#+TITLE: Passepartout Evolutionary Roadmap #+STARTUP: content #+FILETAGS: :docs:roadmap: * The Evolutionary Roadmap Understanding Passepartout as a function in time is not nostalgia. It is architectural guidance. Every decision in v0.x should be made with awareness of where the system is going. Code written today becomes the substrate for v3.0. Skills designed today become the vocabulary the symbolic engine speaks tomorrow. The probabilistic beginning is not a weakness to overcome. It is the bootstrap. The system learns the domain through probabilistic inference, and that learned knowledge becomes the seed for the symbolic engine. By the time the symbolic engine takes over, it has a rich knowledge graph to reason about, grown from thousands of probabilistic interactions. This is how you build a reasoning machine: start with a learner, make it learn to verify by watching itself and its user, let verification become the core. Every blocked action becomes a rule. Every approved exception becomes a pattern. The symbolic layer grows at the probabilistic layer's expense. Remove the learner once it has learned enough. Each version expands the deterministic layer. The Dispatcher writes rules from approved exceptions. Shadow mode runs trial executions. Tool permission tiers mature from simple allow/deny to nuanced context-aware policies. The agent becomes less likely to attempt dangerous actions not because it is smarter but because the guard has more complete information. The roadmap is designed working backwards from SOTA parity (v1.0.0), guiding each version toward a fully autonomous, self-editing agent. Each version builds on the previous, with features designed to be implemented in pure Common Lisp + Org-mode. The TODO states in each version's Tasks section are the authoritative task tracker. The feature tables describe what each version delivers. Feature releases increment the minor version (v0.X.0). Bugfix and hardening releases increment the patch version (v0.X.Y). This ensures that security patches and critical fixes are visible in the version number and can ship independently of feature work. No feature release ships without its prerequisite hardening releases resolved. ** File Update Checklist When a version's state changes (DONE → tested → released), update these locations: 1. ~ROADMAP.org~ — mark item DONE, update LOGBOOK timestamp 2. ~README.org~ — update Current Capabilities table (add new Stable rows for shipped features, remove Planned rows that have shipped) 3. ~~.env.example~ — update version references as needed 4. ~lisp/core-communication.lisp~ — update the ~make-hello-message~ version string (current: ~"0.2.0"~) 5. ~passepartout~ (bash entry point) — update version reference On release: 1. Tag the release on GitHub 2. Extract DONE items from ROADMAP (all items with LOGBOOK timestamps since the last release tag) and use as the release notes body 3. If a ~CHANGELOG.md~ is needed for packaging tools, auto-generate it from ROADMAP DONE items ** v0.1.0: The Autonomous Foundation — RELEASED 2026-04-20 :PROPERTIES: :RETROSPECTIVE: [2026-05-07 Wed] :END: The secure, auditable Lisp kernel. All core infrastructure in place. *** DONE Perceive-Reason-Act pipeline :PROPERTIES: :ID: id-06f10b9a-4054-4dea-a927-b0935fbdcd2f :CREATED: [2026-03-22 Sun] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-20 Mon] :END: This established the three-stage cognitive cycle that all later features plug into. The pipeline is the invariant — skills, gates, actuators, and clients all compose through it. *** DONE Skills engine with jailed loading :PROPERTIES: :ID: id-dc83944f-3923-4142-b324-c317dacd6b0b :CREATED: [2026-03-22 Sun] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-20 Mon] :END: This made the "thin harness, fat skills" identity operational. Skills loading into jailed packages (v0.1.0) is the foundation for the skill sandbox mode (v0.3.2) and the Skill Creator (v0.8.0). *** DONE Policy skill (6 invariants) :PROPERTIES: :ID: id-929c84b7-d6ae-42b9-a8b5-d9df962db826 :CREATED: [2026-03-22 Sun] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-20 Mon] :END: This established the "explanation required" invariant that gates stack above. The policy gate (priority 500) runs first and sets the precedent that every action must justify itself. *** DONE Memory (memory-object + Merkle hashing) :PROPERTIES: :ID: id-3a96b384-cacf-4da0-8faa-1647739feba9 :CREATED: [2026-03-22 Sun] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-20 Mon] :END: The Merkle tree with content-addressed hashing made copy-on-write snapshots (v0.2.0) and MVCC concurrency (v0.6.1) possible. The hash-as-identity property also feeds directly into the foveal-peripheral model's semantic retrieval. *** DONE Scribe + Gardener background workers :PROPERTIES: :ID: id-3f618a38-ec23-4034-ba3c-ef272e212e2b :CREATED: [2026-03-22 Sun] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-20 Mon] :END: These background workers established the heartbeat-driven maintenance pattern. The event orchestrator (v0.3.0) generalizes this into hooks and cron jobs. *** DONE LLM gateway (OpenRouter, Ollama) :PROPERTIES: :ID: id-f5d870e2-cbd2-4c00-a8d4-174ab4118afc :CREATED: [2026-04-11 Sat] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-20 Mon] :END: The provider-agnostic cascade pattern established in v0.1.0 makes the model-tier router (v0.3.0), privacy-aware routing (v0.3.0), and consensus loop (v0.10.0) possible — they all build on the same ~backend-cascade-call~ abstraction. *** DONE Shell actuator, Emacs bridge, credentials vault :PROPERTIES: :ID: id-7ca3167f-8353-4bb7-8b97-c039017716b0 :CREATED: [2026-04-11 Sat] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-20 Mon] :END: The actuator registry pattern makes MCP tools (v0.7.0) possible — they register the same way. *** DONE FiveAM test suite :PROPERTIES: :ID: id-925d4180-764b-4219-8bdc-8e1849572da1 :CREATED: [2026-04-11 Sat] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-20 Mon] :END: The test infrastructure established in v0.1.0 becomes the TDD runner (v0.7.1) and the SWE-bench harness (v0.9.0). ** v0.2.0: Interactive Refinement — RELEASED 2026-04-29 :PROPERTIES: :RETROSPECTIVE: [2026-05-07 Wed] :END: The "Brain" meets the "Machine." Standardization and professionalization of the user interface and environment. *** DONE Text User Interface (Croatoan-based, styled, scrollable) :PROPERTIES: :ID: id-57cef382-fe14-42e6-aade-03e05e3e920b :CREATED: [2026-04-28 Tue] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-29 Wed] :END: The Croatoan-based TUI with model-view separation and dirty-flag rendering is the foundation for all TUI improvements: word wrap in v0.3.3, gate trace in v0.4.0, tool visualization in v0.7.0, and streaming in v0.6.3. *** DONE Self-editing (error detection, surgical fix, hot-reload) :PROPERTIES: :ID: id-459b8275-9979-4d0f-8d61-a9af883930d4 :CREATED: [2026-04-23 Wed] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-29 Wed] :END: The surgical edit + tangle + hot-reload pipeline (text replace → tangle → compile → load) established the self-modification capability that makes the Skill Creator (v0.8.0) safe — skills are generated, tangled, loaded, and verified in the same loop. *** DONE Enhanced utilities (structural Lisp/Org manipulation + REPL) :PROPERTIES: :ID: id-23f37c0d-4e77-4dc3-ab43-52a5987eb426 :CREATED: [2026-04-23 Wed] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-29 Wed] :END: Structural Lisp/Org manipulation tools are the primitives the self-improve module (v0.2.0) and the programming skills (literate block extraction, syntax validation) build on. *** DONE Onboarding wizard (modular Lisp setup for LLM providers) :PROPERTIES: :ID: id-bd497de7-3533-4056-b89f-2c992d2ea28b :CREATED: [2026-04-28 Tue] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-29 Wed] :END: The setup wizard established the "works out of the box" constraint that the gateway QA (v0.4.0) and Emacs bridge (v0.4.0) onboarding flows follow. *** DONE Memory rollback (snapshot and restore) :PROPERTIES: :ID: id-fd2fb6e3-03e7-4e22-b9e9-a7eecfd06718 :CREATED: [2026-04-12 Sun] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-04-29 Wed] :END: Copy-on-write snapshots (deep-copying the memory hash table on every write) gave the pipeline crash recovery. The snapshot mechanism is the root of MVCC concurrency (v0.6.1). ** v0.3.0: Event Orchestration + HITL — DONE, UNRELEASED Unified control plane, Human-in-the-Loop state management, and backfill remediation for stubs and gaps from v0.1.0/v0.2.0. All features are implemented but not yet published. The security hardening patches (v0.3.1–0.3.3) will ship as follow-up point releases before v0.4.0 feature work begins. *** DONE Secret Exposure Gate, Shell Safety, Lisp Validation :PROPERTIES: :ID: id-aa53c128-195b-42d4-9838-2def59faf7cf :CREATED: [2026-05-02 Sat] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-02 Sat] :END: *** DONE Multi-distro deployment (Debian+Fedora, systemd, Docker) :PROPERTIES: :ID: id-783df999-f7fe-45c8-896d-2fd07c604d64 :CREATED: [2026-05-02 Sat] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-02 Sat] :END: *** DONE Project rename to Passepartout (files, packages, env vars) :PROPERTIES: :ID: id-91724874-aa0d-4804-9220-8bc5551f1366 :CREATED: [2026-05-02 Sat] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-02 Sat] :END: *** DONE 31 org files with full literate prose :PROPERTIES: :ID: id-597b2a92-aac6-481a-b2c4-4f9842ced97c :CREATED: [2026-05-02 Sat] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-02 Sat] :END: *** DONE Human-in-the-Loop (HITL) CLOSED: [2026-05-03 Sun 14:00] :PROPERTIES: :ID: id-hitl-complete :CREATED: [2026-05-02 Sat] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-03 Sun 14:00] :END: Continuation-based interaction. The agent can suspend its cognitive loop to ask for permission or clarification and resume precisely where it left off. Builds on the dispatcher's existing Flight Plan mechanism. *** DONE Event Orchestrator (unified hooks+cron+routing) :PROPERTIES: :ID: id-d35aea3d-2e5f-4a12-a9b0-1c2d3e4f5a6b :CREATED: [2026-05-02 Sat 14:00] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-02 Sat 22:36] :END: Unified control plane for hooks, cron, and complexity-based routing. - *hook-registry* + *cron-registry* + tier classifier - Hooks via ~#+HOOK:~ Org-mode properties - Three complexity tiers: ~:REFLEX~ (no LLM), ~:COGNITION~ (light LLM), ~:REASONING~ (full LLM) - Hooked into heartbeat for cron processing - Rule-based tier classifier (overrideable via ~*tier-classifier*~) *** DONE Context Manager (project scoping) CLOSED: [2026-05-05 Tue] :PROPERTIES: :ID: id-context-manager-scoping :CREATED: [2026-05-05 Tue] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-05 Tue] :END: Stack-based project focusing with persistence. - ~push-context~/~pop-context~/~with-context~ stack operations - ~current-scope~ wired into perceive gate ~*scope-resolver*~ - ~/focus~/~/scope~/~/unfocus~ TUI commands - Context stack persisted to ~~/.cache/passepartout/context.lisp~, auto-restores on boot *** DONE Model-Tier Routing (cost optimization) CLOSED: [2026-05-03 Sun 16:00] :PROPERTIES: :ID: id-model-tier-routing :CREATED: [2026-05-02 Sat 23:00] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-03 Sun 16:00] :END: Extend ~*model-selector*~ for quadrant-based routing with per-slot provider cascades. - Privacy filter (local-only for @personal content) — top priority - Quadrant tagging (foreground/background × probabilistic/deterministic) - Complexity classifier (code/plan/chat/background slots), each with its own provider cascade - Model-selector skill registers into =*model-selector*= hook Deferred to v0.5.0: budget tracking per request, per-session cost monitoring. Deferred to v0.10.0: TUI /config command for cascade configuration (env vars for now). *** DONE Memory Scope Segmentation CLOSED: [2026-05-03 Sun 16:30] :PROPERTIES: :ID: id-memory-scope-segmentation :CREATED: [2026-05-02 Sat 23:00] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-03 Sun 16:30] :END: Extend memory-object with ~:scope~ property. - ~:memex~ (permanent knowledge), ~:session~ (ephemeral), ~:project~ (current work) - Scope-aware retrieval in memory layer *** DONE Asynchronous Embedding Gateway CLOSED: [2026-05-05 Tue] :PROPERTIES: :ID: id-async-embedding :CREATED: [2026-05-02 Sat 23:00] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-05 Tue] :END: Provider-agnostic vector generation (Ollama, OpenAI, hashing fallback). - Three backends: local (Ollama-compatible), openai (/v1/embeddings), hashing (SHA-256) - ~embeddings-compute~ and ~*embedding-backend*~ for runtime provider selection - ~ingest-ast~ populates vectors at object creation time - ~mark-vector-stale~ marks vectors as ~:pending~ and queues for re-embedding - ~embed-all-pending~ drains queue, computes vectors, stores in ~*memory-store*~ - Cron job registered with orchestrator: runs every 10m on ~:reflex~ tier - ~EMBEDDING_PROVIDER~ env var for provider selection - Registered as proper skill (~defskill~~:passepartout-system-model-embedding~) *Note:* The default ~:hashing~ backend uses SHA-256-derived vectors. SHA-256 is a cryptographic hash with the avalanche property — one-bit input differences produce entirely different outputs. This makes it a correct integrity check (Merkle tree) but an incorrect similarity function (semantic retrieval). v0.4.0 replaces it with a zero-dependency lexical similarity algorithm that actually captures textual overlap while remaining offline-capable. *** DONE TUI Experience (Daily Driver Quality) CLOSED: [2026-05-05 Tue] :PROPERTIES: :ID: id-tui-experience :CREATED: [2026-05-02 Sat 23:00] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-05 Tue] :END: All P0-P4 items implemented: - P0: Chat scrollback (Page Up/Down), Input history (up/down arrows) - P1: Status bar (connection, mode, msg count, scroll, activity indicator) - P1: Message rendering (timestamps, colors, role icons) - P2: Command palette (~/help~ command listing) - P2: Multi-line input (~\ + Enter~ inserts newline) - P3: Background activity indicator (~…thinking~ spinner) - P4: Tab completion for all ~/~~ commands - P4: Configurable theme (~*tui-theme*~ plist, ~~/theme~~ command) *** DONE v0.2.x Backfill Remediation (stubs and gaps) CLOSED: [2026-05-03 Sun] :PROPERTIES: :ID: id-v02x-remediation :CREATED: [2026-05-03 Sun] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-03 Sun] :END: - P0: vault-get-secret / vault-set-secret wrappers (one-line delegation to vault-get/vault-set with ~:type :secret~) - P0: system-archivist Scribe + Gardener (distill daily logs → atomic notes; scan broken links, orphaned memory-objects) - P0: system-self-improve surgical edit + error fix (read → replace → snapshot → write → balance → tangle → reload) - P0: programming-org org-modify + org-ast-render (locate node by ID, apply changes; convert plist AST → Org text) - P0: programming-literate balance check + tangle sync (verify balanced parens in source blocks; verify .lisp matches tangled output) - P1: system-event-orchestrator bootstrap (scan Org files for HOOK/CRON properties, register via existing registries) - P1: system-memory introspection (structured statistics: object count by type, TODO distribution, orphans, snapshots) - P1: path relic skills/ → lisp/ (update skill-initialize-all and context-skill-source to resolve against lisp/ directory) - P2: core-context semantic retrieval (populate org-object-vector at ingest; fallback: TF-IDF bag-of-words) - P2: core-context subtree-based skill source loading (context-skill-subtree for targeted retrieval by heading name) - P3: Variable name drift normalization (*memory* vs *memory-store*, *skills-registry* vs *skill-registry*) - P4: Eliminate STYLE-WARNINGs from setup output (reorder defuns for same-file forward references; accept cross-skill references) *** DONE Project Renaming (Bouncer → Dispatcher) :PROPERTIES: :ID: id-9e779580-287b-b3d1-37b9-bcefd750bf9e :CREATED: [2026-05-01 Fri 15:40] :END: :LOGBOOK: - State "DONE" from "TODO" [2026-05-02 Sat 22:00] :END: The Dispatcher's role has evolved beyond security guard. It is the seed of the deterministic engine — it learns to execute procedures without invoking the neural net. *** v0.3.1 — TODO Parser RCE elimination Rationale: SBCL's default ~*read-eval* accessor is ~t~, enabling the ~#.~ reader macro to execute arbitrary Lisp forms during parsing. Three code paths in the current codebase process untrusted input with ~read-from-string~ or ~read~ without binding ~*read-eval*~ to ~nil~. Each represents a remote code execution vector that bypasses all deterministic safety gates — the Dispatcher's shell safety check, path protection, secret scanning, and network exfiltration detection never execute because the malicious form is evaluated during parsing, before the action plist is even constructed. - Wrap ~read-from-string~ in ~think()~ (core-loop-reason.lisp:102) with ~(let ((*read-eval* nil)) ...)~ — LLM output is untrusted by definition; parsing it must never execute code. The markdown-strip regex already runs, so the fix immediately follows it. - Wrap ~read~ in ~load-memory-from-disk~ (core-memory.lisp:143) with ~(let ((*read-eval* nil)) ...)~ — the ~memory.snap~ file lives in ~~/ by default and could be corrupted or planted. - Wrap ~read-from-string~ in ~action-system-execute~ (core-loop-act.lisp:62) with ~(let ((*read-eval* nil)) ...)~ — the ~:system :eval~ path executes untrusted payload code. Explicitly assert that this path requires the Dispatcher's approval gate. - Add FiveAM test: inject ~"(#.(shell \"echo pwned\"))"~ into the ~think()~ pipeline and assert no shell execution occurs. *** v0.3.2 — TODO Shell safety & actuator sandboxing Rationale: The ~:system :eval~ actuator path is currently unchecked by the Dispatcher's approval gate — only ~:shell~ and ~:tool "shell"~ trigger HITL. The shell actuator wraps commands through double ~bash -c~ nesting (~system-actuator-shell.lisp:10~), where Lisp's ~format~ with ~s~ produces S-expression-safe strings, not shell-safe strings. A command containing quotes or substitution characters can break out. Additionally, skill files loaded via ~skill-initialize-all~ execute arbitrary Lisp in jailed packages — a skill file containing ~(uiop:run-program "dangerous")~ executes immediately on load before any gate can inspect it. - Fix shell double-wrapping: remove the outer ~bash -c~ in ~actuator-shell-execute~; pass the command string directly to ~uiop:run-program~ with ~:force-shell nil~. The timeout wrapping remains via the OS ~timeout~ binary. - Extend the Dispatcher approval requirement to the ~:system :eval~ path (currently only ~:shell~ and ~:tool "shell"~ trigger HITL). An unbounded ~eval~ should require the same Flight Plan approval as a shell command. - Add skill sandbox mode for ~skill-initialize-all~: load each skill's code into a temporary jailed package, run the registered trigger function in isolation, verify it imports no restricted symbols (from CL package: ~run-program~, ~shell~, ~run-shell-command~), then promote to the live registry on pass. - Add FiveAM test: register a skill containing ~(uiop:run-program "echo test")~ in the body and verify the sandbox blocks its promotion. *** v0.3.3 — TODO TUI Critical Fixes Rationale: The TUI is Passepartout's only interface. OpenClaw distributes across 25+ messaging channels with voice, Canvas, and macOS/iOS apps. Hermes Agent ships multiline editing, slash-command autocomplete, conversation history, interrupt-and-redirect, and streaming tool output in its TUI. Passepartout's Croatoan TUI must carry the product alone, and it currently lacks word wrap, cursor movement, resize handling, connection-loss feedback, a quit command, and persistent history. None of these fixes require daemon changes — they are pure client-side Croatoan work that closes the gap from "proof of concept" to "daily driver." - Word wrap in ~view-chat~: every LLM response longer than the terminal width is silently truncated to one line. Croatoan supports multi-line rendering; ~view-chat~ must calculate per-message line height, adjust visible-message count accordingly, and scroll per message-line rather than per message. For very long messages, add a pager mode where pressing Enter on a message opens it in a scrollable overlay. - Left/Right cursor in input: add ~:left~ and ~:right~ key handlers that move a cursor position index within the ~:input-buffer~ list. Characters are inserted at the cursor position, not always appended. Backspace deletes at the cursor position. - SIGWINCH handler: register a terminal resize signal. On resize, re-measure the root window, destroy and recreate the three sub-windows (~sw~, ~cw~, ~iw~), set all dirty flags to ~t~, and force a full redraw. - Connection-loss detection: the reader thread currently polls ~recv-daemon~ silently on EOF. On disconnection, queue a ~:disconnected~ event, set ~:connected~ to ~nil~, clear ~:busy~, add a red system message "Connection lost — run /reconnect to retry." The ~:disconnected~ event dirties the status bar to show the status indicator. - ~/quit~ command + persistent history: on ~/quit~, save ~:input-history~ to ~~/.cache/passepartout/history~ (one line per entry, most recent first), send a goodbye handshake to the daemon, close the socket, and exit the main loop cleanly. On startup, load history from the save file if it exists. - Scroll offset clamping: clamp ~:scroll-offset~ to ~(max 0 (- msg-count visible-lines))~. The status bar shows ~"msgs:12/45"~ (visible / total) rather than ~"msgs:45"~ (total only) so the user knows when they've scrolled past the oldest message. - Message list storage: replace the O(n²) ~(nth i msgs)~ list indexing with a simple adjustable vector. ~add-msg~ appends; ~view-chat~ iterates with ~aref~. The vector is resized as needed. Same API surface, 100x speedup on message-heavy sessions. - Add FiveAM tests: word-wrap produces correct line count for a 200-character string at 80-column width; cursor left/right wraps at buffer boundaries; SIGWINCH preserves message state; ~/quit~ saves and restores history. ** v0.4.0: Production Hardening The features in this version were originally sequenced as v0.3.x patches but represent feature-level scope. They activate the architectural advantages designed in v0.1.0–v0.3.0, harden the self-build safety boundary, and expand Passepartout's interaction surfaces beyond the terminal TUI. Each feature depends on infrastructure already in place — the wiring, the sandbox, the gate trace — and activates it. *** TODO Semantic retrieval activation Rationale: Two independent failures prevent the foveal-peripheral semantic retrieval path from ever firing. First, ~context-awareness-assemble~ never passes ~:foveal-vector~ to ~context-object-render~, so the renderer receives ~nil~ for ~foveal-vector~ and the similarity calculation always returns 0.0. Second, the default ~:hashing~ embedding backend uses SHA-256 (a cryptographic hash with the avalanche property) as a similarity function. SHA-256 is designed to produce entirely different outputs for nearly identical inputs — the property that makes it secure for integrity verification is precisely what makes it useless for semantic retrieval. A content-addressed Merkle tree correctly uses SHA-256 for identity; a retrieval engine needs a similarity function, not an identity function. The infrastructure for real embeddings (~local~ with Ollama, ~openai~ with the embeddings API) is fully implemented and working — this release activates the last-mile wiring and replaces the semantically blind default with a zero-dependency algorithm that actually captures textual overlap. - Wire ~:foveal-vector~ into ~context-awareness-assemble~: pass ~(memory-object-vector (memory-object-get foveal-id))~ as the ~:foveal-vector~ argument to the ~context-object-render~ call (one line in ~core-context.lisp:148-150~). - Replace ~:hashing~ default backend with character-trigram Jaccard similarity. Pure Lisp, zero external dependencies, works exactly as offline as SHA-256, but captures lexical overlap: "authentication" and "authenticate" share trigrams "aut," "uth," "the," "hen," "ent," etc. The vector is a bloom filter of trigrams; cosine similarity maps to Jaccard (intersection / union). This provides real if crude semantic signal without any server. - Rename existing ~embedding-backend-hashing~ to ~embedding-backend-sha256~ and repurpose it as an explicit ~:sha256~ provider for environments where even trivial Lisp computation is undesirable (embedded, resource-constrained). Document it as "integrity-only, no semantic retrieval capability." - Add ~EMBEDDING_PROVIDER~ guidance to the setup wizard: explain that ~:hashing~ is the default offline fallback, ~:local~ requires Ollama with ~nomic-embed-text~, and ~:openai~ uses the paid embeddings API. - Add FiveAM test: ingest two semantically related nodes ("implement login form" and "add password authentication"), verify cosine similarity > 0.0 with the trigram backend. *** TODO Self-build safety boundary Rationale: Self-building (the agent modifying its own source code) begins at v0.7.1 when the tool ecosystem and test runner are in place. But self-building without path-level write protection means the agent can modify the very pipeline code that is currently executing — the ~core-*~ files that implement the Perceive-Reason-Act cycle, the Merkle-tree memory, the skill engine loader, and the Dispatcher gate stack itself. A hallucination or a logic error during self-building that corrupts ~core-loop-reason.lisp~ destroys the agent's ability to reason about and fix the corruption. The "thin harness" is not privileged code in the architectural sense (homoiconicity means any code can be modified at runtime), but it must be *protected* code — modifications to the harness require a human in the loop, enforced by the Dispatcher's path-protection gate, not by convention. This is the corollary to "thin harness, fat skills": the harness is thin enough to be auditable by a human, and the Dispatcher ensures it stays that way. Skills and system modules expand freely; the core contracts to a minimal, protected kernel. - Add ~core-*~ patterns to ~*dispatcher-protected-paths*~: ~core-*.org~, ~core-*.lisp~, and their tangled equivalents. Any file write, file read-that-prefaces-a-write, or shell command targeting these paths triggers the Dispatcher's blocking gate. - The blocked action produces a Flight Plan (HITL approval required). The human reviews the proposed core change in an Org buffer before approving. This is the same mechanism that governs shell commands and network exfiltration — the core protection is a path-specific instance of the existing gate, not a new gate. - Implement a ~SELF_BUILD_MODE~ env var. When ~SELF_BUILD_MODE=true~ (default ~false~): - Core path protection is active (writes blocked, HITL required) - Non-core writes proceed through the standard Dispatcher gate (permissions table + policy + Bouncer) - ~SELF_BUILD_MODE=false~ disables core protection entirely — useful during initial development when the human is manually editing core files and doesn't want every save to trigger a Flight Plan - Telemetry: track self-build actions (core modifications proposed, core modifications approved, core modifications denied). This is the dataset that the Dispatcher's learning system uses in v3.0.0 to understand which core modifications are safe enough to automate. - Add FiveAM test: simulate a write to ~core-loop.lisp~, verify the Dispatcher returns a ~:LOG~ rejection with ~"protected path"~ in the message. *** TODO TUI Differentiator Visualization Rationale: Three architectural elements exist today in the daemon that no competitor can render — the Dispatcher gate trace, the foveal-peripheral focus map, and the rules-learned counter. All three run in pure Lisp with 0 LLM tokens. None are visible to the user. Making them visible turns Passepartout's architecture from an internal mechanism into a trust-building UX — the user sees exactly which safety gates passed, exactly what the agent is focusing on, and exactly how many rules the Dispatcher has learned from their decisions. No competitor can ship this because none has deterministic gates to trace, foveal-peripheral context to map, or a rule-synthesizing Dispatcher to count. - Gate trace per action: extend the daemon's response plist to include ~:gate-trace~ — a list of ~(:gate :result <:passed | :blocked | :approval>)~ entries produced by ~cognitive-verify~. The TUI renders each entry as a colored line below the corresponding agent message: green ~✓ Dispatcher: path allowed~, red ~✗ Dispatcher: blocked (shell safety)~, yellow ~→ HITL required: /approve HITL-ab12~. Gate trace lines are dim and collapsible (press Tab on a message to toggle trace visibility). This turns the invisible nine-vector safety gate into the user's primary trust mechanism. - Focus map in status bar: add a second status bar line showing ~[Focus: core-loop.lisp:think()] [Scope: passepartout] [3 related nodes]~. The daemon already tracks ~foveal-id~ and ~*scope-resolver*~ in the signal plist; the TUI reads these from the most recent response and renders them. Related node count comes from the number of objects with cosine similarity ≥ threshold in the last context assembly. This shows the user *what the agent is looking at* — the single biggest trust gap in AI agents. - Rule counter in status bar: ~[Rules: 47]~. The Dispatcher's ~*hitl-pending*~ hash table and approved/disallowed memory-object entries provide the count — every HITL decision that produces a rule increments it. The TUI reads the count from a new daemon response field ~:rule-count~. The user watches the counter tick up as they teach the agent their preferences. - Expanded theme: replace the 7-flat-color ~*tui-theme*~ with a 25-color layered system organized by message category (roles, content types, tool visibility, gate states, status). See the design discussion for the full color mapping. Implement a ~/theme ~ command that swaps between named presets (~dark~, ~light~, ~solarized~, ~gruvbox~). Theme change persists to disk and reloads on next session. - Add FiveAM tests: gate trace renders correctly for pass/block/approval states; focus map updates when ~foveal-id~ changes; rule counter increments on HITL approval. *** TODO Gateway QA, Discord, Slack + Emacs Bridge Rationale: Passepartout currently has Telegram and Signal gateways in the codebase, both untested. The setup wizard has Slack as a configurable option with no implementation. Two messaging channels is not competitive — OpenClaw has 25+, Hermes Agent has 6+. But more critically: the Lisp crowd is Passepartout's natural audience, and they live in Emacs. An Emacs bridge that speaks the framed TCP protocol is trivial to implement (the protocol is ~200 lines of Lisp; porting to elisp is straightforward) and turns every Emacs buffer into a Passepartout interaction surface. This is not the deep Emacs integration of v0.10.2 (where the agent controls Emacs) — this is Emacs controlling the agent over TCP. The Emacs user selects a region, hits ~M-x passepartout-send-region~, and the agent responds in a dedicated buffer. They never leave their editor. Gateway: - Integration tests for Telegram gateway: mock the Telegram Bot API, verify message send (POST ~/sendMessage~) and receive (GET ~/getUpdates~) round-trip. Verify HITL commands (~/approve~, ~/deny~) are intercepted before injection. - Integration tests for Signal gateway: mock ~signal-cli~ output, verify JSON message parsing and polling loop. Verify send path constructs correct ~signal-cli send~ arguments. - Add Discord gateway: Discord Bot API (REST + Gateway WebSocket for real-time messages). Register bot, handle ~MESSAGE_CREATE~ events, send via ~POST /channels/{id}/messages~. Map Discord mentions to ~:user-input~ signals. HITL commands work identically to Telegram. - Add Slack gateway: Slack Events API + Web API. Subscribe to ~message.im~ events, send via ~chat.postMessage~. Reuse the SLACK_TOKEN config key already present in the setup wizard. - Each gateway is a skill under ~passepartout.skills.gateway-~ — jail-loaded, hot-reloadable, sandbox-verified. - Gateway configuration surfaced in the setup wizard: after entering a token, offer "send a test message to yourself" as a connection verification step. Surface the result as a green ✓ or red ✗ with the error detail. - Gateway status displayed in ~messaging-list~: platform, configured (yes/no), gateway active (yes/no), last message received (timestamp). Emacs Bridge: - Elisp package: ~passepartout.el~. Connects to daemon on localhost:9105 via ~make-network-process~ (TCP). - Sends: framed plist protocol identical to the TUI (~frame-message~ ported to elisp — write hex length prefix, write prini'd plist). The daemon does not know or care whether the client is the Croatoan TUI, the CLI, or Emacs. - Receives: daemon responses arrive in a ~passepartout-response~ buffer. Each response is rendered as an Org headline: role prefix, timestamp, content. Gate trace (from v0.4.0) is rendered as property drawer entries under the headline. - ~M-x passepartout-send-region~: sends the selected region as a ~:user-input~ signal with the current buffer's file path as context. - ~M-x passepartout-send-buffer~: sends the entire buffer. - ~M-x passepartout-focus~: sets the foveal focus to the Org headline at point (extracts ~:ID:~ property, sends ~:point-update~ signal). Equivalent to the TUI's ~/focus~ command. - ~M-x passepartout-approve~ / ~M-x passepartout-deny~: prompts for HITL token and sends approval/denial. - Agent modifies an Org file → Emacs receives ~:buffer-update~ via the bridge → the buffer is refreshed (~revert-buffer~ or targeted replacement). - The Emacs bridge is the daily driver for Lisp users. The TUI remains for non-Emacs users and for the differentiator visualizations. Emacs users get the gate trace and focus map as Org property drawers in the response buffer — same data, elisp-native rendering. **** TODO Native embedding inference Rationale: The foveal-peripheral model depends on vector similarity to surface semantically related nodes. Without vectors, retrieval is depth-2 truncation with no semantic boosting. The trigram Jaccard fallback provides real lexical signal — "login bug" shares trigrams with "authentication error" — but cannot surface nodes with zero lexical overlap ("password reset flow" vs "login broken"). A real embedding model closes this gap. Embedding inference is 10x simpler than chat LLM inference: single forward pass, no autoregressive decoding, no KV cache, no streaming. The CFFI binding is ~150 lines of Lisp. - FFI binding to llama.cpp's embedding API (~150 lines of CFFI). Call ~llama_get_embeddings~ after a single forward pass. No KV cache, no sampling, no streaming required — embedding models are BERT-family, single-pass. - Ship all-MiniLM-L6-v2 (23MB GGUF) as the bundled embedding model. 384-dimensional vectors, CPU-friendly (<100ms per document on any modern CPU), produces semantically meaningful vectors with zero external dependencies. - ~embedding-backend-local~ detects the bundled model at configure time. If present, uses in-process inference by default. Falls back to Ollama (~EMBEDDING_PROVIDER=local~) or OpenAI (~EMBEDDING_PROVIDER=openai~) if the model is missing or the user prefers external inference. - ~EMBEDDING_PROVIDER~ default becomes ~:native~ (the bundled model). The model file lives at ~~/.local/share/passepartout/models/all-MiniLM-L6-v2.gguf~, downloaded on first ~configure~ if not present. - The trigram Jaccard backend remains as a further fallback for environments where even 23MB is too large (embedded, resource-constrained). SHA-256 hashing is removed entirely — it was semantically blind. - Add FiveAM test: embedding a document with the native backend produces a 384-dimensional float vector; identical documents produce identical vectors. *** Competitive Advantage Analysis — v0.4.0 Summary Production hardening is the process of turning architectural potential into operational strength. The semantic retrieval fix activates the foveal-peripheral model's full power: deep nodes that are topically related to the user's focus now surface automatically. Without this, the context model is "dumb truncation at depth 2." With it, it's genuine semantic awareness — and since the retrieval is deterministic (in-image vector math, zero LLM tokens), the cost advantage over competitors' LLM-assisted search compounds with every query. The self-build safety boundary is a capability no competitor provides: the agent cannot modify its own brain stem without human review. The ~core-*~ path protection means the Dispatcher draws a line at the filesystem level, not the policy document level. Claude Code, OpenClaw, and Hermes all allow agents to modify their own source files without distinction between application code and runtime code. Passepartout's Dispatcher prevents modification of the very pipeline that implements the Perceive-Reason-Act cycle, the Merkle-tree memory, the skill engine loader, and the Dispatcher gate stack itself. This is the operational realization of "thin harness, fat skills" — the harness is thin enough to be auditable by a human, and the Dispatcher ensures it stays that way. The TUI differentiator visualizations are Passepartout's permanent UX advantage. The gate trace, focus map, and rule counter are UX elements that only make sense in Passepartout's architecture — deterministic gates, foveal-peripheral context, and Dispatcher rule synthesis exist nowhere else. No competitor can ship this because none has deterministic gates to trace, foveal-peripheral context to map, or a rule-synthesizing Dispatcher to count. Combined with the TUI critical fixes from v0.3.3, the TUI is competitive on usability and uniquely informative on safety and context transparency. The messaging gateways and Emacs bridge expand Passepartout's interaction surface from a single terminal TUI to four surfaces: terminal, Telegram/Signal/Discord/Slack messaging, Emacs, and voice (via the voice gateway in v0.7.3). The Emacs bridge is strategically critical — the Lisp crowd is Passepartout's natural audience, and they live in Emacs. An Emacs bridge that speaks the framed TCP protocol turns every Emacs buffer into a Passepartout interaction surface. Combined with the gate trace and focus map rendered as Org property drawers in the response buffer, Emacs users get the same differentiator visualizations as TUI users — same data, elisp-native rendering. ** v0.5.0: Token Economics & Prompt Efficiency The architecture's single largest gap versus SOTA: Passepartout currently spends tokens like a research prototype. Every ~think()~ call rebuilds and retransmits the full system prompt — IDENTITY + TOOLS + CONTEXT + LOGS + SKILL_AUGMENTS — with no caching, no budget, and no incremental assembly. The foveal-peripheral model prunes memory content but doesn't touch the fixed overhead. With 20+ skills by v1.0.0, system prompt overhead alone could reach 3,000–8,000 tokens per call before user input is even processed. Competitors (Claude Code, OpenClaw, Copilot) all implement some form of prefix caching — Anthropic's API gives 90% discount on cached tokens, OpenAI caches automatically. Passepartout's prompt structure is already naturally cacheable: IDENTITY, TOOLS, and LOGS format are static across calls. This version turns that structural property into a cost advantage. **Design insight: why token economics is the structural differentiator.** Passepartout's sparse-tree rendering and deterministic safety gates should produce 2–3x fewer tokens than competitors for equivalent coding tasks, and 13–24x fewer for knowledge management. But without caching and budget enforcement, the fixed overhead per call eats these savings. A coding session that touches 30 files with competent context management costs ~72K tokens (Passepartout) versus ~185K (Claude Code). Without caching, the Passepartout number climbs toward ~150K because every call retransmits the static prefix. The architectural advantage exists in theory but requires operational plumbing to materialize. *** TODO Tokenizer integration - Integrate a tokenizer for at minimum the model families used in the provider cascade (cl100k_base for OpenAI, claude-3 tokenizer for Anthropic). Options: FFI binding to tiktoken via CFFI, or a pure-Lisp port of the BPE tokenizer for cl100k_base (the encoding table is ~100KB, the algorithm is ~100 lines). - Expose ~(count-tokens text &key model)~ as a core utility. - Use for three purposes: context budget enforcement (reject assembly if over limit), cost estimation (tokens × provider price), and prompt optimization (measure which sections of the system prompt consume the most budget). *** TODO Prompt prefix caching - Split the system prompt into a static prefix (IDENTITY string, TOOLS section, LOGS format header) and a dynamic suffix (CONTEXT render, current log entries, skill augments, user prompt). - Track a hash of the static prefix; only retransmit when it changes (skill load/unload, identity config change). On cache hit, send the cached prefix with the dynamic suffix appended. - Implement the Anthropic prompt-caching header protocol for providers that support it (claude-3-* models, up to 90% discount on cached tokens). For OpenAI, the automatic caching layer handles prefix detection without explicit headers. - Log cache hit/miss rate to telemetry for cost tracking. *** TODO Incremental context assembly - Cache the last rendered ~context-awareness-assemble~ string with metadata: foveal-id at render time, scope, last memory modification timestamp. - On ~think()~ invocation: if foveal-id, scope, and memory-modification-timestamp are unchanged since the cached render, return the cached string. This eliminates re-rendering on heartbeat ticks, tool-output feedback loops, and multi-turn conversations where the user hasn't changed focus. - Invalidate the cache on any ~ingest-ast~ call, any ~org-modify~, or any focus change. - For heartbeats specifically: skip context assembly entirely — the heartbeat sensor bypasses the reason gate (returns early in ~loop-gate-reason:154~), so building awareness for a signal that won't call the LLM is pure waste. Add an early return in ~think()~ for ~:heartbeat~ / ~:delegation~ sensors. *** TODO Per-call token budget - ~CONTEXT_MAX_TOKENS~ env var (default: 16384, half of a 32K context window to leave room for model response). - In ~think()~: compute total token count (static prefix + dynamic context + user prompt). If over budget, progressively trim: first truncate system logs to 5 lines, then drop skill augments from non-triggered skills, then if still over, downgrade peripheral nodes to title-only (disable ~:foveal-vector~ path, render strict depth ≤ 2). - Log budget violations to telemetry with the trimmed-token count for diagnostics. - The goal: Passepartout never silently exceeds a model's context window. Silent truncation by the model API produces undefined behavior (mid-thought cutoff, lost instructions). A system that knows it's over budget can degrade intentionally. *** TODO Cost tracking - Per-provider pricing lookup table: input/output token costs for each model in the provider cascade (gpt-4o-mini, claude-3-5-sonnet, deepseek-chat, llama-3.1-70b, groq-llama, etc.). - After each ~backend-cascade-call~: compute cost as (input_tokens × input_price + output_tokens × output_price), log to session accumulator, emit ~:cost-update~ telemetry event. - Per-session cumulative cost stored in memory (~*session-cost*~ plist: ~(:total :by-provider :by-task )~). - TUI status bar shows current session cost (optional, off by default, toggled via ~/cost~ command). The cost counter renders as ~[Session: $0.12]~ in the status bar, updating after each ~backend-cascade-call~. Color: green when under 50% of daily budget, yellow at 50-90%, red above 90%. - ~COST_BUDGET_DAILY~ env var with soft cap — warning injected into system prompt when approaching budget, HITL gate on any single action exceeding 25% of remaining budget. **** TODO Self-configuring setup binary Rationale: The current ~passepartout configure~ flow is a bash script that detects Debian or Fedora, installs packages, installs Quicklisp, tangles Org sources, and runs the setup wizard. It handles 2 distro families. It fails on everything else. A self-configuring setup with a small LLM expands coverage to "anything with a package manager" without shipping gigabytes of model data. The key constraint: the LLM follows a decision tree for setup, it does not improvise. This keeps setup reliable while expanding coverage. - The setup binary (~passepartout-setup~) is a ~save-lisp-and-die~ executable (~100MB: SBCL runtime + core Lisp code + native embedding inference from v0.4.0 + 23MB embedding model). No SBCL install required. No Quicklisp. No bash script. The user runs one file. - Deterministic path (default, always runs first): the same distro detection, package installation, and configuration logic from today's bash script, reimplemented in Lisp. Handles Debian and Fedora families. Covers the common case without touching an LLM. - LLM-assisted path (optional, activates on deterministic failure): downloads Qwen2.5-0.5B (~500MB GGUF, pinned by hash, cached to ~~/.local/share/passepartout/models/~). The model reads command output, classifies success/failure/recoverable-error from a finite set of outcomes, and selects the next corrective action from a constrained decision tree. On unrecognized failures, generates a diagnostic for the user. - Model hash verification: the GGUF file is pinned by SHA-256 hash. If the hash doesn't match (wrong version, corrupted download), fall back to deterministic setup with a warning. The bootstrap tool must not fail silently because of a model mismatch. - After setup completes, the binary exits. The user runs ~passepartout daemon~ to start the full system (a live SBCL process, not a sealed binary — REPL, hot-reload, self-modification all available). - The setup binary is a bridge. It gets the system installed and configured, then gets out of the way. The final system is a live Lisp image, not a sealed binary. - Add FiveAM test: the deterministic path succeeds on a system with all dependencies pre-installed; the LLM-assisted path correctly classifies 10 common package-manager error messages. *** Competitive Advantage Analysis — v0.5.0 Summary Token economics is the dimension where the architecture's theoretical advantage becomes operationally real. The foveal-peripheral model and deterministic gates reduce the tokens *needed* per task; prompt caching and incremental assembly reduce the tokens *spent* per task. Combined, the 2–3x coding savings and 13–24x knowledge management savings in the DESIGN_DECISIONS token analysis become achievable rather than aspirational. The cost tracking and budget enforcement are defensive advantages: no competitor gives the user visibility into per-task LLM cost. Claude Code and Copilot obscure cost behind flat-rate subscriptions. Passepartout's transparent cost model is a sovereignty feature — the user knows what the agent spends on their behalf and can cap it. The minimum viable local model advantage is structural: at 2,000–4,000 effective tokens (foveal-peripheral + caching), a 7–8B parameter model on consumer hardware is a daily driver. Competitors at 32K+ effective tokens require 70B+ parameter models and 16–32 GB VRAM. Passepartout runs on a laptop GPU where competitors need a data center card or cloud API. ** v0.6.0: Signal Pipeline, Concurrency & Streaming The current pipeline is strictly sequential — one signal traverses Perceive → Reason → Act before the next signal begins. Background tasks (heartbeat, embedding cron, gardener scans) compete with foreground interactions. A heartbeat that fires during a long tool chain is queued. A Telegram message during a multi-step planning cycle is queued. The system feels sluggish under concurrent load even though the symbolic operations are near-instant (SBCL hash table lookups are microseconds) — the bottleneck is the single-pipeline architecture, not the hardware. *Design insight: why concurrency matters for an agent that is "one brain."* Passepartout rejects multi-agent delegation on principle (see DESIGN_DECISIONS "One Single Agent"). But a single brain handles multiple inputs simultaneously — the human brain processes vision, audio, and proprioception in parallel. Rejecting multi-agent delegation does not require rejecting concurrency within the agent. The key is that all concurrent operations share the same memory space, the same Merkle tree, and the same deterministic gate stack. They are threads of one cognition, not separate agents. *** TODO Priority-queue signal processing - Replace the linear ~process-signal~ call chain with a priority-ordered signal queue. The queue is a sorted plist-list consumed by the main loop. Priority tiers: - ~:user-input~ / ~:chat-message~ — highest priority (the user is waiting) - ~:approval-required~ — high (HITL re-injections need quick resolution) - ~:tool-output~ — medium (feedback from tool execution, needs LLM assessment) - ~:interrupt~ — medium-high (shutdown signal) - ~:heartbeat~ / ~:cron~ / ~:delegation~ — low (background maintenance) - Coalesce duplicate heartbeats: if the queue already contains a ~:heartbeat~ signal when a new one arrives, discard the older one (no value in processing stale ticks). Keep at most one pending heartbeat at any time. - The main loop drains the highest-priority signal from the queue, processes it through the pipeline, and repeats. If the pipeline produces feedback (tool-output → think), the feedback is enqueued at its appropriate priority — it may preempt background signals but won't interrupt the current signal mid-processing. - Add telemetry: average queue depth by priority tier, max wait time per tier. - TUI ~/reconnect~ command: when the connection-loss detection from v0.3.3 fires, the user can reconnect without restarting the TUI. The command closes the stale socket, re-runs ~connect-daemon~ with its retry backoff, and restores the ~:connected~ state on success. *** TODO MVCC memory concurrency - Replace ~*memory-store*~ (mutable global hash table) with a versioned Merkle-root pointer. The root is an ~(or null merkle-node)~ struct containing the tree and a monotonic version counter. - Read threads snapshot the root before beginning their pipeline cycle. All object lookups dereference through the snapshot — they see a consistent view of memory regardless of concurrent writes. Reads never block. - Write threads (ingest-ast, org-modify, snapshot-memory) build new object hashes, construct a new Merkle root, and CAS-replace the global root pointer. If another thread won the CAS race (root version changed), the loser re-reads the new root, replays its changes on the updated tree, and retries the CAS. - Conflict probability is near-zero because concurrent signals almost never touch the same Org headline. The replay-on-conflict path exists for correctness but is rarely exercised. Lock contention is eliminated — the only atomic operation is the CAS on the root pointer. - Remove the single-threaded pipeline assumption: previously, ~process-signal~ was safe because nothing else wrote to ~*memory-store*~ during its execution. With MVCC, multiple signals can process concurrently because each has its own snapshot. The ~*loop-interrupt-lock*~ becomes ~*signal-queue-lock*~ (protecting only the queue, not the memory). - Test: concurrent ingest-ast from two threads writing to different memory objects, verify both commits succeed without corruption. *** TODO Structured output enforcement - Add a plist validation step between ~markdown-strip~ and ~read-from-string~ in ~think()~. Before attempting to parse, validate: (a) the output starts with ~(~ or ~[~, (b) it contains balanced delimiters (count opens vs closes), (c) it doesn't contain ~#.~ (redundant after v0.3.1 ~*read-eval* nil~ but defense-in-depth). - On validation failure: construct a rejection trace (similar to the existing deterministic gate rejection feedback) and re-inject into the LLM prompt. The trace includes the raw output and a diagnostic ("Your response did not produce a valid plist. Ensure it starts with ( and has balanced parentheses."). - Configurable ~LLM_OUTPUT_RETRIES~ (default 2). After exhausting retries, fall through with the raw text as a ~:MESSAGE~ action (current behavior). - Track parse-failure rate per provider in telemetry. Use to guide provider cascade ordering: a provider with 20% parse-failure rate falls behind one with 2%. - If retries are exhausted without a parseable plist, the TUI renders the raw LLM output in a dimmed, collapsible region labeled "Parse failure — could not interpret this response." The user can inspect what the model produced. *** v0.6.3 — TODO Streaming responses Rationale: Every competitor streams — Hermes Agent specifically lists "streaming tool output" as a feature, OpenClaw streams via messaging channels, Claude Code streams via terminal. A spinner followed by a wall of text is v0.1-era UX for an LLM chat interface. Streaming was originally sequenced in the evaluation release (after evaluation harness and computer use), but it depends only on the daemon protocol (chunked frames) and TUI rendering — neither require tools, planning, evaluation, or vision. Moving it to v0.6.3 means Passepartout streams before it ships tools, because streaming makes the existing chat experience competitive. - Add a new frame type (~:type :stream-chunk~) to the daemon-TUI protocol. Chunks are variable-length strings carrying partial LLM output. The final chunk is an empty string, signalling end-of-stream. - ~provider-openai-request~: for providers that support streaming (OpenRouter, OpenAI, Anthropic, Groq, local), send ~"stream": true~ in the request body. Read the SSE stream, extract ~delta.content~ from each chunk, and call a new ~*stream-callback*~ function with the partial text. - The TUI renders partial output in the chat window as it arrives, appending characters to the in-progress agent message. The "…thinking" spinner is replaced by live, building text. - Interrupt-and-redirect: the user pressing a key (Esc or any printable char) during streaming injects an interrupt signal. The partial response is captured as the agent's message, the LLM call is cancelled (HTTP connection closed), and the user's keystroke becomes new input. This replaces the current full-process ~SIGINT~ with a graceful mid-response redirect. - The TUI message for a streamed response shows a ~[streaming]~ indicator that changes to a timestamp when the stream completes. If interrupted, the indicator changes to ~[interrupted]~. - Add FiveAM tests: stream-chunk framing round-trips correctly; interrupt during streaming produces a valid partial message; the TUI correctly renders progressive chunks vs a completed message. *** Competitive Advantage Analysis — v0.6.0 Summary The priority queue eliminates the perception of sluggishness that concurrent load creates. A user typing a query never waits for a heartbeat tick to finish — their signal jumps the queue. The coalescing of duplicate heartbeats eliminates wasted processing. This is table-stakes UX for a daily-driver agent. MVCC concurrency on the Merkle tree is genuinely novel for an AI agent. Most agents use either a single-threaded event loop (Claude Code) or process-level isolation (OpenClaw's subprocess model). Passepartout's approach — concurrent threads sharing a versioned content-addressable tree — combines the coherence of a single-agent memory with the throughput of concurrent execution. The Merkle tree, originally designed for integrity verification, gets a second life as the concurrency control primitive. This is the kind of architectural synergy that single-purpose databases can't match. Structured output enforcement bridges the gap between "Passepartout uses plists, not JSON" and "LLMs sometimes produce malformed syntax." It gives the system the same reliability guarantee that JSON mode gives competitors — the output will parse — without introducing JSON into the architecture. Streaming responses (v0.6.3) close the last remaining table-stakes UX gap with Hermes Agent and Claude Code. The "…thinking" spinner is replaced with live text. Interrupt-and-redirect means the user can course-correct mid-response instead of waiting for a wrong answer to complete. Combined with the TUI critical fixes (v0.3.3) and differentiator visualizations (v0.4.0), the TUI is competitive on responsiveness and uniquely informative on safety and context transparency. ** v0.7.0: Tool Ecosystem (MCP-Native) + Voice Gateway The original roadmap placed MCP at v0.8.0 and planned "10+ cognitive tools" built from scratch for v1.0.0. This is inverted: the ecosystem already provides 50+ tools (filesystem, git, postgres, slack, github, web search, memory servers). Building bespoke tools from scratch duplicates work the community has already done and tested. Passepartout's advantage is not in tool *implementation* but in tool *orchestration* — the deterministic gate stack that verifies every tool invocation before execution. *Why MCP matters for competitive positioning:* Claude Code's native tools (Read, Write, Edit, Bash, Grep, Glob, WebSearch) are implemented in TypeScript within the Claude Code runtime. They are not extensible — you cannot add a tool without modifying the runtime. OpenClaw's tools are similarly baked into the Node.js process. By building a native MCP client, Passepartout gains tool breadth that exceeds both competitors (50+ tools via the MCP ecosystem versus ~10 native tools) without building a single tool implementation. The tool quality is maintained by the ecosystem; the safety verification is maintained by Passepartout's gate stack. This division of labor is the right architecture for a small team building a competitor to well-funded commercial agents. *** TODO MCP native client - Pure Common Lisp MCP client: parse JSON-RPC messages from MCP servers over stdio or SSE. No Python bridge, no Node.js subprocess. The client runs in the same Lisp image as the agent — zero serialization overhead between the agent and the MCP layer. - Implement the MCP protocol lifecycle: initialize handshake, list tools, call tool, handle notifications. Each MCP server registers its tools as entries in Passepartout's ~*cognitive-tool-registry*~ at connection time — the LLM's tool belt prompt automatically expands to include them. - ~MCP_SERVERS~ env var: comma-separated paths to MCP server config files (JSON). Each config specifies the server command, args, and env vars. Example: =MCP_SERVERS=~/.config/passepartout/mcp/filesystem.json,~/.config/passepartout/mcp/git.json=. - Tool invocation route: LLM proposes a tool call → Dispatcher verifies against permission table → MCP client serializes call as JSON-RPC → server executes → result deserialized back to plist → returned to LLM as tool output. The Dispatcher does not distinguish between native tools and MCP tools — the gate stack is uniform. - Register the MCP client as a skill (~defskill~~:passepartout-mcp-client~) so it can be hot-reloaded. The MCP client is not core infrastructure — it is a skill that extends the tool ecosystem. *** TODO Core MCP tools (from existing roadmap items) - Git Steward (deferred from old v0.5.0): status, diff, commit, push, branch via the MCP Git server. Policy gate enforces commit-before-modify: any file write to a git-tracked directory must be preceded by a diff review. - Web Research (deferred from old v0.7.0): headless browser via Puppeteer/Playwright MCP server. Text extraction, screenshot capture, page interaction. - Interactive PTY (deferred from old v0.6.0): stream long-running process output to context window, async interrupt control. *** TODO TUI tool visualization - Tool invocation rendering: when the agent invokes a tool, the TUI renders a color-coded, collapsible region. Pre-execution: ~[Running: bash "npm test"...]~ in magenta with a dim spinner. Post-execution: ~✓ bash: tests passed (1.2s)~ in green, or ~✗ bash: exit code 1~ in red with the error output expanded below. - Tool output is collapsed by default (single line summary). Pressing Enter on a tool invocation row toggles expansion to show the full output. - Diff display: when a file write or git diff is involved, render the diff with standard ~+~ (green) / ~-~ (red) coloring. The diff is shown as a compact inline block with 3 lines of context around each change. - Gate trace for tool invocations: each tool call shows its Dispatcher gate results inline (gate trace from v0.4.0), so the user sees both the tool execution and which safety gates allowed or blocked it. *** TODO Environment Steward - Detect "command not found" in shell actuator output. - Search system PATH and package manager registries for the missing command. - Propose installation command and retry the failed action on user approval. - Cache resolved dependency paths to avoid repeated searches. *** v0.7.3 — TODO Voice Gateway Rationale: OpenClaw ships voice wake words and talk mode on macOS/iOS/Android via ElevenLabs. Hermes Agent has voice memo transcription. Both treat voice as a first-class channel. Passepartout's daemon already handles text — voice is an I/O format conversion. Speech-to-text turns audio into ~:user-input~ signals. Text-to-speech turns agent responses into audio. The architecture requires no changes; the voice gateway is a skill that wraps existing REST APIs. - Speech-to-text: POST audio to OpenAI Whisper API (~/v1/audio/transcriptions~) or local Whisper via Ollama. Receive text. Inject as a ~:user-input~ signal into the pipeline. The daemon processes it identically to a typed message. - Text-to-speech: POST text to ElevenLabs REST API (~/v1/text-to-speech/{voice-id}~) with stream response. Also support system ~say~ (macOS) / ~espeak~ (Linux) as zero-dependency fallbacks. - TUI voice toggle: ~/voice on~ enables voice capture, shows a ~🎤~ (listening) indicator in the status bar. ~/voice off~ returns to text-only. The microphone capture runs in a dedicated thread that feeds audio chunks to the speech-to-text backend. - Voice mode in messaging gateways: on Telegram and Discord, the voice gateway transcribes voice messages into text and injects them as ~:user-input~ signals. Agent responses can be optionally spoken back via text-to-speech if the user's message included a voice note (reply in kind). - The voice gateway is a skill (~defskill~~:passepartout-gateway-voice~). No core daemon changes required. The daemon receives text signals whether they originated from a keyboard, a messaging app, or a microphone. *** Competitive Advantage Analysis — v0.7.0 Summary MCP-native tool architecture gives Passepartout a tool breadth advantage that no single team could achieve through bespoke implementation. The MCP ecosystem is growing faster than any individual agent's tool set. By connecting to it rather than competing with it, Passepartout's tool count scales with the ecosystem — every new MCP server is a new Passepartout tool. The Dispatcher's tool permission table (allow/ask/deny) applies uniformly to MCP tools, giving Passepartout tool-level security granularity that competitors lack. Claude Code's tools are binary: available or not. Passepartout can conditionally allow filesystem writes to ~/projects/*~ while requiring HITL for writes to ~~/.config/*~ — per-path, per-tool, per-session. This is the deterministic gate stack's natural application domain. The Git policy gate (commit-before-modify) is a safety feature no competitor provides. It prevents the most common agent failure mode: modifying files without preserving the prior state. Combined with memory snapshots (v0.2.0), this gives every action a dual audit trail: the git history and the memory object history. v0.7.1 is also the threshold at which Passepartout can safely self-build — modify its own source files outside the core pipeline. The ~core-*~ path protection from v0.4.0 ensures the agent cannot destroy its own brain stem during self-building; the TDD runner catches regressions before commit; the Git policy gate preserves every state change. Together, these four releases (v0.4.0, v0.5.0, v0.6.2, v0.7.1) form the safety, economic, reliability, and tool stack that makes self-hosting viable. The voice gateway (v0.7.3) adds parity with OpenClaw's voice features without architectural changes — speech-to-text and text-to-speech are thin REST wrappers that feed text signals into the existing pipeline. Combined with the Emacs bridge (v0.4.0) and messaging gateways (v0.4.0), Passepartout supports four interaction surfaces by v0.7.3: terminal (TUI), messaging apps, Emacs, and voice. Each surface is a thin client speaking the same framed TCP protocol to the same daemon. ** v0.8.0: Planning, Self-Modification & Deterministic Routing *Design insight: the inverted tier classifier.* The current tier classifier routes "rm", "write-file", and "shell" to ~:REFLEX~ (no LLM). This routes the most dangerous operations to the path with the least oversight. It should be inverted: ~:REFLEX~ handles deterministic lookups (list TODOs, check file existence, query memory), ~:COGNITION~ handles text processing and summarization, ~:REASONING~ handles planning and code generation. Dangerous operations should always route through ~:REASONING~ where the full LLM cycle and Dispatcher gate stack apply. v0.8.1 fixes this. *** TODO Long-horizon planning (task tree DAG) - Decompose complex tasks into Org-mode headline trees. Each task node is a memory-object with terminal states: ~:todo~ → ~:next-action~ → ~:in-progress~ → ~:done~ / ~:blocked~ / ~:stuck~. - The LLM generates the initial task tree from the user's request. The REASONING tier processes each leaf task sequentially, updating node states as it progresses. - Parent nodes summarise child results: when all children of a node reach ~:done~, the parent is promoted to ~:done~ with a synthesised summary. When any child reaches ~:stuck~, the parent is promoted to ~:blocked~ with the blocking child's diagnostic. - Branch pruning: if a child is ~:stuck~ after three retries with different LLM providers, the parent re-plans the branch — the LLM generates alternative decomposition paths for the blocked sub-task. - Task trees persist as Org headlines in ~/memex/system/tasks/~. Survive restarts. Visible to the user as editable Org files. - TUI task tree visualization: a collapsible Org headline tree rendered in the chat area. Each node shows its terminal state with a colored indicator (~○~ todo, ~▶~ next-action, ~◉~ in-progress, ~✓~ done, ~✗~ blocked, ~⏸~ stuck). Nodes expand/collapse on Enter. The tree updates in real time as the agent progresses through subtasks. This is visible in the TUI as an async status region that appears when the agent is executing a long-horizon plan and collapses to a single summary line when complete. *** TODO Tier classifier fix - Invert the current classifier: ~:REFLEX~ = deterministic lookups only (memory query, file-exists-p, check time, list TODOs by tag). ~:COGNITION~ = text processing, summarization, simple Q&A, note formatting. ~:REASONING~ = planning, code generation, multi-step task execution, dangerous operations. - Track classifier accuracy via telemetry: for each classified action, record whether the classification was appropriate (did the ~:REFLEX~ action actually succeed without LLM? did a ~:REASONING~ action turn out to be a simple lookup?). - The classifier function is overrideable via ~*tier-classifier*~, allowing users or skills to customize routing. - The classifier should be a skill, not core infrastructure — reloadable and replaceable without restart. *** TODO Skill Creator - LLM drafts complete skill org-file from natural language description. - Mandatory pipeline: (a) syntax validation via ~lisp-syntax-validate~, (b) sandbox-load in temporary jailed package (v0.3.2), (c) run registered trigger function against mock contexts, (d) run registered deterministic gate against mock proposals, (e) on pass, promote to live registry under ~passepartout.skills.~. - Required ~:repl-verified~ flag on all ~defun~ forms — the existing Dispatcher lint check (core-loop-act.lisp:152–161) warns on writes without verification. The Skill Creator enforces this at creation time. - Skills are the primary extension mechanism for users. The Skill Creator makes skill authoring accessible to non-Lisp-programmers: describe what you want in English, the LLM drafts the Org file, the system verifies it, and the skill is live. This is how Passepartout grows its capability surface without requiring the user to learn Common Lisp. *** Competitive Advantage Analysis — v0.8.0 Summary The task tree DAG with terminal states and branch pruning is Passepartout's planning primitive — analogous to Claude Code's TODO list but structural (Org headlines with parent-child relationships) rather than flat. The advantage: subtask dependencies are explicit in the tree structure, so the agent knows that task C depends on tasks A and B without having to rediscover this from context. Parent summarisation means the LLM can check high-level progress without re-reading every child's output — a token savings multiplier on long-running tasks. The tier classifier fix is a safety correctness issue. The current inverted classifier (dangerous ops → no-LLM path) is actively harmful — it reduces oversight on the operations that need it most. Fixing this means "dangerous by default → maximal oversight" becomes the routing rule, which is the correct security posture. The Skill Creator is the mechanism by which Passepartout escapes the "team of Lisp programmers" constraint. Most agent frameworks require Python/TypeScript to extend. Passepartout's extension language is English — the LLM writes the Lisp, the system verifies it. The sandbox-load and verification pipeline (from v0.3.2) make this safe: a skill that fails verification never enters the running image. ** v0.9.0: Evaluation & Vision With tools (v0.7.0) and planning (v0.8.0) in place, the agent can execute complex multi-step tasks. v0.9.0 answers two questions: (1) how do we *prove* it works? (SWE-bench evaluation harness), and (2) can the agent interact with visual interfaces? (computer use / vision). Streaming has been moved to v0.6.3 — it depends only on the daemon protocol, not on evaluation or vision. *** TODO SWE-bench harness - Automated pipeline: clone a repository from SWE-bench dataset, parse the GitHub issue, feed the issue description into Passepartout's cognitive loop, track the resolution trajectory as an Org headline tree, apply the generated patch, run the repository's test suite, score success (tests pass yes/no). - Trajectory persistence: each benchmark run produces an Org file under ~/memex/system/benchmarks/~ recording every ~think()~ call, every tool invocation, every Dispatcher decision, and the final test result. The trajectory is auditable — a human can read why the agent made each decision and where it went wrong on failures. - Regression mode: run the same benchmark after each version release. Track score trends. A version that regresses on SWE-bench does not ship. - Target: competitive score with Claude Code and OpenClaw on SWE-bench-verified by v1.0.0. The evaluation harness ships in v0.9.0 so there are two full version cycles to iterate and improve before v1.0.0 ships. *** TODO Computer Use / Vision - Screenshot capture: X11 (~xwd~ / ~import~) and Wayland (~grim~) bridge. The agent requests a screenshot of a specific window or the full desktop. - Vision model integration: send screenshot to a vision-capable model (GPT-4V, Claude 3.5, Gemini 2.0 Flash). The model analyzes UI elements and returns structured descriptions. - Coordinate-based interaction: ~xdotool~ / ~ydotool~ for click and type commands at specific screen coordinates. Dispatcher approval gate applies — screen interaction requires HITL by default, overridable per-application via permission table. - Use case: the user says "open Firefox, search for the Passepartout GitHub repo, and star it." The agent captures screenshots, identifies UI elements via the vision model, and issues click/type commands. Each step is verified by a follow-up screenshot to confirm the action succeeded. *** Competitive Advantage Analysis — v0.9.0 Summary SWE-bench evaluation is the industry standard for coding agent capability claims. Without it, "SOTA parity" is a marketing claim. With it, "SOTA parity" is a number. The harness's trajectory persistence is a differentiator: most evaluation harnesses produce a pass/fail score. Passepartout's produces a complete Org-mode audit trail showing exactly where the reasoning succeeded or failed. This turns benchmarking into a debugging tool — failed trajectories point directly to the skill, gate, or model that needs improvement. Vision + screen interaction is table stakes for competing with Claude Code's computer use feature. The Passepartout advantage: every screen interaction passes through the Dispatcher gate stack. A vision model might hallucinate a UI element that doesn't exist — the follow-up screenshot verification catches this deterministically. Competitors' computer use features lack this verification step — they trust the vision model's output. ** v0.10.0: Consensus, GTD & Deep Emacs Integration Near-SOTA. The agent has tools, planning, evaluation, and streaming. v0.10.0 adds reliability (consensus), productivity methodology (GTD), and environment depth (Emacs integration). *** TODO Consensus loop - Multi-provider parallel inference for critical decisions. When the action's impact score exceeds a threshold (file writes outside home directory, shell commands that touch /etc, git pushes to main), the system sends the same prompt to 2–3 independent providers. - Disagreement detection: compare the structured outputs (actions proposed by each provider). If all providers propose the same action (or semantically equivalent actions), proceed with the highest-confidence result. If providers disagree, flag the action for HITL approval and present the user with each provider's proposal and confidence score. - Confidence scoring: when providers agree, use the agreement level as a confidence metric for telemetry. Track which provider combinations produce the highest agreement rates for which task types. - Cost-aware: consensus mode doubles/triples cost for the action. Only trigger when the action's impact exceeds the cost threshold. Configurable via ~CONSENSUS_THRESHOLD~ — actions below the threshold use single-provider mode. - TUI consensus display: when consensus mode fires, the TUI shows a collapsible region listing each provider, its model, its proposal, and its confidence score. Agreement is rendered as ~✓ 3/3 providers agree~ in green; disagreement as ~✗ 2/3 providers agree (1 disagrees)~ in yellow with the dissenting proposal expanded for review. The user can accept the majority or inspect the dissent before approving. *** TODO GTD integration - Full GTD cycle: capture (inbox → process), clarify (what is this? is it actionable?), organize (project, next action, reference, someday/maybe, trash), reflect (weekly review), engage (context-appropriate action lists). - Org properties: ~:TRIGGER:~ (what context makes this actionable — @home, @office, @computer, @phone), ~:BLOCKER:~ (what task must complete first). - Weekly review: the agent scans all projects and tasks, surfaces stalled items, suggests next actions, and generates a review Org file for the user. The review is produced deterministically (no LLM — pure Org tree traversal) and takes zero tokens. - TUI agenda view: a ~/agenda~ command renders the user's Org-agenda (scheduled items, deadlines, habits) as a formatted scrollable region within the chat area. The agent can reference agenda context in its responses without the user having to paste their schedule. *** TODO Deep Emacs integration Rationale: The Emacs bridge (v0.4.0) treats Emacs as a Passepartout client — the user sends text, Emacs displays responses. This is the first direction: Emacs → Passepartout. The deep integration is the second direction: Passepartout → Emacs. The agent reads the user's agenda, clocks time on tasks, refiles headlines, and archives completed work. This builds on the TCP bridge already in place from v0.4.0 — the agent now initiates commands to Emacs, not just responds to user input. - Org-agenda awareness: the agent queries the user's agenda view (scheduled items, deadlines, habits) and incorporates agenda context into planning decisions. "What should I work on today?" considers the agenda, not just the task tree. - Clock time tracking: the agent starts/stops clocks on Org headlines. Produces clock tables for time reporting. This enables the agent to answer "how long did I spend on that feature?" - Refile and archive: the agent refiles headlines between Org files and archives completed items to ~/memex/archives/~. Archive decisions are proposed by the LLM and verified by the Dispatcher (archive policy: DONE items older than 30 days, DONE items with no open child tasks). *** Competitive Advantage Analysis — v0.10.0 Summary The consensus loop is not unique (OpenClaw has a similar feature), but Passepartout's implementation benefits from the structured output enforcement in v0.6.2 — comparing plists for semantic equivalence is simpler and more reliable than comparing free-text responses. The GTD integration and Emacs integration are Passepartout's "unfair advantages" — no competitor has either. Claude Code and Copilot are development tools, not life management tools. Org-mode is the bridge: the same format that holds the agent's memory holds the user's tasks, calendar, and notes. The GTD cycle operates on the same Org trees that the foveal-peripheral model renders into LLM context. There is no import/export, no separate task database, no format conversion. The agent's world model IS the user's Org files. This is the unified format thesis from the DESIGN_DECISIONS document made operational — and it's a capability that JSON-based agents structurally cannot replicate. ** v1.0.0: SOTA Parity (verified) Feature-complete, benchmark-verified, production-hardened. All capabilities from v0.3.0 through v0.10.0 integrated and tested end-to-end. v1.0.0 is not a feature release — it is a verification release. Every feature from the v0.x series is tested under concurrent load, resource starvation, adversarial input, and benchmark scoring. The evaluation harness (v0.9.0) provides the scoring apparatus; v1.0.0 is the scored release. | Area | Parity Target | Verification Method | |-------------------+---------------------------------------------+---------------------------------------| | Self-improvement | Skill Creator + self-edit + hot-reload | Skill regression suite (v0.3.x) | | Planning | Task tree DAG with terminal states | Multi-step integration tests | | Tool ecosystem | 15+ MCP tools + native shell + git | MCP protocol compliance tests | | Context window | Semantic search + foveal-peripheral + caching| Token budget vs competitor audit | | Safety | 9-vector Dispatcher + policy + permissions | Chaos testing (v0.9.0) | | Multi-step tasks | Task trees with terminal states | SWE-bench score (v0.9.0 harness) | | Code editing | Full file read/write via MCP + Org | SWE-bench-verified subset | | Memory | Vector recall + Merkle integrity + MVCC | Concurrency stress test (v0.6.1) | | Emacs integration | Full org-mode control (exceeds Claude Code) | Org-agenda round-trip test | | Streaming | Partial output + early termination | TUI UX latency benchmark | | TUI | Word wrap, cursor, gate trace, focus map, | TUI integration test suite (v0.3.3, v0.4.0) | | | rule counter, cost counter, streaming | | | Packaging | Source install (primary) + save-lisp-and-die | Install test matrix across distros | | | binary for constrained platforms | | | Offline | 100% local capable (7-13B model) | Air-gapped integration test | | Cost | 2-3x fewer tokens than competitors | SWE-bench token audit | | Concurrency | Priority queue + MVCC + parallel signals | Concurrent load test (3 users + bg) | **Performance projection at v1.0.0:** | Scenario | Passepartout v1.0.0 | Claude Code | OpenClaw | |-------------------------------+----------------------------------+------------------------------------+------------------------------------| | Single-turn chat (local 8B) | 2-4s, ~1,500 tok | N/A (cloud-only) | N/A (cloud-only) | | Single-turn chat (cloud) | 1-3s, ~1,500 tok | 1-3s, ~3,000 tok | 1-3s, ~3,500 tok | | Multi-step coding (5 files) | 15-30s, ~30,000 tok | 10-20s, ~65,000 tok | 20-40s, ~85,000 tok | | Knowledge base query (500 nodes)| <1s (in-image vector), 0 LLM tok | 3-5s, ~5,000 tok (LLM-assisted) | 3-5s, ~5,000 tok (LLM-assisted) | | Background maintenance | 0 LLM tok (deterministic cron) | Variable or skipped | Variable or skipped | | Offline operation | Full capability | None | None | | Cost per coding session | ~$0.15 (gpt-4o-mini) | ~$0.45 (gpt-4o-mini) | ~$0.55 (gpt-4o-mini) | Passepartout wins on cost (2-3x savings from sparse trees + deterministic gates + caching), offline capability (unique), and knowledge management (10-40x savings from in-image vector lookup + Org-native format). It is competitive on single-turn latency and slightly behind on multi-step latency (the single-pipeline architecture adds ~5s overhead per tool execution versus competitors' parallel tool dispatch). The key insight at v1.0.0: Passepartout does not beat competitors at everything. It wins decisively where the architecture's structural advantages apply (safety, cost, offline operation, knowledge management) and is competitive where they don't (raw LLM inference speed, parallel tool dispatch). This is a defensible position — the niches Passepartout dominates are exactly the niches that matter for a sovereign, local-first AI assistant. But it is still fundamentally probabilistic at its core. The symbolic engine verifies and constrains, but the generative engine is still the primary reasoning source. The architectural transition to symbolic-first reasoning happens in v3.0.0. ** v2.0.0: Lisp Machine Emergence v2.0.0 is where Passepartout stops being a daemon with clients and becomes the environment. The agent's cognitive loop, the user's editor, the user's shell, and the user's browser run in the same Common Lisp image. The Dispatcher gate stack verifies every action regardless of who initiated it — user or agent. The distinction between "tool" and "self" dissolves. **Why this version matters for UX parity.** v0.4.0 through v1.0.0 give Passepartout four interaction surfaces (TUI, messaging apps, Emacs, voice). This is competitive with Hermes Agent's TUI + messaging but not with OpenClaw's 25+ channels + Canvas + macOS/iOS apps + voice wake words. v2.0.0 doesn't try to match OpenClaw's breadth. It inverts the problem: instead of building more clients, it builds a platform where the agent's environment and the user's environment are the same process, separated not by a sandbox but by the Dispatcher gate stack. The editor IS the agent's prompt. The shell IS the agent's actuator. The browser IS the agent's web research tool. There are no clients — there is one Lisp image, one address space, one org-mode file system. This is only possible because the deterministic safety gates are in-process pure Lisp functions rather than out-of-process sandboxes. **Components:** | Component | What it replaces | Technology | Status at v1.0.0 | |-----------------+------------------------+-------------------------------------+-------------------| | Lish editor | Emacs, VS Code | McCLIM or Croatoan-based TUI | TUI exists (Croatoan) | | Lish shell | Bash, zsh | Common Lisp REPL (already Lisp) | Shell actuator exists | | Nyxt browser | Chrome, Firefox | Nyxt (Common Lisp browser) | Playwright MCP (v0.7.0) | | Web interface | Notion, Obsidian Publish | Org → HTML static site generator | Not started | | Daemon + memory | "the OS" | SBCL image + Org files | Production (v1.0.0) | *** Lish — the Common Lisp editor Not elisp. Not Emacs. A multi-threaded Common Lisp editor built on McCLIM or the Croatoan TUI infrastructure. The complete system prompt lives in an Org buffer — the agent's identity (~AGENTS.md~ equivalent), its skill registry, its memory, and its reasoning are visible and editable as Org text. The user modifies the agent's prompt and the agent reflects the change immediately — the prompt is a file in memory, not a hidden string in a config. Org-babel for interactive evaluation: source blocks in Org files are executable. The user evaluates a ~#+begin_src lisp~ block and the result appears inline. The agent evaluates blocks to verify code before writing. The REPL is not a separate window — it is the Org buffer in which the agent and user both work. The editor and the agent share the same Lisp image. The editor is not a client that connects to a daemon — it IS the daemon process. The TUI from v0.4.0 (with word wrap, streaming, gate trace, focus map) is the editor's rendering surface. The Emacs bridge (v0.4.0) remains for users who prefer Emacs until Lish matures. *** Nyxt — the Common Lisp browser Nyxt is a browser written in Common Lisp that renders web content in buffers. In v2.0.0, it replaces the Playwright MCP bridge (v0.7.0) as the agent's web interaction surface. The agent controls Nyxt by calling Nyxt functions directly — no subprocess, no serialization, no protocol. Navigation, form filling, data extraction, screenshot capture all happen within the same Lisp image. This matters because Playwright (Node.js subprocess, v0.7.0) and vision (screenshot + LLM analysis, v0.9.1) give the agent web access but with a process boundary. Nyxt eliminates the boundary. The agent's browser session shares memory with the agent's cognitive loop. The agent can inspect the DOM as Lisp data structures. It can respond to page events by injecting signals into its own pipeline. The browser also serves as the user's web interface. Org files in ~/memex are rendered as static HTML by a zero-JS Org-to-HTML converter running in Nyxt. The user browses their memex through the same browser the agent uses to research the web. No separate web server. No deployment. It's a directory on disk rendered by a local browser. *** Lish — the Lisp shell Bash is a text-stream protocol. Passepartout speaks plists. The Lish shell replaces text streams with structured data — every command returns a plist, not a byte stream. Pipe becomes function composition. Scripts become Lisp functions that operate on memory objects directly. The agent and the user share the same shell. The user types ~(list-todos :tag "@urgent")~. The agent proposes ~(shell "npm run build")~. The Dispatcher verifies both. The shell is not a separate process — it is a REPL connected to the same Lisp image as the agent's cognitive loop. Org-mode buffers become the file system. The user's memex (~/memex/) is browsable as a tree of Org headlines. File operations (read, write, list, search) operate on Org AST nodes, not byte streams. A "directory listing" is a tree of headlines. A "file read" is a subtree rendered as text. Bash remains available as a backend for running external commands, but it is not the primary interface. The agent and the user interact with the system through plists, not through text parsing. *** Strategic timeline The Emacs bridge (v0.4.0) is the temporary bridge. It gives Lisp users a native Emacs experience while Lish is being built. By v2.0.0, Lish is mature enough to replace it for users who prefer the integrated environment. Emacs users who prefer Emacs keep the bridge — Lish does not require abandoning Emacs. It offers an alternative built on the same principles (single address space, plists everywhere, Org-mode as AST) but without elisp's limitations (single-threaded, no bordeaux-threads, no shared memory with the agent). ** v3.0.0: Neurosymbolic Maturity Deterministic planner takes the wheel. LLM relegated to semantic translation. - Deterministic planner: Pure Lisp task scheduler. No LLM needed for scheduling. - Self-correcting gates: Gates learn from false positives (user override patterns). This is the architectural leap. The system transitions from "probabilistic engine with symbolic verification" to "symbolic engine with probabilistic input and output." The 10-80-10 architecture becomes fully realized: ten percent neural for input translation, eighty percent symbolic for reasoning against a knowledge graph, ten percent neural for output formatting. The symbolic engine maintains facts, relationships, rules, and formal proofs. When the neural engine generates something, the symbolic engine verifies it - not by checking against a blocklist, but by running the proposal through a Prolog/Datalog reasoner that understands the domain constraints. The deterministic planner takes the wheel. The LLM is no longer consulted for planning decisions - it translates human language to structured queries and structured results back to human language. The planning itself is pure Lisp: task graphs generated by a symbolic reasoner that has access to the full knowledge graph. Self-correcting gates replace the learned Bouncer rules. The system learns not just from approved exceptions but from the full history of outcomes - did the plan succeed? Where did it fail? The symbolic engine updates its own rules based on the results. The implications are significant. Hallucination becomes structurally impossible because the symbolic engine will not accept a fact that contradicts its knowledge graph. Safety becomes provable because the formal verification layer can prove properties about the system's behavior. Self-improvement becomes stable because the agent modifies skills that are then verified before execution. ** v4.0.0: AI Stack Internalized The agent understands its own weights. No external inference. - Llama.cpp in Lisp: FFI binding. No Python subprocess. Pure Common Lisp inference. - Weights as sexps: Neural weights as Lisp data structures. Homoiconic model introspection. ** v5.0.0: Hardware The Lisp machine becomes physical. RISC-V with tagged architecture, hardware-enforced type checking, FPGA prototype for the symbolic core. The agent runs not in emulation but on silicon purpose-built for the architecture. This is the long horizon. The symbolic engine runs on logic ASICs optimized for symbolic computation. The neural engine runs on GPU or purpose-built matrix math hardware. Lisp orchestrates both, enforcing at the hardware level what it enforced at the software level in earlier versions. ** v6.0.0: True Agency World models, temporal reasoning, goal persistence across restarts. - World models: Predictive models of user behavior, project dynamics, system state. - Temporal reasoning: Scheduling, deadlines, elapsed duration awareness. - Goal persistence: Goals survive restarts. Long-term projects in memory-objects.