Epic: Evaluate Pi (badlogic/pi-mono) architectural patterns for terraphim-ai #682

Overview

Evaluate architectural patterns from Pi coding agent (badlogic/pi-mono, 24.5k stars, MIT) for adoption in terraphim-ai. Pi is a layered, extensible TypeScript coding agent toolkit with patterns that address known gaps in our current architecture.

Knowledge base article: cto-executive-system/knowledge/external/context-engineering/pi-coding-agent-architecture.md
Comparison article: cto-executive-system/knowledge/claude-code-architecture-deep-dive.md

Evaluation Items

Tree-structured session storage for terraphim_persistence Evaluate: Tree-structured session storage (Pi pattern) for terraphim_persistence #683
Layered crate architecture: extract standalone LLM interaction crate Evaluate: Extract standalone LLM interaction crate (Pi layered architecture pattern) #684
Cross-provider context serialisation for ADF multi-model routing Evaluate: Cross-provider context serialisation for ADF multi-model routing #685
Typed beforeToolCall/afterToolCall hook pattern for terraphim-skills Evaluate: Typed tool hooks (beforeToolCall/afterToolCall) for terraphim-skills #686
Steering and follow-up message queues for ADF agent interaction Evaluate: Steering and follow-up message queues for ADF agent interaction #687

Context

Pi takes a fundamentally different approach from Claude Code: minimal core (4 built-in tools) with a TypeScript extension system that can replace any component. While we are not adopting Pi itself (our stack is Rust + Claude Code), several of its architectural patterns address real gaps:

Session branching: Our current linear JSONL sessions lose history on compaction. Pi's tree structure (id/parentId) preserves all branches in a single file.
Layered architecture: Pi cleanly separates LLM API, agent loop, and UI into independent packages. Our LLM interaction code is scattered across terraphim_orchestrator, terraphim_multi_agent, and ad-hoc scripts.
Cross-provider handoffs: Pi serialises context for mid-session model switching. ADF routes different agents to different models but cannot hand off context between them.
Tool hooks: Pi's typed beforeToolCall/afterToolCall at the agent-core level is simpler than Claude Code's JSON-based shell hooks. Pattern applicable to terraphim-skills.
Message queues: Pi's steering (interrupt) vs follow-up (after completion) queues are missing from ADF agent interaction.

Architecture Decision

Each evaluation item is independent. Successful evaluations may lead to implementation issues. Items that do not justify adoption should be closed with rationale.

What NOT to Adopt

Pi itself (TypeScript, not Rust)
Anti-MCP philosophy (MCP is integral to our workflow)
Anti-sub-agents philosophy (agent teams are core to our approach)
TypeScript extension system (our skills are Markdown + shell)

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all \u003cpre\u003e\u003ccode\u003e blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks"); } } catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); } })(); (function(){ try { var __m = "github.com"; var __re = new RegExp('^' + "github\\.com" + '

Epic: Evaluate Pi (badlogic/pi-mono) architectural patterns for terraphim-ai #682

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opened

Overview

Evaluation Items

Tree-structured session storage for terraphim_persistence Evaluate: Tree-structured session storage (Pi pattern) for terraphim_persistence #683
Layered crate architecture: extract standalone LLM interaction crate Evaluate: Extract standalone LLM interaction crate (Pi layered architecture pattern) #684
Cross-provider context serialisation for ADF multi-model routing Evaluate: Cross-provider context serialisation for ADF multi-model routing #685
Typed beforeToolCall/afterToolCall hook pattern for terraphim-skills Evaluate: Typed tool hooks (beforeToolCall/afterToolCall) for terraphim-skills #686
Steering and follow-up message queues for ADF agent interaction Evaluate: Steering and follow-up message queues for ADF agent interaction #687

Context

Session branching: Our current linear JSONL sessions lose history on compaction. Pi's tree structure (id/parentId) preserves all branches in a single file.
Layered architecture: Pi cleanly separates LLM API, agent loop, and UI into independent packages. Our LLM interaction code is scattered across terraphim_orchestrator, terraphim_multi_agent, and ad-hoc scripts.
Cross-provider handoffs: Pi serialises context for mid-session model switching. ADF routes different agents to different models but cannot hand off context between them.
Tool hooks: Pi's typed beforeToolCall/afterToolCall at the agent-core level is simpler than Claude Code's JSON-based shell hooks. Pattern applicable to terraphim-skills.
Message queues: Pi's steering (interrupt) vs follow-up (after completion) queues are missing from ADF agent interaction.

Architecture Decision

Each evaluation item is independent. Successful evaluations may lead to implementation issues. Items that do not justify adoption should be closed with rationale.

What NOT to Adopt

Pi itself (TypeScript, not Rust)
Anti-MCP philosophy (MCP is integral to our workflow)
Anti-sub-agents philosophy (agent teams are core to our approach)
TypeScript extension system (our skills are Markdown + shell)

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '

Epic: Evaluate Pi (badlogic/pi-mono) architectural patterns for terraphim-ai #682

Open

Labels

opened

Overview

Evaluation Items

Tree-structured session storage for terraphim_persistence Evaluate: Tree-structured session storage (Pi pattern) for terraphim_persistence #683
Layered crate architecture: extract standalone LLM interaction crate Evaluate: Extract standalone LLM interaction crate (Pi layered architecture pattern) #684
Cross-provider context serialisation for ADF multi-model routing Evaluate: Cross-provider context serialisation for ADF multi-model routing #685
Typed beforeToolCall/afterToolCall hook pattern for terraphim-skills Evaluate: Typed tool hooks (beforeToolCall/afterToolCall) for terraphim-skills #686
Steering and follow-up message queues for ADF agent interaction Evaluate: Steering and follow-up message queues for ADF agent interaction #687

Context

Session branching: Our current linear JSONL sessions lose history on compaction. Pi's tree structure (id/parentId) preserves all branches in a single file.
Layered architecture: Pi cleanly separates LLM API, agent loop, and UI into independent packages. Our LLM interaction code is scattered across terraphim_orchestrator, terraphim_multi_agent, and ad-hoc scripts.
Cross-provider handoffs: Pi serialises context for mid-session model switching. ADF routes different agents to different models but cannot hand off context between them.
Tool hooks: Pi's typed beforeToolCall/afterToolCall at the agent-core level is simpler than Claude Code's JSON-based shell hooks. Pattern applicable to terraphim-skills.
Message queues: Pi's steering (interrupt) vs follow-up (after completion) queues are missing from ADF agent interaction.

Architecture Decision

Each evaluation item is independent. Successful evaluations may lead to implementation issues. Items that do not justify adoption should be closed with rationale.

What NOT to Adopt

Pi itself (TypeScript, not Rust)
Anti-MCP philosophy (MCP is integral to our workflow)
Anti-sub-agents philosophy (agent teams are core to our approach)
TypeScript extension system (our skills are Markdown + shell)

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '

Epic: Evaluate Pi (badlogic/pi-mono) architectural patterns for terraphim-ai #682

Open

Labels

opened

Overview

Evaluation Items

Tree-structured session storage for terraphim_persistence Evaluate: Tree-structured session storage (Pi pattern) for terraphim_persistence #683
Layered crate architecture: extract standalone LLM interaction crate Evaluate: Extract standalone LLM interaction crate (Pi layered architecture pattern) #684
Cross-provider context serialisation for ADF multi-model routing Evaluate: Cross-provider context serialisation for ADF multi-model routing #685
Typed beforeToolCall/afterToolCall hook pattern for terraphim-skills Evaluate: Typed tool hooks (beforeToolCall/afterToolCall) for terraphim-skills #686
Steering and follow-up message queues for ADF agent interaction Evaluate: Steering and follow-up message queues for ADF agent interaction #687

Context

Session branching: Our current linear JSONL sessions lose history on compaction. Pi's tree structure (id/parentId) preserves all branches in a single file.
Layered architecture: Pi cleanly separates LLM API, agent loop, and UI into independent packages. Our LLM interaction code is scattered across terraphim_orchestrator, terraphim_multi_agent, and ad-hoc scripts.
Cross-provider handoffs: Pi serialises context for mid-session model switching. ADF routes different agents to different models but cannot hand off context between them.
Tool hooks: Pi's typed beforeToolCall/afterToolCall at the agent-core level is simpler than Claude Code's JSON-based shell hooks. Pattern applicable to terraphim-skills.
Message queues: Pi's steering (interrupt) vs follow-up (after completion) queues are missing from ADF agent interaction.

Architecture Decision

Each evaluation item is independent. Successful evaluations may lead to implementation issues. Items that do not justify adoption should be closed with rationale.

What NOT to Adopt

Pi itself (TypeScript, not Rust)
Anti-MCP philosophy (MCP is integral to our workflow)
Anti-sub-agents philosophy (agent teams are core to our approach)
TypeScript extension system (our skills are Markdown + shell)

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '

Epic: Evaluate Pi (badlogic/pi-mono) architectural patterns for terraphim-ai #682

Open

Labels

opened

Overview

Evaluation Items

Tree-structured session storage for terraphim_persistence Evaluate: Tree-structured session storage (Pi pattern) for terraphim_persistence #683
Layered crate architecture: extract standalone LLM interaction crate Evaluate: Extract standalone LLM interaction crate (Pi layered architecture pattern) #684
Cross-provider context serialisation for ADF multi-model routing Evaluate: Cross-provider context serialisation for ADF multi-model routing #685
Typed beforeToolCall/afterToolCall hook pattern for terraphim-skills Evaluate: Typed tool hooks (beforeToolCall/afterToolCall) for terraphim-skills #686
Steering and follow-up message queues for ADF agent interaction Evaluate: Steering and follow-up message queues for ADF agent interaction #687

Context

Session branching: Our current linear JSONL sessions lose history on compaction. Pi's tree structure (id/parentId) preserves all branches in a single file.
Layered architecture: Pi cleanly separates LLM API, agent loop, and UI into independent packages. Our LLM interaction code is scattered across terraphim_orchestrator, terraphim_multi_agent, and ad-hoc scripts.
Cross-provider handoffs: Pi serialises context for mid-session model switching. ADF routes different agents to different models but cannot hand off context between them.
Tool hooks: Pi's typed beforeToolCall/afterToolCall at the agent-core level is simpler than Claude Code's JSON-based shell hooks. Pattern applicable to terraphim-skills.
Message queues: Pi's steering (interrupt) vs follow-up (after completion) queues are missing from ADF agent interaction.

Architecture Decision

Each evaluation item is independent. Successful evaluations may lead to implementation issues. Items that do not justify adoption should be closed with rationale.

What NOT to Adopt

Pi itself (TypeScript, not Rust)
Anti-MCP philosophy (MCP is integral to our workflow)
Anti-sub-agents philosophy (agent teams are core to our approach)
TypeScript extension system (our skills are Markdown + shell)

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '

Epic: Evaluate Pi (badlogic/pi-mono) architectural patterns for terraphim-ai #682

Open

Labels

opened

Overview

Evaluation Items

Tree-structured session storage for terraphim_persistence Evaluate: Tree-structured session storage (Pi pattern) for terraphim_persistence #683
Layered crate architecture: extract standalone LLM interaction crate Evaluate: Extract standalone LLM interaction crate (Pi layered architecture pattern) #684
Cross-provider context serialisation for ADF multi-model routing Evaluate: Cross-provider context serialisation for ADF multi-model routing #685
Typed beforeToolCall/afterToolCall hook pattern for terraphim-skills Evaluate: Typed tool hooks (beforeToolCall/afterToolCall) for terraphim-skills #686
Steering and follow-up message queues for ADF agent interaction Evaluate: Steering and follow-up message queues for ADF agent interaction #687

Context

Session branching: Our current linear JSONL sessions lose history on compaction. Pi's tree structure (id/parentId) preserves all branches in a single file.
Layered architecture: Pi cleanly separates LLM API, agent loop, and UI into independent packages. Our LLM interaction code is scattered across terraphim_orchestrator, terraphim_multi_agent, and ad-hoc scripts.
Cross-provider handoffs: Pi serialises context for mid-session model switching. ADF routes different agents to different models but cannot hand off context between them.
Tool hooks: Pi's typed beforeToolCall/afterToolCall at the agent-core level is simpler than Claude Code's JSON-based shell hooks. Pattern applicable to terraphim-skills.
Message queues: Pi's steering (interrupt) vs follow-up (after completion) queues are missing from ADF agent interaction.

Architecture Decision

Each evaluation item is independent. Successful evaluations may lead to implementation issues. Items that do not justify adoption should be closed with rationale.

What NOT to Adopt

Pi itself (TypeScript, not Rust)
Anti-MCP philosophy (MCP is integral to our workflow)
Anti-sub-agents philosophy (agent teams are core to our approach)
TypeScript extension system (our skills are Markdown + shell)

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '

Epic: Evaluate Pi (badlogic/pi-mono) architectural patterns for terraphim-ai #682

Open

Labels

opened

Overview

Evaluation Items

Tree-structured session storage for terraphim_persistence Evaluate: Tree-structured session storage (Pi pattern) for terraphim_persistence #683
Layered crate architecture: extract standalone LLM interaction crate Evaluate: Extract standalone LLM interaction crate (Pi layered architecture pattern) #684
Cross-provider context serialisation for ADF multi-model routing Evaluate: Cross-provider context serialisation for ADF multi-model routing #685
Typed beforeToolCall/afterToolCall hook pattern for terraphim-skills Evaluate: Typed tool hooks (beforeToolCall/afterToolCall) for terraphim-skills #686
Steering and follow-up message queues for ADF agent interaction Evaluate: Steering and follow-up message queues for ADF agent interaction #687

Context

Session branching: Our current linear JSONL sessions lose history on compaction. Pi's tree structure (id/parentId) preserves all branches in a single file.
Layered architecture: Pi cleanly separates LLM API, agent loop, and UI into independent packages. Our LLM interaction code is scattered across terraphim_orchestrator, terraphim_multi_agent, and ad-hoc scripts.
Cross-provider handoffs: Pi serialises context for mid-session model switching. ADF routes different agents to different models but cannot hand off context between them.
Tool hooks: Pi's typed beforeToolCall/afterToolCall at the agent-core level is simpler than Claude Code's JSON-based shell hooks. Pattern applicable to terraphim-skills.
Message queues: Pi's steering (interrupt) vs follow-up (after completion) queues are missing from ADF agent interaction.

Architecture Decision

Each evaluation item is independent. Successful evaluations may lead to implementation issues. Items that do not justify adoption should be closed with rationale.

What NOT to Adopt

Pi itself (TypeScript, not Rust)
Anti-MCP philosophy (MCP is integral to our workflow)
Anti-sub-agents philosophy (agent teams are core to our approach)
TypeScript extension system (our skills are Markdown + shell)

to join this conversation on GitHub. Already have an account? Sign in to comment

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();

Epic: Evaluate Pi (badlogic/pi-mono) architectural patterns for terraphim-ai #682

Open

Labels

opened

Overview

Evaluation Items

Tree-structured session storage for terraphim_persistence Evaluate: Tree-structured session storage (Pi pattern) for terraphim_persistence #683
Layered crate architecture: extract standalone LLM interaction crate Evaluate: Extract standalone LLM interaction crate (Pi layered architecture pattern) #684
Cross-provider context serialisation for ADF multi-model routing Evaluate: Cross-provider context serialisation for ADF multi-model routing #685
Typed beforeToolCall/afterToolCall hook pattern for terraphim-skills Evaluate: Typed tool hooks (beforeToolCall/afterToolCall) for terraphim-skills #686
Steering and follow-up message queues for ADF agent interaction Evaluate: Steering and follow-up message queues for ADF agent interaction #687

Context

Session branching: Our current linear JSONL sessions lose history on compaction. Pi's tree structure (id/parentId) preserves all branches in a single file.
Layered architecture: Pi cleanly separates LLM API, agent loop, and UI into independent packages. Our LLM interaction code is scattered across terraphim_orchestrator, terraphim_multi_agent, and ad-hoc scripts.
Cross-provider handoffs: Pi serialises context for mid-session model switching. ADF routes different agents to different models but cannot hand off context between them.
Tool hooks: Pi's typed beforeToolCall/afterToolCall at the agent-core level is simpler than Claude Code's JSON-based shell hooks. Pattern applicable to terraphim-skills.
Message queues: Pi's steering (interrupt) vs follow-up (after completion) queues are missing from ADF agent interaction.

Architecture Decision

Each evaluation item is independent. Successful evaluations may lead to implementation issues. Items that do not justify adoption should be closed with rationale.

What NOT to Adopt

Pi itself (TypeScript, not Rust)
Anti-MCP philosophy (MCP is integral to our workflow)
Anti-sub-agents philosophy (agent teams are core to our approach)
TypeScript extension system (our skills are Markdown + shell)

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