Compare ruflo and HAL systems for running the GAIA benchmark, an AI evaluation. Examine differences in code execution, browsing, file handling, memory, and model selection to prioritize improvements by expected impact and effort.
Side-by-side comparison of ruflo vs HAL vs other GAIA harnesses — capability gaps, design decisions, and improvement roadmap
Compare ruflo with Princeton’s HAL reference implementation for running GAIA, a benchmark that measures AI capabilities. This skill organizes differences in code execution, browser operation, attachment reading, and the number of questions evaluated. It supports reviewing improvement priorities alongside the effort needed to implement them.
Use it when planning the next round of GAIA development or helping a new contributor understand the evaluation code. The comparison covers missing capabilities as well as selecting an answer by majority vote across multiple attempts, choosing models according to difficulty, and using AgentDB for memory. You can focus the review on tools, routing, memory, or cost.
The documented architecture and evaluation figures reflect particular implementation stages. They do not guarantee the current state or the effects of improvements. Searching and saving previous research uses the illustrated @claude-flow/cli memory commands or the memory-related MCP tools in ruflo-core.
This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.
AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.
The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 21, 2026. Treat it as a guide and read the original before installing.
Review the instructions the agent will receive before you install.
Compare ruflo's GAIA benchmark harness against the Princeton HAL reference implementation and other open-source harnesses to understand capability gaps and prioritize improvements.
gaia-bench run
└─ gaia-loader.ts — HF dataset download + cache
└─ gaia-agent.ts — multi-turn Anthropic Messages loop
└─ gaia-tools/ — web_search, file_read, web_browse,
image_describe, python_exec
└─ gaia-voting.ts — Track A self-consistency (N attempts → majority vote)
└─ gaia-hardness/ — Track Q difficulty predictor (ADR-136)
└─ gaia-judge.ts — two-stage LLM-as-judge scorer
HAL uses a similar loop but with:
| Dimension | ruflo | HAL reference | Gap |
|---|---|---|---|
| Question count | 53 (partial L1) | 300 (full L1) | Use --limit 165 for full L1 |
| Web search | DuckDuckGo / Google CSE | BrowserBase live | Add Playwright or Browserless |
| Code execution | python_exec stub | Real Jupyter kernel | Implement real sandbox |
| Image OCR | image_describe (Gemini) | GPT-4V / Gemini | Functionally equivalent |
| File handling | file_read | Full PDF/XLSX/ZIP parser | Expand file_read |
| Self-consistency | voting.ts (Track A) | Not in reference | ruflo advantage |
| Hardness routing | predictor.ts (Track Q) | Not in reference | ruflo advantage |
| Memory | AgentDB HNSW | None | ruflo advantage |
| Pass-rate L1 | ~20.8% (iter 23) | 74.6% (HAL Sonnet 4.5) | ~54 pp gap |
Real code execution — many L2/L3 questions require running Python to
compute a numerical answer. The current python_exec tool is a stub.
Implementing a real sandbox (E2B, Pyodide, or subprocess) is the single
highest-ROI change.
Full question set — running 53/300 L1 questions underestimates true
pass-rate because the first 53 skew easier. Run --limit 165 (full L1)
for a comparable HAL score.
Real browser — web_browse currently fetches raw HTML. Replacing it
with Playwright/Browserless for JavaScript-rendered pages would unlock
many web navigation questions.
Structured file parsing — PDF, XLSX, and ZIP attachments require
dedicated parsers. file_read currently handles plain text and images only.
Turn budget — 12 turns may be insufficient for complex multi-step questions. HAL uses up to 20 turns for L3.
System prompt tuning — HAL's system prompt is more elaborate and explicitly instructs the model to use tools before answering.
Self-consistency voting (Track A) — running N attempts per question and taking the majority answer reduces variance on borderline questions. HAL does not implement this.
Hardness routing (Track Q) — routing each question to an appropriate model and turn budget based on predicted difficulty. This reduces cost on easy questions while providing more resources for hard ones.
AgentDB memory — storing patterns across runs enables the agent to recall successful strategies for similar question types.
| Priority | Change | Expected Lift | Effort |
|---|---|---|---|
| P0 | Real python_exec sandbox (E2B) | +15-25 pp | High |
| P0 | Full 165-Q L1 evaluation | Accurate baseline | Low |
| P1 | Playwright-based web_browse | +5-10 pp | Medium |
| P1 | PDF/XLSX file parser | +3-8 pp | Medium |
| P2 | Increase max-turns to 20 for L2/L3 | +2-5 pp | Low |
| P2 | System prompt tuning (iter 30 research) | +2-5 pp | Low |
| P3 | Google Grounding via Gemini (iter 32) | +3-7 pp | Medium |
| P3 | Multi-provider routing (Gemini Flash for cheap Q's) | Cost reduction | Medium |
npx @claude-flow/cli@latest memory search \
--namespace gaia-patterns \
--query "architecture comparison HAL benchmark"
npx @claude-flow/cli@latest memory store \
--namespace gaia-patterns \
--key "architecture-comparison-$(date +%Y%m%d)" \
--value "HAL gap: 54pp. Primary: python_exec stub. Secondary: browser, file parsing."
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Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.
Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.
Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.
Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.
Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.