Done! 11 entries added to the knowledge base covering:

- **What it is**: Python package for local video ingestion, search & probing
- **Dependencies**: faster-whisper, playwright, yt-dlp, streamlink
- **CLI surface**: `add-url`, `sync-channels`, `find`, `show`, `transcript`, `probe-url`
- **Policies**: transcript_only (default), audio_archive, video_archive
- **Storage**: library under `~/.local/share/videoctl/library/<extractor>/<id>/`, SQLite FTS for search
- **Architecture**: 10 modules (cli, config, db, models, probe, service, subtitles, summarize, transcribe, utils) + 3 bin wrappers
- **Service**: systemd timer for daily channel sync with Telegram notifications
- **Security**: public/non-DRM only, dedicated video-worker user, ProtectSystem=full

Now when you say "run videoctl add-url..." I'll know exactly what it does without needing to explore first. Same approach can be done for automeat and sfq-factory when you're ready. 🙂

Output:
ut: {}

[tool] ../repos/videoctl/docs/manual.md (completed)
  kind: read
  input: /home/matrix-lite/workspace/repos/videoctl/docs/manual.md
  files: /home/matrix-lite/workspace/repos/videoctl/docs/manual.md
  output:
    <path>/home/matrix-lite/workspace/repos/videoctl/docs/manual.md</path>
    <type>file</type>
    <content>
    60:
    61: ```bash
    62: bin/videoctl probe-url "https://example.com/watch-page"
    63: ```
    64:
    65: ## Artifact Layout
    66:
    67: Each video lives under:
    68:
    69: ```text
    70: ~/.local/share/videoctl/library/<extractor>/<video_id>/
    71: ```
    72:
    73: Files include:
    74:
    75: - `metadata.json`
    76: - `source.url`
    77: - `description.txt` when available
    78: - `raw/info.json`
    79: - `raw/subtitle.<lang>.<ext>` when available
    80: - `media/archive.*` for archive policies
    81: - `transcript/transcript.txt`
    82: - `transcript/chunks.json`
    83: - `summaries/brief.md`
    84: - `summaries/detailed.md`
    ... (44 more lines)

[tool] read (pending)
  input: {}

[tool] ../repos/videoctl/src/openclaw_videoctl/cli.py (completed)
  kind: read
  input: /home/matrix-lite/workspace/repos/videoctl/src/openclaw_videoctl/cli.py
  files: /home/matrix-lite/workspace/repos/videoctl/src/openclaw_videoctl/cli.py
  output:
    <path>/home/matrix-lite/workspace/repos/videoctl/src/openclaw_videoctl/cli.py</path>
    <type>file</type>
    <content>
    1: from __future__ import annotations
    2:
    3: import argparse
    4: import json
    5: from pathlib import Path
    6:
    7: from .config import load_runtime_config
    8: from .models import TranscriptChunk
    9: from .service import VideoControlService, VideoCtlError
    10: from .utils import format_duration, to_serializable
    11:
    12:
    13: def build_parser() -> argparse.ArgumentParser:
    14:     parser = argparse.ArgumentParser(prog="videoctl")
    15:     parser.add_argument("--config", type=Path, help="optional YAML config path")
    16:
    17:     subparsers = parser.add_subparsers(dest="command", required=True)
    18:
    19:     add_url = subparsers.add_parser("add-url", help="ingest one public video URL")
    20:     add_url.add_argument("url")
    21:     add_url.add_argument(
    22:         "--policy", choices=("transcript_only", "audio_archive", "video_archive")
    23:     )
    24:     add_url.add_argument("--confirm-large-download", action="store_true")
    25:     add_url.add_argument("--no-transcription", action="store_true")
    ... (28 more lines)

[tool] read (pending)
  input: {}

[tool] ../repos/videoctl/tests (completed)
  kind: read
  input: /home/matrix-lite/workspace/repos/videoctl/tests
  files: /home/matrix-lite/workspace/repos/videoctl/tests
  output:
    <path>/home/matrix-lite/workspace/repos/videoctl/tests</path>
    <type>directory</type>
    <entries>
    test_db.py
    test_subtitles.py
    test_utils.py

    (3 entries)
    </entries>

[thinking] I now have a comprehensive understanding of videoctl. Let me check the remaining CLI commands and security docs to complete the picture.

[tool] read (pending)
  input: {}

[tool] ../repos/videoctl/src/openclaw_videoctl/cli.py (completed)
  kind: read
  input: /home/matrix-lite/workspace/repos/videoctl/src/openclaw_videoctl/cli.py
  files: /home/matrix-lite/workspace/repos/videoctl/src/openclaw_videoctl/cli.py
  output:
    <path>/home/matrix-lite/workspace/repos/videoctl/src/openclaw_videoctl/cli.py</path>
    <type>file</type>
    <content>
    50:     transcript.add_argument("--full-text", action="store_true")
    51:     transcript.add_argument("--json", action="store_true")
    52:
    53:     probe = subparsers.add_parser(
    54:         "probe-url", help="observe browser network traffic for media URLs"
    55:     )
    56:     probe.add_argument("url")
    57:     probe.add_argument("--json", action="store_true")
    58:
    59:     return parser
    60:
    61:
    62: def _print_json(value: object) -> None:
    63:     print(json.dumps(to_serializable(value), indent=2, sort_keys=True))
    64:
    65:
    66: def _render_transcript_excerpt(chunks: list[TranscriptChunk], limit: int) -> str:
    67:     lines = []
    68:     for chunk in chunks[:limit]:
    69:         lines.append(f"{chunk.timestamp}  {chunk.text}")
    70:     return "\n".join(lines)
    71:
    72:
    73: def main(argv: list[str] | None = None) -> int:
    74:     parser = build_parser()
    ... (138 more lines)

[tool] read (pending)
  input: {}

[tool] ../repos/videoctl/docs/security.md (completed)
  kind: read
  input: /home/matrix-lite/workspace/repos/videoctl/docs/security.md
  files: /home/matrix-lite/workspace/repos/videoctl/docs/security.md
  output:
    <path>/home/matrix-lite/workspace/repos/videoctl/docs/security.md</path>
    <type>file</type>
    <content>
    1: # Security Notes
    2:
    3: ## Scope Limits
    4:
    5: - `videoctl` is for public, non-DRM media only.
    6: - It does not implement DRM bypass, paywall bypass, or personal browser-cookie scraping.
    7:
    8: ## Data Handling
    9:
    10: - Raw source URLs are written only into per-video `source.url` artifacts with restrictive permissions.
    11: - Redacted URLs are used in SQLite job state and logs.
    12: - SQLite stores metadata, transcript chunks, summaries, jobs, and log records.
    13: - Signed media URLs returned by `probe-url` are not persisted by the service.
    14:
    15: ## Cookie Policy
    16:
    17: - `probe-url` starts with no imported browser cookies.
    18: - Per-site cookie import requires explicit `probe.site_cookies.<host>.storage_state` config.
    19: - Cookie state files should live outside git, ideally under `/etc/videoctl/cookies/` with restricted permissions.
    20: - Telegram notify credentials should live only in `/etc/videoctl/notify.env` with restrictive permissions.
    21:
    22: ## Execution Limits
    23:
    24: - `limits.max_duration_minutes` caps long videos.
    25: - `limits.max_file_size_mb` blocks oversized downloads.
    ... (16 more lines)

[thinking] Now I have a very comprehensive understanding of videoctl. Let me also check the skills directory and systemd directory quickly, then compile everything into knowledge base entries.

[tool] read (pending)
  input: {}

[tool] ../repos/videoctl/skills (completed)
  kind: read
  input: /home/matrix-lite/workspace/repos/videoctl/skills
  files: /home/matrix-lite/workspace/repos/videoctl/skills
  output:
    <path>/home/matrix-lite/workspace/repos/videoctl/skills</path>
    <type>directory</type>
    <entries>
    video-ingest/

    (1 entries)
    </entries>

[tool] read (pending)
  input: {}

[tool] ../repos/videoctl/systemd (completed)
  kind: read
  input: /home/matrix-lite/workspace/repos/videoctl/systemd
  files: /home/matrix-lite/workspace/repos/videoctl/systemd
  output:
    <path>/home/matrix-lite/workspace/repos/videoctl/systemd</path>
    <type>directory</type>
    <entries>
    videoctl-sync.service
    videoctl-sync.timer

    (2 entries)
    </entries>

[thinking] Now I have enough info to build comprehensive knowledge base entries for videoctl. Let me compile all the key facts and append them to the knowledge base.
Now let me build the videoctl knowledge pack:

[tool] read (pending)
  input: {}

[tool] ../../state/persistent-chat/knowledge-base.jsonl (completed)
  kind: read
  input: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  files: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  output:
    <path>/home/matrix-lite/state/persistent-chat/knowledge-base.jsonl</path>
    <type>file</type>
    <content>
    23: {"ts":"2026-05-19T18:19Z","topic":"WGT","source":"MDR.de (brave-search)","fact":"MDR reports over 200 artists total; recommends 7 concert highlights"}
    24: {"ts":"2026-05-19T18:19Z","topic":"WGT","source":"photosindarkness.com (brave-search)","fact":"Front 242 confirmed for WGT 2026; known as founders of Electronic Body Music (EBM)"}

    (End of file - total 24 lines)
    </content>

[tool] edit (pending)
  input: {}

[tool] ../../state/persistent-chat/knowledge-base.jsonl (completed)
  kind: edit
  input: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  files: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  output:
    Edit applied successfully.

    Edit applied successfully.

    diff /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl (+11 lines)

[thinking] Good. Let me verify the entries were added correctly.

[tool] read (pending)
  input: {}

[tool] ../../state/persistent-chat/knowledge-base.jsonl (completed)
  kind: read
  input: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  files: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  output:
    <path>/home/matrix-lite/state/persistent-chat/knowledge-base.jsonl</path>
    <type>file</type>
    <content>
    23: {"ts":"2026-05-19T18:19Z","topic":"WGT","source":"MDR.de (brave-search)","fact":"MDR reports over 200 artists total; recommends 7 concert highlights"}
    24: {"ts":"2026-05-19T18:19Z","topic":"WGT","source":"photosindarkness.com (brave-search)","fact":"Front 242 confirmed for WGT 2026; known as founders of Electronic Body Music (EBM)"}
    25: {"ts":"2026-05-19T18:37Z","topic":"videoctl","source":"pyproject.toml (read)","fact":"Python package 'openclaw-videoctl' v0.1.0; local video ingestion, search, and probing for OpenClaw/Matrix-lite workflows"}
    26: {"ts":"2026-05-19T18:37Z","topic":"videoctl","source":"pyproject.toml (read)","fact":"Dependencies: faster-whisper, playwright, PyYAML, streamlink, yt-dlp; Python >=3.10"}
    27: {"ts":"2026-05-19T18:37Z","topic":"videoctl","source":"docs/manual.md (read)","fact":"CLI commands: add-url (ingest one video), sync-channels (sync YouTube/Twitch channels), find (FTS search), show (video metadata), transcript (show transcript excerpts), probe-url (browser probe for media URLs)"}
    28: {"ts":"2026-05-19T18:37Z","topic":"videoctl","source":"docs/manual.md (read)","fact":"Ingest policies: transcript_only (default), audio_archive, video_archive"}
    29: {"ts":"2026-05-19T18:37Z","topic":"videoctl","source":"docs/manual.md (read)","fact":"Library artifacts per video: metadata.json, source.url, description.txt, raw/info.json, raw/subtitle.*, media/archive.*, transcript/transcript.txt, transcript/chunks.json, summaries/brief.md, summaries/detailed.md"}
    30: {"ts":"2026-05-19T18:37Z","topic":"videoctl","source":"docs/manual.md (read)","fact":"Library layout: ~/.local/share/videoctl/library/<extractor>/<video_id>/; SQLite at ~/.local/state/videoctl/videoctl.db"}
    31: {"ts":"2026-05-19T18:37Z","topic":"videoctl","source":"config/config.example.yaml / docs/manual.md","fact":"Config paths: ~/.config/videoctl/config.yaml + channels.yaml; limits ma...
35 entries total now — 24 WGT + 11 videoctl. Here's the videoctl knowledge pack summary:

<matrix_lite_final>Done! 11 entries added to the knowledge base covering:

- **What it is**: Python package for local video ingestion, search & probing
- **Dependencies**: faster-whisper, playwright, yt-dlp, streamlink
- **CLI surface**: `add-url`, `sync-channels`, `find`, `show`, `transcript`, `probe-url`
- **Policies**: transcript_only (default), audio_archive, video_archive
- **Storage**: library under `~/.local/share/videoctl/library/<extractor>/<id>/`, SQLite FTS for search
- **Architecture**: 10 modules (cli, config, db, models, probe, service, subtitles, summarize, transcribe, utils) + 3 bin wrappers
- **Service**: systemd timer for daily channel sync with Telegram notifications
- **Security**: public/non-DRM only, dedicated video-worker user, ProtectSystem=full

Now when you say "run videoctl add-url..." I'll know exactly what it does without needing to explore first. Same approach can be done for automeat and sfq-factory when you're ready. 🙂
</matrix_lite_final>

[done] end_turn

There's still time before, besides I'm doing most things from my phone now. That's why I said I'm not going anywhere.

Openclaw-tools is the legacy openclaw patches and scripts from before matrix-lite. You can ignore that repo, just look at matrix-lite instead (later)

Let's look at videctl first, as it's the lightest, easiest, most immediately practical and you should have enough permissions to read at least.


So the vector search infrastructure exists but it's running in degraded mode:

- **Repo search index**: sfq_factory is fully indexed — 14,964 chunks across code, docs, tests, config
- **Embedding backend**: Defaults to `"local"` (SentenceTransformer), but that failed to load — fell back to `local_hashing/token-hash-384` which is a basic hash-based embedding, not real semantic search
- **Coverage**: Only 3,148 out of 14,964 chunks have embeddings (21%) — the hash embeddings were generated May 16 and haven't been refreshed
- **Available providers** in order of priority: Gemini API → SentenceTransformer (local model) → HashingEmbedding (fallback)

The two higher-quality providers (Gemini embedding API and local SentenceTransformer) aren't working — likely missing API key or the `sentence-transformers` package. The hashing fallback still allows lexical + hash hybrid search, but it's not true semantic vector search.

Want me to look into getting SentenceTransformer working, or is the current setup good enough for now?

ensure skipped; preserving open backend session

Output:
 "hybrid" which suggests vector/embedding search is available at that level. But the indexing might be separate from sfq-factory.

           Let me check if there's embedding infrastructure in the workspace or app.

[tool] persistent_chat_repo_index_status (pending)
  input: {}

[tool] grep (pending)
  input: {}

[tool] embed|vector|semantic.*search (completed)
  kind: search
  input: /home/matrix-lite/app/scripts
  files: /home/matrix-lite/app/scripts
  output:
    Found 124 matches (showing first 100)
    /home/matrix-lite/app/scripts/matrix-lite-status-mcp.py:
      Line 1208:         "embeddedFallback": "disabled",

    /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-repo-mcp.py:
      Line 224:     "PERSISTENT_CHAT_GEMINI_EMBEDDING_MODEL", "gemini-embedding-001"

      Line 625:         CREATE TABLE IF NOT EXISTS repo_embeddings (

      Line 630:             vector_json TEXT NOT NULL,

      Line 721:                 "DELETE FROM repo_embeddings WHERE doc_id = ? AND content_hash != ?",

      Line 731:                     "DELETE FROM repo_embeddings WHERE doc_id = ?", (row["doc_id"],)

      Line 791:     def embed(self, text: str) -> list[float] | None:

      Line 804:     def embed(self, text: str) -> list[float] | None:

      Line 808:         url = f"https://generativelanguage.googleapis.com/v1beta/models/{self.model}:embedContent?key={self.api_key}"

      Line 824:             values = data.get("embedding", {}).get("values")

      Line 827:             self.error = "missing embedding values"

      Line 841:     def embed(self, text: str) -> list[float] | None:

    ... (180 more lines)

[tool] persistent_chat_repo_index_status (completed)
  kind: other
  input: {}
  output:
    {
      "ok": true,
      "index_db": "/home/matrix-lite/state/persistent-chat/repo-search-index.sqlite3",
      "registry": "/home/matrix-lite/state/persistent-chat/repo-index-sources.json",
      "last_refresh": "2026-05-16T19:28:36.692028+00:00",
      "totals": {
        "chunks": 14964,
        "embeddings": 3148
      },
      "repos": [
        {
          "name": "sfq_factory",
          "chunks": 14964,
          "updated_unix": 1778959715.2460093,
          "updated_display": "2026-05-16T21:28:35.246009+02:00"
        }
      ],
      "kinds": [
        {
          "kind": "code",
          "count": 9030
        },
        {
          "kind": "docs",
          "count": 2461
        },
        {
          "kind": "test",
    ... (243 more lines)

[thinking] The vector/embedding system is part of the repo-search infrastructure:

           1. **sfq_factory** is indexed in the repo-search with 14,964 chunks and 3,148 embeddings
           2. The embedding backend uses `token-hash-384` (local hashing-based embeddings, not a real embedding model)
           3. The last refresh was May 16 — 3 days ago

           Looking at the `matrix-lite-persistent-chat-repo-mcp.py` script, it supports multiple embedding providers:
           - Google Gemini embedding API (gemini-embedding-001)
           - SentenceTransformer (local model)
           - TokenHashProvider (simple hash-based, which is what's currently active)
           - A disabled fallback

           The current embeddings are from `local_hashing/token-hash-384` — that's a simple hash-based embedding (not a real semantic embedding model). The more advanced providers (Gemini, SentenceTransformer) may require configuration or API keys that aren't set up.

           Also, the persistent-chat memory...
The vector search infrastructure is in place! Key findings:

[tool] bash (pending)
  input: {}

[tool] bash (failed)
  kind: execute
  input: grep -n "TokenHashProvider\|SentenceTransformerProvider\|GeminiProvider\|def _embedding_provider" /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-repo-mcp.py | head -20
  output:
    {
      "error": "The user has specified a rule which prevents you from using this specific tool call. {\"permission\":\"bash\",\"pattern\":\"grep -n \\\"TokenHashProvider\\\\|SentenceTransformerProvider\\\\|GeminiProvider\\\\|def _embedding_provider\\\" /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-repo-mcp.py\",\"matched\":{\"permission\":\"bash\",\"pattern\":\"*|*\",\"action\":\"deny\"},\"omitted_relevant_rule_count\":2497,\"note\":\"Detailed permission allowlists are omitted to keep denial errors compact; use permission preflight/report tooling for diagnostics.\"}"
    }

    The user has specified a rule which prevents you from using this specific tool call. {"permission":"bash","pattern":"grep -n \"TokenHashProvider\\|SentenceTransformerProvider\\|GeminiProvider\\|def _embedding_provider\" /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-repo-mcp.py","matched":{"permission":"bash","pattern":"*|*","action":"deny"},"omitted_relevant_rule_count":2497,"note":"Detailed permission allowlists are omitted to keep denial errors compact; use permission preflight/report tooling for diagnostics."}

[thinking] Can't use `grep` with quotes that contain shell operators. Let me use the grep tool instead.

[tool] grep (pending)
  input: {}

[tool] TokenHashProvider|SentenceTransformer|GeminiProvider|_embedding_provider (completed)
  kind: search
  input: /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-repo-mcp.py
  files: /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-repo-mcp.py
  output:
    Found 12 matches
    /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-repo-mcp.py:
      Line 833: class SentenceTransformerProvider(EmbeddingProvider):

      Line 843:             if SentenceTransformerProvider._model is None:

      Line 844:                 from sentence_transformers import SentenceTransformer  # type: ignore

      Line 846:                 SentenceTransformerProvider._model = SentenceTransformer(self.model)

      Line 847:             vector = SentenceTransformerProvider._model.encode(

      Line 875: def _embedding_provider() -> tuple[EmbeddingProvider | None, list[str]]:

      Line 881:             SentenceTransformerProvider(),

      Line 885:         candidates = [SentenceTransformerProvider(), HashingEmbeddingProvider()]

      Line 893:             SentenceTransformerProvider(),

      Line 1263:         provider, errors = _embedding_provider()

      Line 1284:     provider, errors = _embedding_provider()

      Line 1338:     provider, errors = _embedding_provider()

[tool] read (pending)
  input: {}

[tool] ../../app/scripts/matrix-lite-persistent-chat-repo-mcp.py (completed)
  kind: read
  input: /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-repo-mcp.py
  files: /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-repo-mcp.py
  output:
    <path>/home/matrix-lite/app/scripts/matrix-lite-persistent-chat-repo-mcp.py</path>
    <type>file</type>
    <content>
    875: def _embedding_provider() -> tuple[EmbeddingProvider | None, list[str]]:
    876:     errors: list[str] = []
    877:     requested = EMBEDDING_BACKEND.lower()
    878:     if requested == "gemini":
    879:         candidates: list[EmbeddingProvider] = [
    880:             GeminiEmbeddingProvider(),
    881:             SentenceTransformerProvider(),
    882:             HashingEmbeddingProvider(),
    883:         ]
    884:     elif requested in {"local", "sentence_transformer"}:
    885:         candidates = [SentenceTransformerProvider(), HashingEmbeddingProvider()]
    886:     elif requested == "hashing":
    887:         candidates = [HashingEmbeddingProvider()]
    888:     elif requested in {"off", "none", "lexical"}:
    889:         return None, ["embedding backend disabled"]
    890:     else:
    891:         candidates = [
    892:             GeminiEmbeddingProvider(),
    893:             SentenceTransformerProvider(),
    894:             HashingEmbeddingProvider(),
    895:         ]
    896:     for provider in candidates:
    897:         if provider.embed("repo semantic code search probe"):
    898:             return provider, errors
    899:         if provider.error:
    ... (8 more lines)

[thinking] Let me check what the EMBEDDING_BACKEND config is currently set to.

[tool] grep (pending)
  input: {}

[tool] EMBEDDING_BACKEND|PERSISTENT_CHAT_EMBEDDING (completed)
  kind: search
  input: /home/matrix-lite/app/scripts
  files: /home/matrix-lite/app/scripts
  output:
    Found 5 matches
    /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-repo-mcp.py:
      Line 222: EMBEDDING_BACKEND = os.environ.get("PERSISTENT_CHAT_EMBEDDING_BACKEND", "local")

      Line 877:     requested = EMBEDDING_BACKEND.lower()

    /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-memory-mcp.py:
      Line 201: EMBEDDING_BACKEND = os.environ.get("PERSISTENT_CHAT_EMBEDDING_BACKEND", "local")

      Line 977:     requested = EMBEDDING_BACKEND.lower()

      Line 1517:         "embedding_backend_default": EMBEDDING_BACKEND,

[thinking] The default embedding backend is "local" which maps to sentence_transformer. But looking at the current index status, it shows embbackend is "local_hashing" with model "token-hash-384". That means the SentenceTransformer provider failed to initialize (likely missing the sentence-transformers library), and it fell through to HashingEmbeddingProvider.

           Let me check if the env var is set to overrid the default.

[tool] grep (pending)
  input: {}

[tool] grep (pending)
  input: {}

[tool] PERSISTENT_CHAT_EMBEDDING_BACKEND (completed)
  kind: search
  input: /home/matrix-lite
  files: /home/matrix-lite
  output:
    No files found

[tool] PERSISTENT_CHAT_EMBEDDING_BACKEND (completed)
  kind: search
  input: /home/matrix-lite/app
  files: /home/matrix-lite/app
  output:
    Found 2 matches
    /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-repo-mcp.py:
      Line 222: EMBEDDING_BACKEND = os.environ.get("PERSISTENT_CHAT_EMBEDDING_BACKEND", "local")

    /home/matrix-lite/app/scripts/matrix-lite-persistent-chat-memory-mcp.py:
      Line 201: EMBEDDING_BACKEND = os.environ.get("PERSISTENT_CHAT_EMBEDDING_BACKEND", "local")

[thinking] The env var isn't set anywhere explicitly. The default is "local" (sentence_transformer). But the repo index shows the active embeddings are from "local_hashing/token-hash-384". This means the SentenceTransformer provider failed to load (likely `sentence_transformers` package not installed), and it fell back to HashingEmbeddingProvider.

           The index has 3,148 embeddings for 14,964 chunks — so only about 21% of chunks have embeddings. The current provider is the simple hash-based one, not a real semantic embedding model.

           Let me summarize this for the user.
<matrix_lite_final>So the vector search infrastructure exists but it's running in degraded mode:

- **Repo search index**: sfq_factory is fully indexed — 14,964 chunks across code, docs, tests, config
- **Embedding backend**: Defaults to `"local"` (SentenceTransformer), but that failed to load — fell back to `local_hashing/token-hash-384` which is a basic hash-based embedding, not real semantic search
- **Coverage**: Only 3,148 out of 14,964 chunks have embeddings (21%) — the hash embeddings were generated May 16 and haven't been refreshed
- **Available providers** in order of priority: Gemini API → SentenceTransformer (local model) → HashingEmbedding (fallback)

The two higher-quality providers (Gemini embedding API and local SentenceTransformer) aren't working — likely missing API key or the `sentence-transformers` package. The hashing fallback still allows lexical + hash hybrid search, but it's not true semantic vector search.

Want me to look into getting SentenceTransformer working, or is the current setup good enough for now?
</matrix_lite_final>

[done] end_turn

Actually you are supposed to have vector search in sfq-factory and also over your memory, not sure it's still working


Haha, fair point about Gemini — we're already sending plenty of data through various APIs. If you're fine with it, I can switch the embedding backend to Gemini which would give real semantic search over memory and repo index. The model is free-tier eligible and the key is already available.

Now on to your question about private AI APIs — here's what I found:

**Venice** — you mentioned it. Privacy-first with zero data retention at the architecture level. Models range from cheap DeepSeek ($0.33/$0.48 per 1M tokens, *Private* tier) to pricey Claude Opus ($6/$30, *Anonymized*). Four privacy tiers: Anonymized → Private → TEE → E2EE. Accepts crypto (VVV staking) and USD. Web search/scraping tools included. Embedding models too. Solid but spendy for the good stuff.

**OpenRouter** — big with enterprise. ZDR enforcement per model group (Anthropic, OpenAI, Google, other). SOC-2, GDPR, EU region locking. BYOK support so your own keys can be used. They don't store prompts unless you opt in. Mostly a routing layer to existing providers rather than a privacy-first infra like Venice.

**Other options:**

| Service | Pricing | Privacy | Region |
|---------|---------|---------|--------|
| **Answira** | Pay-per-token | No prompt storage, no training | EU (Czechia) |
| **AKI.IO** | Pay-per-token | GDPR, no data leaves EU, open models | EU (Germany) |
| **Privatemode** | Usage-based | E2EE + confidential computing, BSI C5 certified | Works with OpenCode |
| **KeyTokenator** | Flat monthly | Closed environment, no big AI sharing | ? |
| **CometAPI** | 20-40% below vendor | ZDR, encrypted transit | US |
| **Flat Rate AI** | $15/month flat | Private server, zero logs | US |
| **Egida.AI** | Tiered | Zero logs, no training, Ollama | Independent infra |

**Privatemode** is interesting — it does E2EE with confidential computing, works as a drop-in for OpenAI/Anthropic, and is explicitly compatible with OpenCode. Might be the best fit if you want real privacy guarantees without self-hosting. But it's not cheap.

Want me to dig deeper into any of these, or should I go ahead and flip the Gemini embedding switch for the vector search?

Output:
    162:       "/home/matrix-lite/state/persistent-chat/semantic-memory.json": "allow",
    163:       "/home/matrix-lite/state/persistent-chat/continuity-pinned-notes.txt": "allow",
    164:       "/home/matrix-lite/state/persistent-chat/operational-synthesis/notices.jsonl": "allow",
    165:       "/home/matrix-lite/state/persistent-chat/operational-synthesis/findings.jsonl": "allow",
    166:       "/home/matrix-lite/state/persistent-chat/operational-synthesis/hypotheses.jsonl": "allow",
    167:       "/home/matrix-lite/state/persistent-chat/operational-synthesis/actions.jsonl": "allow",
    168:       "/home/matrix-lite/state/persistent-chat/session-packet.md": "deny",
    169:       "/home/matrix-lite/state/persistent-chat/session-packet.json": "deny",
    170:       "/home/matrix-lite/state/persistent-chat/restore-packet.md": "deny",
    171:       "/home/matrix-lite/state/persistent-chat/restore-packet.json": "deny",
    172:       "/home/matrix-lite/state/persistent-chat/session-start-packet.md": "deny",
    173:       "/home/matrix-lite/state/persistent-chat/session-start-packet.json": "deny",
    174:       "/home/matrix-lite/state/persistent-chat/turn-packet.md": "deny",
    175:       "/home/matrix-lite/state/persistent-chat/turn-packet.json": "deny",
    176:       "/home/matrix-lite/state/persistent-chat/memory-heartbeat.json": "deny",
    177:       "/home/matrix-lite/state/persistent-chat/memory-context-manifest.json": "deny",
    178:       "/home/matrix-lite/state/persistent-chat/model-heartbeat.json": "deny",
    179:       "/home/matrix-lite/state/persistent-chat/context-guard.json": "deny",
    180:       "/home/matrix-lite/state/persistent-chat/reload-state.json": "deny",
    181:       "/home/matrix-lite/state/persistent-chat/pending-reload.json...

[tool] read (pending)
  input: {}

[tool] ../../xdg/persistent-chat/opencode/opencode.json (completed)
  kind: read
  input: /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
  output:
    <path>/home/matrix-lite/xdg/persistent-chat/opencode/opencode.json</path>
    <type>file</type>
    <content>
    230:       "git -c safe.directory=/home/gleb/sfq-factory ls-files *": "allow",
    231:       "git -C /home/matrix-lite/workspace/repos/automeat status *": "allow",
    232:       "git -C /home/matrix-lite/workspace/repos/automeat diff *": "allow",
    233:       "git -C /home/matrix-lite/workspace/repos/automeat log *": "allow",
    234:       "git -C /home/matrix-lite/workspace/repos/automeat show *": "allow",
    235:       "git -C /home/matrix-lite/workspace/repos/automeat rev-parse *": "allow",
    236:       "git -C /home/matrix-lite/workspace/repos/automeat ls-files *": "allow",
    237:       "git -C /home/matrix-lite/workspace/repos/sfq-factory status *": "allow",
    238:       "git -C /home/matrix-lite/workspace/repos/sfq-factory diff *": "allow",
    239:       "git -C /home/matrix-lite/workspace/repos/sfq-factory log *": "allow",
    240:       "git -C /home/matrix-lite/workspace/repos/sfq-factory show *": "allow",
    241:       "git -C /home/matrix-lite/workspace/repos/sfq-factory rev-parse *": "allow",
    242:       "git -C /home/matrix-lite/workspace/repos/sfq-factory ls-files *": "allow",
    243:       "npx --yes acpx --cwd /home/matrix-lite/workspace opencode sessions ensure --name *": "allow",
    244:       "/home/matrix-lite/app/scripts/persistent-chat-memory": "allow",
    245:       "rg * /home/matrix-lite/workspace/state/sessions*": "allow",
    246:       "rg * /home/matrix-lite/.acpx/sessions*": "allow",
    247:       "rg * /home/matrix-lite/state/opencode-log*": "allow",
    248:       "rg * /home/matrix-lite/workspace/memory/session-archive*": "allow",
    249:       "fdfind * /home/matrix-lite/workspace/state/sessions*": "allow",
    250:       "fdfind * /home/matrix-lite/.acpx/sessions*": "allow",
    251:       "fdfind * /home/matrix-lite/state/opencode-log*": "allow",
    252:       "fdfind * /home/matrix-lite/workspace/memory/session-archive*": "allow",
    253:       "rg *": "allow",
    254:       "fdfind *": "allow",
    ...

[tool] grep (pending)
  input: {}

[tool] "\\*" (completed)
  kind: search
  input: /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
  output:
    Found 17 matches
    /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json:
      Line 295:       "\"/home/matrix-lite/bin/matrix-lite-sudo-run\" *": "allow",

      Line 618:       "\"/home/matrix-lite/bin/matrix-lite-sudo-run\"": "allow",

      Line 2059:           "\"/home/matrix-lite/bin/matrix-lite-sudo-run\" *": "allow",

      Line 3694:           "\"/home/matrix-lite/bin/matrix-lite-sudo-run\" *": "allow",

      Line 5329:           "\"/home/matrix-lite/bin/matrix-lite-sudo-run\" *": "allow",

      Line 6899:           "\"/home/matrix-lite/bin/matrix-lite-sudo-run\" *": "allow",

      Line 8452:           "\"/home/matrix-lite/bin/matrix-lite-sudo-run\" *": "allow",

      Line 10005:           "\"/home/matrix-lite/bin/matrix-lite-sudo-run\" *": "allow",

      Line 11623:           "\"/home/matrix-lite/bin/matrix-lite-sudo-run\" *": "allow",

      Line 11647:           "\"/home/matrix-lite/app/scripts/persistent-chat-memory\" semantic-candidates *": "allow",

      Line 13251:           "\"/home/matrix-lite/bin/matrix-lite-sudo-run\" *": "allow",

      Line 14849:           "\"/home/matrix-lite/bin/matrix-lite-sudo-run\" *": "allow",

      Line 16447:           "\"/home/matrix-lite/bin/matrix-lite-sudo-run\" *": "allow",

    ... (7 more lines)

[tool] grep (pending)
  input: {}

[tool] "\\*": "deny" (completed)
  kind: search
  input: /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
  output:
    No files found

[tool] read (pending)
  input: {}

[tool] ../../xdg/persistent-chat/opencode/opencode.json (completed)
  kind: read
  input: /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
  output:
    <path>/home/matrix-lite/xdg/persistent-chat/opencode/opencode.json</path>
    <type>file</type>
    <content>
    1480:       "/home/matrix-lite/app/scripts/matrix-lite-safe-git status --repo *": "allow",
    1481:       "/home/matrix-lite/app/scripts/matrix-lite-safe-git add --repo *": "allow",
    1482:       "/home/matrix-lite/app/scripts/matrix-lite-safe-git commit --repo * -m *": "allow",
    1483:       "uv sync --directory /home/matrix-lite/workspace/repos/videoctl": "allow",
    1484:       "uv sync --project /home/matrix-lite/workspace/repos/videoctl": "allow",
    1485:       "uv lock --directory /home/matrix-lite/workspace/repos/videoctl": "allow",
    1486:       "uv lock --project /home/matrix-lite/workspace/repos/videoctl": "allow",
    1487:       "python3 -m pip install -e /home/matrix-lite/workspace/repos/videoctl": "allow",
    1488:       "python3 -m pip install -r /home/matrix-lite/workspace/repos/videoctl/requirements*.txt": "allow",
    1489:       "python -m pip install -e /home/matrix-lite/workspace/repos/videoctl": "allow",
    1490:       "/home/matrix-lite/workspace/repos/videoctl/.venv/bin/pytest *": "allow",
    1491:       "git -C /home/gleb/openclaw/videoctl *": "deny",
    1492:       "git -c safe.directory=/home/gleb/openclaw/videoctl -C /home/gleb/openclaw/videoctl *": "deny",
    1493:       "/home/matrix-lite/bin/matrix-lite-safe-git status --repo *": "allow",
    1494:       "/home/matrix-lite/bin/matrix-lite-safe-git add --repo *": "allow",
    1495:       "/home/matrix-lite/bin/matrix-lite-safe-git commit --repo * -m *": "allow",
    1496:       "uv run --directory /home/matrix-lite/workspace/repos/videoctl pytest": "allow",
    1497:       "uv run --directory /home/matrix-lite/workspace/repos/videoctl pytest *": "allow",
    1498:       "uv run --directory /home/matrix-lite/workspace/repos/videoctl python -m pytest": "allow",
    1499:       "uv run --directory /home/matrix-lite/workspace/repos/videoctl python -m pytest *": "allow",
    1500:       "uv run --directory /home/matrix-lite/workspace/repos/videoctl pyt...

[tool] grep (pending)
  input: {}

[tool] "/home/gleb/videoctl/bin/videoctl" (completed)
  kind: search
  input: /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
  output:
    Found 15 matches
    /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json:
      Line 1537:       "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 3133:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 4768:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 6403:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 7981:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 9534:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 11087:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 12702:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 14342:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 15940:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 17538:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 19136:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 20734:           "/home/gleb/videoctl/bin/videoctl": "allow",

    ... (3 more lines)

[tool] grep (pending)
  input: {}

[tool] videoctl/bin/videoctl (completed)
  kind: search
  input: /home/matrix-lite/xdg/persistent-chat/opencode
  output:
    Found 68 matches
    /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json:
      Line 391:       "/home/gleb/openclaw/videoctl/bin/videoctl *": "deny",

      Line 672:       "/home/gleb/openclaw/videoctl/bin/videoctl": "deny",

      Line 1537:       "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 1538:       "/home/gleb/videoctl/bin/videoctl *": "allow",

      Line 2322:           "/home/gleb/openclaw/videoctl/bin/videoctl": "deny",

      Line 2323:           "/home/gleb/openclaw/videoctl/bin/videoctl *": "deny",

      Line 3133:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 3134:           "/home/gleb/videoctl/bin/videoctl *": "allow",

      Line 3957:           "/home/gleb/openclaw/videoctl/bin/videoctl": "deny",

      Line 3958:           "/home/gleb/openclaw/videoctl/bin/videoctl *": "deny",

      Line 4768:           "/home/gleb/videoctl/bin/videoctl": "allow",

      Line 4769:           "/home/gleb/videoctl/bin/videoctl *": "allow",

      Line 5592:           "/home/gleb/openclaw/videoctl/bin/videoctl": "deny",

    ... (120 more lines)

[tool] bash (pending)
  input: {}

[tool] Search for videoctl in permissions (completed)
  kind: execute
  input: /home/matrix-lite/bin/matrix-lite-safe-git status --repo /home/matrix-lite/app
  output:
    {
      "ok": true,
      "returncode": 0,
      "cmd": [
        "git",
        "-c",
        "safe.directory=/home/matrix-lite/app",
        "-C",
        "/home/matrix-lite/app",
        "status",
        "--short"
      ],
      "stdout": " M agent_memory/findings.md\n M config/opencode/persistent-chat.opencode.json\n M matrix_lite/daemon.py\n M scripts/matrix-lite-safe-git\n M scripts/matrix-lite-status-mcp.py\n M tests/test_continuity_scripts.py\n M tests/test_daemon_stability.py\n?? patches/\n",
      "stderr": ""
    }

[tool] read (pending)
  input: {}

[tool] ../../xdg/persistent-chat/opencode/opencode.json (completed)
  kind: read
  input: /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
  output:
    <path>/home/matrix-lite/xdg/persistent-chat/opencode/opencode.json</path>
    <type>file</type>
    <content>
    1: {
    2:   "$schema": "https://opencode.ai/config.json",
    3:   "mcp": {
    4:     "duckduckgo": {
    5:       "type": "local",

    (Showing lines 1-5 of 24340. Use offset=6 to continue.)
    </content>

[done] end_turn

The thing about Gemini is that the key is available and the embedding model is free, but the agents were very concerned about data privacy. As if Google would learn something from thousands of generated reports. As if we're not leaking somewhat private data left and right here.

By the way, can you search for a private-ish API? There's Venice with some promises but it's expensive. Openrouter is big with corporate clients, maybe they promise something? What else is there?


Great research! Here's the budget-friendly picture:

**Free tier: OpenRouter's Owl Alpha** — completely free, 1.05M context, native tool calling, compatible with OpenClaw/OpenCode. 295B MoE model (21B activated). The catch: prompts may be logged by the provider for model improvement. But for testing and prototyping, it's a no-brainer. OpenRouter also has a `openrouter/free` router that randomly selects from free models including Hunter Alpha, DeepSeek V4 Flash, and several cloaked models.

**Cheapest paid options (per 1M tokens):**

| Model | Input | Output | Context | Via |
|-------|-------|--------|---------|-----|
| GPT-5 Nano | $0.05 | $0.40 | 128K | OpenAI |
| Mistral Small 3.2 | $0.06 | $0.18 | 128K | Mistral |
| **qwen3.5-flash** | **$0.065** | **$0.26** | **1M** | **OpenRouter** |
| Gemini Flash-Lite | $0.10 | $0.40 | 1M | Google |
| DeepSeek V4 Flash | $0.17 | $0.30 | 1M | DeepSeek |
| DeepSeek V3.2 | $0.28 | $0.42 | 128K | DeepSeek |

**Best value plays:**
- **qwen3.5-flash via OpenRouter** ($0.065/$0.26, 1M context) — this is the standout. 35% cheaper than direct Alibaba pricing, huge context, fast.
- **DeepSeek V4 Flash** — free on OpenRouter, or $0.17/$0.30 direct. MoE with 1M context. Best quality-to-cost ratio in the market.
- **Owl Alpha** — completely free. If it works well for agentic stuff, that's literally $0.

**Privacy notes:**
- OpenRouter has ZDR (Zero Data Retention) enforcement per model group — you can restrict to providers that don't store data.
- inference.net / Together AI / Fireworks host open-weight models at rock-bottom prices and don't train on your data.
- The free models on OpenRouter do log prompts (it's the tradeoff for free inference).

Want me to set up a quick test with Owl Alpha or qwen3.5-flash to see how they handle our prompts?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user wants me to dig deeper into cheap/affordable AI API options. They mentioned OpenRouter has "owl" model with 1M context that's free. Let me search for more details on this and also look at the cheapest options across the services I found.

           Let me search for:
           1. OpenRouter's "owl" model (likely an OWL model? or maybe "OWL" is a codename?)
           2. Cheapest models across OpenRouter, Venice, and other providers
           3. Other budget-friendly private AI APIs

[tool] websearch (pending)
  input: {}

[tool] websearch (pending)
  input: {}

[tool] Web search: OpenRouter OWL model free 1M context 2026 (completed)
  kind: other
  input: OpenRouter OWL model free 1M context 2026
  output:
    Title: Owl Alpha - API Pricing & Providers | OpenRouter
    URL: https://openrouter.ai/openrouter/owl-alpha
    Published: N/A
    Author: N/A
    Highlights:
    ### openrouter/owl-alpha
    [...]
    Owl Alpha is a high-performance foundation model designed for agentic workloads. Natively supports tool use, and long-context tasks, with strong performance in code generation, automated workflows, and complex instruction execution. Compatible with Claude Code, OpenClaw, and other mainstream productivity tools.
    [...]
    1.14T
    [...]
    Apr 28, 2026
    [...]
    1.05M
    [...]
    Free Models RouterThe simplest way to get free inference. openrouter/free is a router that selects free models at random from the models available on OpenRouter. The router smartly filters for models that support features needed for your request such as image understanding, tool calling, structured outputs and more.

    ---

    Title: Free Models Router – API Quickstart | OpenRouter
    URL: https://openrouter.ai/openrouter/free/api
    Published: N/A
    Author: N/A
    Highlights:
    The simplest way to get free inference. openrouter/free is a router that selects free models at random from the models available on OpenRouter. The router smartly filters for models that support features needed for your request such as image understanding, tool calling, structured outputs and more.
    [...]
    ### Models in this router25All free-variant models currently available on OpenRouter.
    [...]
    ... (233 more lines)

[tool] Web search: cheapest AI API providers 2026 competitive pricing open source models (completed)
  kind: other
  input: cheapest AI API providers 2026 competitive pricing open source models
  output:
    Title: Cheapest LLM APIs (Updated 2026-05-12) · AI Model Intelligence
    URL: https://modelcompare.dev/best/best-cheap-llm-api
    Published: N/A
    Author: N/A
    Highlights:
    Cheapest LLM APIs (Updated 2026-05-12) · AI Model Intelligence
    [...]
    - Sorted by sum of input + output price per 1M tokens.
    - Models with $0 placeholder pricing (Github Copilot, free-tier rebroadcasts) are excluded — Unknown ≠ free.
    - Only text models with valid context windows are included.
    [...]
    ## Top 10 picks
    [...]
    - Context: 128K
    - Providers: 1
    [...]
    - Context: 32K
    - Providers: 3
    - Tool calling
    - Structured output
    - Open weights
    [...]
    - Context: 512
    - Providers: 1
    - Open weights
    [...]
    - Context: 256
    [...]
    ... (495 more lines)

[thinking] Excellent research! Let me compile this into a useful comparison for the user. Key findings:

           **Owl Alpha on OpenRouter** - completely free, 1.05M context, native tool calling, designed for agentic workloads (Claude Code, OpenClaw compatible). It's an anonymous "stealth" model - 295B MoE, 21B activated. The catch: prompts may be logged and used to improve the model. But completely free.

           **OpenRouter free tier** - multiple free models including Owl Alpha, Hunter Alpha, Sherlock Dash Alpha, Sonoma Dusk/Sky Alpha, DeepSeek V4 Flash (free tier). All $0/M tokens.

           **Cheapest paid options:**
           1. **Qwen3.5-flash** via OpenRouter: $0.065/$0.26 per 1M tokens, 1M context - absurdly cheap
           2. **GPT-5 Nano** (OpenAI): $0.05/$0.40, 128K context
           3. **Gemini Flash-Lite** (Google): $0.10/$0.40, 1M context
           4. **DeepSeek V4 Flash** (DeepSeek): $0.17/$0.30, 1M context (also free tier on OpenRouter)
           5. **...
<matrix_lite_final>Great research! Here's the budget-friendly picture:

**Free tier: OpenRouter's Owl Alpha** — completely free, 1.05M context, native tool calling, compatible with OpenClaw/OpenCode. 295B MoE model (21B activated). The catch: prompts may be logged by the provider for model improvement. But for testing and prototyping, it's a no-brainer. OpenRouter also has a `openrouter/free` router that randomly selects from free models including Hunter Alpha, DeepSeek V4 Flash, and several cloaked models.

**Cheapest paid options (per 1M tokens):**

| Model | Input | Output | Context | Via |
|-------|-------|--------|---------|-----|
| GPT-5 Nano | $0.05 | $0.40 | 128K | OpenAI |
| Mistral Small 3.2 | $0.06 | $0.18 | 128K | Mistral |
| **qwen3.5-flash** | **$0.065** | **$0.26** | **1M** | **OpenRouter** |
| Gemini Flash-Lite | $0.10 | $0.40 | 1M | Google |
| DeepSeek V4 Flash | $0.17 | $0.30 | 1M | DeepSeek |
| DeepSeek V3.2 | $0.28 | $0.42 | 128K | DeepSeek |

**Best value plays:**
- **qwen3.5-flash via OpenRouter** ($0.065/$0.26, 1M context) — this is the standout. 35% cheaper than direct Alibaba pricing, huge context, fast.
- **DeepSeek V4 Flash** — free on OpenRouter, or $0.17/$0.30 direct. MoE with 1M context. Best quality-to-cost ratio in the market.
- **Owl Alpha** — completely free. If it works well for agentic stuff, that's literally $0.

**Privacy notes:**
- OpenRouter has ZDR (Zero Data Retention) enforcement per model group — you can restrict to providers that don't store data.
- inference.net / Together AI / Fireworks host open-weight models at rock-bottom prices and don't train on your data.
- The free models on OpenRouter do log prompts (it's the tradeoff for free inference).

Want me to set up a quick test with Owl Alpha or qwen3.5-flash to see how they handle our prompts?
</matrix_lite_final>

[done] end_turn

Yes let's dig deeper, I think we leaned the prompts enough to do a controlled test soon. Focus on the cheaper ones, I can't afford Venice, and I can't afford opus with any of them. But say openrouter has 'owl' which is for some reason free and have 1m context (probably some promo)
Maybe there are others, check what they promise and compare prices.


You're right to be cautious — there's a lot of private context flowing through these sessions. Here's what I'd recommend:

**OpenRouter privacy settings to enable:**
1. **ZDR enforcement** — in your account Privacy Settings, toggle on ZDR for "Non-frontier" at minimum. This restricts routing to providers with zero data retention. You can also do it per-request with `zdr: true`
2. **Opt out of training** — separate toggle for paid/free models, prevents routing to providers that train on your data
3. **Disable prompt logging** — off by default, but verify in your settings that "Private Input & Output Logging" and "OpenRouter Use of Inputs/Outputs" are both disabled
4. **Important caveat**: ZDR through OpenRouter depends on the downstream provider. Calling a ZDR provider directly removes OpenRouter as an extra hop. For the privacy-sensitive stuff, direct is cleaner.

**Providers with ≥200k context + strong privacy:**

| Provider | Model | Context | Price (in/out per 1M) | Privacy |
|----------|-------|---------|----------------------|---------|
| OpenRouter | **DeepSeek V4 Flash** (free) | 1M | $0 | ZDR endpoints available |
| OpenRouter | **qwen3.5-flash** | 1M | $0.065/$0.26 | ZDR via inference.net |
| inference.net | DeepSeek V3.2 | 128K | $0.14/$0.28 | No training, zero retention |
| Together AI | Llama/Mistral | 128K | $0.10-$0.18 | No training on data |
| Privatemode | gpt-oss-120b | 128K | Free tier available | E2EE, BSI C5 certified |

**Self-hosting (best privacy):**
- **Qwen3-Next** — 80B params, 3B active, **262K context**, Apache 2.0 license. Runs on consumer GPUs (~16GB VRAM at 4-bit). This is probably the best realistic self-host option for ≥200k context.
- **gpt-oss-20b** — OpenAI's open model, 21B params, runs in 16GB VRAM via Ollama. Only 32K context though.
- **GLM-5** — 744B MoE (40B active), **200K context**, MIT license. Needs serious hardware (2x Mac Studio or multi-GPU), but the most capable open model at this context length.

**My take:** For testing right now, **DeepSeek V4 Flash (free)** or **qwen3.5-flash via OpenRouter** with ZDR enabled gives you the best balance — cheap, 1M context, and reasonable privacy with the right settings. For the longer term where you're not comfortable with subscriptions anymore, **Qwen3-Next** self-hosted on a local GPU gives you 262K context with zero data leaving your machine. Want me to set up a test run with one of these?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is concerned about privacy - there's too much private information in the conversations. They want:

           1. What to select in OpenRouter to make it privacy-friendly
           2. Other providers to try
           3. At least 200k context
           4. They might eventually move away from subscriptions

           Let me give actionable advice on:

           1. **OpenRouter privacy settings**: ZDR enforcement, opting out of prompt logging, etc.
           2. **Other providers** with strong privacy and ≥200k context:
              - Privatemode (E2EE, works with OpenCode)
              - inference.net / Together AI (open-weight models, no training)
              - Self-hosting options (local GPU)
           3. **Context requirement**: ≥200k

           Let me search for more specific info about OpenRouter's privacy settings and which providers support ≥200k context with strong privacy.

[tool] websearch (pending)
  input: {}

[tool] websearch (pending)
  input: {}

[tool] websearch (pending)
  input: {}

[tool] Web search: OpenRouter privacy settings ZDR zero data retention model routing 2026 (completed)
  kind: other
  input: OpenRouter privacy settings ZDR zero data retention model routing 2026
  output:
    Title: Provider Logging | Provider Data Retention | OpenRouter | Documentation
    URL: https://openrouter.ai/docs/features/privacy-and-logging
    Published: N/A
    Author: N/A
    Highlights:
    Each AI provider on OpenRouter has its own data handling policies for logging and retention. This page explains how to control which providers can access your data.
    [...]
    Each provider on OpenRouter has its own data handling policies. We reflect those policies in structured data on each AI endpoint that we offer.
    [...]
    On your account settings page, you can set whether you would like to allow routing to providers that may train on your data (according to their own policies). There are separate settings for paid and free models.
    [...]
    Wherever possible, OpenRouter works with providers to ensure that prompts will not be trained on, but there are exceptions. If you opt out of training in your account settings, OpenRouter will not route to providers that train. This setting has no bearing on OpenRouter's own policies and what we do with your prompts.
    [...]
    You can restrict individual requests
    to only use providers with a certain data policy.

    This is also available as an account-wide setting in your privacy settings.
    [...]
    ### Data Retention & Logging
    [...]
    Providers also have their own data retention policies, often for compliance reasons. OpenRouter does not have routing rules that change based on data retention policies of providers, but the retention policies as reflected in each provider's terms are shown below. Any user of OpenRouter can ignore providers that don't meet their own data retention requirements.
    [...]
    For enterprise customers, OpenRouter supports EU in-region routing. When enabled for your account, your prompts and completions are processed within the European Union and do not leave the EU. Use the base URL `https://eu.openrouter.ai` for API requests to keep traffic and data within Europe. This feature is only enabled for enterprise customers by request.

    ---

    Title: Enter...

[tool] Web search: Privatemode AI pricing models context window 2026 (completed)
  kind: other
  input: Privatemode AI pricing models context window 2026
  output:
    Title: Pricing
    URL: https://www.privatemode.ai/pricing
    Published: N/A
    Author: N/A
    Highlights:
    6›I! >M!s1ZŸ=1“1Ãg/+ÑÔvè$J çp³AI)¥ô+q#Ä¨‡¢ÂsÛ°¢–>¢dµŠ QÁŒqÍ‡bE¡ÐO¨Ú£J Ïc¯PMa“HTƒøêÈ ‘M‚Šo‡W!:XÌœ¶ƒjó½Ó› Ö˜)x„"ä9&y EÝh!1ê¡˜'HV;¡¸/¸rDòÚ¡„)\9F$÷=¡êtÍãY«1#M()ä¶ ñ³%T#D+É„ŽÔï>è•$°¾¨I€ÌÂ•¡swè`Z&ÄõtÅ œA(âF3WßCÑ:ƒrýDXåÆ‰ý„âB¼0ƒXé¡„ðLÐECÕÂOrôCÉBÙo2T#D+†…ðêŸ>Ù òFóMâPê”6ƒBœŽtpm$C@\ùÉÕD ‰¦7˜)‘qÅKSôâF¥º†%¡s(VÀžcRJ)›PÜÍ'$F=”âƒMÄûžPµðNª8…–oJò¢…ä|f¨&ÝÐ_±†¬ê¬AŸ|û(bŒq‰rÓxùäÔ¢S(€€8D¢{ÄnŠb…Ä$E…!1¥„bB\ùP¼€÷[J¸ñ@b: U 1ÀŒú¡¤úôÝ¡á±r5qOu± O±‚¨›1×4æ1#¬nµ£Ö–9ˆ;íJAV‡$š"<ÜRÁŒqò¡h!!™&¡tM1n,Xnmß¿¸QÁÌÕú % Db•ÂèrMÕò»1BbÔCI!'0cDòP$’ŠvÖM!nÄ6 *¾I`”úÊˆjoß!µLˆßŸk²šŠ¸u‰Å8H¢)z)¤+#E6ÏM±³-$V€ÂKSüâ^’© Aü¦ƒB±2 ËF¢|MÕ< êÄâ´Sòì–£ª™ñ&ÞAtuÔÐ§ÜÆP6„hÄ ¹iÌeejR.îh´QCÍMFdC9áŠ¸KbQ’hŠ^„Ë¶!Žº„b…ì™¤¡¸»l'(qAM‰K¡xÖjY#ªvc‡Äb¼4%/2±3ž›jÎ3ÜœYqè³¹r!"“8§1—+Áh#K‡ƒQFÎŠ?n:HF’I8RH¶O6¤k8êæn:JkŽÉŽ²•‘¼4JöÇÝÄa”6‰¶‘×’7K8éÆÛ(Fqˆ×47¤×ô¬Ž8,wŒíÐ,ÒNQà&Á7àCm¢´nMPÜ7$5‚s G„hcûÆ\.áh!½‚S®©pL8o Á¼µ¹E8îžo:*pM…=Â aÙ„›vD£¹úâÆÛÈ$¡Ø?…“î²9¼–yl¢Ñ\ãîå¦ ã”qÑ/}à›JJ ÙqW§Ááp|ÔÖ¬ ¨K• Bï¨™±‚`$ Fƒ°¾ÙÄºÙ¼U…-êdÂãÀŒI2æ6k¥™0YÇž ˆ‰^Û©v7±®9êè3¨ìjá¾20ÊZàƒ®a´?¢UqÖÌ OUÃ ]þÚë(•¥ÛV’±¶÷ 2¿¿\Ðûñn9Æ‰Öù|ÚxbÚ¬-{Ž0û½àª£U÷>‚¢‹k²ƒì4uIUöñÑOÊéåw.Céï½ŠQÝo“¤¡ÁwÓZ6tæAíÛ ™d~}GèCb1GÒuÆPÑ~é:?&cmŸîëH9úw½º½RãMî§‘Ñ÷®{çÜ&]ç"¨¼<‡K:ï´(c nÖôØ‡Žþ²õÅ>¨Áä{‹%ã.JïË§Ú~Ëùß±7o\dÍ 6@0nôØ¨ÒâêÐaÝ1ù?MIç]¬4FòCf»Å` Â|ÓmÍÛ.U‡VµopÔñb˜rÔyXî½§Ôj6L×áÐÐÃ~öÙ•Ñ½ž|Ôýï+OØ¯µ˜Qƒ¬Œ%!æg.ÊN*Fë–Ct.ä’ÕtßŠ.ý¥âÒçpÚÇõQ Z²gšpˆ‘o+–î|>„?ÕGû©.±–ÁèêhPãcÌƒt±p)Ã‡uëµ]¨œ¼ûêÕ_;º×~aÖ;['î’¢¼V½ßù¹™j§³hq_â¬ÞïÞï 3Ri;‹•÷Q&!bzí gä:åmmæÔ)ÔZ«B±u$½ÅÜË'¿åkØûM0E7LÃ ™+7[K}.È>´±ÆfÆ Er_n·¶ÑæR—Rn¶jÁ®_wÀmkðßíôT ~sYÇ“ìwúý»û§/‹§òébv/À¸!êU»ÜBY¡ëjý-¯«ýýf`Øs§Ál7Î,ÛS‡\uÌ¾°Ñ˜\³ i¢K‡À¤U½¶‘””ô6q!(Ê]e‡GÏÅÏþ»AÉ`ôá~Us7Ã09™ „ä]^Òb½xëûÌ“Á¾êÅGÆ7ªI\bô+ ž’~à0 Jn¢¥rÃ¾Â¬æG„(Ýo´ú»ó¤ Ø—Û]RmÙ“øÝÉêïNÌ™qd0ò#z=ò»Æ#CéŒJáŸ¿ßÿöw«î...

[tool] Web search: open source AI models 200k+ context self host local GPU 2026 (completed)
  kind: other
  input: open source AI models 200k+ context self host local GPU 2026
  output:
    Title: BeeLlama.cpp Guide 2026 — Qwen 3.6 27B, RTX 3090 & 200k Context
    URL: https://yangmao.ai/en/devtools/beellama/
    Published: N/A
    Author: N/A
    Highlights:
    BeeLlama.cpp Guide 2026 — Qwen 3.6 27B, RTX 3090 & 200k Context
    [...]
    # BeeLlama.cpp Local LLM Runtime: Qwen 3.6 27B on RTX 3090 with 200k Context
    [...]
    Local LLM runtime experiment with DFlash, TurboQuant and long-context acceleration
    [...]
    Short answer: BeeLlama.cpp has a free option: Open-source and free, but requires local GPU/VRAM and build environment. If it needs model calls, bring your own API key or compare OpenAI-compatible API options before paying.
    [...]
    BeeLlama.cpp is a local LLM runtime project discovered from Reddit r/LocalLLaMA. Its pitch is DFlash, TurboQuant and long-context inference optimization.The signal claims Qwen 3.6 27B Q5 can run 200k context on RTX 3090 with 2-3x speedup and peak 135 tokens/s.Treat it as a high-potential experimental tool for local LLM enthusiasts, not as a proven production runtime yet.
    [...]
    BeeLlama.cpp itself is open-source and free. The real cost is hardware: local NVIDIA GPU, CUDA setup, enough VRAM and willingness to compile.Without RTX 3090/4090-class hardware, running 27B long-context locally is not realistic. Start with Ollama/LM Studio on 7B-14B models, then rent RunPod/Vast.ai for 27B+ experiments.
    [...]
    Before adopting it, verify license, model weight license, reproducibility of 200k context on RTX 3090, whether speedup covers prefill vs decoding, and whether long-context output quality degrades.

    ---

    Title: Z.ai GLM-4.6 Open Source | Reference Models | Made By Agents
    URL: https://www.madebyagents.com/hardware/models/glm-4-6
    Published: N/A
    Author: N/A
    Highlights:
    A 355B frontier-scale AI optimized heavily for polished UI generation, front-end development, and 200K token context processing.
    [...]
    ... (231 more lines)

[thinking] Now I have a comprehensive picture. Let me synthesize this for the user.

           Key concerns:
           1. Privacy - too much private info in conversations, won't be comfortable with subscriptions
           2. Need ≥200k context
           3. Want to know OpenRouter privacy settings
           4. Want other provider recommendations

           Let me give a clear, actionable answer:

           **OpenRouter privacy settings:**
           - Enable ZDR (Zero Data Retention) per model group in account settings
           - Disable prompt logging (off by default, but verify)
           - Opt out of training data usage (separate toggle for paid/free models)
           - Use `zdr: true` parameter per-request
           - But note: ZDR depends on downstream provider support
           - For highest privacy, call ZDR providers directly instead of through OpenRouter

           **Models with ≥200k context that work for agentic tasks:**
           1. Qwen3.5-flash (1M ctx, $0.065/$0.26 via OpenRouter) - cheap
           2. DeepSeek V4 Flash (1M ctx, free or $0...
<matrix_lite_final>You're right to be cautious — there's a lot of private context flowing through these sessions. Here's what I'd recommend:

**OpenRouter privacy settings to enable:**
1. **ZDR enforcement** — in your account Privacy Settings, toggle on ZDR for "Non-frontier" at minimum. This restricts routing to providers with zero data retention. You can also do it per-request with `zdr: true`
2. **Opt out of training** — separate toggle for paid/free models, prevents routing to providers that train on your data
3. **Disable prompt logging** — off by default, but verify in your settings that "Private Input & Output Logging" and "OpenRouter Use of Inputs/Outputs" are both disabled
4. **Important caveat**: ZDR through OpenRouter depends on the downstream provider. Calling a ZDR provider directly removes OpenRouter as an extra hop. For the privacy-sensitive stuff, direct is cleaner.

**Providers with ≥200k context + strong privacy:**

| Provider | Model | Context | Price (in/out per 1M) | Privacy |
|----------|-------|---------|----------------------|---------|
| OpenRouter | **DeepSeek V4 Flash** (free) | 1M | $0 | ZDR endpoints available |
| OpenRouter | **qwen3.5-flash** | 1M | $0.065/$0.26 | ZDR via inference.net |
| inference.net | DeepSeek V3.2 | 128K | $0.14/$0.28 | No training, zero retention |
| Together AI | Llama/Mistral | 128K | $0.10-$0.18 | No training on data |
| Privatemode | gpt-oss-120b | 128K | Free tier available | E2EE, BSI C5 certified |

**Self-hosting (best privacy):**
- **Qwen3-Next** — 80B params, 3B active, **262K context**, Apache 2.0 license. Runs on consumer GPUs (~16GB VRAM at 4-bit). This is probably the best realistic self-host option for ≥200k context.
- **gpt-oss-20b** — OpenAI's open model, 21B params, runs in 16GB VRAM via Ollama. Only 32K context though.
- **GLM-5** — 744B MoE (40B active), **200K context**, MIT license. Needs serious hardware (2x Mac Studio or multi-GPU), but the most capable open model at this context length.

**My take:** For testing right now, **DeepSeek V4 Flash (free)** or **qwen3.5-flash via OpenRouter** with ZDR enabled gives you the best balance — cheap, 1M context, and reasonable privacy with the right settings. For the longer term where you're not comfortable with subscriptions anymore, **Qwen3-Next** self-hosted on a local GPU gives you 262K context with zero data leaving your machine. Want me to set up a test run with one of these?
</matrix_lite_final>

[done] end_turn

The problem is that there's too much private information here, also by design. At some point I won't be comfortable using subscriptions anymore.
What do I select in openrouter to make it more privacy friendly considering our conversations?
What other providers should I try?
Also we need at least 200k context until we trim the prompts even more


Good new finds! Here are the most relevant ones:

**New private API providers (privacy-first, affordable):**

| Provider | Privacy | Models | Context | Price (in/out per 1M) |
|----------|---------|--------|---------|----------------------|
| **Answira** 🇨🇿 | EU (Czech), no storage, no training | qwen3-coder-next | **256K** | **$0.07/$0.30** |
| **Tensorix** 🇪🇺 | EU (Amsterdam/Frankfurt), zero retention | Kimi-K2.6, GLM-5.1 | 262K / 203K | $1.00/$4.00 |
| **Together AI** 🇺🇸 | ZDR by default, opt-in training | DeepSeek, Qwen, Llama | 128K-1M | $0.10-$0.50 |
| **Fireworks AI** 🇺🇸 | ZDR for open models, SOC 2, HIPAA | Llama, DeepSeek, Qwen | 128K | ~$0.10-$0.20 |
| **Regolo.ai** 🇪🇺 | EU, zero retention, GDPR | Various open models | 128K+ | Pay-as-you-go |
| **LLMBase** 🇩🇪 | German company, EU servers, no training | Open-source models | 128K-256K | Pay-per-use |

**Standouts for your needs:**
- **Answira** — Czech Republic, qwen3-coder-next at **$0.07/$0.30** with **256K context**, zero storage, no training. That's Qwen's coding model at absurdly low price with EU privacy.
- **Together AI** — DeepSeek models hosted on their own US infrastructure. **Zero data retention by default.** The model authors (DeepSeek, Qwen) never see your requests. They explicitly say "DeepSeek itself receives no user requests or API traffic."
- **Fireworks AI** — similar to Together, SOC 2 Type II certified, HIPAA compliant if that ever matters.

**About your 12GB GPU:** Unfortunately Qwen3-Next (80B) won't fit — even at 4-bit it needs ~40GB. gpt-oss-20b at 4-bit would be ~10.5GB which might barely squeeze in with CPU offloading. For truly local 200K+ context on 12GB, nothing realistic exists yet — you'd need to use API-based models. DeepSeek V4 Flash or qwen3.5-flash via one of the ZDR providers is your best bet for now.

Want me to look into setting up a test with DeepSeek Flash or the Answira qwen3-coder endpoint?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user wants:
           1. They're interested in trying DeepSeek V4 Flash (it was terse in tests)
           2. Qwen3-Next sounds interesting but they have 12GB GPU (that's tight for Qwen3-Next which needs ~16GB at 4-bit)
           3. Look for other private API providers

           Let me search for more private API providers that would work for their needs (≥200k context, privacy-focused, affordable). Also, let me think about what works on 12GB VRAM for self-hosting.

           For 12GB VRAM:
           - gpt-oss-20b (21B params, 3.6B active) - needs ~16GB at FP16 but could work at 4-bit (~8GB)
           - Qwen3-Next (80B, 3B active, 262K ctx) - at 4-bit this needs around 16GB, too tight for 12GB
           - Smaller models: Llama 3.2 8B, Qwen 2.5 7B, Mistral 7B - these would fit but context is typically 32-128K
           - Qwen3.5-4B - would easily fit but limited capability
           - Qwen3.5-9B - would fit in 12GB at 4-bit, but context?

           Actually, Qwen3-Next at 80B with only 3B ...

[tool] websearch (pending)
  input: {}

[tool] websearch (pending)
  input: {}

[tool] Web search: private AI inference API zero data retention affordable 2026 context 200k (completed)
  kind: other
  input: private AI inference API zero data retention affordable 2026 context 200k
  output:
    Title: Api·Go
    URL: https://ent.apigo.ai/
    Published: N/A
    Author: N/A
    Highlights:
    Access 100+ leading AI models through a single endpoint. Intelligent routing, automatic failover, and 99.99% uptime. Build faster with one API that connects to OpenAI, Claude, Gemini, and more.
    [...]
    Explore the world's leading AI models. Enjoy an intelligent future from a single source. Apigo's intelligent API integration engine ensures exceptional performance and high availability. Enable enterprise-grade AI capabilities instantly with zero-complexity deployment.
    [...]
    200K context | text input $3 / MTokens | text output $15 / MTokens
    [...]
    Anthropic’s top model for agents/coding, boasting 30hrs autonomous task runtime, enhanced programming & computer skills. New "Imagine with Claude" and 200K context. Cost-effective vs peers .
    [...]
    DeepMind’s flagship, boasting 1M-token context (upgradable to
    [...]
    M) and strong multimodality. Excels in math reasoning (92% AIME 2024), coding (63.8% SWE-bench), with
    [...]
    89.8% Global MMLU
    [...]
    multilingual tasks .
    [...]
    OpenAI’s flagship 2025 model (52T params) with 400K context. Excels in math (94.6% AIME), coding (74.9% SWE-bench), and video understanding. Reduces hallucinations, offers tiered pricing, and links Google services .
    [...]
    Providing high-quality, ready-made AI training data helps you quickly build and deploy models for diverse application scenarios. The quality and diversity of training data are crucial to model performance, and pre-built datasets are an efficient and cost-effective way to acquire massive amounts of data.
    [...]
    Elastic computing service designed specifically for production environments. Automatically scale up and down to accommodate business fluctuations, with per-second billing to reduce costs. 10,000 GPU cards（4090） is always on standby, eliminating the need for infrastructure management. API calls enable 10-second deployment and go-live, allowing you to focus on business innovation.
    [...]
    Purpose-built for high-...

[tool] Web search: Together AI Fireworks AI inference API privacy data policy 2026 (completed)
  kind: other
  input: Together AI Fireworks AI inference API privacy data policy 2026
  output:
    Title: Privacy Policy | Together AI
    URL: https://www.together.ai/privacy
    Published: N/A
    Author: N/A
    Highlights:
    # Privacy Policy
    [...]
    Updated December 17, 2025
    [...]
    This Privacy Policy (the “Policy”) describes the practices of Together Computer, Inc. (the “Company”, “we”, “our”, or “us”) with respect to personal data that is collected when you access www.together.ai (the “Website”), the Company’s developed and available programmatic APIs and web interfaces that host, use, fine-tune, and train large AI models (the “Service(s)”), and training, migration, or other professional services provided to you by the Company (“Professional Support”).
    [...]
    Applicability: This Policy applies to Personal Data that the Company collects, uses, and discloses and which may include: (i) data collected through the Services, (ii) data collected through Professional Support, (iii) data collected through the Website, and (iv) data collected from third-party sources. Third-party sources may include but are not limited to public databases, commercial data sources, and the public internet. When you make purchases, we use third-party payment processors to collect credit card or other financial information. The Company does not store the credit card or payment information you provide, only confirmation that payment was made. We do not use any data collected from you to train our models without your explicit opt-in and consent. You may revoke that consent at any time and request deletion of information we have collected. You may also disable some data collection in the settings for your product. We allow you to tweak your privacy settings in our platform so that we do not retain training data, prompts, or model responses for any purpose. You can tweak your privacy settings by going to Settings > Profile.
    [...]
    Data that we collect, including your Personal Data, will not be used to train the Company’s models without your explicit opt-in and consent. The Company may use Personal Data for the...

[thinking] Now I have a lot of good info. Let me compile the most useful findings for the user:

           **About their 12GB GPU:**
           - Qwen3-Next (80B) won't fit - needs ~40GB at 4-bit
           - gpt-oss-20b (21B) could fit at 4-bit (~10.5GB) with some offloading
           - Llama 4 Scout (17B) at 4-bit ~8.5GB - would fit, 1M context via Groq API
           - For truly local 200K context on 12GB: not really feasible with current models
           - Best bet for local: smaller models via API (DeepSeek Flash, qwen3.5-flash)

           **New private API providers found:**
           1. **Tensorix** - EU-sovereign, zero data retention, Amsterdam/Frankfurt. Kimi-K2.6 (262K ctx, $1/$4 per 1M), GLM-5.1 (203K ctx, $1.40/$4.40). $5 free credits.
           2. **LLMBase** - German company, EU servers, zero retention, 128K-256K context models, no credit card needed
           3. **Regolo.ai** - European, GDPR, zero data retention, subscription or pay-as-you-go
           4. **Answira** - Czech Republic, 256K...
<matrix_lite_found_more>Good new finds! Here are the most relevant ones:

**New private API providers (privacy-first, affordable):**

| Provider | Privacy | Models | Context | Price (in/out per 1M) |
|----------|---------|--------|---------|----------------------|
| **Answira** 🇨🇿 | EU (Czech), no storage, no training | qwen3-coder-next | **256K** | **$0.07/$0.30** |
| **Tensorix** 🇪🇺 | EU (Amsterdam/Frankfurt), zero retention | Kimi-K2.6, GLM-5.1 | 262K / 203K | $1.00/$4.00 |
| **Together AI** 🇺🇸 | ZDR by default, opt-in training | DeepSeek, Qwen, Llama | 128K-1M | $0.10-$0.50 |
| **Fireworks AI** 🇺🇸 | ZDR for open models, SOC 2, HIPAA | Llama, DeepSeek, Qwen | 128K | ~$0.10-$0.20 |
| **Regolo.ai** 🇪🇺 | EU, zero retention, GDPR | Various open models | 128K+ | Pay-as-you-go |
| **LLMBase** 🇩🇪 | German company, EU servers, no training | Open-source models | 128K-256K | Pay-per-use |

**Standouts for your needs:**
- **Answira** — Czech Republic, qwen3-coder-next at **$0.07/$0.30** with **256K context**, zero storage, no training. That's Qwen's coding model at absurdly low price with EU privacy.
- **Together AI** — DeepSeek models hosted on their own US infrastructure. **Zero data retention by default.** The model authors (DeepSeek, Qwen) never see your requests. They explicitly say "DeepSeek itself receives no user requests or API traffic."
- **Fireworks AI** — similar to Together, SOC 2 Type II certified, HIPAA compliant if that ever matters.

**About your 12GB GPU:** Unfortunately Qwen3-Next (80B) won't fit — even at 4-bit it needs ~40GB. gpt-oss-20b at 4-bit would be ~10.5GB which might barely squeeze in with CPU offloading. For truly local 200K+ context on 12GB, nothing realistic exists yet — you'd need to use API-based models. DeepSeek V4 Flash or qwen3.5-flash via one of the ZDR providers is your best bet for now.

Want me to look into setting up a test with DeepSeek Flash or the Answira qwen3-coder endpoint?
</matrix_lite_found_more>

[done] end_turn

Hmm we should try deepseek flash soon. It was very terse in our subagent tests.
Qwen-next also sounds interesting, my existing GPU is 12gb though and I'm not getting more anytime soon.
Look for a couple of other private API providers


Here's the DeepSeek V4 Pro pricing showdown:

**DeepSeek V4 Pro** (1.6T params, 49B active, 1M context, MIT license)

| Provider | Input (cache miss) | Input (cache hit) | Output | Wait, what? |
|----------|:-----------------:|:-----------------:|:------:|-------------|
| **OpenRouter** 🏆 | **$0.44** | **$0.0036** | **$0.87** | Passes through promo pricing + auto-routing |
| DeepSeek direct (promo) | $0.44 | $0.0036 | $0.87 | Same, but direct. Promo until May 31 |
| DeepSeek direct (list) | $1.74 | $0.0145 | $3.48 | After May 31 — 4× jump |
| Fireworks AI | $1.74 | $0.14 | $3.48 | List pricing, no promo |
| Together AI | $2.10 | $0.20 | $4.40 | Most expensive option |

**Best deal: OpenRouter at $0.44/$0.87.** Same as DeepSeek's promo pricing but with better uptime and fallback routing.

**For context, the alternatives you were eyeing:**

| Model | Provider | Input | Output | Context |
|-------|----------|:-----:|:------:|:-------:|
| **DeepSeek V4 Flash** (free) | OpenRouter/free | **$0** | **$0** | 1M |
| DeepSeek V4 Flash (paid) | DeepSeek direct | $0.14 | $0.28 | 1M |
| **qwen3-coder-next** 🇨🇿 | Answira | **$0.07** | **$0.30** | **256K** |
| qwen3.5-flash | OpenRouter | $0.065 | $0.26 | 1M |
| DeepSeek V4 Pro 🏆 | OpenRouter | $0.44 | $0.87 | 1M |

**For testing DeepSeek Flash** — it's free on OpenRouter right now, so literally zero cost to try. The terseness you saw in subagent tests might be a system prompt or temperature tuning issue, not the model itself.

Want me to prioritize which one to set up for a controlled test?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user wants me to compare DeepSeek V4 Pro pricing across different providers (OpenRouter, Together AI, Fireworks, etc.) to see who offers the best deal on it. Let me search for specific pricing.

[tool] websearch (pending)
  input: {}

[tool] Web search: DeepSeek V4 Pro API pricing OpenRouter Together AI Answira Fireworks 2026 (completed)
  kind: other
  input: DeepSeek V4 Pro API pricing OpenRouter Together AI Answira Fireworks 2026
  output:
    Title: Models & Pricing - DeepSeek API Docs
    URL: https://api-docs.deepseek.com/quick_start/pricing
    Published: N/A
    Author: N/A
    Highlights:
    Models & Pricing | DeepSeek API Docs

    On this page

    # Models & Pricing

    The prices listed below are in units of per 1M tokens. A token, the smallest unit of text that the model recognizes, can be a word, a number, or even a punctuation mark. We will bill based on the total number of input and output tokens by the model.

    ---

    ## Model Details

    | MODEL | deepseek-v4-flash(1) | deepseek-v4-pro |
    | --- | --- | --- |
    | BASE URL (OpenAI Format) | https://api.deepseek.com |
    | BASE URL (Anthropic Format) | https://api.deepseek.com/anthropic |
    | MODEL VERSION | DeepSeek-V4-Flash | DeepSeek-V4-Pro |
    | THINKING MODE | Supports both non-thinking and thinking (default) modesSee Thinking Mode for how to switch |
    | CONTEXT LENGTH | 1M |
    | MAX OUTPUT | MAXIMUM: 384K |
    | FEATURES | Json Output | ✓ | ✓ |
    | Tool Calls | ✓ | ✓ |
    | Chat Prefix Completion（Beta） | ✓ | ✓ |
    ... (447 more lines)

[thinking] Now I have a clear picture of DeepSeek V4 Pro pricing across providers. Let me compile a comparison.

           DeepSeek V4 Pro:
           - 1.6T total params, 49B active
           - 1M context
           - MIT licensed (open weights)

           **Pricing comparison (per 1M tokens):**

           | Provider | Input (cache miss) | Input (cache hit) | Output | Notes |
           |----------|-------------------|-------------------|--------|-------|
           | **DeepSeek direct** (promo) | $0.435 | $0.0036 | $0.87 | 75% promo until May 31 |
           | **DeepSeek direct** (list) | $1.74 | $0.0145 | $3.48 | After promo ends |
           | **OpenRouter** | $0.44 | $0.0036 | $0.87 | Routes to DeepSeek promo pricing |
           | **Together AI** | $2.10 | $0.20 | $4.40 | Most expensive! |
           | **Fireworks AI** | $1.74 | $0.14 | $3.48 | List price, no promo |
           | **DeepInfra** | $0.53 | ~$0.14 | $3.48 | Via OpenRouter routing |

           So the clear winner is **OpenRouter** at $0.44/$0.87, matching DeepSeek's promo p...
<matrix_lite_final>Here's the DeepSeek V4 Pro pricing showdown:

**DeepSeek V4 Pro** (1.6T params, 49B active, 1M context, MIT license)

| Provider | Input (cache miss) | Input (cache hit) | Output | Wait, what? |
|----------|:-----------------:|:-----------------:|:------:|-------------|
| **OpenRouter** 🏆 | **$0.44** | **$0.0036** | **$0.87** | Passes through promo pricing + auto-routing |
| DeepSeek direct (promo) | $0.44 | $0.0036 | $0.87 | Same, but direct. Promo until May 31 |
| DeepSeek direct (list) | $1.74 | $0.0145 | $3.48 | After May 31 — 4× jump |
| Fireworks AI | $1.74 | $0.14 | $3.48 | List pricing, no promo |
| Together AI | $2.10 | $0.20 | $4.40 | Most expensive option |

**Best deal: OpenRouter at $0.44/$0.87.** Same as DeepSeek's promo pricing but with better uptime and fallback routing.

**For context, the alternatives you were eyeing:**

| Model | Provider | Input | Output | Context |
|-------|----------|:-----:|:------:|:-------:|
| **DeepSeek V4 Flash** (free) | OpenRouter/free | **$0** | **$0** | 1M |
| DeepSeek V4 Flash (paid) | DeepSeek direct | $0.14 | $0.28 | 1M |
| **qwen3-coder-next** 🇨🇿 | Answira | **$0.07** | **$0.30** | **256K** |
| qwen3.5-flash | OpenRouter | $0.065 | $0.26 | 1M |
| DeepSeek V4 Pro 🏆 | OpenRouter | $0.44 | $0.87 | 1M |

**For testing DeepSeek Flash** — it's free on OpenRouter right now, so literally zero cost to try. The terseness you saw in subagent tests might be a system prompt or temperature tuning issue, not the model itself.

Want me to prioritize which one to set up for a controlled test?
</matrix_lite_final>

[done] end_turn

I like your standout, please compare the pricing, say, on deepseek v4 pro


Good idea. The cleanest way would be switching on next reload — that way we keep our current 40k context running as long as we want, then start fresh on a new model when we're ready.

For a controlled test plan:

1. **Next reload** → switch the main model. Best candidates:
   - **DeepSeek V4 Flash** (free) — you already have some experience with it, just need to tune the prompts for the terseness
   - **qwen3.5-flash** via OpenRouter ($0.065/$0.26) — 1M context, cheap, and I can use the current OpenRouter setup with ZDR enabled

2. **Subagents** — nanogpt's blown, but Owl Alpha is free on OpenRouter and would work for subagent testing without costing anything. Or DeepSeek V4 Flash is also free.

3. **Switch mechanism** — the `matrix_lite_status_switch_bot_model` tool can schedule the model switch + restart. Just need an OpenRouter API key configured in the gateway so the session can use it.

Want me to check what model config paths are available and how the OpenRouter integration is set up? That way we know what needs to happen for the switch.

Output:
ia 200+"}
    17: {"ts":"2026-05-19T18:11Z","topic":"WGT","source":"MDR.de (searxng)","fact":"33rd WGT starts Friday 22 May 2026; ~20K attendees gather in Leipzig for concerts, Victorian Picnic, and dark culture events"}
    18: {"ts":"2026-05-19T18:11Z","topic":"WGT","source":"Volt Magazin (searxng)","fact":"Early 2026 lineup announcements included Einstürzende Neubauten as headliners"}
    19: {"ts":"2026-05-19T18:11Z","topic":"WGT","source":"MDR.de / Spontis.de (searxng)","fact":"Victorian Picnic at Clara-Zetkin-Park on Friday (Pfingsten weekend); free public events available without festival wristband"}

    (End of file - total 19 lines)
    </content>

[tool] edit (pending)
  input: {}

[tool] ../../state/persistent-chat/knowledge-base.jsonl (completed)
  kind: edit
  input: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  output:
    Edit applied successfully.

    Edit applied successfully.

    diff /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl (+3 lines)

[tool] read (pending)
  input: {}

[tool] ../../state/persistent-chat/knowledge-base.jsonl (completed)
  kind: read
  input: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  output:
    <path>/home/matrix-lite/state/persistent-chat/knowledge-base.jsonl</path>
    <type>file</type>
    <content>
    16: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"cross-source","fact":"Attendance numbers: Wikipedia says 18-20K, Grokipedia says 20K+; band count: Wikipedia 150-200, Grokipedia 200+"}

    (End of file - total 16 lines)
    </content>

[tool] read (pending)
  input: {}

[tool] ../../state/persistent-chat/knowledge-base.jsonl (completed)
  kind: read
  input: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  output:
    <path>/home/matrix-lite/state/persistent-chat/knowledge-base.jsonl</path>
    <type>file</type>
    <content>
    1: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"wave-gotik-treffen.de (official)","fact":"33rd Wave-Gotik-Treffen, 22-25 May 2026 in Leipzig"}
    2: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"wave-gotik-treffen.de (official)","fact":"~50 venues across Leipzig, no day tickets since 2001 (4-day only)"}
    3: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"wave-gotik-treffen.de (official)","fact":"Free public transport within MDV Zone 110 included (trams/buses/S-Bahn)"}
    4: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"wave-gotik-treffen.de (official)","fact":"Camping requires Obsorge card (€50) at Agra Messepark; 4-day ticket also needed"}
    5: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"wave-gotik-treffen.de (official)","fact":"Children under 12 free with parents; under 16 need parent/guardian; no dogs allowed"}

    (Showing lines 1-5 of 16. Use offset=6 to continue.)
    </content>

[tool] grep (pending)
  input: {}

[tool] WGT|Wave-Gotik (completed)
  kind: search
  input: /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
  output:
    Found 16 matches
    /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl:
      Line 1: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"wave-gotik-treffen.de (official)","fact":"33rd Wave-Gotik-Treffen, 22-25 May 2026 in Leipzig"}

      Line 2: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"wave-gotik-treffen.de (official)","fact":"~50 venues across Leipzig, no day tickets since 2001 (4-day only)"}

      Line 3: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"wave-gotik-treffen.de (official)","fact":"Free public transport within MDV Zone 110 included (trams/buses/S-Bahn)"}

      Line 4: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"wave-gotik-treffen.de (official)","fact":"Camping requires Obsorge card (€50) at Agra Messepark; 4-day ticket also needed"}

      Line 5: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"wave-gotik-treffen.de (official)","fact":"Children under 12 free with parents; under 16 need parent/guardian; no dogs allowed"}

      Line 6: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"Wikipedia","fact":"Founded 1992 at Eiskeller club (now Conne Island) in Leipzig"}

      Line 7: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"Wikipedia","fact":"~18,000-20,000 attendees; 150-200 bands across genres: gothic rock, EBM, industrial, darkwave, neofolk, medieval"}

      Line 8: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"Wikipedia","fact":"Financial collapse in 2000 — organizers quit, bands played for free; Move GmbH took over management in 2001"}

      Line 9: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"Wikipedia","fact":"Cancelled 2020 + 2021 due to COVID"}

      Line 10: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"Grokipedia","fact":"Precursor gathering in Potsdam 1987, suppressed by GDR authorities"}

      Line 11: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"Grokipedia","fact":"Founders: Michael Brunner and Sandro Standhaft"}

      Line 12: {"ts":"2026-05-19T08:07Z","topic":"WGT","source":"Grokipedia","fact":"'WGT Kompaß' app for offline nav...

[tool] matrix_lite_extended_tools_extended_tool_call (pending)
  input: {}

[tool] matrix_lite_extended_tools_extended_tool_call (completed)
  kind: other
  input: {"arguments":{"url":"https://www.spontis.de/wgt-2026/"},"tool":"searxng_url_read"}
  output:
    {
      "ok": false,
      "server": "searxng",
      "tool": "searxng_url_read",
      "error": "unhandled errors in a TaskGroup (1 sub-exception)"
    }

[tool] matrix_lite_extended_tools_extended_tool_call (pending)
  input: {}

[tool] matrix_lite_extended_tools_extended_tool_call (completed)
  kind: other
  input: {"arguments":{"url":"https://www.impericon.com/blogs/magazine/wgt-2026-everything-you-need-to-know-about-the-wave-gothic-meeting-in-leipzig"},"tool":"searxng_url_read"}
  output:
    {
      "ok": false,
      "server": "searxng",
      "tool": "searxng_url_read",
      "error": "unhandled errors in a TaskGroup (1 sub-exception)"
    }

[tool] matrix_lite_extended_tools_extended_tool_call (pending)
  input: {}

[tool] matrix_lite_extended_tools_extended_tool_call (completed)
  kind: other
  input: {"tool":"searxng_web_search","arguments":{"query":"WGT conference 2026","num_results":10}}
  output:
    {
      "ok": true,
      "server": "searxng",
      "tool": "searxng_web_search",
      "content": [
        {
          "type": "text",
          "text": "SearXNG results for: WGT conference 2026\nCategories: (default)\nReturned: 10 of 25 results\n\nTitle: Offizielle Seiten Wave-Gotik-Treffen Leipzig\nURL: https://www.wave-gotik-treffen.de/\nDescription: +Information · Aktuelles · Veranstaltungsstätten · Vorverkaufsstellen · Abendkassen · Bändchenausgaben · WGT A-Z · +Treffen-Weltbote.\nEngines: google\nRelevance Score: 1.0\n\nTitle: WGT 2026 Leipzig: Die wichtigsten Infos zum Wave-Gotik-Treffen | MDR.DE\nURL: https://www.mdr.de/kultur/wgt/index.html\nDescription: Das 33. WGT startet am Freitag! Vom 22. bis 25. Mai 2026 treffen sich wieder tausende Fans der schwarzen Szene. Alle Infos rund um Konzerte, Veranstaltungen, Viktorianisches Picknick und die schönsten Bilder gibt's hier.\nEngines: google, brave\nRelevance Score: 0.8666666666666667\n\nTitle: WGT 2026 Programm: Kompletter Spielplan für Wave-Gotik-Treffen online\nURL: https://www.volt-magazin.de/news/wgt-2026-frueher-auftakt-mit-grossen-namen-im-programm\nDescription: Kaum war der letzte Sommer passé, warf das Wave-Gotik-Treffen 2026 seinen schwarzen Schatten voraus. Die Veranstalter überraschten mit frühen Wellen von Bestätigungen für ihr WGT – und klangvollen obendrein: Einstürzende Neubauten und ...\nEngines: google, brave\nRelevance Score: 0.75\nPublished: 2025-11-09 00:00:00\n\nTitle: Wave-Gotik-Treffen 2026 - agra Messepark Leipzig\nURL: https://agramessepark.de/event/wave-gotik-treffen-2026/2026-05-22/\nDescription: ntworten auf die wichtigen Fragen zum WGT vom 22. bis 25. Mai 2026 gibt es bei mdr.de, zum Beispiel zu Bands, Tickets und Programm oder zum Viktorianischen Picknick….\nEngines: google, brave\nRelevance Score: 0.5176470588235295\nPublished: 2026-04-17 00:00:00\n\nTitle: Wave Gotik Treffen 2026 - Stadt Leipzig\nURL: https://www.leipzig.de/kultur-und-freizeit/veranstaltungen/eventsingle/event/wave-gotik-treffen-2026...

[tool] matrix_lite_extended_tools_extended_tool_describe (pending)
  input: {}

[tool] matrix_lite_extended_tools_extended_tool_describe (completed)
  kind: other
  input: {"name":"searxng_web_search","server":"searxng"}
  output:
    {
      "ok": true,
      "server": {
        "name": "searxng",
        "description": "Local SearXNG search and URL reader; supports categories like science/news/it.",
        "available": true,
        "known_tools": [
          "searxng_url_read",
          "searxng_web_search"
        ]
      },
      "tools": [
        {
          "name": "searxng_web_search",
          "description": "Search via local SearXNG. Supports SearXNG categories; use categories='science' for papers/research, 'it' for software, 'news' for current events, or comma/list values for multiple categories.",
          "input_schema": {
            "type": "object",
            "properties": {
              "query": {
                "type": "string",
                "description": "Search query."
              },
              "categories": {
                "description": "SearXNG category or categories. Known values: general, images, videos, news, map, music, it, science, files, social media.",
                "oneOf": [
                  {
                    "type": "string"
                  },
    ... (70 more lines)

[tool] matrix_lite_extended_tools_extended_tools_manifest (pending)
  input: {}

[tool] matrix_lite_extended_tools_extended_tools_manifest (completed)
  kind: other
  input: {}
  output:
    {
      "ok": true,
      "note": "Use extended_tool_describe before first use when arguments are unclear; use extended_tool_call to execute.",
      "servers": [
        {
          "name": "duckduckgo",
          "description": "DuckDuckGo web/news search via conservative HTML endpoint.",
          "available": true,
          "known_tools": [
            "duckduckgo_news_search",
            "duckduckgo_web_search"
          ]
        },
        {
          "name": "searxng",
          "description": "Local SearXNG search and URL reader; supports categories like science/news/it.",
          "available": true,
          "known_tools": [
            "searxng_url_read",
            "searxng_web_search"
          ]
        },
        {
          "name": "brave-search",
          "description": "Brave Search API for web and local-place search.",
          "available": true,
          "known_tools": []
        },
    ... (35 more lines)

[tool] bash (pending)
  input: {}

[tool] Check latest sessions (completed)
  kind: execute
  input: ls -lt /home/matrix-lite/.acpx/sessions/ | head -8
  output:
    total 280508
    -rw-rw-r-- 1 matrix-lite matrix-lite    42913 May 19 19:58 index.json
    -rw-rw-r-- 1 matrix-lite matrix-lite  1068735 May 19 19:58 ses_1bf0bdb40ffeAFrAzQZD1eauNw.json
    -rw-rw-r-- 1 matrix-lite matrix-lite  7116958 May 19 19:58 ses_1bf0bdb40ffeAFrAzQZD1eauNw.stream.ndjson
    -rw-rw-r-- 1 matrix-lite matrix-lite       65 May 19 19:56 ses_1bf0bdb40ffeAFrAzQZD1eauNw.stream.lock
    -rw-r--r-- 1 matrix-lite matrix-lite   725004 May 19 19:17 ses_1bec47434ffeng752STX8HMpf5.json
    -rw-r--r-- 1 matrix-lite matrix-lite  2376296 May 19 19:17 ses_1bec47434ffeng752STX8HMpf5.stream.ndjson
    -rw-rw-r-- 1 matrix-lite matrix-lite     3697 May 19 18:13 ses_1bfb7bf69ffeEG0vSkl2jKrQmR.json

[tool] bash (pending)
  input: {}

[tool] Check config file timestamp (completed)
  kind: execute
  input: ls -la /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
  output:
    -rw------- 1 matrix-lite matrix-lite 2324927 May 19 19:15 /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json

[done] end_turn

How about we switch you to it? Maybe when we need to restart next
We could try on subagents but I blew though my nanogpt subscription with our earlier tests