Split monolithic tai.py into tai_pkg package, keep tai-orig.py as original

tai.py (8643 lines) is now a thin facade over tai_pkg/ (33 feature modules, one class or feature per file). tai-orig.py preserves the pre-split single-file version byte for byte (sha256 verified against snapshot); import tai and ./tai.py behavior unchanged.

Equivalence proof, all run on this tree immediately pre-commit: differential battery tai-orig.py vs facade 3718 checks 0 failures (surface, pure fuzz, tool dispatch, store/audit, release ops, fake-HOME install roundtrip, CLI runs); test_tai.py 261 OK; test_seal.py 16 OK; --version/--help byte-identical; dir(tai) zero removals.

Fix included: opencode wire-protocol schema echo leaked into replies; opencode_parse now scans with JSONDecoder.raw_decode capped at 200 scans (13 OpencodeParseTests). Test count 261 = 237 HEAD + 8 pre-existing uncommitted ToolArgumentsTests (now committed with this change, disclosed) + 13 OpencodeParseTests + 3 EmbeddedStringsTests. Patch-target-only test migration (24 hunks), zero assertion changes. tai_pkg clean-regenerated after mid-verification tamper was found and reverted.

README.md

@@ -2,9 +2,9 @@ retoor <retoor@molodetz.nl>
 
 # tai
 
-**One file. Zero dependencies. A real agent.**
+**Zero dependencies. A real agent.**
 
-`tai` is a single-file autonomous AI agent written in Python — the entire implementation lives in `tai.py` (~7,600 lines) and imports **only the Python standard library**. No `pip install`, no `requirements.txt`, no build step, no lockfile to rot. It runs as an interactive REPL or a one-shot command, reasons through an OpenAI-compatible backend, acts through 38 tools, keeps per-profile memory in a self-healing SQLite vault, seals that vault at rest, ranks every search with real BM25 (no vector database, no embeddings bill, no GPU), chunks and indexes anything worth remembering for retrieval, and can wall shell/file work off inside a disposable container sandbox.
+`tai` is an autonomous AI agent written in Python — the implementation lives in the `tai_pkg/` package (one feature per module, grouped into classes) behind the thin `tai.py` command, and imports **only the Python standard library**. No `pip install`, no `requirements.txt`, no build step, no lockfile to rot. It runs as an interactive REPL or a one-shot command, reasons through an OpenAI-compatible backend, acts through 38 tools, keeps per-profile memory in a self-healing SQLite vault, seals that vault at rest, ranks every search with real BM25 (no vector database, no embeddings bill, no GPU), chunks and indexes anything worth remembering for retrieval, and can wall shell/file work off inside a disposable container sandbox.
 
 Nothing here is decorative. Every mechanism below exists because something specific was expensive — context tokens, API calls, attacker surface, disk writes, or your attention — and got engineered out.
 
@@ -185,7 +185,7 @@ This is the part worth being loud about: **tai does retrieval-augmented generati
 
 A chunk hit from `search` returns **the actual chunk text**, not just FTS's highlighted snippet — the whole point of chunk-level retrieval is to hand back something directly usable as context, and the parent record id rides along so `record_read` can pull more surrounding material on demand (hierarchical retrieval: chunk for precision, parent for context, one hop away, never both loaded by default).
 
-**Why no vectors.** Embeddings mean a model call (cost, latency, an API dependency) or a local model (a real dependency, a real download, real RAM) for *every single thing written* — directly incompatible with the single-file, stdlib-only, zero-install rule this whole project is built on. BM25 over SQLite FTS5 costs nothing to build, nothing to maintain, and handles exactly what this vault actually needs to search well: command output, notes, transcripts, terminal history — content where the literal words (an error string, a flag name, a path) *are* the signal. This was an explicit, considered tradeoff, not a missing feature.
+**Why no vectors.** Embeddings mean a model call (cost, latency, an API dependency) or a local model (a real dependency, a real download, real RAM) for *every single thing written* — directly incompatible with the stdlib-only, zero-install rule this whole project is built on. BM25 over SQLite FTS5 costs nothing to build, nothing to maintain, and handles exactly what this vault actually needs to search well: command output, notes, transcripts, terminal history — content where the literal words (an error string, a flag name, a path) *are* the signal. This was an explicit, considered tradeoff, not a missing feature.
 
 **Chunking is wired into the system, not bolted onto one feature.** `record_save`, the shell/subagent output spill path (`spill_output`), and terminal log export all chunk through the identical `chunk_lines()` call — "RAG" here means a property of the vault, not a special case for logs. It is deliberately *not* wired into `add_record`'s lower-level, highest-frequency caller — `upsert_file_record`, which fires on every single file write and edit for audit bookkeeping — because exploding the chunk table on every keystroke-adjacent save would be pure waste for content `read_file`/`grep` already serves perfectly well. And deleting a record deletes its chunks and their index rows with it (`Store.delete_record`); there is no orphan path to worry about.
 
@@ -345,6 +345,10 @@ Install asks for the bot token up front, verifies it against `getMe`, stores it
 
 ## Layout
 
-    tai.py         the entire agent — tools, vault, search, skills, orchestration, sandbox, all of it
+    tai.py         CLI entry + stable import surface (thin facade, no logic of its own)
+    tai_pkg/       the entire agent, one feature per module: agent loop, tools,
+                   vault, backends (muse/opencode/grok/claude/codex/gemini),
+                   search, skills, scheduler, telegram, plexer, sandbox,
+                   install/release, config, rendering — grouped into classes
     test_seal.py   seal regression tests
     test_tai.py    agent, tools, records, graph, search/RAG, skills, scheduler tests