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obsidian-vault/300 areas/350 AI/Pi Subagent.md

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2026-06-15 2026-06-15 note
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pi-subagents

Pi Subagent System

Persistent named subagents with provider-pinned KV cache continuity.

What It Is

Pi-subagents (@tintinweb v0.10.2) lets you spawn named, persistent subagents that:

  • Run in background — don't block the main session
  • Keep their context — resume later with full conversation history
  • Pin to a provider — KV cache stays warm across resume calls (= massive token savings)
  • Have persistent memory — memory: project gives each agent its own memory directory
  • Survive session restarts — patched to keep agents alive 24h, not 10 minutes

Key Concepts

Named Agents

Every subagent gets a human-readable name so you can reference it later:

  • coder-login — coder-pro working on auth
  • searcher-1 — chat-search for web lookups
  • obsidian-docs — obsidian agent for note editing

Multiple instances of the same type can run simultaneously:

  • coder-login + coder-nav-bar — two coder-pro agents, different tasks
  • searcher-1 + searcher-2 — parallel web searches

Provider Pinning

Each agent is pinned to a specific OpenRouter provider via only: [...] in models.json. This ensures:

  • KV cache continuity — same provider = cache hits on resume
  • Predictable latency — always route through the same serving host
  • Cost control — token savings when context prefix is cached

See Pi Agent Extensions & Skills#models.json for the full routing table.

Persistent Memory

Agents with memory: project in their frontmatter get a persistent directory:

.pi/agent-memory/<agent-name>/     ← per-project, committed to git
.pi/agent-memory-local/<agent-name>/ ← per-project, gitignored
~/.pi/agent-memory/<agent-name>/   ← global, across all projects

Memory survives across resume calls. Agents build up knowledge over time.

Session-Scoped Registry

Agents belong to a pi session (per-project directory). When you /new or /resume, the session's agents are available.

Creating Agents

Ask the LLM

Simply describe what you need. The LLM will ask for a name if you don't provide one:

You: "Find all authentication files"
LLM: "What should I name this agent?"
You: "auth-finder"
LLM: *spawns agent, saves to registry*
     "Created 'auth-finder' (chat-search, Google). ID: 7efad0d8"

Agent Types Available

Type Model Provider Memory Tools Use For
chat-search gemini-2.5-flash:free Google project read, bash, grep, find Web search, quick lookups
coder-basic deepseek-chat DeepInfra project read, bash, write, grep, find Simple code edits
coder-pro deepseek-v4-pro DeepInfra project read, bash, write, grep, find, edit Complex architecture
code-analysis deepseek-r1-distill-qwen-32b opencode-go project read, bash, grep, find Security review, analysis
code-ingest gemini-2.5-flash:free Google project read, bash, grep, find Scan/ingest docs, GitHub
database deepseek-r1-distill-qwen-32b opencode-go project read, bash, write, grep SQL, schema design
devops-basic deepseek-chat DeepInfra project read, bash, write, grep, find Docker, YAML, NixOS
devops-pro deepseek-v4-pro DeepInfra project read, bash, write, grep, find, edit Complex infrastructure
document-writer deepseek-r1-distill-qwen-32b opencode-go project read, bash, write Documents, letters
file-ops qwen-coder-32b-instruct opencode-go project read, bash, grep, find, write Filesystem, drives
home-automation deepseek-r1-distill-qwen-32b opencode-go project read, bash, write, grep MQTT, Home Assistant
image-maker flux-1-schnell Venice project read, bash, write Image generation
iot-coder qwen-coder-32b-instruct opencode-go project read, bash, write, grep, find Arduino, ESP32
iot-hardware kimi-k2.6 Moonshot AI project read, bash, grep, find Hardware specs, boards
obsidian deepseek-r1-distill-qwen-14b opencode-go project read, bash, write, grep, find Obsidian notes
research gemini-2.5-flash:free Google project read, bash, grep, find Web search, docs
video-analyze qwen-2.5-vl Alibaba project read, bash, grep, find Video/image analysis
vscode-setup qwen-3-coder-next StreamLake project read, bash, grep, find VS Code, AI tooling
Explore haiku/inherit default — read, bash, grep, find, ls Fast codebase search (built-in)
Plan inherit default — read, bash, grep, find, ls Implementation planning (built-in)
general-purpose inherit default — all tools General tasks (built-in)

Managing Agents

List Agents

Type /agents to open the interactive menu:

Running agents (2) — 1 running, 1 done
Agent types (21)
Create new agent
Settings

Select "Running agents" to see active agents with status, tool uses, and duration.

View Conversation

From /agents → "Running agents" → select an agent:

  • Live scrolling overlay of the agent's full conversation
  • Auto-follows new output
  • Press x to stop a running agent
  • Scroll up to pause auto-follow

Resume an Agent

You: "Resume auth-finder and also check the logout module"
LLM: *looks up agent ID from context*
     Agent({ subagent_type: "chat-search", resume: "7efad0d8-...", prompt: "Also check the logout module" })

The agent continues with its full context preserved. Provider KV cache is warm.

Steer a Running Agent

You: "Tell auth-finder to focus on the API routes only"
LLM: steer_subagent({ agent_id: "7efaf0d8-...", message: "Focus on API routes only, ignore UI" })

The agent receives the message after its current tool execution and adjusts course.

Delete an Agent

You: "Delete auth-finder"
LLM: *removes from registry, aborts if running*
     "Deleted 'auth-finder'"

Or delete all agents for a session:

You: "Clear all subagents for computer-software"
LLM: *removes all matching entries*
     "Deleted 4 subagents from session 'computer-software'"

Function Calls

Create

Agent({
  subagent_type,    // string — agent type (e.g. "coder-pro")
  prompt,           // string — the task
  description,      // string — 3-5 word summary
  model?,           // string — provider/model override
  thinking?,        // string — off|minimal|low|medium|high|xhigh
  max_turns?,       // number — max agentic turns (default: unlimited)
  run_in_background?, // boolean — run async (default: false)
  resume?,          // string — agent ID to resume a previous session
  isolated?,        // boolean — no extension/MCP tools
  isolation?,       // "worktree" — run in isolated git worktree
  inherit_context?  // boolean — fork parent conversation into agent
})

Retrieve

get_subagent_result({
  agent_id,   // string — the agent ID from spawn notification
  wait?,      // boolean — block until complete (default: false)
  verbose?    // boolean — include full conversation (default: false)
})

Steer

steer_subagent({
  agent_id,   // string — running agent ID
  message     // string — message injected after current tool execution
})

Compress

compress_for_agent({
  content     // string — content >20K chars to compress via Headroom
})
// Returns: { compressed, originalLength, compressedLength, tokensBefore, tokensAfter, savingsPercent }

Token Savings

The entire purpose is to minimize context tokens on the API provider:

Scenario Context Uploaded Cost
Fresh agent every request Full system prompt + context (~10K tokens) ~$0.50-1.00 per request
Resume same agent Only new prompt (~100 tokens) + cached prefix hits KV cache ~$0.01-0.05 per request
Savings 90-95% reduction ~$0.50 vs $5.00 for 5 questions

Files

File Purpose
~/.local/share/npm-global/lib/node_modules/@tintinweb/pi-subagents/ Extension source
~/.pi/agent/agents/*.md Agent type definitions (18 custom + 3 built-in)
~/.pi/agent/models.json OpenRouter provider pinning (only: provider)
.pi/agent-memory/<name>/ Per-agent persistent memory (project scope)
.pi/agent-memory-local/<name>/ Per-agent persistent memory (gitignored)
~/.pi/agent/subagents.json pi-subagents settings (maxConcurrent, etc.)

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