A network of agentic research specialists coordinated by a principal investigator agent. V1: web-search researcher MCP server + CLI shim.
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Previously the depth parameter (shallow/balanced/deep) was passed only as a text hint inside the agent's user message, with no mechanical effect on iterations, token budget, or source count. The flag was effectively cosmetic — the LLM was expected to "interpret" it. Add DEPTH_PRESETS table and constraints_for_depth() helper in researchers.web.models: shallow: 2 iters, 5,000 tokens, 5 sources balanced: 5 iters, 20,000 tokens, 10 sources (= historical defaults) deep: 8 iters, 60,000 tokens, 20 sources Wired through the stack: - WebResearcher.research(): when constraints is None, builds from the depth preset instead of bare ResearchConstraints() - MCP server `research` tool: max_iterations and token_budget now default to None; constraints are built via constraints_for_depth with explicit values overriding the preset - CLI `ask` command: --max-iterations and --budget default to None; the CLI only forwards them to the MCP tool when set, so unset flags fall through to the depth preset balanced is unchanged from the historical defaults so existing callers see no behavior difference. Explicit --max-iterations / --budget always win over the preset. Tests cover each preset's values, balanced backward-compat, unknown depth fallback, full override, and partial override. 116/116 tests passing. Live-verified: --depth shallow on a simple question now caps at 2 iterations and stays under budget. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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| cli | ||
| obs | ||
| orchestrator | ||
| researchers | ||
| scripts | ||
| tests | ||
| .dockerignore | ||
| .gitignore | ||
| CLAUDE.md | ||
| CONTRIBUTING.md | ||
| Dockerfile | ||
| pyproject.toml | ||
| README.md | ||
Marchwarden
A network of agentic research specialists coordinated by a principal investigator agent.
Marchwarden researchers are stationed at the frontier of knowledge — they watch, search, synthesize, and report back what they find. Each specialist is self-contained, fault-tolerant, and exposed via MCP. The PI agent orchestrates them to answer complex, multi-domain questions.
V1: Single web-search researcher + CLI shim for development.
V2+: Multiple specialists (arxiv, database, internal docs, etc.) + PI orchestrator.
Quick start
# Clone
git clone https://forgejo.labbity.unbiasedgeek.com/archeious/marchwarden.git
cd marchwarden
# Install
pip install -e .
# Ask a question
marchwarden ask "What are ideal crops for a garden in Utah?"
# Replay a research session
marchwarden replay <trace_id>
Docker test environment
A reproducible container is available for running the test suite and the CLI without depending on the host's Python install:
scripts/docker-test.sh build # build the image
scripts/docker-test.sh test # run pytest
scripts/docker-test.sh ask "question" # run `marchwarden ask` end-to-end
# (mounts ~/secrets ro and ~/.marchwarden rw)
scripts/docker-test.sh replay <id> # replay a trace from ~/.marchwarden/traces
scripts/docker-test.sh shell # interactive bash in the container
Documentation
- Architecture — system design, researcher contract, MCP flow
- Development Guide — setup, running tests, debugging
- Research Contract — the
research()tool specification - Contributing — branching, commits, PR workflow
Status
- V1 scope: Issue #1
- Branch:
main(development) - Tests:
pytest tests/
Stack
- Language: Python 3.10+
- Agent framework: Anthropic Claude Agent SDK
- MCP server: Model Context Protocol
- Web search: Tavily API
License
(TBD)