheticPaths": [".venv", ".env.local"] }

syntheticPaths must be relative to the worktree root. They are removed before diff capture so helper files do not pollute patches. Tracked files are never excluded; marking a tracked path as synthetic fails setup. Default timeout is 30000 ms.

Files, logs, and observability

Each chain run creates a user-scoped temp directory like:

<tmpdir>/pi-subagents-<scope>/chain-runs/{runId}/

It may contain files such as context.md, plan.md, progress.md, and parallel-{stepIndex}/.../output.md. Directories older than 24 hours are cleaned up on extension startup.

Debug artifacts live under {sessionDir}/subagent-artifacts/ or a user-scoped temp artifact directory. Per task you may see:

• {runId}_{agent}_input.md
• {runId}_{agent}_output.md
• {runId}_{agent}.jsonl
• {runId}_{agent}_meta.json

Metadata records timing, usage, exit code, final model, attempted models, and fallback attempt outcomes.

Session files are stored under a per-run session directory. With context: "fork", each child starts with --session <branched-session-file> produced from the parent’s current leaf. That is a real session fork, not an injected summary.

Async completions notify only the originating session. The result watcher emits subagent:async-complete, and the extension consumes that event to render completion notifications.

Async runs write:

<tmpdir>/pi-subagents-<scope>/async-subagent-runs/<id>/
  status.json
  events.jsonl
  output-<n>.log
  subagent-log-<id>.md

status.json powers the widget and subagent({ action: "status" }) output. events.jsonl contains wrapper events plus child Pi JSON events annotated with run and step metadata. output-<n>.log is a live human-readable tail. Fallback information is persisted so background runs are debuggable after completion.

Live progress

Foreground runs show compact live progress for single, chain, and parallel modes: current tool, recent output, token counts, duration, activity freshness, and current-tool duration.

Press Ctrl+O to expand the full streaming view with complete output per step.

Sequential chains show a flow line like done scout → running planner. Chains with parallel steps show per-step cards instead.

Session sharing

Pass share: true to export a full session to HTML, upload it to a secret GitHub Gist through your gh credentials, and return a https://shittycodingagent.ai/session/?<gistId> URL.

{ agent: "scout", task: "...", share: true }

This is disabled by default. Session data may contain source code, paths, environment variables, credentials, or other sensitive output. You need gh installed and authenticated.

Recursion guard

Subagents can call subagent, which can get expensive and hard to observe. A depth guard prevents unbounded nesting.

By default, nesting is limited to two levels: main session → subagent → sub-subagent. Deeper calls are blocked with guidance to complete the current task directly.

Configure the limit with:

1. PI_SUBAGENT_MAX_DEPTH before starting Pi
2. config.maxSubagentDepth
3. maxSubagentDepth in agent frontmatter, which can only tighten the inherited limit

export PI_SUBAGENT_MAX_DEPTH=3
export PI_SUBAGENT_MAX_DEPTH=1
export PI_SUBAGENT_MAX_DEPTH=0

PI_SUBAGENT_DEPTH is internal and propagated automatically. Do not set it manually.

Events

Async events:

• subagent:async-started
• subagent:async-complete

Intercom delivery events:

• subagent:control-intercom
• subagent:result-intercom

The result watcher emits subagent:async-complete; src/extension/index.ts registers the notification handler that consumes it. Control/attention events are surfaced as visible parent notices and persisted for async runs. With pi-intercom, needs-attention notices and grouped parent-side subagent result deliveries can reach the orchestrator over intercom.

Prompt-template integration

pi-subagents works standalone through natural language, the subagent tool, slash commands, and the packaged prompt shortcuts listed near the top of this README. If you use pi-prompt-template-model, you can also wrap subagent delegation in your own reusable prompt templates.

Example:

---
description: Take a screenshot
model: claude-sonnet-4-20250514
subagent: browser-screenshoter
cwd: /tmp/screenshots
---
Use url in the prompt to take screenshot: $@

Then /take-screenshot https://example.com switches to Sonnet, delegates to browser-screenshoter with /tmp/screenshots as cwd, and restores your model when done. Runtime overrides like --cwd=<path> and --subagent=<name> work too.

For more reusable workflows on top of subagents, including /chain-prompts and compare-style prompts such as /best-of-n, install pi-prompt-template-model separately and copy the examples you want into ~/.pi/agent/prompts/.

Runtime files

The main runtime files are:

File	Purpose
src/extension/index.ts	Extension registration, tool registration, message/render wiring.
src/agents/agents.ts	Agent and chain discovery, frontmatter parsing.
src/runs/foreground/subagent-executor.ts	Main execution routing for single, parallel, chain, management, status, interrupt, and doctor actions.
src/runs/foreground/execution.ts	Core foreground runSync handling.
src/runs/background/subagent-runner.ts	Detached async runner.
src/runs/background/async-execution.ts	Background launch support.
src/runs/background/async-status.ts	Status discovery and formatting for async runs.
src/runs/foreground/chain-execution.ts / src/agents/chain-serializer.ts	Chain orchestration and .chain.md parsing.
src/shared/settings.ts	Chain behavior, instructions, and config helpers.
src/runs/shared/worktree.ts	Git worktree isolation.
src/intercom/intercom-bridge.ts	Runtime intercom bridge instructions and diagnostics.
src/extension/schemas.ts / src/shared/types.ts	Tool schemas, shared types, and event constants.
test/unit/ / test/integration/	Unit and loader-based integration tests.