Adds an append-only JSONL ledger of every research() call at
~/.marchwarden/costs.jsonl, supplementing (not replacing) the
per-call cost_metadata field returned to callers. The ledger is
the operator-facing source of truth for spend tracking, queryable
via the upcoming `marchwarden costs` command (M2.5.3).
Fields per entry: timestamp, trace_id, question (truncated 200ch),
model_id, tokens_used, tokens_input, tokens_output, iterations_run,
wall_time_sec, tavily_searches, estimated_cost_usd, budget_exhausted,
confidence.
Cost estimation reads ~/.marchwarden/prices.toml, which is
auto-created with seed values for current Anthropic + Tavily rates
on first run. Operators are expected to update prices.toml
manually when upstream rates change — there is no automatic
fetching. Existing files are never overwritten. Unknown models
log a WARN and record estimated_cost_usd: null instead of
crashing.
Each ledger write also emits a structured `cost_recorded` log line
via the M2.5.1 logger, so cost data ships to OpenSearch alongside
the ledger file with no extra plumbing.
Tracking changes in agent.py:
- Track tokens_input / tokens_output split (not just total)
- Count tavily_searches across iterations
- _synthesize now returns (result, synth_in, synth_out) so the
caller can attribute synthesis tokens to the running counters
- Ledger.record() called after research_completed log; failures
are caught and warn-logged so a ledger write can never poison
a successful research call
Tests cover: price table seeding, no-overwrite of existing files,
cost estimation for known/unknown models, tavily-only cost,
ledger appends, question truncation, env var override.
End-to-end verified with a real Anthropic+Tavily call:
9107 input + 1140 output tokens, 1 tavily search, $0.049 estimated.
104/104 tests passing.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Adds an operational logging layer separate from the JSONL trace
audit logs. Operational logs cover system events (startup, errors,
MCP transport, research lifecycle); JSONL traces remain the
researcher provenance audit trail.
Backend: structlog with two renderers selectable via
MARCHWARDEN_LOG_FORMAT (json|console). Defaults to console when
stderr is a TTY, json otherwise — so dev runs are human-readable
and shipped runs (containers, automation) emit OpenSearch-ready
JSON without configuration.
Key features:
- Named loggers per component: marchwarden.cli,
marchwarden.mcp, marchwarden.researcher.web
- MARCHWARDEN_LOG_LEVEL controls global level (default INFO)
- MARCHWARDEN_LOG_FILE=1 enables a 10MB-rotating file at
~/.marchwarden/logs/marchwarden.log
- structlog contextvars bind trace_id + researcher at the start
of each research() call so every downstream log line carries
them automatically; cleared on completion
- stdlib logging is funneled through the same pipeline so noisy
third-party loggers (httpx, anthropic) get the same formatting
and quieted to WARN unless DEBUG is requested
- Logs to stderr to keep MCP stdio stdout clean
Wired into:
- cli.main.cli — configures logging on startup, logs ask_started/
ask_completed/ask_failed
- researchers.web.server.main — configures logging on startup,
logs mcp_server_starting
- researchers.web.agent.research — binds trace context, logs
research_started/research_completed
Tests verify JSON and console formats, contextvar propagation,
level filtering, idempotency, and auto-configure-on-first-use.
94/94 tests passing.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The loop previously checked the token budget at the *bottom* of each
iteration, after the LLM call and tool work had already happened. By
the time the cap was caught the budget had been exceeded and the
overshoot was unbounded by the iteration's cost.
Move the check to the *top* of the loop so a new iteration is never
started past the budget. Document the policy explicitly: token_budget
is a soft cap on the tool-use loop only; the synthesis call is always
allowed to complete so callers get a structured ResearchResult rather
than a fallback stub. Capping synthesis is a separate, larger design
question (would require splitting the budget between loop and
synthesis up-front).
Verified: token_budget=5000, max_iterations=10 now stops after 2
iterations with budget_exhausted=True and a complete answer with
10 citations.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Both the MCP server and WebResearcher defaulted to
claude-sonnet-4-5-20250514, which 404s against the Anthropic API.
Update both defaults to claude-sonnet-4-6, which is current as of
2026-04.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The synthesis step was passing max_tokens=4096 to Claude, which was
not enough for a full ResearchResult JSON over a real evidence set
(28 sources). The model's output got cut mid-string, json.loads
failed, and the agent fell back to a stub answer with zero citations.
The trace logger then truncated the raw_response to 1000 chars before
recording it, hiding the actual reason for the parse failure (the
truncated JSON suffix) and making the bug invisible from traces.
Fixes:
- Bump synthesis max_tokens to 16384
- Capture and log Claude's stop_reason on synthesis_error so future
truncation cases are diagnosable from the trace alone
- Log the parser exception text alongside the raw_response
- Stop slicing raw_response — record the full string
Verified end-to-end against the Utah crops question:
- Before: 0 citations, confidence 0.10, fallback stub
- After: 9 citations, confidence 0.88, real synthesized answer
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
FastMCP server exposing a single 'research' tool:
- Delegates to WebResearcher with keys from ~/secrets
- Accepts question, context, depth, max_iterations, token_budget
- Returns full ResearchResult as JSON
- Configurable model via MARCHWARDEN_MODEL env var
- Runnable as: python -m researchers.web
4 tests: secret reading, JSON response validation, default parameters.
Refs: archeious/marchwarden#1
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
New field on ResearchResult: open_questions — follow-up questions that
emerged from the research itself. Distinct from gaps (backward: what
failed) and discovery_events (sideways: what's lateral). Open questions
look forward: 'based on what I found, this needs deeper investigation.'
- OpenQuestion model: question, context, priority (high/medium/low),
source_locator
- Updated agent synthesis prompt to produce open_questions
- Updated agent result builder to parse open_questions from JSON
- 3 new tests for OpenQuestion model
- Updated existing tests for new field
77 tests passing.
Refs: archeious/marchwarden#1
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
WebResearcher — the core agentic research loop:
- Tool-use loop: Claude decides when to search (Tavily) and fetch (httpx)
- Budget enforcement: stops at max_iterations or token_budget
- Synthesis step: separate LLM call produces structured ResearchResult JSON
- Fallback: valid ResearchResult even when synthesis JSON is unparseable
- Full trace logging at every step (start, search, fetch, synthesis, complete)
- Populates all contract fields: raw_excerpt, categorized gaps,
discovery_events, confidence_factors, cost_metadata with model_id
9 tests: complete research loop, budget exhaustion, synthesis failure
fallback, trace file creation, fetch_url tool integration, search
result formatting.
Refs: archeious/marchwarden#1
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
- Directory layout: researchers/web/, orchestrator/, cli/, docs/wiki/
- README with quick start and vision
- CONTRIBUTING with workflow and testing guidelines
- pyproject.toml with dependencies and build config
- .gitignore for Python projects
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
