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wiki: add Internals code tour, fix Architecture cache + survey, replace Roadmap with status pointer (#53)

Jeff Smith 2026-04-06 23:21:37 -06:00
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78
Architecture.md

@ -1,5 +1,9 @@
# Architecture # Architecture
> This page is the high-level map. For a code-level walkthrough with
> file:line references — how the dir loop actually works, where to add a
> tool, what the cache really stores — read [Internals](Internals).
## Overview ## Overview
Luminos is a zero-dependency Python CLI at its base. The `--ai` flag layers an Luminos is a zero-dependency Python CLI at its base. The `--ai` flag layers an
@ -49,54 +53,94 @@ scan(target)
``` ```
analyze_directory(report, target) analyze_directory(report, target)
│
└── _run_investigation()
│
├── _get_investigation_id() new UUID, or resume an existing one
│ │
├── _discover_directories() find all dirs, sort leaves-first ├── _discover_directories() find all dirs, sort leaves-first
│ │
├── per-directory loop (each dir, up to max_turns=14) ├── _run_survey() single short loop, max 3 turns
│ _build_dir_context() list files + sizes │ inputs: survey_signals + 2-level tree preview
│ Tools: submit_survey
│ output: shared description, approach, relevant_tools,
│ skip_tools, domain_notes, confidence
│ (skipped via _default_survey() on tiny targets)
│
├── _filter_dir_tools(survey) remove skip_tools (if confidence ≥ 0.5)
│
├── per-directory loop (each uncached dir, up to max_turns=14)
│ _build_dir_context() list files + sizes + MIME
│ _get_child_summaries() read cached child summaries │ _get_child_summaries() read cached child summaries
│ _run_dir_loop() agent loop: read files, parse structure, │ _format_survey_block() inject survey context into prompt
│ write cache entries, submit_report │ _run_dir_loop() agent loop with budget check on
│ Tools: read_file, list_directory, │ every iteration; flushes a partial
│ run_command, parse_structure, │ cache entry on budget breach
│ write_cache, think, checkpoint, │ Tools: read_file, list_directory, run_command,
│ parse_structure, write_cache, think, checkpoint,
│ flag, submit_report │ flag, submit_report
│ │
├── _run_synthesis() one-shot aggregation of dir summaries ├── _run_synthesis() single loop, max 5 turns
│ reads all "dir" cache entries │ reads all "dir" cache entries
│ produces brief (2-4 sentences) + detailed (free-form) │ produces brief (2-4 sentences) + detailed (free-form)
│ Tools: read_cache, list_cache, flag, submit_report │ Tools: read_cache, list_cache, flag, submit_report
│ fallback: _synthesize_from_cache() if out of turns
│ │
└── returns (brief, detailed, flags) └── returns (brief, detailed, flags)
``` ```
Token usage and the context budget are tracked by `_TokenTracker` in
`ai.py`. The budget check uses the *most recent* call's `input_tokens`,
not the cumulative sum across turns — see #44 and the
[Internals](Internals) page §4.4 for why.
--- ---
## Cache ## Cache
Location: `/tmp/luminos/<investigation_id>/` Location: `/tmp/luminos/<investigation_id>/`
Two entry types, both stored as JSONL: Layout:
**File entries** (`files.jsonl`):
``` ```
{path, relative_path, size_bytes, category, summary, notable, meta.json investigation metadata
notable_reason, cached_at} files/<sha256>.json one JSON file per cached file entry
dirs/<sha256>.json one JSON file per cached directory entry
flags.jsonl JSONL — appended on every flag tool call
investigation.log JSONL — appended on every tool call
``` ```
**Dir entries** (`dirs.jsonl`): File and dir entries are stored as one sha256-keyed JSON file per entry
(not as JSONL) so that `has_entry(path)` is an O(1) `os.path.exists()`
check rather than a file scan. Only `flags.jsonl` and `investigation.log`
are JSONL.
**File entries** (`files/<sha256>.json`):
``` ```
{path, relative_path, child_count, summary, dominant_category, {path, relative_path, size_bytes, category, summary, cached_at,
notable_files, cached_at} [confidence], [confidence_reason], [notable], [notable_reason]}
``` ```
**Dir entries** (`dirs/<sha256>.json`):
```
{path, relative_path, child_count, dominant_category, summary, cached_at,
[confidence], [confidence_reason], [notable_files],
[partial], [partial_reason]}
```
`partial: true` marks a dir entry written by the budget-breach early-exit
path — the agent didn't reach `submit_report` and the summary was
synthesized from already-cached file entries.
**Flags** (`flags.jsonl`): **Flags** (`flags.jsonl`):
``` ```
{path, finding, severity} severity: info | concern | critical {path, finding, severity} severity: info | concern | critical
``` ```
Cache is reused across runs for the same target. `--fresh` ignores it. Investigation IDs are persisted in `/tmp/luminos/investigations.json`
`--clear-cache` deletes it. keyed by absolute target path. Cache is reused across runs for the same
target. `--fresh` mints a new investigation ID. `--clear-cache` deletes
the entire cache root.
--- ---

5
DevelopmentGuide.md

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# Development Guide # Development Guide
> This page covers **how to set up, run, and test** Luminos. For a
> code-level walkthrough of how the AI pipeline actually works — the dir
> loop, the cache, the survey pass, where to add a tool — read
> [Internals](Internals).
## Running Luminos ## Running Luminos
```bash ```bash

5
Home.md

@ -21,8 +21,9 @@ of what the directory contains and why.
| Page | Contents | | Page | Contents |
|---|---| |---|---|
| [Architecture](Architecture) | Module breakdown, data flow, AI pipeline | | [Architecture](Architecture) | Module breakdown, data flow, AI pipeline |
| [Development Guide](DevelopmentGuide) | Git workflow, naming conventions, commands | | [Internals](Internals) | Code-level tour: dir loop, cache, prompts, where to make changes |
| [Roadmap](Roadmap) | Planned phases and open design questions | | [Development Guide](DevelopmentGuide) | Setup, git workflow, testing, commands |
| [Roadmap](Roadmap) | Phase status — pointer to PLAN.md and open issues |
| [Session Retrospectives](SessionRetrospectives) | Full session history | | [Session Retrospectives](SessionRetrospectives) | Full session history |
--- ---

514
Internals.md Normal file

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# Internals
A code tour of how Luminos actually works. Read this after
[Development Guide](DevelopmentGuide) and [Architecture](Architecture). The
goal is that a developer who knows basic Python but has never built an
agent loop can finish this page and start making non-trivial changes.
All file:line references are accurate as of the date this page was last
edited — verify with `git log` or by opening the file before relying on a
specific line number.
---
## 1. The two layers
Luminos has a hard internal split:
| Layer | What it does | Imports |
|---|---|---|
| **Base scan** | Walks the directory, classifies files, counts lines, ranks recency, measures disk usage, prints a report. | stdlib only + GNU coreutils via subprocess. **No pip packages.** |
| **AI pipeline** (`--ai`) | Runs a multi-pass agent investigation via the Claude API on top of the base scan output. | `anthropic`, `tree-sitter`, `python-magic` — all imported lazily. |
The split is enforced by lazy imports. `luminos.py:156` is the only place
that imports from `luminos_lib.ai`, and it sits inside `if args.ai:`. You
can grep the codebase to verify: nothing in the base scan modules imports
anything from `ai.py`, `ast_parser.py`, or `prompts.py`. This means
`python3 luminos.py /target` works on a stock Python 3 install with no
packages installed at all.
When you change a base-scan module, the question to ask is: *does this
introduce a top-level import of anything outside stdlib?* If yes, you've
broken the constraint and the change must be rewritten.
---
## 2. Base scan walkthrough
Entry: `luminos.py:main()` parses args, then calls `scan(target, ...)` at
`luminos.py:45`. `scan()` is a flat sequence — it builds a `report` dict
by calling helpers from `luminos_lib/`, one per concern, in order:
```
scan(target)
build_tree() → report["tree"], report["tree_rendered"]
classify_files() → report["classified_files"]
summarize_categories() → report["file_categories"]
survey_signals() → report["survey_signals"] ← input to AI survey
detect_languages() → report["languages"], report["lines_of_code"]
find_large_files() → report["large_files"]
find_recent_files() → report["recent_files"]
get_disk_usage() → report["disk_usage"]
top_directories() → report["top_directories"]
return report
```
Each helper is independent. You could delete `find_recent_files()` and the
report would just be missing that field. The flow is procedural, not
event-driven, and there is no shared state object — everything passes
through the local `report` dict.
The progress lines you see on stderr (`[scan] Counting lines... foo.py`)
come from `_progress()` in `luminos.py:23`, which returns an `on_file`
callback that the helpers call as they work. If you add a new helper that
walks files, plumb a progress callback through the same way for
consistency.
After `scan()` returns, `main()` either runs the AI pipeline or jumps
straight to `format_report()` (`luminos_lib/report.py`) for terminal
output, or `json.dumps()` for JSON. The AI pipeline always runs *after*
the base scan because it needs `report["survey_signals"]` and
`report["file_categories"]` as inputs.
---
## 3. AI pipeline walkthrough
The AI pipeline is what makes Luminos interesting and is also where
almost all the complexity lives. Everything below happens inside
`luminos_lib/ai.py` (1438 lines as of writing), called from
`luminos.py:157` via `analyze_directory()`.
### 3.1 The orchestrator
`analyze_directory()` (`ai.py:1408`) is a thin wrapper that checks
dependencies, gets the API key, builds the Anthropic client, and calls
`_run_investigation()`. If anything fails it prints a warning and returns
empty strings — the rest of luminos keeps working.
`_run_investigation()` (`ai.py:1286`) is the real entry point. Read this
function first if you want to understand the pipeline shape. It does six
things, in order:
1. **Get/create an investigation ID and cache** (`ai.py:1289–1294`).
Investigation IDs let you resume a previous run; see §5 below.
2. **Discover all directories** under the target via
`_discover_directories()` (`ai.py:715`). Returns them sorted
*leaves-first* — the deepest paths come first. This matters because
each dir loop reads its child directories' summaries from cache, so
children must be investigated before parents.
3. **Run the survey pass** (`ai.py:1300–1334`) unless the target is below
the size thresholds at `ai.py:780–781`, in which case
`_default_survey()` returns a synthetic skip.
4. **Filter out cached directories** (`ai.py:1336–1349`). If you're
resuming an investigation, dirs that already have a `dir` cache entry
are skipped — only new ones get a fresh dir loop.
5. **Run a dir loop per remaining directory** (`ai.py:1351–1375`). This
is the heart of the system — see §4.
6. **Run the synthesis pass** (`ai.py:1382`) reading only `dir` cache
entries to produce `(brief, detailed)`.
It also reads `flags.jsonl` from disk at the end (`ai.py:1387–1397`) and
returns `(brief, detailed, flags)` to `analyze_directory()`.
### 3.2 The survey pass
`_run_survey()` (`ai.py:1051`) is a short, single-purpose loop. It exists
to give the dir loops some shared context about what they're looking at
*as a whole* before any of them start.
Inputs go into the system prompt (`_SURVEY_SYSTEM_PROMPT` in
`prompts.py`):
- `survey_signals` — extension histogram, `file --brief` outputs, filename
samples (built by `filetypes.survey_signals()` during the base scan)
- A 2-level tree preview from `build_tree(target, max_depth=2)`
- The list of tools the dir loop will have available
The survey is allowed only `submit_survey` as a tool (`_SURVEY_TOOLS` at
`ai.py:356`). It runs at most 3 turns. The agent must call `submit_survey`
exactly once with six fields:
```python
{
"description": "plain language — what is this target",
"approach": "how the dir loops should investigate it",
"relevant_tools": ["read_file", "parse_structure", ...],
"skip_tools": ["parse_structure", ...], # for non-code targets
"domain_notes": "anything unusual the dir loops should know",
"confidence": 0.0–1.0,
}
```
The result is a Python dict that gets passed into every dir loop as
`survey=...`. If the survey fails (API error, ran out of turns), the dir
loops still run but with `survey=None` — the system degrades gracefully.
### 3.3 How the survey shapes dir loops
Two things happen with the survey output before each dir loop runs:
**Survey block injection.** `_format_survey_block()` (`ai.py:803`) renders
the survey dict as a labeled text block, which gets `.format()`-injected
into the dir loop system prompt as `{survey_context}`. The dir agent sees
the description, approach, domain notes, and which tools it should lean on
or skip.
**Tool filtering.** `_filter_dir_tools()` (`ai.py:824`) returns a copy of
`_DIR_TOOLS` with anything in `skip_tools` removed — but only if the
survey's confidence is at or above `_SURVEY_CONFIDENCE_THRESHOLD = 0.5`
(`ai.py:775`). Below that threshold the agent gets the full toolbox. The
control-flow tool `submit_report` is in `_PROTECTED_DIR_TOOLS` and can
never be filtered out — removing it would break loop termination.
This is the only place in the codebase where the agent's available tools
change at runtime. If you add a new tool, decide whether it should be
protectable.
---
## 4. The dir loop in depth
`_run_dir_loop()` is at `ai.py:845`. This is a hand-written agent loop and
you should expect to read it several times before it clicks. The shape is:
```
build system prompt (with survey context, child summaries, dir contents)
build initial user message ("investigate this directory now")
reset per-loop token counter
for turn in range(max_turns): # max_turns = 14
if budget exceeded: flush partial cache and break
call API (streaming)
record token usage
print text blocks and tool decisions to stderr
append assistant response to message history
if no tool calls: nudge agent to call submit_report; continue
execute each tool call, build tool_result blocks
append tool_results to message history as user message
if submit_report was called: break
return summary
```
A few non-obvious mechanics:
### 4.1 The message history grows monotonically
Every turn appends an assistant message (the model's response) and a user
message (the tool results). Nothing is ever evicted. This means
`input_tokens` on each successive API call grows roughly linearly — the
model is re-sent the full conversation every turn. On code targets we see
~1.5–2k tokens added per turn. At `max_turns=14` this stays under the
budget; raising the cap would expose this. See **#51**.
### 4.2 Tool dispatch
Tools are not class methods. They're plain functions in `ai.py:486–642`,
registered into `_TOOL_DISPATCH` at `ai.py:645`. `_execute_tool()`
(`ai.py:659`) is a 16-line function that looks up the handler by name,
calls it, logs the turn to `investigation.log`, and returns the result
string. **The two control-flow tools — `submit_report` and `think`/
`checkpoint` for narration — are NOT in `_TOOL_DISPATCH`** because the
loop body handles them specially:
- `submit_report` is recognized in the tool-use scan at `ai.py:977`, sets
`done = True`, and doesn't go through dispatch
- `think`, `checkpoint`, and `flag` *are* in dispatch, but they have side
effects that just print to stderr or append to `flags.jsonl` — the
return value is always `"ok"`
When you add a tool: write the function, add it to `_TOOL_DISPATCH`, add
its schema to `_DIR_TOOLS`. That's it.
### 4.3 Pre-loaded context
Before the loop starts, two helpers prepare static context that goes into
the system prompt:
- `_build_dir_context()` (`ai.py:736`) — `ls`-style listing of the dir
with sizes and MIME types via `python-magic`. The agent sees this
*before* it makes any tool calls, so it doesn't waste a turn just
listing the directory.
- `_get_child_summaries()` (`ai.py:758`) — looks up each subdirectory in
the cache and pulls its `summary` field. This is how leaves-first
ordering pays off: by the time the loop runs on `src/`, all of
`src/auth/`, `src/db/`, `src/middleware/` already have cached summaries
that get injected as `{child_summaries}`.
If `_get_child_summaries()` returns nothing, the prompt says
`(none — this is a leaf directory)`.
### 4.4 The token tracker and the budget check
`_TokenTracker` (`ai.py:94`) is a tiny accumulator with one important
subtlety, captured in **#44**:
> Cumulative input tokens are NOT a meaningful proxy for context size:
> each turn's `input_tokens` already includes the full message history,
> so summing across turns double-counts everything. Use `last_input` for
> budget decisions, totals for billing.
So `budget_exceeded()` (`ai.py:135`) compares `last_input` (the most
recent call's input_tokens) to `CONTEXT_BUDGET` (`ai.py:40`), which is
70% of 200k. This is checked at the *top* of each loop iteration, before
the next API call.
When the budget check trips, the loop:
1. Prints a `Context budget reached` warning to stderr
2. If no `dir` cache entry exists yet, builds a *partial* one from any
`file` cache entries the agent already wrote (`ai.py:889–937`), marks
it with `partial: True` and `partial_reason`, and writes it
3. Breaks out of the loop
This means a budget breach doesn't lose work — anything the agent already
cached survives, and the synthesis pass will see a partial dir summary
rather than nothing.
### 4.5 What the loop returns
`_run_dir_loop()` returns the `summary` string from `submit_report` (or
the partial summary if the budget tripped). `_run_investigation()` then
writes a normal `dir` cache entry from this summary at `ai.py:1363–1375`
— *unless* the dir loop already wrote one itself via the partial-flush
path, in which case the `cache.has_entry("dir", dir_path)` check skips it.
### 4.6 The streaming API caller
`_call_api_streaming()` (`ai.py:681`) is a thin wrapper around
`client.messages.stream()`. It currently doesn't print tokens as they
arrive — it iterates the stream, drops everything, then pulls the final
message via `stream.get_final_message()`. The streaming API is used for
real-time tool decision printing, which today happens only after the full
response arrives. There's room here to add live progress printing if you
want it.
---
## 5. The cache model
Cache lives at `/tmp/luminos/{investigation_id}/`. Code is
`luminos_lib/cache.py` (201 lines).
### 5.1 Investigation IDs
`/tmp/luminos/investigations.json` maps absolute target paths to UUIDs.
`_get_investigation_id()` (`cache.py:40`) looks up the target and either
returns the existing UUID (resume) or creates a new one (fresh run).
`--fresh` forces a new UUID even if one exists.
### 5.2 What's stored
Inside `/tmp/luminos/{uuid}/`:
```
meta.json investigation metadata (model, start time, dir count)
files/<sha256>.json one file per cached file entry
dirs/<sha256>.json one file per cached directory entry
flags.jsonl JSONL — appended on every flag tool call
investigation.log JSONL — appended on every tool call
```
**File and dir cache entries are NOT in JSONL** — they are one
sha256-keyed JSON file per entry. The sha256 is over the path string
(`cache.py:13`). Only `flags.jsonl` and `investigation.log` use JSONL.
Required fields are validated in `write_entry()` (`cache.py:115`):
```python
file: {path, relative_path, size_bytes, category, summary, cached_at}
dir: {path, relative_path, child_count, dominant_category, summary, cached_at}
```
The validator also rejects entries containing `content`, `contents`, or
`raw` fields — the agent is explicitly forbidden from caching raw file
contents, summaries only. If you change the schema, update the required
set in `write_entry()` and update the test in `tests/test_cache.py`.
### 5.3 Confidence support already exists
`write_entry()` validates an optional `confidence` field
(`cache.py:129–134`) and a `confidence_reason` string.
`low_confidence_entries(threshold=0.7)` (`cache.py:191`) returns all
entries below a threshold, sorted ascending. The agent doesn't currently
*set* these fields in any prompt — that lights up when Phase 1 work
actually wires the prompts.
### 5.4 Why one-file-per-entry instead of JSONL
Random access by path. The dir loop calls `cache.has_entry("dir", path)`
once per directory during the `_get_child_summaries()` lookup; with
sha256-keyed files this is an `os.path.exists()` call. With JSONL it
would be a full file scan.
---
## 6. Prompts
All prompt templates live in `luminos_lib/prompts.py`. There are three:
| Constant | Used by | What it carries |
|---|---|---|
| `_SURVEY_SYSTEM_PROMPT` | `_run_survey` | survey_signals, tree_preview, available_tools |
| `_DIR_SYSTEM_PROMPT` | `_run_dir_loop` | dir_path, dir_rel, max_turns, context, child_summaries, survey_context |
| `_SYNTHESIS_SYSTEM_PROMPT` | `_run_synthesis` | target, summaries_text |
Each is a Python f-string-style template with `{name}` placeholders. The
caller assembles values and passes them to `.format(...)` immediately
before the API call. There is no template engine — it's plain string
formatting.
When you change a prompt, the only thing you need to keep in sync is the
set of placeholders. If you add `{foo}` to the template, the caller must
provide `foo=...`. If you remove a placeholder from the template but
leave the kwarg in the caller, `.format()` silently ignores it. If you
add a placeholder and forget to provide it, `.format()` raises `KeyError`
at runtime.
`prompts.py` has no logic and no tests — it's listed in
[Development Guide](DevelopmentGuide) as exempt from unit testing for
that reason.
---
## 7. Synthesis pass
`_run_synthesis()` (`ai.py:1157`) is structurally similar to the dir loop
but much simpler:
- Reads all `dir` cache entries via `cache.read_all_entries("dir")`
- Renders them into a `summaries_text` block (one section per dir)
- Stuffs that into `_SYNTHESIS_SYSTEM_PROMPT`
- Loops up to `max_turns=5` waiting for `submit_report` with `brief` and
`detailed` fields
Tools available: `read_cache`, `list_cache`, `flag`, `submit_report`
(`_SYNTHESIS_TOOLS` at `ai.py:401`). The synthesis agent can pull
specific cache entries back if it needs to drill in, but it cannot read
files directly — synthesis is meant to operate on summaries, not raw
contents.
There's a fallback: if synthesis runs out of turns without calling
`submit_report`, `_synthesize_from_cache()` (`ai.py:1262`) builds a
mechanical brief+detailed from the cached dir summaries with no AI call.
This guarantees you always get *something* in the report.
---
## 8. Flags
The `flag` tool is the agent's pressure valve for "I noticed something
that should not be lost in the summary." `_tool_flag()` (`ai.py:629`)
prints to stderr *and* appends a JSONL line to
`{cache.root}/flags.jsonl`. At the end of `_run_investigation()`
(`ai.py:1387–1397`), the orchestrator reads that file back and includes
the flags in its return tuple. `format_report()` then renders them in a
dedicated section.
Severity is `info | concern | critical`. The agent is told to flag
*immediately* on discovery, not save findings for the report — this is in
the tool description at `ai.py:312`.
---
## 9. Where to make common changes
A cookbook for the kinds of changes that come up most often.
### 9.1 Add a new tool the dir agent can call
1. Write the implementation: `_tool_<name>(args, target, cache)` somewhere
in the tool implementations section of `ai.py` (~lines 486–642).
Return a string.
2. Add it to `_TOOL_DISPATCH` at `ai.py:645`.
3. Add its schema to `_DIR_TOOLS` at `ai.py:151`. The schema must follow
Anthropic tool-use shape: `name`, `description`, `input_schema`.
4. Decide whether the survey should be able to filter it out (default:
yes — leave it out of `_PROTECTED_DIR_TOOLS`) or whether it's
control-flow critical (add to `_PROTECTED_DIR_TOOLS`).
5. Update `_DIR_SYSTEM_PROMPT` in `prompts.py` if the agent needs
instructions on when to use the new tool.
6. There is no unit test for tool registration today (`ai.py` is exempt).
If you want coverage, the test would mock `client.messages.stream` and
assert that the dispatch table contains your tool.
### 9.2 Add a whole new pass
(Phase 3's planning pass is the immediate example.) The pattern:
1. Define a new system prompt constant in `prompts.py`
2. Define a new tool list in `ai.py` for the pass-specific submit tool
3. Write `_run_<pass>()` in `ai.py`, modeled on `_run_survey()` — single
submit tool, low max_turns, returns a dict or `None` on failure
4. Wire it into `_run_investigation()` between existing passes
5. Pass its output downstream by adding a kwarg to `_run_dir_loop()` (or
wherever it's needed) and threading it through
The survey pass is the cleanest reference implementation because it's
short and self-contained.
### 9.3 Change a prompt
Edit the constant in `prompts.py`. If you add a `{placeholder}`, also
update the corresponding `.format(...)` call in `ai.py`. Search the
codebase for the constant name to find the call site:
```
grep -n SURVEY_SYSTEM_PROMPT luminos_lib/ai.py
```
There is no prompt versioning today. Investigation cache entries don't
record which prompt version produced them, so re-running with a new
prompt against an existing investigation will mix old and new outputs
unless you `--fresh`.
### 9.4 Change cache schema
1. Update the required-fields set in `cache.py:write_entry()`
(`cache.py:119–123`)
2. Update `_DIR_TOOLS`'s `write_cache` description in `ai.py:228` so the
agent knows what to write
3. Update `_DIR_SYSTEM_PROMPT` in `prompts.py` if the agent needs to know
*how* to populate the new field
4. Update `tests/test_cache.py` — schema validation is the part of the
cache that *is* covered
### 9.5 Add a CLI flag
Edit `luminos.py:88` (`main()`'s argparse setup) to define the flag, then
plumb it through whatever functions need it. New AI-related flags
typically need to be added to `analyze_directory()`'s signature
(`ai.py:1408`) and then forwarded to `_run_investigation()`.
---
## 10. Token budget and cost
Budget logic is in `_TokenTracker.budget_exceeded()` and is checked at the
top of every dir loop iteration (`ai.py:882`). The budget is **per call**,
not cumulative — see §4.4. The breach handler flushes a partial dir cache
entry so work isn't lost.
Cost reporting happens once at the end of `_run_investigation()`
(`ai.py:1399`), using the cumulative `total_input` and `total_output`
counters multiplied by the constants at `ai.py:43–44`. There is no
running cost display during the investigation today. If you want one,
`_TokenTracker.summary()` already returns the formatted string — just
call it after each dir loop.
---
## 11. Glossary
| Term | Meaning |
|---|---|
| **base scan** | The non-AI phase: tree, classification, languages, recency, disk usage. Stdlib + coreutils only. |
| **dir loop** | Per-directory agent loop in `_run_dir_loop`. Up to 14 turns. Produces a `dir` cache entry. |
| **survey pass** | Single short loop before any dir loops, producing a shared description and tool guidance. |
| **synthesis pass** | Final loop that reads `dir` cache entries and produces `(brief, detailed)`. |
| **leaves-first** | Discovery order in `_discover_directories`: deepest paths first, so child summaries exist when parents are investigated. |
| **investigation** | One end-to-end run, identified by a UUID, persisted under `/tmp/luminos/{uuid}/`. |
| **investigation_id** | The UUID. Stored in `/tmp/luminos/investigations.json` keyed by absolute target path. |
| **cache entry** | A JSON file under `files/` or `dirs/` named by sha256(path). |
| **flag** | An agent finding written to `flags.jsonl` and reported separately. info / concern / critical. |
| **partial entry** | A `dir` cache entry written when the budget tripped before `submit_report`. Marked with `partial: True`. |
| **survey signals** | The histogram + samples computed by `filetypes.survey_signals()` during the base scan, fed to the survey prompt. |
| **last_input** | The `input_tokens` count from the most recent API call. The basis for budget checks. NOT the cumulative sum. |
| **CONTEXT_BUDGET** | 70% of 200k = 140k. Trigger threshold for early exit. |
| **`_PROTECTED_DIR_TOOLS`** | Tools the survey is forbidden from filtering out of the dir loop's toolbox. Currently `{submit_report}`. |

144
Roadmap.md

@ -1,125 +1,37 @@
# Roadmap # Roadmap
Full design notes and open questions live in `PLAN.md` in the repo root. The roadmap used to live here as a static phase list. It drifted out of
This page tracks phase status. sync with reality (Phase 2 was marked "Not started" months after it
shipped) so it has been replaced with pointers to the two sources that
actually stay current.
--- ## Where the roadmap lives now
## Core Philosophy **Design and rationale** → [`PLAN.md`](https://forgejo.labbity.unbiasedgeek.com/archeious/luminos/src/branch/main/PLAN.md)
in the repo root. Phase descriptions, philosophy, file map, known
unknowns, concerns. This is the long-form *why*.
Move from a **pipeline with AI steps** to **investigation driven by curiosity**. **Current status and active work** → [Open issues](https://forgejo.labbity.unbiasedgeek.com/archeious/luminos/issues)
The agent should decide what it needs to know and how to find it out — not on Forgejo. Issues track what is in flight, what is done (closed), and
execute a predetermined checklist. what is queued. Each phase corresponds to a set of issues; closed issues
are the ground truth for "is this shipped."
--- ## Phase status at a glance
## Phases | Phase | Topic | Status |
### Phase 1 — Confidence Tracking
Add `confidence` + `confidence_reason` to file and dir cache entries.
Agent sets this when writing cache. Enables later phases to prioritize
re-investigation of uncertain entries.
**Status:** Not started
---
### Phase 2 — Survey Pass
Lightweight pre-investigation pass. Agent looks at file type distribution
and tree structure, then answers: what is this, how should I investigate it,
which tools are relevant?
Replaces hardcoded domain detection with AI-driven characterization.
Survey output injected into dir loop system prompts as context.
**Status:** Not started
---
### Phase 3 — Investigation Planning
After survey, a planning pass allocates investigation depth per directory.
Replaces fixed max_turns-per-dir with a global turn budget the agent manages.
Priority dirs get more turns; trivial dirs get fewer; generated/vendored dirs
get skipped.
**Status:** Not started
---
### Phase 4 — External Knowledge Tools
Resolution strategies for uncertainty beyond local files:
- `web_search` — unfamiliar library, format, API
- `package_lookup` — PyPI / npm / crates.io metadata
- `fetch_url` — follow URLs referenced in local files
- `ask_user` — interactive mode, last resort
All gated behind `--no-external` flag. Budget-limited per session.
**Status:** Not started
---
### Phase 5 — Scale-Tiered Synthesis
Calibrate synthesis input and depth to target size:
| Tier | Size | Approach |
|---|---|---| |---|---|---|
| small | <5 dirs / <30 files | Per-file cache entries as synthesis input | | 1 | Confidence tracking | ✅ shipped |
| medium | 5–30 dirs | Dir summaries (current) | | 2 | Survey pass | ✅ shipped |
| large | 31–150 dirs | Multi-level synthesis | | 2.5 | Context budget reliability (#44) | ✅ shipped |
| xlarge | >150 dirs | Multi-level + subsystem grouping | | 3 | Investigation planning | ⏳ next |
| 3.5 | MCP backend abstraction (#39) | planned |
| 4 | External knowledge tools | planned |
| 4.5 | Unit of analysis (#48) | planned |
| 5 | Scale-tiered synthesis | planned |
| 6 | Multi-level synthesis | planned |
| 7 | Hypothesis-driven synthesis | planned |
| 8 | Refinement pass | planned |
| 9 | Dynamic report structure | planned |
**Status:** Not started For details on any phase, read the matching section of `PLAN.md` and
search open issues for the phase number or feature name.
---
### Phase 6 — Multi-Level Synthesis
For large/xlarge: grouping pass identifies logical subsystems from dir
summaries (not directory structure). Final synthesis receives 3–10 subsystem
summaries rather than hundreds of dir summaries.
**Status:** Not started
---
### Phase 7 — Hypothesis-Driven Synthesis
Synthesis reframed from aggregation to conclusion-with-evidence. Agent
forms a hypothesis, looks for confirming/refuting evidence, considers
alternatives, then submits.
Produces analytical output rather than descriptive output.
**Status:** Not started
---
### Phase 8 — Refinement Pass
Post-synthesis targeted re-investigation. Agent receives current synthesis,
identifies gaps and contradictions, goes back to actual files (or external
sources), submits improved report.
Triggered by `--refine` flag. `--refine-depth N` for multiple passes.
**Status:** Not started
---
### Phase 9 — Dynamic Report Structure
Synthesis produces a superset of possible output fields; report formatter
renders only populated ones. Output naturally scales from minimal (small
simple targets) to comprehensive (large complex targets).
**Status:** Not started
---
## Open Design Questions
See `PLAN.md` — Known Unknowns and Concerns sections.
Key unresolved items:
- Which search API to use for web_search
- Whether external tools should be opt-in or opt-out by default
- How to handle confidence calibration (numeric vs categorical)
- Config file format and location for tunable thresholds
- Progressive output / interactive mode UX design