AI financial audit and task economics: connect usage, supplier charges and business outcomes to calculate task costs, cost per verified success and ROI.
Track LLM token usage by user, session and model without a metric label for every user. Prometheus or any OTLP metrics backend.
dscache 给你的 DeepSeek Agent 装上一个 prefix-cache 利润表：每次请求看清命中还是被打爆，再给出一条可直接套用的 prefix 重排建议，把上下文缓存折扣稳稳拿回来。
Investigate LiteLLM spend from your database in one read-only command: what changed, who drove it, which models served each route, and which requests are missing
Deterministic benchmark for agent instruction adherence, with token and round-trip cost verified by a recording proxy
Local, deterministic, report-only Token ROI and multi-agent worktree accounting
Embeddable LLM benchmark speed measurement and offline Codex/Claude Desktop analysis, with explicit timing boundaries and output verification.
为 DeepSeek Harness 提供透明、可审计的 token 记账：可重启的账本，加上一个从原始日志重算账本并比对差异的 CLI | Transparent, auditable token accounting for DeepSeek Harness: a restart-safe ledger, plus a CLI that recomputes it from raw logs and diffs the result.
Run repeatable agent experiments in Docker: capture inputs, track tool calls, token usage and cost, grade outputs, and compare baseline vs candidate with evidence for every result.
Local LLM token accounting, cache usage reports and offline evaluation for AI agents. Includes an optional OpenClaw observation plugin.
Trace creator asset delivery token use and processing exceptions with one Java configuration.
Computes cost-per-resolved-task, prefix cache savings, and unit margin economics across LLM inference workloads | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550
OpenCode plugin for persistent goals, automatic continuation, token budgets, and evidence-based completion checks.
Conformance invariants and runnable harnesses for tools that count LLM tokens and cost from agent transcripts. Two catalogs, eleven upstream fixes.
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