shandu 3.0.0

Multi-agent research system powered by Blackgeorge

Shandu 3.0

Shandu 3.0 is a Blackgeorge-powered, lead-orchestrated multi-agent research system.

• Architecture deep dive: ARCH.md
• Example long-form output: see the examples directory.

Architecture

• Lead orchestrator plans iterative research loops.
• Parallel search subagents retrieve and extract web evidence.
• Citation subagent builds the final reference ledger.
• SQLite-backed memory tracks run context across steps.
• Rich CLI control deck renders run metrics and timeline.
• Scraper pipeline normalizes URLs, strips boilerplate HTML, and favors main-content blocks.

Quick Start

uv sync --dev
source .venv/bin/activate
cp .env.example .env
# edit .env with your provider/model settings

API Key Configuration (LiteLLM Style)

shandu configure now asks for:

• Default model (example: deepseek/deepseek-chat, openrouter/minimax/minimax-m2.5)
• API key env var name (example: DEEPSEEK_API_KEY, OPENROUTER_API_KEY, ANYSUPPORTED_API_KEY)
• API key value (hidden input)

Shandu saves these in user config storage and exports the configured env var at runtime for LiteLLM if it is not already set in your shell.

Examples:

# DeepSeek
shandu configure
# model: deepseek/deepseek-chat
# env var name: DEEPSEEK_API_KEY
# key value: <your key>

# OpenRouter
shandu configure
# model: openrouter/minimax/minimax-m2.5
# env var name: OPENROUTER_API_KEY
# key value: <your key>

You can still configure keys only through shell env vars if you prefer:

export OPENROUTER_API_KEY="your_real_key"

Environment Variables (Without shandu configure)

If you prefer not to use interactive configuration, set env vars directly.

Provider/model:

• SHANDU_MODEL (primary model selector, example deepseek/deepseek-chat)
• OPENAI_MODEL_NAME (compatibility fallback if SHANDU_MODEL is not set)

Provider API key routing:

• SHANDU_API_KEY_ENV (name of provider key env var, example OPENROUTER_API_KEY)
• SHANDU_API_KEY (actual key value that Shandu exports into SHANDU_API_KEY_ENV at runtime if missing)

Direct LiteLLM-style provider key env vars (examples):

• DEEPSEEK_API_KEY
• OPENROUTER_API_KEY
• ANTHROPIC_API_KEY
• OPENAI_API_KEY
• Any other provider key name LiteLLM supports, for example ANYSUPPORTED_API_KEY

Generation/runtime controls:

• SHANDU_TEMPERATURE (default 0.2)
• SHANDU_MAX_TOKENS (default 8192)
• SHANDU_STORAGE_DIR (default .blackgeorge)
• SHANDU_PROXY (optional proxy for scraping)

Precedence:

1. If your provider key env var (for example OPENROUTER_API_KEY) is already set in shell, Shandu uses it.
2. Otherwise, Shandu uses SHANDU_API_KEY_ENV + SHANDU_API_KEY from config/env.

CLI

shandu run "Who is the current president of the United States?" \
  --max-iterations 1 \
  --parallelism 2 \
  --max-results-per-query 2 \
  --max-pages-per-task 2 \
  --output report.md

--parallelism controls the maximum number of subagent tasks that execute concurrently inside each iteration. If set to 2, the lead planner creates at least two independent tasks when possible, and the orchestrator runs up to two tasks at the same time.

During shandu run, progress events stream live in the terminal:

• BOOTSTRAP / PLAN / SEARCH / SYNTHESIZE / CITE / REPORT / COMPLETE
• Per-task search events (Task <id> started and Task <id> completed) with metrics
• Iteration index and task IDs for long-running model calls

shandu aisearch "latest state of open-source browser automation in 2026" \
  --max-results 8 \
  --max-pages 3 \
  --detail-level high \
  --output aisearch.md

aisearch returns classic behavior: web search + synthesized explanation with source citations.

Citation behavior:

• Final reports enforce numeric citation markers ([1], [2], ...).
• Raw internal evidence IDs are removed from the rendered markdown.
• The final ## References section is rendered from the citation ledger to keep numbering stable.

Other commands:

• shandu info
• shandu configure
• shandu aisearch <query>
• shandu inspect <run_id>
• shandu clean

Python API

from shandu import ResearchRequest, ShanduEngine

engine = ShanduEngine.from_config()
result = engine.run_sync(
    ResearchRequest(
        query="AI inference infrastructure 2026",
        max_iterations=2,
        parallelism=3,
    )
)
print(result.report_markdown)

Development

uv run ruff check .
uv run pytest -q

Scraper Notes

• Requests use a browser-like user agent and language headers.
• URLs are canonicalized and deduplicated before fetch.
• Extraction prioritizes article / main / role=main content, then falls back to body text.
• Repeated or very short blocks are filtered to reduce navigation/cookie noise.

MIT license.