A scalable Graph-of-Thoughts (GoT) agent service for web research using task decomposition and parallel reasoning to increase accuracy and reduce cost.
Euglena is an agent with web crawling and retrieval-augmented generation. Tasks decompose into parallel subproblems (search, visit, save, think) and merge into structured deliverables. Context persists in ChromaDB. Cost efficiency is maximized through dynamic beam-width and a token-efficient workflow that benefits cheaper models through structured reasoning.
Live: https://euglena.vercel.app/
Note: The visit system does not work on deployment due to website anti-bot measures that detect AWS datacenter IPs. The agent demonstrates resilience by working with search results and other available data sources. This website will be available until 2026-03-16 00:00 PST.
64 runs across 16 tests x 2 models x 2 execution variants.
Graph-of-Thought scores 26.8% higher than sequential and uses 29% fewer tokens.
| Metric | Graph | Sequential | Delta | % Change |
|---|---|---|---|---|
| Avg Score | 0.921 | 0.727 | +0.195 | +26.8% |
| Pass Rate | 90.6% | 46.9% | +43.8pp | |
| Avg Cost | $0.04 | $0.06 | -$0.01 | -22.5% |
| Avg Tokens | 22.6k | 32.0k | -9.4k | -29.3% |
| Avg Duration | 688s | 349s | +339s | +97.2% |
| Rank | System | Avg Score | Median | Std | Pass % | $/run | Time |
|---|---|---|---|---|---|---|---|
| 1 | gpt-5.2 [graph] | 0.930 | 0.979 | 0.094 | 93.8% | $0.07 | 471s |
| 2 | gpt-5-mini [graph] | 0.912 | 0.931 | 0.097 | 87.5% | $0.01 | 904s |
| 3 | gpt-5.2 [sequential] | 0.729 | 0.746 | 0.172 | 50.0% | $0.09 | 200s |
| 4 | gpt-5-mini [sequential] | 0.724 | 0.697 | 0.164 | 43.8% | $0.03 | 497s |
- Graph-of-Thought reasoning: Tasks decompose into parallel subproblems (search, visit, think, save), then merge results upward through the DAG into structured deliverables
- Dual execution modes:
graph(parallel branching with best-first selection) andsequential(generate then pick, single path depth first) for A/B comparison - Bot-resistant web access: Primary
aiohttpconnector with automaticundetected-chromedriverfallback on 403/401 - Long-term memory (RAG): Crawled content is chunked and embedded into ChromaDB, queryable across tasks and reasoning steps
- Dynamic beam width: Branching factor adapts to score quality. Expands exploration when scores are low, narrows when confident
- Deduplication and pruning: Candidate thoughts are deduplicated by embedding similarity. Low-scoring nodes are pruned to save budget
- Elastic worker fleet: ECS autoscaling matches demand via CloudWatch queue-depth metrics, winds down when idle
- User-scoped quotas: Supabase enforces per-user daily usage limits with JWT authentication
- Comprehensive test suite: 39 priority-ordered tests with programmatic and LLM-based validation
Structured telemetry at every layer without cluttering business logic.
| Layer | What Is Tracked | Where |
|---|---|---|
| Connectors | Every HTTP request, LLM call, search query, browser fetch. Timing, status, payload size | ConnectorBase._record_timing, _record_io |
| AgentIO | Unified interface telemetry. Visit/search/store/retrieve with fallback tracking | AgentIO methods |
| Engine | Step-by-step DAG traversal. Expansion, evaluation, selection, merge, pruning events | IdeaDagEngine logger |
| GoT Operations | Embedding, deduplication hits, dynamic beam decisions, prune events | GoTOperations |
| Memory | Chunk storage, retrieval counts, namespace isolation | MemoryManager |
| Test Runner | Per-test scores, pass/fail, cost, tokens, duration, graph structure metrics | idea_test_runner.py |
| Visualization | 4-page core dashboard, heatmaps, efficiency frontiers, difficulty rankings | testing/visualization_* |
Connector base classes handle I/O logging so action classes stay focused on logic (see OOP conventions).
idea_test_runner > JSON results > visualization_summary > terminal report
> visualization_core > 4-page PNG dashboard
> visualization_plots > detailed plot gallery
Results are written to agent/idea_test_results/ as timestamped JSON. The visualizer can filter by run ID (--latest, --run-id) and generates executive dashboards, heatmaps, efficiency frontiers, and per-test breakdowns.
Regenerating Visualizations:
# From services/ directory, run visualization in Docker
docker compose run --rm agent python -m app.testing.idea_test_visualize --latest --core-only
# Or generate all plots (including detailed gallery)
docker compose run --rm agent python -m app.testing.idea_test_visualize --latest
# List available test runs
docker compose run --rm agent python -m app.testing.idea_test_visualize --list-runs
# Generate and copy benchmark plots to docs/benchmark/ (from project root)
python scripts/generate_benchmark_plots.pyVisualization Improvements:
- Executive Summary: Score heatmap (test × system) replaces model leaderboard table for better visual insight
- Efficiency Dashboard: Violin plots with all datapoints replace cramped tables, showing full score distributions
- Larger fonts: All text increased for better readability (titles 32-48pt, labels 18-22pt)
- All datapoints visible: Individual test runs shown as scatter points overlaid on distributions
- Clear trends: Graph vs Sequential advantage highlighted with annotations and visual comparisons
Visualizations are automatically generated after test runs and saved to agent/idea_test_results/plots_<run_id>/.
| Layer | Technology |
|---|---|
| Frontend | React, Vite, Supabase Auth |
| Backend | FastAPI, RabbitMQ, Redis, ChromaDB, Supabase |
| Agent | Graph-of-Thought engine, OpenAI LLMs, Brave Search, undetected-chromedriver |
| Infra | AWS ECS, ECR, CloudWatch, Lambda autoscaling |
cd services
cp keys.env.example keys.env # add OPENAI_API_KEY + SEARCH_API_KEY
docker compose up -d # starts gateway, agent, rabbitmq, redis, chromaThe system will be available at:
- Frontend:
http://localhost:5173(Vite dev server) - Gateway API:
http://localhost:8000 - RabbitMQ Management:
http://localhost:15672(guest/guest) - ChromaDB:
http://localhost:8001
# Run specific tests
IDEA_TEST_IDS=019,025 docker compose run --profile test visit-test
# Run full test suite
docker compose run --profile test idea-test
# Benchmark mode (top 8 tests, 3 models, 3 runs each)
IDEA_TEST_MODE=benchmark docker compose run --profile test idea-testKey environment variables for testing:
IDEA_TEST_IDS: Comma-separated test IDs (e.g., "019,025,033")IDEA_TEST_MODE: "default" or "benchmark"IDEA_TEST_RUNS: Number of runs per test/model pairIDEA_TEST_CONCURRENCY: Max parallel executionsIDEA_TEST_MODELS: Comma-separated models (e.g., "gpt-5.2,gpt-5-mini")IDEA_TEST_EXECUTION_VARIANTS: "graph", "sequential", or both
services/
agent/ Agent service (GoT engine, connectors, tests)
gateway/ FastAPI gateway, task intake, Supabase sync
shared/ Connector configs, models, storage helpers
metrics/ CloudWatch queue-depth publisher
lambda_autoscaling/ ECS autoscaler
frontend/ React web UI
scripts/ Deployment, diagnostics, audits
docs/ Architecture, security, benchmark plots
- System Architecture - Overall system design and message flow
- Agent Architecture - Graph-of-Thought engine internals
- Test Suite - Test structure and validation
- Deployment - Deployment guide
- Debugger - Debugging tools and techniques
- Scripts - Deployment and diagnostic scripts