docs: game AI example + full zh-CN README rewrite - #7
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Four deliverables: - config: add llm/coding_agent/history sections; add max_total_usd and max_total_tokens to hard_stops for configurable cost guard - hard_stops: accumulate total_usd/total_tokens in state; precheck and record_outcome enforce cost limits alongside iteration limits - governor: inject history[] into planner_input each round (last N reflections from ledger); history_max_entries is configurable - cli: add --loop flag; _run_loop drives multi-round evolution until any hard stop triggers; _make_governor helper deduplicates setup - roles/planner.py: LLM planner (anthropic/openai, configurable via config.json in run dir) - roles/executor.sh: coding-agent wrapper (aider/claude-code, configurable) - roles/evaluator.py: LLM evaluator; reports cost_usd + tokens_used so kernel can enforce cost guard All choices (LLM provider/model/key, coding tool) are config-driven, nothing hardcoded. 39/39 tests pass. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- executor.sh: use jq to encode output JSON (was unsafe shell interpolation) - planner.py: wrap json.loads in try/except JSONDecodeError; fall back to plain-text plan instead of crashing on malformed LLM output - config.py: explicit type check before float/int conversion in _parse_hard_stops; raises ConfigError with clear message instead of raw ValueError - cli.py: add _safe_cost() helper with defensive float/int parsing for cost_usd/tokens_used fields; fix exit codes to return 3 consistently on halt in both single-run and --loop modes; call _record_halted() when record_outcome triggers a halt in loop mode (was missing audit entry) - governor.py: store plan_summary in reflection.json; use it in _build_history() so planner sees actual attempt descriptions, not generic decision reason strings; simplify unreachable else branch in slice Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Lead with value ("Give an LLM a goal") not architecture internals
- Add concrete coverage-improvement example with realistic output
including a rejection + recovery round showing history injection
- Add ledger directory tree with all artifacts annotated
- Add full configuration reference with all PR4 fields
- Add capabilities table: working vs coming-next (PR5/6/7)
- Fix CI badge URL: hitome0123 → Protocol-zero-0/evolution-kernel
- Remove Token-Ignition mentions from main README
- Remove legacy --goal CLI docs (code unchanged, just not advertised)
- Move architecture diagram after value sections, not before
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Replace test coverage example with game AI evolution (35→72% win rate) — more visceral, shows self-correction on rejection, closer to AlphaEvolve spirit - Add progress bar visual (before/after) and round-by-round terminal output - Add callout on why Round 3 rejection matters (memory / no repeated mistakes) - Update architecture Mermaid with emoji labels and loop subgraph label - Full rewrite of README.zh.md as faithful Chinese translation (was stale v0) - Fix zh-CN badge URLs and remove Token-Ignition/legacy references Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Pull request overview
This PR updates Evolution Kernel’s documentation and examples while also introducing new runtime capabilities (cost/token budget hard stops, history injection into planner input, and a multi-round --loop CLI mode), plus reference role implementations and new tests covering these features.
Changes:
- Add cost/token budget tracking to hard stops and expose it through config + CLI (including
--loopmulti-round execution). - Inject prior run reflections into planner input (“memory”) with configurable history length.
- Rewrite README (EN + zh-CN) around a game-AI evolution example and update example config/roles to the new shape.
Reviewed changes
Copilot reviewed 11 out of 11 changed files in this pull request and generated 16 comments.
Show a summary per file
| File | Description |
|---|---|
evolution_kernel/cli.py |
Adds --loop, integrates cost/token accounting into hard stops, and prints halt info. |
evolution_kernel/config.py |
Adds new config sections (llm, coding_agent, history) and new hard stop fields. |
evolution_kernel/governor.py |
Injects ledger-derived history into planner_input.json and records plan summaries in reflections. |
evolution_kernel/hard_stops.py |
Persists and enforces total_usd/total_tokens in precheck + record_outcome. |
roles/planner.py |
Adds a reference LLM planner role (Anthropic/OpenAI) that writes plan.json plus cost/token metadata. |
roles/evaluator.py |
Adds a reference LLM evaluator role (Anthropic/OpenAI) that outputs accept/reject plus cost/token metadata. |
roles/executor.sh |
Adds a reference executor role that calls aider/claude-code and writes executor output JSON. |
tests/test_pr4.py |
Adds tests for new config fields, cost guard behavior, history injection, and --loop. |
examples/evolution.yml |
Updates example config to include new fields and reference role scripts. |
README.md |
Full rewrite of English README and CLI/config examples around game-AI evolution story. |
README.zh.md |
Full rewrite of zh-CN README and updated badges/examples to match new docs. |
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| max_total_tokens=cfg.hard_stops.max_total_tokens, | ||
| ) | ||
| hard_stops.save_state(args.ledger, new_state) | ||
| _print_result(result, halted=new_state.halted, halt_reason=new_state.halt_reason) |
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| return LLMConfig(provider=provider.strip(), model=model.strip(), api_key_env=api_key_env.strip()) | ||
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| tool = value.get("tool", "aider") | ||
| if not isinstance(tool, str) or not tool.strip(): | ||
| raise ConfigError("`coding_agent.tool` must be a non-empty string") | ||
| return CodingAgentConfig(tool=tool.strip()) |
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| def _call_anthropic(prompt: str, model: str, api_key_env: str) -> tuple[str, int, float]: | ||
| import anthropic # type: ignore | ||
| client = anthropic.Anthropic(api_key=os.environ[api_key_env]) | ||
| msg = client.messages.create( |
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| PLAN_PATH="$(python3 -c "import json,sys; d=json.load(open('$INPUT')); print(d.get('plan_path',''))" 2>/dev/null || echo "")" | ||
| if [[ -z "$PLAN_PATH" || ! -f "$PLAN_PATH" ]]; then | ||
| PLAN_PATH="$RUN_DIR/plan.json" | ||
| fi | ||
| SUMMARY="$(python3 -c "import json; d=json.load(open('$PLAN_PATH')); print(d.get('summary','improve the codebase'))" 2>/dev/null || echo "improve the codebase")" | ||
| STEPS="$(python3 -c "import json; d=json.load(open('$PLAN_PATH')); print('\n'.join(d.get('steps',[])) or 'Apply the plan.')" 2>/dev/null || echo "Apply the plan.")" | ||
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| # Load coding agent tool from config.json | ||
| TOOL="aider" | ||
| CONFIG_PATH="$RUN_DIR/config.json" | ||
| if [[ -f "$CONFIG_PATH" ]]; then | ||
| TOOL="$(python3 -c "import json; d=json.load(open('$CONFIG_PATH')); print(d.get('coding_agent',{}).get('tool','aider'))" 2>/dev/null || echo "aider")" |
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| roles: | ||
| planner: ["python3", "roles/planner.py"] | ||
| executor: ["bash", "roles/executor.sh"] | ||
| evaluator: ["python3", "roles/evaluator.py"] |
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| roles: | ||
| planner: ["python3", "roles/planner.py"] | ||
| executor: ["bash", "roles/executor.sh"] | ||
| evaluator: ["python3", "roles/evaluator.py"] |
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| roles: | ||
| planner: ["python3", "bots/planner.py"] | ||
| executor: ["python3", "bots/executor.py"] | ||
| evaluator: ["python3", "bots/evaluator.py"] | ||
| planner: ["python3", "roles/planner.py"] | ||
| executor: ["bash", "roles/executor.sh"] | ||
| evaluator: ["python3", "roles/evaluator.py"] |
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| ```bash | ||
| # 1. Install | ||
| pip install evolution-kernel | ||
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| # 1. 安装 | ||
| pip install evolution-kernel | ||
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Owner
Author
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Closing — superseded by docs/readme-update branch which cherry-picks cleanly onto main. |
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Summary
--goalreferences from both READMEsDocumentation-only change — no code modified.
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