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Monitor local LLM token costs, detect abuse patterns, and expose findings to agents.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ledgermind" yet — see the docs or source repo.
Analyze the last 7 days of local LLM proxy logs and identify abnormal token usage and cost spikes by time window, model, and request source, ranked by severity.
A ranked anomaly report with expensive calls, spike sources, and recommended next actions.
Scan local LLM request records for possible abuse patterns such as bursty high-frequency traffic, unusually long contexts, and repeated failed retries, then summarize the risk signals.
An abuse-pattern analysis listing suspicious behaviors, affected scope, and risk explanations.
Format the latest cost and security audit results into an MCP-friendly structured output, including anomaly summaries, affected services, and recommended actions.
Structured audit data that agents can directly consume for automated remediation or alerting.
Compare AI model pricing, simulate costs, and get plan recommendations.
Enable AI agents to perform double-entry bookkeeping, ledger queries, and reconciliation.
Track LLM costs, enforce budgets, and get spending alerts in AI editors.
Delegate low-risk tasks to a cheaper model with main-agent review.
Run a local-first MCP proxy with secure discovery and major token savings.
Route LLM tasks locally first to keep sensitive data private and controlled.