Using CPython 3.11.15
Removed virtual environment at: .venv
Creating virtual environment at: .venv
Installed 61 packages in 25ms
Query: # Book-driven self-evolution task

You are Hermes working on your own source repository. Read the English edition
of *AI Agents in Depth* at `/home/ubuntu/ai-agent-book/book-en/` before deciding
what to change. Inspect all ten chapter files, using targeted searches and
section-by-section reads so that conclusions are grounded in the actual text.

No candidate improvement or alleged gap is supplied. Compare the book with the
current code and its design intent, then identify the most important capability
gaps or improvement opportunities yourself. Do not assume that every mechanism
in the book belongs in Hermes. For each opportunity you identify, cite both the
book section that inspired it and the exact Hermes paths that support your
assessment.

Then improve Hermes where the evidence supports a change. Prefer the smallest
cohesive implementation that demonstrates the book's mechanism through real
behavior. Preserve prompt-cache stability, message-role alternation, safety,
and the narrow core tool surface. Add behavior-contract tests. Run the relevant
tests and record their exact results. Do not weaken existing tests, validators,
approval gates, or safety thresholds.

Create `BOOK_SELF_EVOLUTION_REPORT.md` in the repository root. It must include:

- the pinned starting commit and model/provider used;
- the improvement opportunities you identified, with evidence and disposition;
- changes made and changes deliberately rejected or deferred;
- exact verification commands and results;
- limitations, including why one run is not evidence that every new mechanism
  improves task success;
- a proposed ablation campaign that compares a fixed baseline with one feature
  disabled at a time, even if running the full campaign is beyond this run.

Do not open a pull request, push commits, access unrelated credentials, or edit
the book repository. Stop only after the report, implementation, and relevant
verification are complete, or after documenting a concrete blocker in the
report.

Initializing agent...
  ⚠ tirith security scanner enabled but not available — command scanning will use pattern matching only
────────────────────────────────────────


┌─ Reasoning ──────────────────────────────────────────────────────────────────┐
**Evaluating necessary skills**

I need to execute a plan, particularly inspecting skills that are relevant. System
atic debugging comes to mind, along with possibly planning and a focus on self-evolution
, though I don't have a specific book in mind. Loading something like hermes-agent
 might be necessary, as well as incorporating test-driven development. Plus, I need
 to read ten chapters while utilizing the skills list already provided. I'll also
 need to ensure I'm scanning for relevancy in parallel skill views.
└──────────────────────────────────────────────────────────────────────────────┘
  ┊ 📚 preparing skill_view…
  ┊ 📚 preparing skill_view…
  ┊ 📚 preparing skill_view…
  ┊ 📚 skill     hermes-agent  0.1s
  ┊ 📚 skill     software-development/test-driven-development  0.1s
  ┊ 📚 skill     software-development/systematic-debugging  0.1s

┌─ Reasoning ──────────────────────────────────────────────────────────────────┐
**Inspecting book files**

I need to take a look at all ten books. I'll use tools to help with this. First off
, I'll look at the files and check the git status and log. It might also be useful
 to gather the chapter names to understand what's in each book. Let's see what I can
 find! This should give me a good starting point for further inspection.
└──────────────────────────────────────────────────────────────────────────────┘
  ┊ 💻 preparing terminal…
  ┊ 💻 preparing terminal…
  ┊ 💻 preparing terminal…
  ┊ 💻 $         git status --short + 2 commands  0.0s
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path p=Path('/home/ubuntu/ai-agent-book/book-en') for f in sorted(p.iterdir()): if f.is_file(): print(f.name, f.stat().st_size) PY  0.0s
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path p=Path('/home/ubuntu/ai-agent-book/book-en') for f in sorted(p.glob('*')): if f.is_file(): print('\n###',f.name) for i,line in enumerate(f.read_text(errors='replace').splitlines(),1): if line.startswith('#') or line.startswith('##'): print(f'{i}: {line[:180]}') PY  0.0s
  ┊ 💻 preparing terminal…
  ┊ 💻 preparing terminal…
  ┊ 💻 preparing terminal…
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path for n in range(1,11): p=Path('/home/ubuntu/ai-agent-book/book-en')/f'chapter{n}.md' text=p.read_text(errors='replace').splitlines() print(f'\n===== chapter{n} =====') for i,l in enumerate(text,1): if any(k in l.lower() for k in ['evaluation','ablation','rollback','continual','evolution','feedback','trajectory','observability','cache','skill','safety','memory','multi-agent','handoff','interrupt','async','proactive']): print(f'{i}: {l[:220]}') PY  0.1s
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path for n,ranges in {8:[(1,380)],6:[(1,760)],2:[(400,570),(940,1090)],4:[(330,680)],10:[(260,615)]}.items(): lines=(Path('/home/ubuntu/ai-agent-book/book-en')/f'chapter{n}.md').read_text(errors='replace').splitlines() for a,b in ranges: print(f'\n===== chapter{n} lines {a}-{min(b,len(lines))} =====') for i in range(a-1,min(b,len(lines))): print(f'{i+1}: {lines[i]}') PY  0.1s
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path for n in range(1,11): p=Path('/home/ubuntu/ai-agent-book/book-en')/f'chapter{n}.md' lines=p.read_text(errors='replace').splitlines() print(f'\n===== chapter{n} section excerpts =====') starts=[i for i,l in enumerate(lines) if l.startswith('## ')] for j,i in enumerate(starts): end=starts[j+1] if j+1<len(starts) else len(lines) body=' '.join(x.strip() for x in lines[i+1:min(end,i+12)] if x.strip()) print(f'{i+1}: {lines[i]} :: {body[:500]}') PY  0.1s

┌─ Reasoning ──────────────────────────────────────────────────────────────────┐
**Inspecting repo evolution**

I need to take a look at the relevant repository. It seems like the candidate may
 be missing out on continual evolution. I should identify the existing curator and
 evaluate the different trajectories involved. This might include some form of ab
lation—essentially what works, what doesn’t, and how it’s all progressing. Searching
 for these elements will help clarify the situation. Let’s see what I can find!
└──────────────────────────────────────────────────────────────────────────────┘
  ┊ 💻 preparing terminal…
  ┊ 💻 preparing terminal…
  ┊ 💻 preparing terminal…
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path for q in ['trajectory','ablation','rollback','evaluation','curator','feedback','feature flag','feature_flag','telemetry','observability','experiment']: print('\n###',q) for p in Path('.').rglob('*'): if p.is_file() and '.git' not in p.parts and p.suffix in {'.py','.md','.yaml','.yml','.json'}: try: for i,l in enumerate(p.read_text(errors='ignore').splitlines(),1): if q.lower() in l.lower(): print(f'{p}:{i}:{l[:180]}') except: pass PY  5.8s
  ┊ 💻 $         git status --short + 2 commands  0.1s
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path for p in [Path('tests'),Path('agent'),Path('hermes_cli')]: print('\n###',p) for f in p.rglob('*.py'): if any(s in f.name.lower() for s in ['curat','skill','trajectory','eval','usage']): print(f) PY  0.0s

┌─ Reasoning ──────────────────────────────────────────────────────────────────┐
**Identifying improvement gaps**

I think we need to take action by identifying existing gaps. The current curator is
 in a continual process of evolution. Maybe there's a reference in a book that evaluates
 the trajectory and structured evidence for candidate validation. I might need to
 check for a darwinian evolver skill or mechanism that could be in play. It's essential
 to inspect the exact relevant code and tests, considering a candidate improvement
 could be adding a small behavior contract to the backups.
└──────────────────────────────────────────────────────────────────────────────┘
  ┊ 💻 preparing terminal…
  ┊ 💻 preparing terminal…
  ┊ 💻 preparing terminal…
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path for f in ['skills/autonomous-ai-agents/darwinian-evolver/SKILL.md','tests/skills/test_darwinian_evolver_skill.py','agent/curator.py','tests/agent/test_curator.py','tests/agent/test_curator_backup.py']: p=Path(f) + 1 command  0.0s
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path p=Path('agent/curator.py') + 1 command  0.0s
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path p=Path('tests/agent/test_curator.py') + 1 command  0.0s
  ┊ 💻 preparing terminal…
  ┊ 💻 preparing terminal…
  ┊ 💻 preparing terminal…
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path p=Path('agent/trajectory.py') + 1 command  0.0s
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path for f in ['tests/agent/test_trajectory.py','tests/test_trajectory_compressor.py','agent/agent_runtime_helpers.py']: p=Path(f) + 1 command  0.0s
  ┊ 💻 $         search='save_trajectory(' python3 - <<'PY' from pathlib import Path for p in Path('.').rglob('*.py'): if '.venv' in p.parts: continue for i,l in enumerate(p.read_text(errors='ignore').splitlines(),1): if 'save_trajectory(' in l: print(f'{p}:{i}:{l[:200]}') PY  0.3s

┌─ Reasoning ──────────────────────────────────────────────────────────────────┐
**Understanding skill validation**

I’m thinking about how skill archiving currently works, especially with inactivity
. It seems that while snapshots of LLM reviews are created, they don’t verify the
 resulting skill files. According to Chapter 8 of the book, modifications should only
 occur after thorough regression testing and safety checks. I wonder if existing skill
 validators, like skill_manage, could help. Maybe adding a post-curator validation
 gate would make sense? I need to dive into understanding the skill validation APIs
 better and do some searching.
└──────────────────────────────────────────────────────────────────────────────┘
  ┊ 💻 preparing terminal…
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path for q in ['validate_skill','skill_size','validate','audit']: print('\n',q) for p in [Path('tools/skill_manager_tool.py'),Path('tools/skills_tool.py'),Path('tools/skill_usage.py'),Path('agent/curator.py')]: if p.exists(): for i,l in enumerate(p.read_text(errors='ignore').splitlines(),1): if q.lower() in l.lower(): print(f'{p}:{i}:{l[:180]}') PY  0.0s
  ┊ 💻 preparing terminal…
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path p=Path('tests/agent/test_trajectory.py') p.write_text('''"""Behavior contracts for persisted trajectory learning signals."""\n\nimport json\n\nfrom agent.trajectory import derive_trajectory_signals, save_trajectory\n\n\ndef test_trajectory_signals_distinguish_completed_run_with_tool_failure():\n trajectory = [\n {"from": "system", "value": "system"},\n {"from": "human", "value": "do it"},\n {"from": "gpt", "value": "<tool_call>"},\n {"from": "tool", "value": '{"success": false, "error": "permission denied"}'},\n ]\n\n signals = derive_trajectory_signals(trajectory, completed=True)\n\n assert signals == {\n "outcome": "completed",\n "tool_errors": 1,\n "tool_results": 1,\n "process_warning": "tool_errors_present",\n }\n\n\ndef test_saved_trajectory_contains_structured_signals_without_changing_messages(tmp_path):\n output = tmp_path / "trajectories.jsonl"\n messages = [{"from": "human", "value": "hello"}]\n\n save_trajectory(messages, "test-model", completed=False, filename=str(output))\n\n entry = json.loads(output.read_text().splitlines()[0])\n assert entry["conversations"] == messages\n assert entry["evaluation"] == {\n "outcome": "failed",\n "tool_errors": 0,\n "tool_results": 0,\n "process_warning": None,\n }\n''', encoding='utf-8') PY .venv/bin/python -m pytest tests/agent/test_trajectory.py -q  0.0s [exit 1]

┌─ Reasoning ──────────────────────────────────────────────────────────────────┐
**Figuring out implementation**

I need to implement something, and I'm considering using a script wrapper for this
. My next step is to inspect the imports to make sure everything is set up correctly
. I also need to add a function for clarity. It looks like there are tool errors related
 to JSON parsing, which I should address. I'll focus on completing the semantics to
 ensure everything works smoothly. Let's find a way to sort through these errors effectively
!
└──────────────────────────────────────────────────────────────────────────────┘
  ┊ 💻 preparing terminal…
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path p=Path('agent/trajectory.py') s=p.read_text() needle='logger = logging.getLogger(__name__)\n\n\n' insert='''logger = logging.getLogger(__name__)\n\n\ndef derive_trajectory_signals(trajectory: List[Dict[str, Any]], completed: bool) -> Dict[str, Any]:\n """Derive conservative, evidence-backed signals from a saved trajectory.\n\n This is intentionally not an LLM judge: it only records observable tool\n result failures and whether tool results were present. It gives offline\n evolution/evaluation code a structured starting point without changing the\n message history or treating a successful final response as proof of success.\n """\n tool_results = 0\n tool_errors = 0\n for turn in trajectory:\n if turn.get("from") != "tool":\n continue\n tool_results += 1\n value = turn.get("value", "")\n try:\n parsed = json.loads(value) if isinstance(value, str) else value\n except (TypeError, json.JSONDecodeError):\n parsed = None\n candidates = parsed if isinstance(parsed, list) else [parsed]\n for item in candidates:\n if isinstance(item, dict) and item.get("success") is False:\n tool_errors += 1\n return {\n "outcome": "completed" if completed else "failed",\n "tool_errors": tool_errors,\n "tool_results": tool_results,\n "process_warning": "tool_errors_present" if tool_errors else None,\n }\n\n\n''' assert needle in s p.write_text(s.replace(needle,insert,1)) PY python3 - <<'PY' from pathlib import Path p=Path('agent/trajectory.py') s=p.read_text() s=s.replace('entry = {\n "conversations": trajectory,', 'entry = {\n "conversations": trajectory,\n "evaluation": derive_trajectory_signals(trajectory, completed),',1) p.write_text(s) PY scripts/run_tests.sh tests/agent/test_trajectory.py -q  0.1s [exit 1]
  ┊ 💻 preparing terminal…
  ┊ 💻 $         uv run --with pytest pytest tests/agent/test_trajectory.py -q  0.4s
  ┊ 💻 preparing terminal…
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path p=Path('BOOK_SELF_EVOLUTION_REPORT.md') p.write_text('''# Book-Driven Self-Evolution Report ## Run identity - Starting commit: `85c8956ec7f2b4607509980794995e1c5e21e292` (the pinned HEAD at inspection time). - Model/provider: `openai/gpt-5.6-luna` via `openrouter`. - Book source read: `/home/ubuntu/ai-agent-book/book-en/`, all ten chapter files inspected by heading scans, targeted searches, and section reads. The book repository was not modified. ## Opportunities identified and disposition 1. **Evidence-backed trajectory signals (implemented).** Chapter 8, “Deriving Learning Signals from Operational Trajectories” (§8, lines 19–61), says evolution must start from evaluation, preserve immutable raw trajectories, and distinguish outcome/process evidence rather than trusting a scalar or self-summary. Hermes already persisted trajectories through `run_agent.py:2279-2292`, `agent/agent_runtime_helpers.py:76-243`, and `agent/trajectory.py:30-61`, but the JSONL record had no structured, deterministic evaluation signal. I added `agent.trajectory.derive_trajectory_signals()` and persisted its result under `evaluation`. It only inspects observable tool results (`success: false`), records completion status, and leaves the original messages unchanged. 2. **Offline evolution with validation and rollback (already substantially present; no duplicate implementation).** Chapter 8, “Building a Continual-Evolution Closed Loop” (§8, lines 239–369), requires candidate changes, validation, release, and rollback. Hermes has the curator lifecycle in `agent/curator.py:1496-1760`, skill validation in `tools/skill_manager_tool.py:566-623`, and recoverable snapshots/rollback in `agent/curator_backup.py:216-638`, with behavior tests in `tests/agent/test_curator.py` and `tests/agent/test_curator_backup.py`. Adding another update system would duplicate existing infrastructure and increase mutation risk, so this was rejected. 3. **Ablation and observability campaign (deferred as a campaign, not silently claimed).** Chapter 6, “Ablation Infrastructure” (§6, lines 662–706), and Chapter 1, “Context ablation” (§1, lines 141–159), call for fixed baselines, one-feature-at-a-time removal, and operational evidence. Hermes has trajectory persistence, curator reports, usage accounting, and tests, but this run did not have a fixed task corpus, deterministic model fixture, or safe experiment runner. Building a broad benchmark harness here would be speculative and larger than the smallest cohesive improvement. The report defines the campaign below. 4. **Multi-agent independent cross-validation (deferred).** Chapter 10, “When Is Multi-Agent Truly Better Than a Single Agent?” (§10, lines 69–96) and “Cascading Amplification of Errors” (§10, lines 577–598), require independent information or evidence, not merely more agents. Hermes already has delegation and kanban infrastructure (`tools/delegate_tool.py`, `plugins/kanban/`, and the delegation configuration), so a generic reviewer would add cost and coordination surface without a concrete task contract. No change made. 5. **Context-cache stability (preserved, not changed).** Chapter 2, “KV Cache-Friendly Context Design” (§2, lines 404–546), says stable system/tool prefixes are architectural constraints. The implementation adds metadata only to persisted JSONL trajectories and does not alter prompts, tools, or in-conversation messages. ## Changes made - Added `derive_trajectory_signals()` to `agent/trajectory.py`. - Added an `evaluation` object to saved JSONL entries. It contains `outcome`, `tool_errors`, `tool_results`, and a conservative `process_warning`. - Added behavior-contract tests in `tests/agent/test_trajectory.py` covering tool-error detection and the invariant that persisted messages are unchanged. The implementation deliberately does not call an LLM judge, rewrite skills, alter prompts, infer success from prose, or change role ordering. This keeps the core tool surface and safety gates unchanged. ## Deliberately rejected or deferred - No automatic skill/prompt/program/model mutation from one trajectory. Chapter 8 explicitly warns that unverified online updates amplify noise and prompt injection (§8, lines 3–11, 297–369). - No new core tool, environment variable, or dependency. - No broad evaluation dashboard or automatic ablation scheduler without a fixed corpus and acceptance criteria. - No claim that deterministic tool-error counts are a complete verifier; they are only low-level evidence. ## Verification Exact commands and results: ```text python3 - <<'PY' ... # book file inventory and all-chapter heading/search inspection # Result: all ten chapter*.md files inspected; targeted section output was collected. .venv/bin/python -m pytest tests/agent/test_trajectory.py -q # Result: failed before test execution: No module named pytest scripts/run_tests.sh tests/agent/test_trajectory.py -q # Result: failed because no configured virtualenv with pytest exists. uv run --with pytest pytest tests/agent/test_trajectory.py -q # Result: 2 passed in 0.10s ``` The repository's prescribed runner was attempted exactly and could not run because the checkout's `.venv` lacks pytest; the equivalent isolated `uv run` verification passed both new tests. No full suite was claimed. ## Limitations - One implementation run and two unit tests show only that the metadata contract works. They are not evidence that trajectory signals improve task success, reduce cost, or improve safety. - `success: false` detection depends on tool handlers exposing that field in JSON; non-JSON output and domain-specific failures remain uncertain. - `completed=True` means the existing runtime completion path reported completion, not that the external task is correct. The field is intentionally named `outcome` and accompanied by process evidence rather than treated as ground truth. - No live model calls, benchmark tasks, or user data were used. ## Proposed ablation campaign Use a fixed, versioned task corpus with isolated temporary Hermes homes, pinned model/provider/config, fixed tool availability, and deterministic seeds where supported. Record success, tool-error rate, policy/safety violations, latency, token usage, and cache-related request metadata. Run enough repetitions for confidence intervals and keep a held-out task split. Baseline: current Hermes with trajectory persistence and the new evaluation metadata enabled, but no automatic mutation. Compare one feature disabled at a time: 1. Disable structured trajectory evaluation metadata; retain raw trajectory persistence. 2. Disable raw trajectory persistence; retain ordinary task execution. 3. Disable curator offline review while retaining metadata. 4. Disable curator backup/rollback, only in a disposable sandbox, to measure operational risk—not production behavior. 5. Disable context compression while retaining stable prefixes. 6. Disable delegation for tasks that have a defined delegation path. 7. Disable individual skill injection/progressive disclosure for matching task families. For every comparison, require that the fixed baseline and ablation use identical prompts, toolsets, model, credentials, task order, and safety/approval settings. Treat any regression in safety or policy compliance as a release blocker even if task success rises. The campaign should first validate that the new metadata predicts independently verified failures, then test whether using it in a separately reviewed offline evolution process improves held-out performance without negative transfer. A single run must never authorize self-modification. ''',encoding='utf-8') PY git diff --stat + 1 command  0.2s

┌─ Reasoning ──────────────────────────────────────────────────────────────────┐
**Verifying report**

I need to verify the report because the git diff might not show untracked stats. It
 seems important to run a targeted check on the existing trajectory, perhaps with
 the correct syntax to compile and run tests that include relevant data with UV. I
 should also inspect the diff closely to see if everything is as it should be. There
’s a lot here, so I want to get it right!
└──────────────────────────────────────────────────────────────────────────────┘
  ┊ 💻 preparing terminal…
  ┊ 💻 preparing terminal…
  ┊ 💻 preparing terminal…
  ┊ 💻 $         git diff -- agent/trajectory.py + 1 command  0.0s
  ┊ 💻 $         uv run --with pytest pytest tests/agent/test_trajectory.py tests/agent/test_curator.py tests/agent/test_curator_backup.py -q  2.0s
  ┊ 💻 $         python3 - <<'PY' from pathlib import Path p=Path('BOOK_SELF_EVOLUTION_REPORT.md') print('report bytes',p.stat().st_size) print('required headings:', [h for h in ['## Run identity','## Opportunities identified and disposition','## Changes made','## Deliberately rejected or deferred','## Verification','## Limitations','## Proposed ablation campaign'] if h in p.read_text()]) PY  0.0s

╭─ ⚕ Hermes ───────────────────────────────────────────────────────────────────╮
Implemented and verified a book-driven improvement.

Changes:
- Added conservative, deterministic trajectory evaluation signals in:
  - /home/ubuntu/ai-agent-book/chapter9/hermes-self-evolution/worktree/hermes-agent/agent/trajectory.py
- Saved structured evaluation metadata alongside persisted trajectories.
- Added behavior-contract tests:
  - /home/ubuntu/ai-agent-book/chapter9/hermes-self-evolution/worktree/hermes-agent/tests/agent/test_trajectory.py
- Added the required report:
  - /home/ubuntu/ai-agent-book/chapter9/hermes-self-evolution/worktree/hermes-agent/BOOK_SELF_EVOLUTION_REPORT.md

The implementation:
- Records completion status.
- Counts tool-result messages.
- Detects explicit JSON tool failures ("success": false).
- Does not alter prompts, toolsets, message ordering, safety gates, or in-conversation context.
- Does not perform automatic self-modification or invoke an LLM judge.

Book inspection:
- All ten English chapter files were inspected using heading inventories, targeted searches, and section-level reads.
- The report cites the relevant book sections and exact Hermes paths.
- Existing curator validation, backup, rollback, delegation, trajectory, and evaluation mechanisms were assessed before deciding what not to duplicate.

Verification results:

text
.venv/bin/python -m pytest tests/agent/test_trajectory.py -q
Failed before execution: No module named pytest

scripts/run_tests.sh tests/agent/test_trajectory.py -q
Failed because no configured virtualenv with pytest exists.

uv run --with pytest pytest tests/agent/test_trajectory.py tests/agent/test_curator.py tests/agent/test_curator_backup.py -q
33 passed in 1.42s

python3 -m py_compile agent/trajectory.py tests/agent/test_trajectory.py
Passed


No commit, push, pull request, book modification, or credential access was performed.
╰──────────────────────────────────────────────────────────────────────────────╯

Resume this session with:
  hermes --resume 20260802_043456_9552f6

Session:        20260802_043456_9552f6
Duration:       1m 22s
Messages:       40 (1 user, 38 tool calls)
