Agent-Blackbox

KO | EN

gitlite — search

#python #react #hacktoberfest #typescript #game #cpp #machine-learning #docker #go #golang #vue #c

★ 70 Open GitHub ↗

Local-first flight recorder for coding agents : replay every run as a live session map, score the context bill, and write the fix back into AGENTS.md — no API key, one npx command.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Agent-Blackbox

**Open your coding agent's black box.**

English ·
 <a href="./README.ko.md">한국어</a> ·
 <a href="./README.zh.md">中文</a> ·
 <a href="./README.ja.md">日本語</a>

Agent-Blackbox is a **local-first flight recorder and context-efficiency profiler for coding agents.** It turns every agent run into a **live, replayable operational graph** — what the agent read, changed, ran, decided, delegated, blocked on, and verified — reconstructed from observed events, not from the agent's own summary. Then it scores the run on **two axes** — how economically it used its context window, *and* whether the task actually landed — judged on a yardstick that **fits the task type** (research / debug / ops…) and **your own past runs**, and tells you, concretely, how to make the next one cheaper and faster.

**Works with [Claude Code](https://www.claude.com/product/claude-code), [Codex](https://developers.openai.com/codex/), and [OpenCode](https://opencode.ai)** — same recorder, same map, same efficiency score. Record one host or all of them at once.

> *"The transcript is what the agent said. The black box is what it did — and what it cost."*

[**taewoopark.com** — author site](https://taewoopark.com)

---

## Why Agent-Blackbox

You can't just **ask** the agent what a task cost. A 2026 study of eight frontier models on agentic coding (SWE-bench Verified) found they predict their own token usage with a correlation of just **0.39 — and systematically underestimate** the real bill. Same task, same model: runs vary **up to 30×** in tokens. Expert difficulty ratings barely track real cost. And agentic runs already burn **~1000× more tokens** than ordinary coding, almost all of it *input* context.

> So don't ask — **measure.** Agent-Blackbox replays every run as an observed session map, scores exactly what it cost, and writes the fix back.

Bai et al., *How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks*, [arXiv:2604.22750](https://arxiv.org/abs/2604.22750) (2026).

---

## Quickstart

**One command. Works with Claude Code, Codex, and OpenCode** (needs Node 20+):

```bash
# Record Claude Code — nothing to install; the daemon tails the session
# transcripts it already writes (~/.claude/projects/)
npx @taewooopark/agent-blackbox up --host claude-code

# …or record Codex — CLI and desktop app, also with no recorder install
npx @taewooopark/agent-blackbox up --host codex

# …or record OpenCode (installs the recorder into OpenCode's global plugin dir)
npx @taewooopark/agent-blackbox up

# …or record every supported host at once, into one dashboard
npx @taewooopark/agent-blackbox up --host all
```

Either way it starts the daemon and **opens the dashboard** (`http://127.0.0.1:5173/`; add `--no-open` to skip). Now use the agent exactly the way you already do — the map fills in live:

```bash
claude            # Claude Code, in any folder — zero setup, just run it
codex              # Codex CLI (desktop-app tasks are captured too)
opencode          # …or OpenCode (terminal or the desktop app)
```

- **Claude Code needs no install at all** — the daemon tails the JSONL transcripts the CLI already writes, so any folder, any session is recorded the moment you run `claude`. (Add `--optimize` to also install the opt-in in-run actuator hooks.)
- **Codex needs no recorder install either** — `--host codex` tails `$CODEX_HOME/sessions` (default `~/.codex/sessions`) across the CLI and desktop app. Add `--optimize` to install the optional Codex actuator, then trust it once with `/hooks`.
- **OpenCode** records via a recorder dropped into its **global** plugin directory (`~/.config/opencode/plugins/`) — any session, any folder, the desktop app included.
- **Gajae-Code** *(experimental)* — `--host gjc` tails [Gajae-Code](https://github.com/Yeachan-Heo/gajae-code) sessions (`~/.gjc/agent/sessions/`), no install; also covered by `--host all`.

Stop recording any time with `npx @taewooopark/agent-blackbox uninstall`.

<details>
<summary>Scope OpenCode to one project, or run from source</summary>

```bash
# Record just one OpenCode project (recorder lands in <dir>/.opencode instead of globally)
npx @taewooopark/agent-blackbox up --project /path/to/your/project

# From source (development / contributing)
git clone https://github.com/TaewoooPark/Agent-Blackbox
cd Agent-Blackbox && npm install && npm run build:cli
node packages/cli/dist/cli.js up --host claude-code # or: up | up --host all
```
</details>

The map assembles itself live. That's it.

### Recipes

```bash
# Just watch Claude Code — start it once, then use `claude` anywhere
npx @taewooopark/agent-blackbox up --host claude-code
claude   # in any folder; the dashboard fills in live

# Record both hosts together, with tailored fixes from a free/local model
npx @taewooopark/agent-blackbox up --host all --suggest ollama --suggest-model qwen2.5-coder

# Multi-agent: just delegate in your normal session — each subagent forks into its own lane
claude "Delegate exploration, implementation, and tests to subagents, then summarize."

# Resume elsewhere: open the run, click Handoff, copy the Markdown into the next session

# Pick a different port if 47831/5173 are taken (the recorder is re-stamped to match)
npx @taewooopark/agent-blackbox up --host claude-code --port 48000 --ui-port 4000

# Stop recording (removes the global recorder + any Claude Code hooks)
npx @taewooopark/agent-blackbox uninstall
```

---

## Two things at once

**1 · See what the agent actually did.** A coding agent reads a dozen files, runs commands, edits code, spawns subagents, and hands you back a tidy summary. Your only window into that is a scrolling transcript and a summary you take on faith. Agent-Blackbox replaces it with a structured, evidence-backed **session map** you read at a glance.

**2 · See — and shrink — what it cost.** Context is money, latency, and a hard window limit. Agent-Blackbox scores how economically each run used its context (cache reuse, redundant re-reads, read-vs-edit amplification, oversized tool dumps, retry waste) and surfaces **specific optimizations** — rule-based by default, or tailored by a **free, local model with no API key**.

| Reading the transcript | Agent-Blackbox |
|---|---|
| Scroll a linear log | A **session map** you read at a glance |
| Trust the agent's summary | Reconstructed from **observed events** |
| "It passed the tests" | See the **fail → fix → pass** loop |
| Lose the thread on long runs | **Scrub and replay** any moment in time |
| One opaque agent | **Subagent genealogy** — who delegated what |
| No idea what it cost | A **context-efficiency score** + reclaimable tokens |
| "Did it actually work?" | A second **outcome** score — efficient-but-failed ≠ wasteful-but-shipped |
| One yardstick for every task | **Task-tailored** (research / debug / ops) + scored vs **your own past runs** |
| "Why is this run so expensive?" | **Concrete fixes**, optionally written by a local model |
| Re-read everything to resume | One-click **handoff** summary |
| Your code & prompts leave the machine | **Local-first**, minimal capture, **no API key** |

---

## Watch it happen — live

The map is not a post-mortem. It is built **as the agent works**: the recorder streams events to a local daemon, and the dashboard updates over a WebSocket — moments appear, files connect by sweeping arcs, tokens tick, a failed test marks oxblood, the fix resolves it. No refresh, no replay required.

That is the whole idea: **open the black box while the flight is still in the air.**

---

## What you get

- **Live session map** — the run forms in real time as a spine of meaningful moments; consecutive repeats aggregate (`Created 12 files`, `Tests passed ×6`) so even large runs stay scannable.
- **Narrative-structure aesthetic** — a flat, monochrome "Mark Lombardi" diagram: hollow ring nodes, sweeping ring-to-ring arcs, serif labels. Graphite on paper (light) or silverpoint on ink (dark); the lone accent is **oxblood, used only for risk/failure**.
- **Replay** — drag the navigation-chart timeline to any sequence point; the graph and files reflect state at exactly that moment.
- **Click to focus** — select any moment for a detail popover (evidence, files, tokens); select an agent to isolate its lane; click a file to highlight every moment that touched it, with arcs drawn from each node's ring.
- **Subagent genealogy** — real delegations (the `task` tool / child sessions / workflow fan-out) fork into their own branch, attributed to the subagent that did the work. Each lane is named by **role** — its spawn type, or distilled from its task prompt (`"You are a literature-search specialist…"` → `literature-search specialist`) — so a run with dozens of parallel agents reads as roles, not paragraphs. A dense run opens at a **readable zoom** (the % button resets to fit the whole tree), and a finished run's lanes read **DONE**, not stuck on ACTIVE.
- **Map navigation** — it's a pan-and-zoom canvas that adapts to your input. **Trackpad:** two-finger swipe pans, pinch zooms. **Mouse:** the wheel zooms (anchored under the cursor) and a middle-button (wheel) drag pans. Either way, drag empty space to rubber-band **select** nodes, click a node to focus it, and the toolbar's `−` / `%` / `+` zoom (the **%** resets to fit the whole tree), **Tracing** follows the newest node live or pins the view, and **Auto layout** re-centers.
- **Context efficiency** — a live score from **11 metrics** (context pressure, cache hit, redundant reads, read amplification, large injections, retry waste, yield density, tool overhead, edit churn, large file reads, unused reads) with one-tap optimization notations — **rule-based, or tailored by a free model with no API key**. `--suggest free` rotates a pool of free models across independent quotas (OpenCode Zen + Ollama cloud + local), failing over and cooling down on rate limits, so the AI suggestions stay free and durable over long sessions.
- **Task-tailored & multi-axis scoring** — the score is judged on a yardstick that **fits the task** (a research run isn't dinged for reading widely; debug runs weigh retries/rework harder), shown as an archetype chip. A separate **outcome** score answers *did the task land?* (from edits, verification, failures) so an efficient-but-failed run reads differently from a wasteful-but-shipped one. And every run is scored against **your own past runs of the same kind in the same project** — *"score 40 vs your usual 87 for research."* (Full reference: **[docs/analysis.md](docs/analysis.md)**.)
- **Custom checks** — drop a `.agent-blackbox/rules.json` to add project rules on top of the built-ins (e.g. *never read `node_modules`*, *run tests before committing*); findings surface in the panel, separate from the score.
- **Handoff export** — a structured continuation summary (objective, files in play, decisions, commands, failures, blockers, next safe action), one click to copy as Markdown.
- **Run picker** — one project log can hold many runs; the console follows the most recently *active* run and lets you pin any past one.
- **Full event coverage** — whichever model you run, every action is captured (reads, edits, bash, skills, custom/MCP tools, permissions, todos, subagents, **slash commands, `/compact` context compaction, agent/model switches**) — keyed off the host event, never the model. Known noise (LSP, pty, file-watcher, MCP registry) is filtered; anything not yet modeled still surfaces as a labeled node, so nothing is silently dropped.
- **One-command bootstrap** — `npm run up` installs the recorder plugin, starts the daemon, and serves the dashboard.

---

## Context efficiency — the part that pays for itself

Every run gets a score from observed sizes and token snapshots — never the agent's self-report. Each flagged metric expands into a concrete fix.

| Metric | What it catches |
|---|---|
| **Context pressure** | how large the prompt grew at its peak |
| **Cache hit ratio** | how much of the prompt was served from cache |
| **Redundant re-reads** | the same file pulled in more than once (with reclaimable tokens) |
| **Read amplification** | reading far more than was edited — read the slice, not the file |
| **Large injections** | a single tool output flooding the window |
| **Retry waste** | failing commands re-run before the cause was fixed |
| **Yield density** | how much concrete change each 1k tokens produced |
| **Tool overhead** | tool calls relative to concrete outcomes |
| **Edit churn** | one file rewritten many times (rework / unsettled approach) |
| **Large file reads** | a single oversized file pulled in whole — read it in ranges |
| **Unused reads** | read text never edited — push wide exploration into a sub-agent |

The score is **task-tailored and multi-axis** — a research run isn't judged on an edit run's yardstick, and "did it use context well" is kept separate from "did the task land":

- **Task archetype** (research / debug / ops / feature / edit) conditions the score so reading widely on a research run isn't penalised; shown as a chip, applied only once the classification is confident.
- **Effectiveness** — a second score (*did the task actually land?*) from outcome + verification + failure signals, with a confidence flag, so an efficient-but-failed run and a wasteful-but-shipped run read differently.
- **Relative baselines** — *"score 40 vs your usual 87 for research"*, compared against your past runs of the same kind **in the same project**.
- **Custom checks** — drop a `.agent-blackbox/rules.json` to add project rules (e.g. "never read node_modules", "run tests before committing").

See **[docs/analysis.md](docs/analysis.md)** for the full reference — every metric + threshold, the archetype profiles, the effectiveness heuristic, the rules.json schema, and the honest known-limitations.

<img src="./docs/screenshots/efficiency-panel.jpeg" alt="The Agent-Blackbox context-efficiency panel for a real debug run: a 62 efficiency score with a 'debug' task-archetype chip, a separate 'OUTCOME · Succeeded · 100' axis (the task shipped — tests passed and committed — even though the run was wasteful), an 'Optimize future runs → write a reversible memory to CLAUDE.md' button, a 'Custom checks' section flagging a WARN rule ('Reading vendored code — skip node_modules' → index.js), and the eleven metric meters (redundant re-reads and yield density flagged in oxblood)." width="332">

One run, both axes: efficient-but-wasteful (62) yet the task shipped (outcome 100) — plus a task-archetype chip, a project rule check, and the reversible-memory button.

Suggestions are **rule-based by default** (always on, no dependencies). To have them tailored by a model — with **no API key** — point `up` at a local/free model:

```bash
# Free, durable, default: rotate a pool of free models across independent quotas
npx @taewooopark/agent-blackbox up --suggest free

# Ollama: local, no key
npx @taewooopark/agent-blackbox up --suggest ollama --suggest-model qwen2.5-coder

# Any OpenAI-compatible localhost server (LM Studio, llama.cpp)
npx @taewooopark/agent-blackbox up --suggest openai-compat --suggest-base-url http://127.0.0.1:1234

# Reuse OpenCode's free model via your installed binary
npx @taewooopark/agent-blackbox up --suggest opencode --suggest-model opencode/deepseek-v4-flash-free
```

**`--suggest free`** (and the default `auto`) rotate a curated pool of **free** models across **independent quota pools** — OpenCode Zen (`opencode/*-free`) + Ollama cloud + a local model — one model per call, rotated to spread load, **failing over and cooling down a model that hits a rate limit (429)** for 10 minutes, and falling back to rule-based only if every pool is exhausted. So the AI suggestions stay free and keep working over long sessions without you babysitting a single quota. Only a **redacted, derived digest** is ever sent, even to a local model: metric statuses, counts, and sizes, plus coarse **offender labels — file basenames and command verbs** (e.g. `billing.ts ×2`, `deploy ×2`) so the advice can name what to fix — but **never file contents, directory paths, command arguments, prompts, or secrets**.

### What the advice is built on

The suggestions aren't generic tips. Both the always-on rule-based floor and the local-model prompt encode a per-metric **fix playbook**, and every action is required to cite the run's own numbers, name the offending file/command, state a concrete mechanism, and give the expected effect. The playbook is distilled from published context-engineering research and production practice:

| Source | What it contributes | Metrics it informs |
|---|---|---|
| Anthropic — [Effective context engineering for AI agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) | **Compaction** (summarize resolved turns → fresh window), clearing tool outputs already acted on, **sub-agent context isolation** (explore in a child that returns a ~1–2k-token summary), and **just-in-time retrieval** (grep/glob, read on demand instead of pre-loading whole files) | `context-pressure`, `read-amplification`, `redundant-reads`, `yield-density` |
| Manus — [Context Engineering for AI Agents: Lessons from Building Manus](https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus) | **KV-cache hit rate** as the primary cost lever (cached tokens ≈ 10× cheaper), a byte-stable prompt prefix (no timestamps/volatile data), append-only context, masking tools instead of adding/removing them, the file system as external memory, and **recitation** of the goal each step | `cache-hit`, `large-injections`, `retry-waste` |
| Liu et al. — [Lost in the Middle: How Language Models Use Long Contexts](https://arxiv.org/abs/2307.03172) | Models systematically **under-use the middle** of long contexts (U-shaped accuracy, ~30%+ degradation) — so advice favors trimming/repositioning and goal recitation over "add more context" | `context-pressure`, `yield-density` |
| Anthropic — [Building effective agents](https://www.anthropic.com/engineering/building-effective-agents) | Minimal, **non-overlapping tool sets** and unambiguous tool boundaries; batch related actions instead of exploratory call chains | `tool-overhead` |
| Schulhoff et al. — [The Prompt Report: A Systematic Survey of Prompt Engineering Techniques](https://arxiv.org/abs/2406.06608) | Contrastive few-shot exemplars (a bad-vague vs good-specific pair), grounding answers in the provided numbers, and strict structured output — so even small local models return specific, actionable JSON | *(shapes the advisor prompt itself)* |

Verified end-to-end on a small local model: a redundant-reads finding turns from "read each file once" into **"`calculator.js` was read 2× (~282 reclaimable) — read it once and cache it, then after each edit re-read only the changed line range instead of the whole file."**

### Close the loop — write the fix back *(experimental)*

Advice you have to re-apply by hand is friction. `optimize` turns your findings into a small, **cache-safe** memory block in the file the agent already reads as context — **`CLAUDE.md` for Claude Code, `AGENTS.md` for OpenCode** — so the *next* run avoids the waste before it happens. It **accumulates across runs**: a pattern seen repeatedly ranks first (annotated `×N`) and one-offs fade, so the block reflects your project's real habits, not just the last run. It's the actuator half of the recorder: observe → diagnose → **write → measure → roll back if it didn't help.**

```bash
# Preview what it would write (no changes)
npm run optimize -- --project ~/code/my-app

# Apply: append a managed block to CLAUDE.md / AGENTS.md + record the baseline score
npm run optimize -- --project ~/code/my-app --apply

# After the next run, confirm it helped — auto-rolls-back on a clear score drop
npm run optimize -- --project ~/code/my-app --check

# Undo at any time
npm run optimize -- --project ~/code/my-app --revert
```

The block is written **between markers at the end of the file**, so the stable prompt-cache prefix is never disturbed. It names the concrete offenders (files to read once, large outputs to scope, verified build/test commands to reuse) and is fully reversible — every write is marked, opt-in, and never silent.

Prefer the dashboard? The **Optimize future runs** button in the right rail opens a popup that previews the *exact* block before anything is written — with the reclaimable-token target and the target path — then applies, updates, or reverts it in one click. A real, reversible file change, not advice:

#### Measured on a real run

A fair before/after on a real **oh-my-openagent `ultrawork`** run (Claude Sonnet driving the whole multi-agent team; same task — *"add a modulo operation end-to-end"*). Run A's explore subagents re-read 9 files; Agent-Blackbox flagged it and pinned *"read `calculator.js`, `parser.js`, `formatter.js` once"* to `AGENTS.md`. Run B — **same task, same model, fresh cold session, only the memory added** — read each once:

| | Before (run A) | After (run B) |
|---|---|---|
| Context-efficiency score | 80 | **99** |
| Redundant re-reads | 9 files (~1.8k reclaimable) | **none** |
| Total tokens | 939k | **521k** (−44%) |
| Tool calls / events | 619 | **253** |
| Yield density | 63/k | **154/k** |

Both runs started from the identical pristine repo (git-reset between them) and a brand-new OpenCode session — no carried context. The redundant-reads elimination (9 files → none) is the memory working directly; OMO is stochastic, so part of the token/event drop is run-to-run variance, but the lever ABB pinned is exactly the waste that disappeared.

> ⚠️ **This `--check` two-run cycle is a benchmark to *validate the mechanism* — not the production workflow.** Re-running the same task to measure would spend tokens twice. In real use you apply once and the memory pays off on *future, different* tasks in that repo (reused commands, files to read once) with **no extra run**.

### In-run optimizer — cut the waste live, no re-run *(opt-in)*

The cross-run memory above pays off on *future* tasks. The in-run optimizer cuts waste **inside the current run** — the recorder stops being purely passive and serves re-reads cheaply through the OpenCode tool hooks. Enable it with `AGENT_BLACKBOX_OPTIMIZE=1` (or `--optimize` at install); off by default.

- **Re-reads served as a no-op or a diff.** When the agent re-reads a file it already read this run, the `tool.execute.after` hook rewrites the result: *unchanged* → a one-line "reuse your earlier copy" note; *edited* → only the changed line range. Measured on a 120-line file: **96% fewer tokens on an unchanged re-read, 94% on an edited one** — in the same run, zero re-run.
- **Correct by construction.** Re-reads are never blocked (you might genuinely need one). The no-op/diff only fires when **no compaction has happened since** the file was last served — so the content is provably still in context. After a compaction the full file is served again, because the agent may have lost it.
- **Working-set memory injected live.** Via `experimental.chat.system.transform`, a tiny always-current block (hot files + verified commands, derived from observed events) is appended to the system prompt, so the agent recalls instead of re-reading.

#### What's next

- **Longitudinal trend** — Agent-Blackbox records every run; chart the efficiency score across your *real* runs and show whether it rises after the memory + optimizer land — measurement from actual work, not a benchmark.
- **Diff-serving across compaction** — keep a small local content cache so even post-compaction re-reads can be served as diffs (today they fall back to full).

---

## Pairs with oh-my-openagent — profile and shrink heavy multi-agent runs

[**oh-my-openagent (OMO)**](https://github.com/code-yeongyu/oh-my-openagent) turns OpenCode into a multi-agent *tokenmaxxer* harness — 11 specialists, parallel execution, a relentless loop that spends tokens hard to ship complex work. Agent-Blackbox is the instrument built for exactly that workload: **OMO floors the accelerator; Agent-Blackbox is the dyno and the telemetry.**

They're both OpenCode plugins and coexist with zero config — run OMO with the recorder installed and the whole team shows up:

- **See the whole team.** Every SDK-spawned subagent (Sisyphus, explore, librarian, plan, oracle…) gets its own lane; delegation forks the trunk; files connect by sweeping arcs. The map was built for runs this complex.
- **See — and shrink — the cost.** A "tokenmaxxer" run is exactly where context economy matters most. Agent-Blackbox scores it (context pressure, redundant re-reads, read amplification, tool overhead) and names the precise offenders — the cost you can't see from inside the harness.
- **Close the loop.** Pin the findings to `AGENTS.md` for the next run, and turn on the in-run optimizer (`AGENT_BLACKBOX_OPTIMIZE=1`) to serve a re-read as a no-op/diff — savings inside the *same* run, no re-run.

A real OMO `ultrawork` run, recorded live by Agent-Blackbox — named specialist lanes on the left, and a context-efficiency score with reclaimable tokens and tailored fixes on the right:

```bash
# Both install globally — start ABB once, then run OMO however you like. Watch :5173.
npx @taewooopark/agent-blackbox up --suggest free
opencode "ultrawork: refactor the auth module and add tests"   # OMO + recorder both active
```

---

## Handoff — pick the run up anywhere

When you need to continue the run elsewhere — a teammate, the next agent, or the same agent after a context reset — export a structured **handoff**:

---

## How it works

```
 Claude Code transcripts (tailed) ─┐
 Codex rollout sessions (tailed) ──┼─▶ host adapter ─▶ daemon ─▶ dashboard
 OpenCode hooks → recorder plugin ─┘
                                       redact+normalize  NDJSON    live session map
                                                         + graph   + efficiency
```

- **`packages/core`** — canonical `TraceEvent`s, the workflow graph model, redaction, replay, audit, handoff generation, and the context-efficiency engine.
- **`packages/claude-code-adapter`** — tails the JSONL transcripts Claude Code writes (`~/.claude/projects/`) and normalizes them into canonical, redacted events — no plugin, no install. Optional hooks add the in-run actuator.
- **`packages/codex-adapter`** — tails Codex rollout sessions (`$CODEX_HOME/sessions`) from the CLI and desktop app, including token, patch, search, MCP, compaction, and subagent signals. Optional trusted hooks add safe read-dedup + working-set context.
- **`packages/opencode-adapter`** — a thin OpenCode plugin that turns host events and tool calls into canonical, redacted events (with content *sizes*, never content) and ships them to the daemon, best-effort with retries.
- **`apps/daemon`** — ingests events to a local NDJSON log, materializes the graph, replays it to any point, computes the efficiency report, routes suggestions, and pushes live snapshots over WebSocket.
- **`apps/dashboard`** — the operator console: live session map, replay, inspector, efficiency co-pilot, and handoff.

---

## Philosophy — observe, don't trust the narrator

> **Derive the truth from observed events, never from free-form self-report.**

- **Behavior, not narration.** Every node is an event the agent actually emitted — a read, an edit, a command and its exit code, a delegation — not a sentence it wrote about itself.
- **Cost is evidence too.** The efficiency score and every suggestion come from observed sizes and token snapshots, not from the model's account of its own thrift.
- **Local-first, no key.** Traces stay on your machine. Raw prompts, secrets, and file contents are redacted by default; even the optional model suggestions run locally and receive only a redacted digest.
- **Host-agnostic core.** A canonical event + graph core with thin host adapters, so the same black box can sit behind any agent harness — **Claude Code, Codex, and OpenCode** ship today.

---

## Daemon API

| Method & path | Purpose |
|---|---|
| `POST /events` | Ingest a canonical `TraceEvent` |
| `GET /events` | The durable event log |
| `GET /graph?seq=<n>` | Replay the graph up to a sequence |
| `GET /snapshot?seq=<n>` | Events, graph, audit checks, efficiency + **effectiveness** reports, **baselines**, **rule packs**, and handoff markdown |
| `GET /audit` | Promise / claim checks |
| `GET /efficiency?seq=<n>` | Context-efficiency report (scores + metrics + archetype) |
| `POST /suggest` | Optimization suggestions for a posted report + optional causal timeline (deterministic or local-model) |
| `GET·POST /optimize[/apply\|/revert]?runId=<id>` | Preview / apply / revert the memory block for a run (reversible file write) |
| `GET /handoff` | Generated handoff markdown |
| `WS /stream` | Live snapshots pushed after each ingest |

---

## Project layout

```text
apps/
  daemon/             local ingest, replay, efficiency, suggestion routing, static dashboard, websocket
  dashboard/          operator console (session map, replay, inspector, efficiency, handoff)
packages/
  core/                 canonical events, graph, redaction, replay, audit, handoff, efficiency,
                        archetypes, effectiveness, baselines, accumulative memory, timeline, rule packs
  storage/              NDJSON persistence
  claude-code-adapter/  Claude Code transcript tailer + in-run actuator hooks
  codex-adapter/        Codex CLI/app rollout tailer + in-run actuator hooks
  opencode-adapter/     OpenCode plugin / SDK bridge
```

Per-project state the daemon writes (all local, best-effort, reversible) lives under `<project>/.agent-blackbox/`: `optimization.json` + `efficiency-profile.json` (the accumulated memory), and you can add `rules.json` (custom checks). Cross-run baselines live next to the daemon's event store as `baselines.json`. See **[docs/analysis.md](docs/analysis.md)**.

## Development

```bash
npm install
npm run check   # typecheck + tests
npm run build
```

---

## Roadmap

- More host adapters (PI and other harnesses) on the same canonical core — **Claude Code, Codex, and OpenCode** ship today.
- **Shipped recently:** a second **outcome** axis, **task-archetype** scoring, **per-project baselines** ("vs your usual run"), **accumulative** optimize memory, and **custom rule packs** — see **[docs/analysis.md](docs/analysis.md)**.
- **Fleet-wide** efficiency-trend charts across many runs (the per-run baseline data already lands locally; the longitudinal view is next).
- Deeper audit: richer claim-vs-evidence verification and risky-command surfacing.

---

## Contact

Local-first. No API key. Observe, don't trust the narrator.

Agent-Blackbox

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README