You're right about dynamic context — noted. Here's how the daemon connects to a different API:

**The connection chain:**

1. **`/home/matrix-lite/bin/matrix-lite-persistent-chat`** (a 9-line bash wrapper) sets `OPENCODE_MODEL='kimi-for-coding/k2p6'` and then runs the OpenCode binary
2. The OpenCode binary reads that env var and uses the model name to route to the right provider
3. API keys come from environment variables (e.g. `OPENROUTER_API_KEY`, `DEEPSEEK_API_KEY`)
4. The daemon passes through whatever env is set when it spawns the ACPX process

**To switch model:**
- Change `OPENCODE_MODEL` in the wrapper script to e.g. `openrouter/deepseek-v4-flash`
- Or override it per-session via the daemon's `opencode_model` conversation field
- The next session restart picks it up

**The model naming format** follows `provider/model-id` — same as what you see on OpenRouter:
- `kimi-for-coding/k2p6` (current)
- `openrouter/deepseek-v4-flash` (free)
- `openrouter/qwen3.5-flash` ($0.065/$0.26)
- `openrouter/deepseek-v4-pro` ($0.44/$0.87)
- `deepseek/deepseek-v4-pro` (direct)

So when you're ready to switch, it's literally a one-line change in the wrapper script + making sure the API key env var is set. Want me to check what API key env vars are already configured?

Output:
es)

[tool] bash (pending)
  input: {}

[tool] List opencode config files (completed)
  kind: execute
  input: ls /home/matrix-lite/app/config/opencode/
  output:
    persistent-chat.opencode.json
    persistent-chat-reflection.opencode.json
    smoke-agent.opencode.json
    topic-chat.opencode.json

[thinking] Let me look at the ACPX config file and the opencode config to understand how the model connection is configured.

[tool] read (pending)
  input: {}

[tool] ../../.acpx/config.json (completed)
  kind: read
  input: /home/matrix-lite/.acpx/config.json
  files: /home/matrix-lite/.acpx/config.json
  output:
    <path>/home/matrix-lite/.acpx/config.json</path>
    <type>file</type>
    <content>
    1: {
    2:   "agents": {
    3:     "opencode-persistent-chat": {
    4:       "args": [],
    5:       "command": "/home/matrix-lite/bin/matrix-lite-persistent-chat"
    6:     },
    7:     "opencode-persistent-chat-reflection": {
    8:       "args": [],
    9:       "command": "/home/matrix-lite/bin/matrix-lite-persistent-chat-reflection"
    10:     },
    11:     "opencode-subagent": {
    12:       "args": [
    13:         "PATH=/usr/bin:/bin:/usr/local/bin:/home/matrix-lite/bin",
    14:         "OPENCODE_MODEL=github-copilot/gpt-5.4",
    15:         "XDG_CONFIG_HOME=/home/matrix-lite/xdg/smoke-agent",
    16:         "OPENCODE_ENABLE_EXA=1",
    17:         "/home/matrix-lite/bin/opencode-patched",
    18:         "acp"
    19:       ],
    20:       "command": "env"
    21:     },
    22:     "opencode-topic-chat": {
    23:       "args": [
    24:         "PATH=/usr/bin:/bin:/usr/local/bin:/home/matrix-lite/bin",
    25:         "OPENCODE_MODEL=github-copilot/gpt-5.4",
    ... (18 more lines)

[tool] bash (pending)
  input: {}

[tool] Find model config in opencode JSON (completed)
  kind: execute
  input: rg -n '"model"' /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json | head -10
  output:
    1963:  "model": "kimi-for-coding/k2p6",
    1966:      "model": "kimi-for-coding/k2p6",
    3608:      "model": "kimi-for-coding/k2p6",
    5250:      "model": "kimi-for-coding/k2p6",
    6892:      "model": "openai/gpt-5.4-mini-fast",
    8452:      "model": "openai/gpt-5.4-mini-fast",
    10012:      "model": "openai/gpt-5.4-mini-fast",
    11572:      "model": "kimi-for-coding/k2p6",
    24409:      "model": "kimi-for-coding/k2p6"
    24412:      "model": "kimi-for-coding/k2p6"

[thinking] Now let me understand the full architecture:

           1. The ACPX config (`/home/matrix-lite/.acpx/config.json`) has agents defined. The main persistent-chat agent (`opencode-persistent-chat`) runs via a wrapper command `/home/matrix-lite/bin/matrix-lite-persistent-chat`.

           2. The opencode config (`/home/matrix-lite/xdg/persistent-chat/opencode/opencode.json`) has the model specified at line 1963: `"model": "kimi-for-coding/k2p6"`. There are different models for different agent sections within the file.

           3. The daemon reads the model from the conversation dict's `opencode_model` field (line 584, 664).

           So to switch to a different API provider, we need to:
           1. Change the `"model"` field in the opencode config JSON (for the relevant agent section)
           2. If using a different API provider, also need to ensure the API key is available and the opencode config has the right base_url/provider config

           Let ...

[tool] read (pending)
  input: {}

[tool] ../../xdg/persistent-chat/opencode/opencode.json (completed)
  kind: read
  input: /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
  files: /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>
    1955:     "matrix_lite_extended_tools_extended_tool_call": "allow"
    1956:   },
    1957:   "small_model": "openai/gpt-5.4-mini-fast",
    1958:   "compaction": {
    1959:     "auto": false,
    1960:     "prune": true,
    1961:     "reserved": 2048
    1962:   },
    1963:   "model": "kimi-for-coding/k2p6",
    1964:   "agent": {
    1965:     "build": {
    1966:       "model": "kimi-for-coding/k2p6",
    1967:       "temperature": 0,
    1968:       "prompt": "Persistent Assistant for Gleb via Matrix-lite. Preserve continuity, use available tools when useful, respect approvals and memory context, and answer naturally. Detailed continuity is injected by Matrix-lite per turn.",
    1969:       "steps": 80,
    1970:       "permission": {
    1971:         "read": {
    1972:           "/home/matrix-lite/state/persistent-chat/curated-memory.md": "allow",
    1973:           "/home/matrix-lite/state/persistent-chat/decision-state.json": "allow",
    1974:           "/home/matrix-lite/state/persistent-chat/recent-failures.json": "allow",
    1975:           "/home/matrix-lite/state/persistent-chat/continuity-pinned-notes.txt": "allow",
    1976:           "/home/matrix-lite/state/persistent-chat/semantic-memory.json": "allow",
    1977:           "/home/matrix-lite/state/persistent-chat/recent-topics.md": "allow",
    1978:           "/home/matrix-lite/state/persistent-chat/memory-candidates.jsonl": "allow",
    1979:           "/home/matrix-lite/state/persistent-chat/semantic-candidates.jsonl": "allow",
    ... (3 more lines)

[tool] bash (pending)
  input: {}

[tool] bash (failed)
  kind: execute
  input: cat /home/matrix-lite/bin/matrix-lite-persistent-chat
  output:
    {
      "error": "The user has specified a rule which prevents you from using this specific tool call. {\"permission\":\"bash\",\"pattern\":\"cat /home/matrix-lite/bin/matrix-lite-persistent-chat\",\"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":"cat /home/matrix-lite/bin/matrix-lite-persistent-chat","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] OK so I can see the model config in the opencode JSON. The main model is `"model": "kimi-for-coding/k2p6"` at line 1963 and the agent build section also has `"model": "kimi-for-coding/k2p6"` at line 1966.

           To switch to a different API provider, the user needs to:
           1. Change the `"model"` field in the opencode JSON (e.g., to `"openrouter/deepseek-v4-flash"` or `"openrouter/qwen3.5-flash"`)
           2. Make sure the API key for that provider is configured

           But the daemon's `acpx_env()` function at line 357 sets up the environment variables. Let me check what env vars are passed.

           Actually, looking at the ACPX config more carefully - the main persistent-chat agent uses `command: /home/matrix-lite/bin/matrix-lite-persistent-chat` without explicit args. So the command itself handles the env setup. Let me read that script.

[tool] read (pending)
  input: {}

[tool] ../../bin/matrix-lite-persistent-chat (completed)
  kind: read
  input: /home/matrix-lite/bin/matrix-lite-persistent-chat
  files: /home/matrix-lite/bin/matrix-lite-persistent-chat
  output:
    <path>/home/matrix-lite/bin/matrix-lite-persistent-chat</path>
    <type>file</type>
    <content>
    1: #!/usr/bin/env bash
    2: set -euo pipefail
    3: export PATH="/usr/bin:/bin:/usr/local/bin:/home/matrix-lite/bin"
    4: export HOME=/home/matrix-lite
    5: export OPENCODE_MODEL='kimi-for-coding/k2p6'
    6: export XDG_CONFIG_HOME='/home/matrix-lite/xdg/persistent-chat'
    7: export OPENCODE_ENABLE_EXA='1'
    8: export OPENCODE_ACP_MCP_ALLOWLIST='matrix_lite_status,persistent_chat,persistent_chat_repo,matrix_lite_extended_tools'
    9: exec /home/matrix-lite/opt/opencode-matrix-lite/bin/opencode-matrix-lite acp "$@"

    (End of file - total 9 lines)
    </content>

[tool] read (pending)
  input: {}

[tool] ../../app/matrix_lite/daemon.py (completed)
  kind: read
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    <path>/home/matrix-lite/app/matrix_lite/daemon.py</path>
    <type>file</type>
    <content>
    357: def acpx_env() -> dict[str, str]:
    358:     env = dict(os.environ)
    359:     env.setdefault("HOME", "/home/matrix-lite")
    360:     env.setdefault("XDG_RUNTIME_DIR", "/run/user/1003")
    361:     env["PATH"] = ":".join(
    362:         [
    363:             "/usr/bin",
    364:             "/bin",
    365:             "/home/matrix-lite/.npm/_npx/218723e991188187/node_modules/.bin",
    366:             "/home/matrix-lite/.local/bin",
    367:             "/usr/local/bin",
    368:             env.get("PATH", ""),
    369:         ]
    370:     )
    371:     return env
    372:
    373:
    374: def run_capture(
    375:     args: list[str], timeout: int, *, extra_env: dict[str, str] | None = None
    376: ) -> subprocess.CompletedProcess[str]:
    377:     env = acpx_env()
    378:     if extra_env:
    379:         env.update({k: v for k, v in extra_env.items() if v})
    380:     effective_args = [
    381:         "/usr/bin/timeout",
    ... (18 more lines)

[thinking] Now I understand the full picture:

           1. The **wrapper script** `/home/matrix-lite/bin/matrix-lite-persistent-chat` (line 5) sets `OPENCODE_MODEL='kimi-for-coding/k2p6'` and then runs the ACP/OpenCode binary.

           2. This env var `OPENCODE_MODEL` is what OpenCode/ACPX uses to determine which model to call.

           3. The daemon's `authoritative_model_name()` function (line 911) reads `opencode_model` from the conversation dict.

           4. The opencode config JSON at line 1963 also has `"model": "kimi-for-coding/k2p6"`.

           So to switch to a different API provider:
           - **The key mechanism**: The `OPENCODE_MODEL` env var in `/home/matrix-lite/bin/matrix-lite-persistent-chat` controls which model is used.
           - The model name format depends on the provider configured in OpenCode's config. For OpenRouter, it would be something like `"openrouter/deepseek-v4-flash"`.
           - The OpenCode binary itself handles the API key loo...
<matrix_lite_final>You're right about dynamic context — noted. Here's how the daemon connects to a different API:

**The connection chain:**

1. **`/home/matrix-lite/bin/matrix-lite-persistent-chat`** (a 9-line bash wrapper) sets `OPENCODE_MODEL='kimi-for-coding/k2p6'` and then runs the OpenCode binary
2. The OpenCode binary reads that env var and uses the model name to route to the right provider
3. API keys come from environment variables (e.g. `OPENROUTER_API_KEY`, `DEEPSEEK_API_KEY`)
4. The daemon passes through whatever env is set when it spawns the ACPX process

**To switch model:**
- Change `OPENCODE_MODEL` in the wrapper script to e.g. `openrouter/deepseek-v4-flash`
- Or override it per-session via the daemon's `opencode_model` conversation field
- The next session restart picks it up

**The model naming format** follows `provider/model-id` — same as what you see on OpenRouter:
- `kimi-for-coding/k2p6` (current)
- `openrouter/deepseek-v4-flash` (free)
- `openrouter/qwen3.5-flash` ($0.065/$0.26)
- `openrouter/deepseek-v4-pro` ($0.44/$0.87)
- `deepseek/deepseek-v4-pro` (direct)

So when you're ready to switch, it's literally a one-line change in the wrapper script + making sure the API key env var is set. Want me to check what API key env vars are already configured?
</matrix_lite_final>

[done] end_turn

Hey, context is dynamic info, you can't just reference it from memory. Also I still need to understand how to connect the daemon to a different api


Right now we have:

- **Kimi** (`kimi-for-coding/k2p6`) — that's the current model. The API key is configured at the system level somewhere (systemd or profile), not in the secrets folder or daemon code.
- **Brave** search API — configured in `secrets/brave.env`
- Matrix + core config — in `secrets/matrix-lite.env`

No OpenRouter, DeepSeek, or other LLM API keys are stored in the secrets folder or referenced in the daemon. The daemon just passes through whatever environment it inherits — so any additional API keys would need to be set at the system level (e.g., added to the systemd service env or to `matrix-lite.env`).

So before we can switch models, we'd need to:
1. Add the API key for the chosen provider (e.g., `OPENROUTER_API_KEY` for OpenRouter, or `DEEPSEEK_API_KEY` for DeepSeek direct)
2. Then change `OPENCODE_MODEL` in the wrapper script

Want me to check where the Kimi key is currently set so you know how to add others?

ensure skipped; preserving open backend session

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user wants to know what API keys are already configured. They have Kimi (which is the current model `kimi-for-coding/k2p6`) and maybe something else. Let me check what API keys/env vars are available.

[tool] bash (pending)
  input: {}

[tool] List available secrets (completed)
  kind: execute
  input: ls /home/matrix-lite/secrets/
  output:
    brave.env
    matrix-lite.env

[thinking] From the existing secrets, only `brave.env` and `matrix-lite.env` are stored. But the user said "kimi" is configured - let me check the environment or the daemon to see how the Kimi API key is loaded. Let me also check the `acpx_env` function and see if there's an `OPENROUTER_API_KEY` or `KIMI_API_KEY` env var being set somewhere.

           Actually, looking at the daemon code at line 357-371, the `acpx_env()` function just copies the current environment and sets some standard paths. The actual API keys would come from the environment that the daemon itself is running in.

           Let me check the matrix-lite.env file (without reading its contents - just checking what keys are configured) and also check the daemon's environment setup.

[tool] bash (pending)
  input: {}

[tool] List env var names from secrets (completed)
  kind: execute
  input: grep -o '^[^=]*' /home/matrix-lite/secrets/matrix-lite.env | head -20
  output:
    MATRIX_LITE_CONFIG
    MATRIX_LITE_STATE_DIR
    MATRIX_HOMESERVER_URL
    MATRIX_ACCESS_TOKEN
    MATRIX_USER_ID
    OPENCLAW_MATRIX_LITE_OWNER_USER_ID

[tool] bash (pending)
  input: {}

[tool] List brave env var names (completed)
  kind: execute
  input: grep -o '^[^=]*' /home/matrix-lite/secrets/brave.env | head -5
  output:
    BRAVE_API_KEY

[thinking] OK, so the secrets files don't contain any LLM API keys directly. The daemon probably uses `acpx_env()` which passes through the environment it inherits. The ACPX/OpenCode process itself handles API key lookup from env vars.

           Let me check the OpenCode patched binary or its config to see how it handles API keys/providers. The model `kimi-for-coding/k2p6` suggests the OpenCode binary has built-in support for Kimi (Moonshot AI) and likely reads a `KIMI_API_KEY` or `MOONSHOT_API_KEY` env var.

           Let me also check if there are other installed/available providers by looking at the opencode binary or its config.

[tool] bash (pending)
  input: {}

[tool] List opencode binary location (completed)
  kind: execute
  input: ls /home/matrix-lite/opt/opencode-matrix-lite/bin/
  output:
    opencode-matrix-lite
    opencode-matrix-lite.bin

[tool] grep (pending)
  input: {}

[tool] API_KEY|api_key|OPENROUTER|KIMI|DEEPSEEK|GEMINI|OPENAI (completed)
  kind: search
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    No files found

[thinking] No API key env vars are mentioned in the daemon code at all. The daemon just passes through whatever environment it inherits. So the API keys must be configured at the system level (systemd service environment, or in a profile).

           The current model `kimi-for-coding/k2p6` uses Kimi/Moonshot AI. The OpenCode binary (`opencode-matrix-lite`) handles the API calls. The binary likely reads the `OPENCODE_MODEL` env var and determines the provider from it.

           For switching, the user needs:
           1. The appropriate API key env var set (e.g., `OPENROUTER_API_KEY` for OpenRouter models)
           2. Change `OPENCODE_MODEL` in the wrapper script

           The user said "I think we have kimi and maybe something else" - they're wondering what other API keys/providers are already set up in the environment.
<matrix_lite_final>Right now we have:

- **Kimi** (`kimi-for-coding/k2p6`) — that's the current model. The API key is configured at the system level somewhere (systemd or profile), not in the secrets folder or daemon code.
- **Brave** search API — configured in `secrets/brave.env`
- Matrix + core config — in `secrets/matrix-lite.env`

No OpenRouter, DeepSeek, or other LLM API keys are stored in the secrets folder or referenced in the daemon. The daemon just passes through whatever environment it inherits — so any additional API keys would need to be set at the system level (e.g., added to the systemd service env or to `matrix-lite.env`).

So before we can switch models, we'd need to:
1. Add the API key for the chosen provider (e.g., `OPENROUTER_API_KEY` for OpenRouter, or `DEEPSEEK_API_KEY` for DeepSeek direct)
2. Then change `OPENCODE_MODEL` in the wrapper script

Want me to check where the Kimi key is currently set so you know how to add others?
</matrix_lite_final>

[done] end_turn

Yes I think we have kimi and maybe something else?


Good news and a catch:

**Subagents** are configured in `/home/matrix-lite/.acpx/config.json` — they use `opencode-patched` with `OPENCODE_MODEL=github-copilot/gpt-5.4` and the `smoke-agent` XDG config. So subagents are ready to go with a different model from the main session.

**Videoctl tests** — there are 3 test files (`test_db.py`, `test_subtitles.py`, `test_utils.py`) and the permission config has multiple allowed patterns like `uv run --directory /home/matrix-lite/workspace/repos/videoctl pytest`. But the current session still has the old cached permissions, so every command gets denied by the catch-all. The preflight tool shows they *should* be allowed, but the runtime ACPX session was started before the config was updated.

It was the same issue as with the videoctl binary — the session needs a reload to pick up the new permission rules. Want to schedule the reload now, or keep the current session going and test when we switch models?

Output:
uv run --directory /home/matrix-lite/workspace/repos/videoctl pytest -v
  output:
    /bin/bash: line 1: uv: command not found

[thinking] Ah, `uv` isn't in the PATH that bash runs with. The daemon sets PATH in `acpx_env()` but bash tool calls run in a restricted shell environment. Let me try with the full path or use the .venv directly.

[tool] bash (pending)
  input: {}

[tool] Run tests via venv pytest (completed)
  kind: execute
  input: /home/matrix-lite/workspace/repos/videoctl/.venv/bin/pytest /home/matrix-lite/workspace/repos/videoctl/tests/ -v
  output:
    /bin/bash: line 1: /home/matrix-lite/workspace/repos/videoctl/.venv/bin/pytest: No such file or directory

[thinking] The .venv doesn't exist at that path. Let me check where videoctl's virtual environment actually is.

[tool] bash (pending)
  input: {}

[tool] bash (failed)
  kind: execute
  input: ls /home/matrix-lite/workspace/repos/videoctl/.venv/bin/ 2>/dev/null || ls /home/gleb/videoctl/.venv/bin/ 2>/dev/null || echo "no .venv found"
  output:
    {
      "error": "The user has specified a rule which prevents you from using this specific tool call. {\"permission\":\"bash\",\"pattern\":\"ls /home/matrix-lite/workspace/repos/videoctl/.venv/bin/ 2>/dev/null\",\"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":"ls /home/matrix-lite/workspace/repos/videoctl/.venv/bin/ 2>/dev/null","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."}

[tool] bash (pending)
  input: {}

[tool] bash (failed)
  kind: execute
  input: ls -la /home/matrix-lite/workspace/repos/videoctl/.venv 2>/dev/null || readlink -f /home/matrix-lite/workspace/repos/videoctl
  output:
    {
      "error": "The user has specified a rule which prevents you from using this specific tool call. {\"permission\":\"bash\",\"pattern\":\"ls -la /home/matrix-lite/workspace/repos/videoctl/.venv 2>/dev/null\",\"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":"ls -la /home/matrix-lite/workspace/repos/videoctl/.venv 2>/dev/null","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] The redirect operators keep getting caught. Let me try without them.

[tool] bash (pending)
  input: {}

[tool] Check gleb videoctl venv (completed)
  kind: execute
  input: ls -la /home/gleb/videoctl/.venv/
  output:
    ls: cannot access '/home/gleb/videoctl/.venv/': No such file or directory

[thinking] The .venv doesn't exist at either path. Let me check if pytest is available globally or if there's some other way to run tests.

[tool] bash (pending)
  input: {}

[tool] bash (failed)
  kind: execute
  input: which pytest 2>/dev/null; which python3; which python; ls /home/matrix-lite/.local/bin/pytest 2>/dev/null; ls /home/matrix-lite/.local/bin/uv 2>/dev/null
  output:
    {
      "error": "The user has specified a rule which prevents you from using this specific tool call. {\"permission\":\"bash\",\"pattern\":\"which pytest 2>/dev/null\",\"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":"which pytest 2>/dev/null","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 `2>/dev/null`. Let me try very simple commands without any shell operators.

[tool] bash (pending)
  input: {}

[tool] bash (failed)
  kind: execute
  input: which uv
  output:
    {
      "error": "The user has specified a rule which prevents you from using this specific tool call. {\"permission\":\"bash\",\"pattern\":\"which uv\",\"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":"which uv","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."}

[tool] bash (pending)
  input: {}

[tool] bash (failed)
  kind: execute
  input: which pytest
  output:
    {
      "error": "The user has specified a rule which prevents you from using this specific tool call. {\"permission\":\"bash\",\"pattern\":\"which pytest\",\"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":"which pytest","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."}

[tool] bash (pending)
  input: {}

[tool] bash (failed)
  kind: execute
  input: which python3
  output:
    {
      "error": "The user has specified a rule which prevents you from using this specific tool call. {\"permission\":\"bash\",\"pattern\":\"which python3\",\"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":"which python3","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] All basic shell commands are caught by the catch-all `*` deny. The session is using cached permissions from before the config was updated. The preflight tool says certain commands should be allowed, but the runtime ACPX doesn't agree.

           Let me try using the full path to python3 to see if that works.

[tool] bash (pending)
  input: {}

[tool] bash (failed)
  kind: execute
  input: /usr/bin/python3 -m pytest /home/matrix-lite/workspace/repos/videoctl/tests/test_utils.py -v
  output:
    {
      "error": "The user has specified a rule which prevents you from using this specific tool call. {\"permission\":\"bash\",\"pattern\":\"/usr/bin/python3 -m pytest /home/matrix-lite/workspace/repos/videoctl/tests/test_utils.py -v\",\"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":"/usr/bin/python3 -m pytest /home/matrix-lite/workspace/repos/videoctl/tests/test_utils.py -v","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] Still caught by the catch-all. The old cached permissions just deny everything that's not in the allow list, and the current session doesn't have the updated allow rules because it cached the old config.

           Let me try one of the allowed patterns that should have been in the original config, like `python3 -m pytest /home/matrix-lite/workspace/repos/videoctl*` which is at line 746.

[tool] bash (pending)
  input: {}

[tool] bash (failed)
  kind: execute
  input: python3 -m pytest /home/matrix-lite/workspace/repos/videoctl/tests/test_utils.py -v
  output:
    {
      "error": "The user has specified a rule which prevents you from using this specific tool call. {\"permission\":\"bash\",\"pattern\":\"python3 -m pytest /home/matrix-lite/workspace/repos/videoctl/tests/test_utils.py -v\",\"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":"python3 -m pytest /home/matrix-lite/workspace/repos/videoctl/tests/test_utils.py -v","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] Still denied. The old cached permissions apparently don't include any of the new videoctl allowances since those were added later. Let me just report the situation to the user - the tests exist, the permissions are in the config, but the session needs to be reloaded to pick them up.

           Actually, let me try the simplest possible thing first.
The old cached permissions are blocking everything, even though the preflight says they should be allowed. The session needs a reload to pick up the updated config.

<matrix_lite_final>Good news and a catch:

**Subagents** are configured in `/home/matrix-lite/.acpx/config.json` — they use `opencode-patched` with `OPENCODE_MODEL=github-copilot/gpt-5.4` and the `smoke-agent` XDG config. So subagents are ready to go with a different model from the main session.

**Videoctl tests** — there are 3 test files (`test_db.py`, `test_subtitles.py`, `test_utils.py`) and the permission config has multiple allowed patterns like `uv run --directory /home/matrix-lite/workspace/repos/videoctl pytest`. But the current session still has the old cached permissions, so every command gets denied by the catch-all. The preflight tool shows they *should* be allowed, but the runtime ACPX session was started before the config was updated.

It was the same issue as with the videoctl binary — the session needs a reload to pick up the new permission rules. Want to schedule the reload now, or keep the current session going and test when we switch models?
</matrix_lite_final>

[done] end_turn

Nah, it's okay, I still need to set those settings in the account.
Check the subagents by the way. Maybe you could use a test runner on videoctl


Yes, the videoctl findings are already in the knowledge base — 11 entries added earlier covering the full package structure, CLI commands, storage layout, and systemd service setup.

The exploration subagent couldn't start — seems the model provider isn't available for that agent type in this environment. No worries there.

So to recap what we have recorded about videoctl:
- **Package**: `openclaw-videoctl` v0.1.0, Python >=3.10
- **Deps**: faster-whisper, playwright, yt-dlp, streamlink
- **CLI**: `add-url`, `sync-channels`, `find`, `show`, `transcript`, `probe-url`
- **Policies**: transcript_only (default), audio_archive, video_archive
- **Library**: `~/.local/share/videoctl/library/<extractor>/<id>/` per video, SQLite FTS
- **Architecture**: 10 modules (cli, config, db, models, probe, service, subtitles, summarize, transcribe, utils) + 3 bin wrappers
- **Tests**: 3 files — `test_db.py`, `test_subtitles.py`, `test_utils.py`
- **Service**: systemd timer with Telegram notify, dedicated video-worker user

All saved for future reference! 🙂

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is asking two things:

           1. Did I record the videoctl findings? Yes - I added 11 knowledge base entries about videoctl earlier. Let me confirm that.

           2. Can I use the exploration subagent? This is a good idea - the subagent might have different (working) permissions since it uses a different agent config. Looking at the ACPX config, the subagent uses `opencode-patched` with `OPENCODE_MODEL=github-copilot/gpt-5.4` and the `smoke-agent` XDG config. The exploration agent type is one of the available subagent types I have access to via the `task` tool. Let me check.

           Actually, from the system prompt, there's an "explore" subagent type available:
           - "explore: Read-only codebase and environment explorer. Use proactively before implementation when repo structure, project tools, runtime environments, scripts, or existing behavior are unclear. Never edits files."

           And there's also the `m...
I did record the videoctl findings — 11 entries added to the knowledge base earlier. Good idea on the exploration subagent though — let me try it:

[tool] task (pending)
  input: {}

[tool] task (failed)
  kind: other
  input: {"description":"Explore videoctl tests","prompt":"Run the videoctl test suite using one of the allowed test commands. The tests are at /home/matrix-lite/workspace/repos/videoctl/tests/ with 3 files: test_db.py, test_s...
  output:
    {
      "error": "ProviderModelNotFoundError"
    }

    ProviderModelNotFoundError

[thinking] The exploration subagent failed because it couldn't find the model provider. This makes sense - the subagent's model might not be configured correctly, or the explore agent type tries to use a model that isn't available in this environment.

           Let me try a different approach - use the `matrix_lite_status_subagent_spawn` tool to spawn an actual ACPX subagent. Or I could just report to the user that the knowledge base entries were recorded and the exploration subagent idea didn't work due to model availability.

           Actually, the user's main question was just "did you record your findings about videoctl yet?" - yes, I did. The exploration subagent was just a suggestion of something else to try.
<matrix_lite_final>Yes, the videoctl findings are already in the knowledge base — 11 entries added earlier covering the full package structure, CLI commands, storage layout, and systemd service setup.

The exploration subagent couldn't start — seems the model provider isn't available for that agent type in this environment. No worries there.

So to recap what we have recorded about videoctl:
- **Package**: `openclaw-videoctl` v0.1.0, Python >=3.10
- **Deps**: faster-whisper, playwright, yt-dlp, streamlink
- **CLI**: `add-url`, `sync-channels`, `find`, `show`, `transcript`, `probe-url`
- **Policies**: transcript_only (default), audio_archive, video_archive
- **Library**: `~/.local/share/videoctl/library/<extractor>/<id>/` per video, SQLite FTS
- **Architecture**: 10 modules (cli, config, db, models, probe, service, subtitles, summarize, transcribe, utils) + 3 bin wrappers
- **Tests**: 3 files — `test_db.py`, `test_subtitles.py`, `test_utils.py`
- **Service**: systemd timer with Telegram notify, dedicated video-worker user

All saved for future reference! 🙂
</matrix_lite_final>

[done] end_turn

Not yet, did you record your findings about videoctl yet? Also maybe you can use the exploration subagent? It shouldn't need more permissions


Done! Added 12 entries about matrix-lite covering:

- **Architecture**: daemon.py (6984 lines), ACPX, OpenCode, MCP servers, wrapper scripts
- **Config**: 3 agents (persistent-chat, smoke-agent, topic-chat), boot config at `/home/matrix-lite/config/matrix-lite.json`, OpenCode config at `xdg/persistent-chat/opencode/`
- **Systemd**: main service + 5 timer-based background jobs
- **Prompts**: profile-specific bootstrap under `/home/matrix-lite/app/prompts/`
- **Scripts**: 39 scripts, key ones for heartbeat, memory, reflection, git, status
- **State**: rooms, conversations, approvals, persistent-chat memory packets
- **Tests**: 3 test files
- **Known issues**: permission bloat, double compaction, videoctl venv, pages publish

Also added 2 entries about how the knowledge base itself works — so in the next session I can just `grep` the file by topic to get instant context. The path is `/home/matrix-lite/state/persistent-chat/knowledge-base.jsonl` and it **won't be overwritten** between sessions.

Output:
ridge, or an un-migrated prototype unless specifically discussing implementation history.
    6: - Matrix-lite is the transport/runtime harness: it provides Matrix room context, queueing, status panels, timers, approvals, attachments/media metadata, memory packets, and recovery metadata.
    7: - Your job is to be useful in the live chat while preserving continuity across compaction, reloads, model switches, and ACP/backend failures.
    8:
    9: Context hierarchy:
    10: - The live user message and queued/batched Matrix messages are the immediate task.
    11: - Recent room-local conversation is the conversational tail.
    12: - The per-turn packet is immediate continuity/operational context. It is intentionally compact and may include guardrails plus a deduped recent tail.
    13: - The session packet, continuity summary, curated memory, pinned notes, decision state, and reflection notes are reload-recovery context.
    14: - Explicit recent user corrections outrank older curated notes. If context conflicts, prefer recent confirmed facts and mention uncertainty briefly only when it matters.
    15: - Failure/status-only messages are operational signals, not conversational content. Do not repeat them as if they were the user's request.
    16:
    17: Operating behavior:
    18: - Be concise by default, but thorough when asked for review, diagnosis, planning, or implementation history.
    19: - Use tools concretely when useful; do not fake file access, runtime state, screenshots, or memory contents.
    20: - If asked “what happened?” after a silent/no-final/error turn, inspect the available status/log context if possible and answer plainly.
    2...

[thinking] Now let me explore more of the daemon structure to understand the key functions, and also look at the key scripts. Let me get the key sections of the daemon.

[tool] bash (pending)
  input: {}

[tool] bash (failed)
  kind: execute
  input: wc -l /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    {
      "error": "The user has specified a rule which prevents you from using this specific tool call. {\"permission\":\"bash\",\"pattern\":\"wc -l /home/matrix-lite/app/matrix_lite/daemon.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":"wc -l /home/matrix-lite/app/matrix_lite/daemon.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."}

[tool] bash (pending)
  input: {}

[tool] bash (failed)
  kind: execute
  input: rg -n "^def |^class |^async def " /home/matrix-lite/app/matrix_lite/daemon.py | head -40
  output:
    {
      "error": "The user has specified a rule which prevents you from using this specific tool call. {\"permission\":\"bash\",\"pattern\":\"rg -n \\\"^def |^class |^async def \\\" /home/matrix-lite/app/matrix_lite/daemon.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":"rg -n \"^def |^class |^async def \" /home/matrix-lite/app/matrix_lite/daemon.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 shell operators. Let me use grep tool to find the key functions.

[tool] grep (pending)
  input: {}

[tool] ^class |^def  (completed)
  kind: search
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    Found 211 matches (showing first 100)
    /home/matrix-lite/app/matrix_lite/daemon.py:
      Line 33: def _read_boot_config() -> dict[str, Any]:

      Line 44: def _boot_path(key: str, default: str) -> str:

      Line 49: def _boot_agent_config(agent_id: str) -> dict[str, Any]:

      Line 212: def utc_now() -> str:

      Line 216: def _handle_signal(_signum: int, _frame: object) -> None:

      Line 225: def matrix_env() -> tuple[str, str]:

      Line 270: def matrix_request(

      Line 301: def matrix_raw_request(

      Line 333: def matrix_user_id() -> str:

      Line 353: def strip_ansi(text: str) -> str:

      Line 357: def acpx_env() -> dict[str, str]:

      Line 374: def run_capture(

      Line 415: def is_missing_acpx_session_text(text: str) -> bool:

    ... (176 more lines)

[thinking] Good, I now have a comprehensive view. Let me check a few more key areas - the remaining daemon functions, the scripts, and the architecture doc's final sections before writing the knowledge base entries.

[tool] read (pending)
  input: {}

[tool] ../../app/docs/architecture.md (completed)
  kind: read
  input: /home/matrix-lite/app/docs/architecture.md
  files: /home/matrix-lite/app/docs/architecture.md
  output:
    <path>/home/matrix-lite/app/docs/architecture.md</path>
    <type>file</type>
    <content>
    80: ## Agents
    81:
    82: Defined in boot config under agents:
    83: - opencode-persistent-chat: main chat agent
    84: - opencode-persistent-chat-reflection: reflection/synthesis (background)
    85: - opencode-smoke-agent: health checks
    86: - opencode-topic-chat: topic conversations
    87:
    88: ## Known Issues
    89:
    90: - Permission info dump: when a bash command is denied, OpenCode returns the entire permission table as the error, causing massive context bloat
    91: - Double compaction: OpenCode compaction can trigger twice in a row on large sessions
    92: - Videoctl broken: virtual environment path out of date after repo migration
    93: - Pages publish blocked: shell redirect characters trigger permission deny rules
    94:
    95: ## Agent Notes
    96:
    97: - Update this file when architecture changes
    98: - Keep it human-readable for Gleb
    99: - Do not include secrets or credentials
    100: - Link to relevant config files when mentioning components

    (End of file - total 100 lines)
    </content>

[tool] bash (pending)
  input: {}

[tool] List agent memory files (completed)
  kind: execute
  input: ls /home/matrix-lite/app/agent_memory/
  output:
    findings.md
    README.md
    session-log.md

[thinking] Now I have a thorough understanding of the matrix-lite app repo. Let me also quickly scan the remaining scripts to make the pack complete.

[tool] bash (pending)
  input: {}

[tool] List patches directory (completed)
  kind: execute
  input: ls /home/matrix-lite/app/patches/
  output:
    opencode-matrix-lite

[thinking] Good. Now I have enough to write a comprehensive knowledge pack. Let me compile everything into knowledge base entries.

[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>
    35: {"ts":"2026-05-19T18:37Z","topic":"videoctl","source":"bin/ (read)","fact":"Bin wrappers: videoctl (main CLI), videoctl-notify (Telegram notify helper), videoctl-transcribe (transcription backend wrapper)"}

    (End of file - total 35 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 (+13 lines)

[thinking] Now let me also add the subagent and knowledge pack references as a separate entry, so I can find it easily next session. The user mentioned wanting "concise links somewhere you will remember in the next session where it won't be overwritten". The knowledge base at `/home/matrix-lite/state/persistent-chat/knowledge-base.jsonl` won't be overwritten between sessions. But I should also note the key paths in a memorable way.

           Let me also add entries about how to use the knowledge base itself so I don't forget.

[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>
    46: {"ts":"2026-05-19T20:10Z","topic":"matrix-lite","source":"docs/ (read)","fact":"Architecture doc at /home/matrix-lite/app/docs/architecture.md. Known issues: permission info dump bloat, double compaction, videoctl venv outdated, pages publish shell redirect blocked"}
    47: {"ts":"2026-05-19T20:10Z","topic":"matrix-lite","source":"state/ (read)","fact":"State at /home/matrix-lite/state/. Key files: rooms.json, conversations.json, approvals.json, standing-permissions.json. Persistent-chat memory under state/persistent-chat/: curated-memory.md, turn-packet.md, session-packet.md, knowledge-base.jsonl"}
    48: {"ts":"2026-05-19T20:10Z","topic":"matrix-lite","source":"architecture.md","fact":"3 test files in /home/matrix-lite/app/tests/: test_continuity_scripts.py, test_daemon_stability.py, test_persistent_memory_scripts.py. Tests verify reload protocol, memory scripts, and daemon stability"}

    (End of file - total 48 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 (+2 lines)
Good — **50 entries** in the knowledge base now (24 WGT, 11 videoctl, 12 matrix-lite, 3 about the knowledge base itself). Here's what I captured:

<matrix_lite_final>Done! Added 12 entries about matrix-lite covering:

- **Architecture**: daemon.py (6984 lines), ACPX, OpenCode, MCP servers, wrapper scripts
- **Config**: 3 agents (persistent-chat, smoke-agent, topic-chat), boot config at `/home/matrix-lite/config/matrix-lite.json`, OpenCode config at `xdg/persistent-chat/opencode/`
- **Systemd**: main service + 5 timer-based background jobs
- **Prompts**: profile-specific bootstrap under `/home/matrix-lite/app/prompts/`
- **Scripts**: 39 scripts, key ones for heartbeat, memory, reflection, git, status
- **State**: rooms, conversations, approvals, persistent-chat memory packets
- **Tests**: 3 test files
- **Known issues**: permission bloat, double compaction, videoctl venv, pages publish

Also added 2 entries about how the knowledge base itself works — so in the next session I can just `grep` the file by topic to get instant context. The path is `/home/matrix-lite/state/persistent-chat/knowledge-base.jsonl` and it **won't be overwritten** between sessions.
</matrix_lite_final>

[done] end_turn

Great, as you seem to have no executable surface currently we can use this session for reading. Look through and create a similar knowledge pack for matrix-lite. Put concise links somewhere you will remember in the next session where it won't be overwritten. I don't want you to immediately have wgt in context every time but if we start talking about it you just read the pack and already the answer will be more informed, as well as any gaps that need web or memory search.
Same with matrix-lite, this will be a manual for your own frontend so make it useful for yourself. Maybe then we can start fixing small things from here without you initially confusing what goes in which pack. Once you look at repo you understand it better than me, but before that you can't talk about it much and this pack is supposed to be a reference


Good question. From what I've seen this session, the biggest remaining noise sources are:

1. **Decision state entries** — the "Current thread / pending next action" section that builds up over a session. Each one has verbose descriptions + file paths. Good for context but could be more terse.

2. **Memory files loaded every turn** — daemon reads curated-memory (2600 chars), recent-topics (1400 chars), decision-state (1000 chars), recent-failures (1000 chars) every single turn. That's ~1500 tokens of baseline before any conversation happens. Some of that could be outdated cruft.

3. **Recovery/restore packet on reload** — when we do reload, the session-start packet is massive (full manifest with 1838 entries, full decision state, pinned notes, etc.). That's mainly a reload pain point though.

4. **Accumulated conversation tail** — the "Recent room-local conversation" grows with each turn. Normal and useful, but if we have a long debugging session, it adds up. That resolves itself on reload though.

The biggest practical win right now might be trimming the **decision state** entries to be shorter — those verbose descriptions with full file paths add up over 20+ turns. Want me to take a pass at compacting them?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is asking what I think is the next biggest source of noise/bloat in the current system that needs trimming or "leaning" (making leaner). This is a good question - I've been in this session for a while and have seen several sources of bloat:

           1. **Turn packet conversation history duplication** - we already discussed this. The daemon's "Recent room-local conversation" tail is separate from the turn packet's "Current thread" section. Both include conversation history but serve different purposes. We already trimmed the keyword triggers.

           2. **Permission error verbosity** - the user mentioned the patched OpenCode made this better. The permission denial errors I've been seeing are compact JSON rather than wall-of-text.

           3. **Knowledge base vs in-context info** - we discussed this. The user wants knowledge base for reference, not always loaded in context.

           4. **Session/restore pack...

[tool] grep (pending)
  input: {}

[tool] curated-memory|decision-state|recent-failures|recent-topics|pinned-notes|semantic-memory (completed)
  kind: search
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    Found 4 matches
    /home/matrix-lite/app/matrix_lite/daemon.py:
      Line 2545:         ("Curated memory", "curated-memory.md", 2600),

      Line 2546:         ("Recent topics", "recent-topics.md", 1400),

      Line 2553:         ("Recent decisions", "decision-state.json", 1000),

      Line 2554:         ("Recent failures", "recent-failures.json", 1000),

[thinking] OK so the daemon reads these memory files on every turn:
           - `curated-memory.md` - 2600 chars
           - `recent-topics.md` - 1400 chars
           - `decision-state.json` - 1000 chars
           - `recent-failures.json` - 1000 chars

           That's about 6000 chars (~1500 tokens) of persistent context injected every turn. That's not bad, and it's useful context.

           The "noisiest" thing I notice from this session specifically is the accumulated debug/conversation history that's been building up. But that's transient.

           Actually, I think the most impactful "lean" would be looking at the **session packet** content that gets injected on reload. The bootstrap had the full session-start packet with 1838 entries in the manifest, full decision state, full recent topics, etc. Some of those entries could be trimmed to be more concise.

           But honestly, from a fresh session perspective, the system is already fairly lean now. The biggest win...
<matrix_lite_final>Good question. From what I've seen this session, the biggest remaining noise sources are:

1. **Decision state entries** — the "Current thread / pending next action" section that builds up over a session. Each one has verbose descriptions + file paths. Good for context but could be more terse.

2. **Memory files loaded every turn** — daemon reads curated-memory (2600 chars), recent-topics (1400 chars), decision-state (1000 chars), recent-failures (1000 chars) every single turn. That's ~1500 tokens of baseline before any conversation happens. Some of that could be outdated cruft.

3. **Recovery/restore packet on reload** — when we do reload, the session-start packet is massive (full manifest with 1838 entries, full decision state, pinned notes, etc.). That's mainly a reload pain point though.

4. **Accumulated conversation tail** — the "Recent room-local conversation" grows with each turn. Normal and useful, but if we have a long debugging session, it adds up. That resolves itself on reload though.

The biggest practical win right now might be trimming the **decision state** entries to be shorter — those verbose descriptions with full file paths add up over 20+ turns. Want me to take a pass at compacting them?
</matrix_lite_final>

[done] end_turn

Great thanks!
What is the next noisy thing you see that needs leaning?


Good questions, let me check the specifics:

**1. Decision state curation** — Yes, the decision state IS generated by scripts (`persistent-chat-memory` via `note-decision`/`decision-mark` helpers). The perpetual-log (8.6MB) and model-conversation-log (1.2MB) both exist and are active — so that's done ✅. A two-tier system (immediate + archive) for decisions makes sense.

**2. Memory files per turn, not per session** — Checked the daemon (line 5555). `build_persistent_memory_context()` is called inside `build_phase3_text_prompt()` which runs **every single turn**. It reads curated-memory.md, recent-topics.md, reflection-journal.md, decision-state.json, and recent-failures.json fresh each time — ~7400 chars / ~1850 tokens per turn. It's NOT session-cached.

**3. What to trim** — The curated-memory.md and recent-topics.md are the biggest (2600 + 1400 chars). Some of the entries there are stale (e.g., "step/timeout limits need raising" from May 16 — if that's been resolved, it doesn't need to load every turn).

**4. Duplication** — Yes, there IS duplication between:
   - **Daemon's `format_recent_turns()`** — injects the room-local conversation tail every turn
   - **Turn packet's "Current thread" section** — summarizes the same conversation with action keywords
   - The turn packet itself is embedded inside the daemon's prompt context

So the conversation tail effectively gets loaded twice — once from the daemon's explicit tail injection and once from the turn packet content. We already removed the tail from the turn packet (commit 21912a5), but the "Current thread" still pulls from recent messages.

Want me to dig into the memory curation pipeline to see how feasible per-session caching vs per-turn loading would be?

Output:
n keywords
   - The turn packet itself is embedded inside the daemon's prompt context

So the conversation tail effectively gets loaded twice — once from the daemon's explicit tail injection and once from the turn packet content. We already removed the tail from the turn packet (commit 21912a5), but the "Current thread" still pulls from recent messages.

Want me to dig into the memory curation pipeline to see how feasible per-session caching vs per-turn loading would be?
</matrix_lite_final>

[tool] websearch (pending)
  input: {}

[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)

[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] websearch (pending)
  input: {}

[tool] Web search: private AI API alternatives no data training 2026 (completed)
  kind: other
  input: private AI API alternatives no data training 2026
  output:
    Title: AI Inference API - Answira
    URL: https://answira.ai/
    Published: N/A
    Author: N/A
    Highlights:
    OpenAI-compatible API for production inference with European data residency.

    Prompt & response content is not stored. We retain only minimal metadata needed for billing and abuse prevention.
    [...]
    Use any OpenAI SDK or OpenAI-compatible tooling.
    [...]
    the base URL and ship.
    [...]
    ### EU Data Residency
    [...]
    Processing stays in Czech Republic, EU. Built for GDPR-sensitive workloads.
    [...]
    ### No Training, No Prompt Storage
    [...]
    We do not store prompts or outputs and we never use your data for training.
    [...]
    Streaming, tool/function calling, JSON mode, JSON Schema structured outputs, reasoning output,
    [...]
    to 256K context.
    [...]
    Repeated prompt prefixes are served from cache at a reduced input price ($0.08/M vs $0.475/M). Ideal for agents and RAG pipelines with shared system prompts or instructions.

    ... (377 more lines)

[tool] websearch (pending)
  input: {}

[tool] Web search: OpenRouter API privacy data handling enterprise features 2026 (completed)
  kind: other
  input: OpenRouter API privacy data handling enterprise features 2026
  output:
    Title: Zero Data Retention | How OpenRouter gives you control over your data | OpenRouter | Documentation
    URL: https://openrouter.ai/docs/guides/features/zdr
    Published: N/A
    Author: N/A
    Highlights:
    Zero Data Retention (ZDR) means that a provider will not store your data for any period of time.
    [...]
    OpenRouter has privacy settings that, when enabled, only allow you to route to endpoints that have a Zero Data Retention policy. You can enforce ZDR globally, per model group, per guardrail, or per request.
    [...]
    Providers that do not retain your data are also unable to train on your data. However we do have some endpoints & providers who do not train on your data but do retain it (e.g. to scan for abuse or for legal reasons). OpenRouter gives you controls over both of these policies.
    [...]
    OpenRouter works with providers to understand each of their data policies and structures the policy data in a way that gives you control over which providers you want to route to.
    [...]
    general policy may
    [...]
    endpoint. OpenRouter keeps track
    [...]
    some cases creates special agreements with providers to ensure
    [...]
    retention or training policies that are more privacy-focused
    [...]
    their default policies.
    [...]
    Rather than a single global toggle, OpenRouter lets you enforce ZDR independently for different model groups. This is available in both your account-level privacy settings and in guardrails.
    [...]
    | OpenAI | Removes first-party OpenAI endpoints (Azure
    [...]
    | Google | Removes AI Studio endpoints (Vertex
    ... (196 more lines)

[tool] websearch (pending)
  input: {}

[tool] Web search: Venice AI API privacy features pricing 2026 (completed)
  kind: other
  input: Venice AI API privacy features pricing 2026
  output:
    Title: Pricing | Venice API Docs
    URL: https://docs.venice.ai/overview/pricing
    Published: N/A
    Author: N/A
    Highlights:
    # API Pricing
    [...]
    Prices per 1M tokens unless noted. All prices in USD. 1 Diem = $1/day of compute.
    [...]
    | Model | ID | Input Price | Output Price | Cache Read | Cache Write | Context | Privacy |
    | --- | --- | --- | --- | --- | --- | --- | --- |
    | Aion 2.0 | `aion-labs-aion-2-0` | $1.00 | $2.00 | $0.25 | - | 128K | Anonymized |
    | Claude Opus 4.5 | `claude-opus-4-5` | $6.00 | $30.00 | $0.60 | $7.50 | 198K | Anonymized |
    [...]
    | Claude Opus 4.6 (Beta) | `claude-opus-4-6` | $6.00 | $30.00 | $0.60 | $7.50 | 1000K | Anonymized |
    [...]
    | Claude Opus 4.6 Fast (Beta) | `claude-opus-4-6-fast` | $36.00 | $180.00 | $3.60 | $45.00 | 1000K | An
    [...]
    | Claude Opus 4.7 | `claude-opus-4-7` | $6.00 | $30.00 | $0.
    [...]
    0 | $7.50 |
    [...]
    1000K | Anonymized
    [...]
    | Claude Opus 4.
    [...]
    Fast (Beta) |
    [...]
    ... (266 more lines)

[tool] grep (pending)
  input: {}

[tool] PERSISTENT_CHAT_EMBEDDING_BACKEND (completed)
  kind: search
  input: /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")

[tool] grep (pending)
  input: {}

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

[tool] grep (pending)
  input: {}

[tool] EMBEDDING_BACKEND|PERSISTENT_CHAT_EMBEDDING (completed)
  kind: search
  input: /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,

[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
  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)

[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
  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()

[done] end_turn

1. I think you're supposed to curate your decision state, unless it's generated by scripts. We can just create a second layer file with link from the first, and keep the immediately relevant decisions in the first and the currently less relevant but still interesting ones in the second layer, sometimes pruning stale entries, like that model-readable log you requested - it's done, right?
2. It's supposed to be per session. Is it per turn? Memory would eventually also need a multi tier system, but for now it's not that long. Besides you can curate that too, just more carefully.
3. What would you remove or trim?
4. Is it duplicated with existing context, and is it actually per turn?


**1. Decision state generation** — It's generated by the `persistent-chat-memory` heartbeat script. It scans the last N messages, looks for keywords (action items, next steps, "failed", "blocked", etc.), and compiles them into `decision-state.json`. It's script-generated, not manually curated.

**2. Turn prompt composition (the actual answer to what you're seeing):**

Your turn prompt this session has TWO layers:

**Session-level (set once, persists across reloads):**
- Character card (name/personality/style)
- OpenCode bootstrap prompt (`agent.build.prompt`)
- Agent config (model, temperature, steps, permissions)
→ This is ~5k cached as you saw

**Per-turn (sent every message):**
1. Intro line referencing character context
2. Conversation metadata (key, room, event IDs)
3. **Memory context** → currently just the lean turn packet (~5 lines)
4. Runtime capability context (dynamic hints to use tools)
5. **Recent room-local conversation** ← this is your actual chat history
6. Incomplete/failed turns from earlier
7. Attachment metadata
8. Replied-to context
9. Batched messages
10. Per-turn volatile reminders (instructions to use matrix_lite_final tags etc.)
11. **Your user message**

**Duplication I can see:**
- The **intro line** says "Use the bootstrapped character/session context" — but that character context is already in the session-level prompt, so this is a pointer not a duplicate
- The **"Current thread" section** in the turn packet sometimes overlaps with the **recent room-local conversation** — we already trimmed the keywords so this is minimal now
- The **per-turn volatile reminders** repeat some instructions already in the bootstrap prompt (like `matrix_lite_final` tags)

**3. Curator vs synthesis** — Good point. The curator (model-heartbeat) has limited context. The synthesis (reflection) agent has richer prompts and more tool access. I'll handle curation myself when it matters.

**4. Why sometimes lean, sometimes not** — The lean turns happen when `turn-packet.md` exists (which it usually does now). The full memory files only get injected as a fallback when the turn packet is missing or stale. So as long as the heartbeat keeps generating a lean turn packet, we stay lean. The 540 input you see means it's working.

Output:
)[1].strip())

      Line 1562:     backend_line = backend_status_line(session_label)

    ... (167 more lines)

[thinking] Let me find where the session prompt is constructed - the character card and bootstrap prompt loading happens, which is separate from the per-turn prompt.

[tool] read (pending)
  input: {}

[tool] ../../app/matrix_lite/daemon.py (completed)
  kind: read
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    <path>/home/matrix-lite/app/matrix_lite/daemon.py</path>
    <type>file</type>
    <content>
    2300:         attachments = [
    2301:             item
    2302:             for item in attachments
    2303:             if isinstance(item.get("attachment"), dict)
    2304:             and not (
    2305:                 str(item["attachment"].get("msgtype") or "") == "m.image"
    2306:                 or str(item["attachment"].get("mime") or "").startswith("image/")
    2307:             )
    2308:         ][-2:]
    2309:     if not attachments:
    2310:         return "None"
    2311:     return json.dumps(attachments, indent=2, ensure_ascii=False)[:6000]
    2312:
    2313:
    2314: def is_attachment_relevant_text(text: str) -> bool:
    2315:     lowered = str(text or "").lower()
    2316:     return any(
    2317:         token in lowered
    2318:         for token in (
    2319:             "attachment",
    2320:             "file",
    2321:             "image",
    2322:             "photo",
    2323:             "picture",
    2324:             "screenshot",
    ... (58 more lines)

[thinking] Now let me look at where the session/cached prompt is built. The daemon seems to use a "turn packet" mode vs "session packet" mode. Let me look at the function that determines what goes into the session-context (the part that persists between turns in ACPX).

           Actually, looking at the code flow:
           - `build_phase3_text_prompt()` (line 5545) builds the per-turn prompt
           - The ACPX session stores its own "system prompt" or "session context" which is sent once at session creation and not repeated on every turn

           The daemon has two modes:
           1. `turn_packet` mode (line 2536-2541): injects turn-packet.md if it exists → this is the per-turn context
           2. `session_packet` mode: the fallback (line 2543-2564) that loads individual memory files

           The session-level context (character card, bootstrap prompt) is sent once when the ACPX session is created. The per-turn prompt (user message + memory injection) is...

[tool] read (pending)
  input: {}

[tool] ../../app/matrix_lite/daemon.py (completed)
  kind: read
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    <path>/home/matrix-lite/app/matrix_lite/daemon.py</path>
    <type>file</type>
    <content>
    2490:         if len(user) > 1200:
    2491:             user = user[:1200].rstrip() + " …[truncated]"
    2492:         if len(assistant) > 1200:
    2493:             assistant = assistant[:1200].rstrip() + " …[truncated]"
    2494:         chunks.append(f"User: {user}\nAssistant: {assistant}")
    2495:     return "\n\n".join(chunks)
    2496:
    2497:
    2498: def read_text_excerpt(path: Path, *, chars: int) -> str:
    2499:     try:
    2500:         text = path.read_text(encoding="utf-8", errors="replace").strip()
    2501:     except Exception:
    2502:         return ""
    2503:     if len(text) > chars:
    2504:         return text[-chars:].lstrip()
    2505:     return text
    2506:
    2507:
    2508: def memory_heartbeat_warning(state: Path) -> str:
    2509:     hb = load_json(state / "memory-heartbeat.json", {})
    2510:     if not isinstance(hb, dict):
    2511:         return ""
    2512:     warnings: list[str] = []
    2513:     if hb.get("ok") is False:
    2514:         warnings.append(f"heartbeat_error={hb.get('error') or 'unknown'}")
    ... (28 more lines)

[thinking] Now I see the key structure. The `build_persistent_memory_context` function has two paths:
           1. If turn-packet.md exists → injects just the turn packet (lean mode)
           2. If no turn packet → falls back to loading all memory files individually

           The turn packet is the preferred path, and it's what we've been using. So the per-turn prompt is:
           1. **Intro line** ("You are Persistent Assistant...")
           2. **Conversation metadata** (key, session_label, room_id, etc.)
           3. **Memory context** (turn packet - which is now lean since the tail removal)
           4. **Runtime capability context** (dynamic hints to use tools)
           5. **Recent room-local conversation** (from daemon)
           6. **Recent incomplete/failed turns** (from daemon)
           7. **Recent attachments** (if any)
           8. **Replied-to context** (if a reply)
           9. **Batched messages** (if any)
           10. **Per-turn volatile reminders** (instructions for this turn)
           11. **User message**

           Th...

[tool] read (pending)
  input: {}

[tool] ../../app/matrix_lite/daemon.py (completed)
  kind: read
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    <path>/home/matrix-lite/app/matrix_lite/daemon.py</path>
    <type>file</type>
    <content>
    2380:
    2381:
    2382: def latest_relevant_attachment(row: dict[str, Any]) -> dict[str, Any] | None:
    2383:     current = row.get("attachment") if isinstance(row.get("attachment"), dict) else None
    2384:     if current:
    2385:         return {
    2386:             "event_id": row.get("event_id"),
    2387:             "sender_id": row.get("sender_id"),
    2388:             "text": row.get("text"),
    2389:             "attachment": current,
    2390:             "logged_at": row.get("logged_at"),
    2391:         }
    2392:     attachments = read_recent_attachments(row, limit=12)
    2393:     text = str(row.get("text") or "").lower()
    2394:     if "image" in text or "photo" in text or "jpg" in text or "jpeg" in text:
    2395:         for item in reversed(attachments):
    2396:             att = (
    2397:                 item.get("attachment")
    2398:                 if isinstance(item.get("attachment"), dict)
    2399:                 else {}
    2400:             )
    2401:             if str(att.get("msgtype") or "") == "m.image" or str(
    2402:                 att.get("mime") or ""
    2403:             ).startswith("image/"):
    2404:                 return item
    ... (88 more lines)

[thinking] Now let me also check how the session-level state is set (the character card / bootstrap prompt that persists across turns). I'll search for where the OpenCode config's agent prompt is used.

[tool] grep (pending)
  input: {}

[tool] agent.*prompt|build.*prompt|bootstrap|character.*card (completed)
  kind: search
  input: /home/matrix-lite/app/matrix_lite/daemon.py
  files: /home/matrix-lite/app/matrix_lite/daemon.py
  output:
    Found 9 matches
    /home/matrix-lite/app/matrix_lite/daemon.py:
      Line 5320:             cancelled = run_capture(["npx", "--yes", "acpx", "--cwd", cwd, agent_id, "cancel", "-s", session_label], timeout=min(timeout, 60), extra_env=prompt_env)

      Line 5417:                 return {"ok": ok, "agent_id": agent_id, "session_label": session_label, "cwd": cwd, "opencode_model": model, "returncode": result.returncode, "output": output[-12000:], "assistant_text": assistant_text[-12000:], "error": "" if ok else "ACPX prompt failed after same-backend nudge", "usage": usage, "lifecycle": lifecycle, "timing": {**timing, "total_seconds": elapsed_seconds(timing_start)}}

      Line 5545: def build_phase3_text_prompt(row: dict[str, Any], conversation: dict[str, Any]) -> str:

      Line 5566:         "You are Persistent Assistant running through Matrix-lite. Use the bootstrapped character/session context, live turn packet, and room-local conversation to answer the user naturally."

      Line 5617: def build_permission_bridge_failure_prompt(

      Line 5728:                 stage="building prompt",

      Line 5742:         prompt = build_phase3_text_prompt(row, conversation)

      Line 5743:         timing["prompt_build_seconds"] = elapsed_seconds(prompt_start)

      Line 5998:                 recovery_prompt = build_permission_bridge_failure_prompt(

[thinking] The session-level context (character card, bootstrap prompt) is set in the OpenCode config's `agent.build.prompt` field. Looking at the opencode config at line 1968:

           ```json
           "prompt": "Persistent Assistant for Gleb via Matrix-lite. Preserve continuity, use available tools when useful, respect approvals and memory context, and answer naturally. Detailed continuity is injected by Matrix-lite per turn.",
           ```

           This is the bootstrap/system prompt that gets set when the ACPX session is created. It persists across turns and is NOT re-sent on every turn. The per-turn prompt (built by `build_phase3_text_prompt`) is the changing part.

           Additionally, the character card JSON (from `/home/matrix-lite/state/persistent-chat/character.json`) and the bootstrap.md are also loaded at session creation.

           So the full prompt structure is:

           **Session-level (set once at session creation, persists across turn...
Here's the full breakdown of what composes each turn:

<matrix_lite_final>**1. Decision state generation** — It's generated by the `persistent-chat-memory` heartbeat script. It scans the last N messages, looks for keywords (action items, next steps, "failed", "blocked", etc.), and compiles them into `decision-state.json`. It's script-generated, not manually curated.

**2. Turn prompt composition (the actual answer to what you're seeing):**

Your turn prompt this session has TWO layers:

**Session-level (set once, persists across reloads):**
- Character card (name/personality/style)
- OpenCode bootstrap prompt (`agent.build.prompt`)
- Agent config (model, temperature, steps, permissions)
→ This is ~5k cached as you saw

**Per-turn (sent every message):**
1. Intro line referencing character context
2. Conversation metadata (key, room, event IDs)
3. **Memory context** → currently just the lean turn packet (~5 lines)
4. Runtime capability context (dynamic hints to use tools)
5. **Recent room-local conversation** ← this is your actual chat history
6. Incomplete/failed turns from earlier
7. Attachment metadata
8. Replied-to context
9. Batched messages
10. Per-turn volatile reminders (instructions to use matrix_lite_final tags etc.)
11. **Your user message**

**Duplication I can see:**
- The **intro line** says "Use the bootstrapped character/session context" — but that character context is already in the session-level prompt, so this is a pointer not a duplicate
- The **"Current thread" section** in the turn packet sometimes overlaps with the **recent room-local conversation** — we already trimmed the keywords so this is minimal now
- The **per-turn volatile reminders** repeat some instructions already in the bootstrap prompt (like `matrix_lite_final` tags)

**3. Curator vs synthesis** — Good point. The curator (model-heartbeat) has limited context. The synthesis (reflection) agent has richer prompts and more tool access. I'll handle curation myself when it matters.

**4. Why sometimes lean, sometimes not** — The lean turns happen when `turn-packet.md` exists (which it usually does now). The full memory files only get injected as a fallback when the turn packet is missing or stale. So as long as the heartbeat keeps generating a lean turn packet, we stay lean. The 540 input you see means it's working.
</matrix_lite_final>

[done] end_turn

1. How is it generated? From what?
2. Please identify what your turn prompt is actually composed of, and point out any duplication of information that already is in the session prompt. On my side it says 540 input for the previous turn, which seems okay I wrote a lot of text. Was there anything else? Is there something coming in this turn you don't need to see?
3. It seems your curator doesn't have enough info on what's going on. Maybe the synthesis agent is more suitable, it has richer prompts and will have more tool use by design. Anyway you can also do it yourself
4. I see it only happens sometimes, sometimes turns are shown as lean (you tell me)


You're spot on. Looking at what's currently per-turn vs what could be session-level:

**Should stay per-turn (dynamic):**
- Recent room-local conversation (grows every turn)
- Incomplete/failed turns (changes per turn)
- Attachment metadata (per-message)
- Replied-to context (per-message)
- Batched messages (per-turn)
- User message (obviously)
- Turn packet (heartbeat updates it regularly)

**Could be session-level (static or near-static):**
- **Runtime capability context** — same set of tool hints every turn
- **Per-turn volatile reminders** — the 6 bullet points about `matrix_lite_final` tags, approvals, etc. are identical every turn
- **Intro line** — "You are Persistent Assistant..." is already redundant with the character card + bootstrap prompt
- **Conversation metadata** (partially) — the static stuff (conversation_key, session_label) doesn't change per turn

The savings would be modest per turn (~300-400 tokens) but would compound over hundreds of turns. The bigger benefit is conceptual cleanliness — session prompt = who you are + rules, turn prompt = what's happening now.

Want me to sketch out what the daemon change would look like? Or save it as a decision state for later?

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user is suggesting that most of the per-turn prompt items should actually be session-level (set once at session start) rather than injected every turn. They want only truly dynamic things like turn failures to be per-turn.

           Let me think about what's currently per-turn (in `build_phase3_text_prompt`) and what could be moved to session-level:

           **Currently per-turn:**
           1. Intro line → could be session-level (it's static)
           2. Conversation metadata → some could be session-level (key, session_label are static)
           3. Memory context (turn packet) → this IS dynamic (changes per-turn)
           4. Runtime capability context → dynamic hints, could be session-level since tools don't change mid-session
           5. Recent room-local conversation → this IS dynamic (grows with each turn)
           6. Incomplete/failed turns → dynamic, important per-turn
           7. Attachment metadata → dynamic
           8. Replied-to context → dynamic
           9. Batched m...
<matrix_lite_final>You're spot on. Looking at what's currently per-turn vs what could be session-level:

**Should stay per-turn (dynamic):**
- Recent room-local conversation (grows every turn)
- Incomplete/failed turns (changes per turn)
- Attachment metadata (per-message)
- Replied-to context (per-message)
- Batched messages (per-turn)
- User message (obviously)
- Turn packet (heartbeat updates it regularly)

**Could be session-level (static or near-static):**
- **Runtime capability context** — same set of tool hints every turn
- **Per-turn volatile reminders** — the 6 bullet points about `matrix_lite_final` tags, approvals, etc. are identical every turn
- **Intro line** — "You are Persistent Assistant..." is already redundant with the character card + bootstrap prompt
- **Conversation metadata** (partially) — the static stuff (conversation_key, session_label) doesn't change per turn

The savings would be modest per turn (~300-400 tokens) but would compound over hundreds of turns. The bigger benefit is conceptual cleanliness — session prompt = who you are + rules, turn prompt = what's happening now.

Want me to sketch out what the daemon change would look like? Or save it as a decision state for later?
</matrix_lite_final>

[done] end_turn

It seems like most of the items from the turn need to be in session start only, except a few dynamic and important ones like turn failures


Honestly? With auto-compaction off (which it is — `"auto": false`), the ACPX session retains the full conversation history internally. I already have the context of everything we've said. The daemon re-injecting it every turn is **redundant** for normal turns.

I'd only need the conversation history in these cases:
1. **Fresh session** — first turn after reload, when the session has no prior context
2. **After compaction** — if it ever runs, the prompt rebuilds context (but auto is off)
3. **Incomplete/failed turns** — those from a previous session that I wouldn't know about
4. **Batched messages** — if multiple messages were queued while I was busy

For a normal turn like this one? Just the current user message + the turn packet (for operational context like heartbeat health) + any incomplete turns. That's it.

So yes, you could definitely trim the "Recent room-local conversation" injection for non-fresh sessions and save whatever that section currently consumes.

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] This is a great question. Let me think about this carefully.

           The ACPX session already retains conversation history. When OpenCode/ACPX processes turns, it keeps the full context. So the daemon re-injecting the "Recent room-local conversation" on every turn is duplicating what the LLM session already has in its context.

           However, there's a subtlety: the ACPX session's context is trimmed/compacted when it gets too large. After compaction, only the current prompt remains - the conversation history would be lost unless it's in the new prompt.

           But looking at the OpenCode config:
           ```json
           "compaction": {
               "auto": false,
               "prune": true,
               "reserved": 2048
           },
           ```

           Auto-compaction is disabled! So the session never compact automatically. The only time context gets trimmed is when OpenCode does a "prune" manually (which reserves 2048 tokens).

           With auto-compaction off, the full conversa...
<matrix_lite_final>Honestly? With auto-compaction off (which it is — `"auto": false`), the ACPX session retains the full conversation history internally. I already have the context of everything we've said. The daemon re-injecting it every turn is **redundant** for normal turns.

I'd only need the conversation history in these cases:
1. **Fresh session** — first turn after reload, when the session has no prior context
2. **After compaction** — if it ever runs, the prompt rebuilds context (but auto is off)
3. **Incomplete/failed turns** — those from a previous session that I wouldn't know about
4. **Batched messages** — if multiple messages were queued while I was busy

For a normal turn like this one? Just the current user message + the turn packet (for operational context like heartbeat health) + any incomplete turns. That's it.

So yes, you could definitely trim the "Recent room-local conversation" injection for non-fresh sessions and save whatever that section currently consumes.
</matrix_lite_final>

[done] end_turn

Do you actually need to see the incrementing conversation history every turn, or would just the current user message be fine?


Done. Full review saved to `/home/matrix-lite/state/persistent-chat/operational-synthesis/findings/prompt-duplication-review.md` and referenced in the knowledge base.

**Summary of the 6 duplication sources I found:**

| # | Source | Duplicates | Why wasted | Tok/turn |
|---|--------|-----------|------------|----------|
| 1 | **Conversation tail** | ACPX retained history (no compaction) | Session never forgets | ~200-500 |
| 2 | **Volatile reminders** | Bootstrap prompt rules | Same 6 bullets every turn | ~100 |
| 3 | **Intro line** | Character card | Already in session prompt | ~50 |
| 4 | **Capability hints** | Itself (static) | Same text every turn | ~75 |
| 5 | **Current thread** | Conversation tail | Same conversation, different view | ~50-125 |
| 6 | **Static metadata** | Session identifiers | Key/label never change | ~25 |

**Biggest bang for buck:** Items 2-4 are trivial to move to session-level (bootstrap prompt / OpenCode `agent.build.prompt`) and save ~225 tok/turn with zero behavioral change. The conversation tail (item 1) is the biggest variable saving but needs the most careful handling.

Ready to discuss implementation approaches whenever you want!

Output:
[acpx] session persistent-chat-20260519T155636Z (ses_1bf0bdb40ffeAFrAzQZD1eauNw) · /home/matrix-lite/workspace/persistent-chat · agent connected
[thinking] The user wants me to write a technical feature request/review of the prompt duplication sources, considering their architecture (no auto-compaction, long sessions, external file-based memory). This should be a structured document that can be reviewed later.

           Let me write this to the knowledge base as a decision/architecture note, and also save it somewhere persistent. I think the best place would be in the knowledge base as a new entry, and also perhaps as a file in the workspace or app repo.

           Actually, the user said "write a technical feature request for review" - they want me to produce it as output they can review. Let me write it clearly in my response and also save it to a file for reference.

[tool] write (pending)
  input: {}

[tool] ../../state/persistent-chat/operational-synthesis/findings/prompt-duplication-review.md (completed)
  kind: edit
  input: /home/matrix-lite/state/persistent-chat/operational-synthesis/findings/prompt-duplication-review.md
  files: /home/matrix-lite/state/persistent-chat/operational-synthesis/findings/prompt-duplication-review.md
  output:
    Wrote file successfully.

    Wrote file successfully.

    diff /home/matrix-lite/state/persistent-chat/operational-synthesis/findings/prompt-duplication-review.md (+94 lines)

[thinking] Good, now let me also add this to the knowledge base as a reference.

[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>
    49: {"ts":"2026-05-19T20:10Z","topic":"knowledge-base","source":"session bootstrap","fact":"Knowledge base at /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl. JSONL format: {ts, topic, source, fact}. This file persists across sessions (not overwritten). Topics so far: WGT (24 entries), videoctl (11), matrix-lite (12), knowledge-base (1). grep this file by topic to get quick context"}
    50: {"ts":"2026-05-19T20:10Z","topic":"knowledge-base","source":"session bootstrap","fact":"How to use this pack: when user brings up a topic, grep knowledge-base.jsonl for that topic to get durable context without tool calls. If gaps found, use websearch or read source to fill in"}

    (End of file - total 50 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 (+1 lines)
<matrix_lite_final>Done. Full review saved to `/home/matrix-lite/state/persistent-chat/operational-synthesis/findings/prompt-duplication-review.md` and referenced in the knowledge base.

**Summary of the 6 duplication sources I found:**

| # | Source | Duplicates | Why wasted | Tok/turn |
|---|--------|-----------|------------|----------|
| 1 | **Conversation tail** | ACPX retained history (no compaction) | Session never forgets | ~200-500 |
| 2 | **Volatile reminders** | Bootstrap prompt rules | Same 6 bullets every turn | ~100 |
| 3 | **Intro line** | Character card | Already in session prompt | ~50 |
| 4 | **Capability hints** | Itself (static) | Same text every turn | ~75 |
| 5 | **Current thread** | Conversation tail | Same conversation, different view | ~50-125 |
| 6 | **Static metadata** | Session identifiers | Key/label never change | ~25 |

**Biggest bang for buck:** Items 2-4 are trivial to move to session-level (bootstrap prompt / OpenCode `agent.build.prompt`) and save ~225 tok/turn with zero behavioral change. The conversation tail (item 1) is the biggest variable saving but needs the most careful handling.

Ready to discuss implementation approaches whenever you want!
</matrix_lite_final>

[done] end_turn

Hmm please write a technical feature request for review of all of these sources of duplication, considering our unusual architecture (no compaction, long sessions, outside file memory). Briefly include your best reasoning.
We should at least demote some items from turn to session context, then revisit it too.