Optional local model aliases and per-million-token price overrides.

July 25, 2026 · View on GitHub

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AgentTrace

Local-first TUI and reports for AI coding-agent session history, cost, tokens, time, and slow-run diagnosis.

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agenttrace running locally against real AI coding agent session logs


agenttrace is a local-first terminal TUI and report generator for AI coding-agent session history. It reads Claude Code, Codex CLI, Gemini CLI, Qwen Code, Cline, Aider, Cursor exports, Hermes Agent, OpenCode, OpenClaw, Pi, Oh My Pi, Kimi CLI, Copilot-style logs, and generic JSON/JSONL traces, then helps with two daily jobs: see what multiple agents spent across cost, tokens, and time; and diagnose why a task ran slowly.

One agenttrace binary provides both interfaces: run it without a report action to open the TUI, or pass flags such as --sessions and --overview for CLI output.

Why agenttrace?

AI coding agents now behave like small build systems: they call tools, retry, stall, and spend tokens while you only see the final answer.

agenttrace reads the logs your agents already write and puts cost-heavy or slow sessions first.

It helps you answer:

  • What did my agents spend? Compare historical sessions by agent source, model, input/output/cache tokens, estimated cost, and wall-clock time.
  • Why was this task slow? Catch long gaps, hanging sessions, retry loops, slow tool calls, large parameters, and context pressure.
  • Did a run regress? Compare against a local baseline when supplied, then inspect incident timelines and conservative tool authority categories in reports.
  • What should I inspect first? Rank sessions by cost, duration, turns, health, failures, anomalies, model, source, or text search.
  • Can I inspect this privately? Everything runs locally; prompts, code, and logs do not need to leave your machine.

Real local run

These screenshots and figures are a redacted sample of the latest 500 real local sessions captured for the v0.7.1 source tree. They are not --demo output, current telemetry, or test fixtures.

agenttrace
OverviewCritical sessions
agenttrace overview showing real local AI coding agent sessions, token cost, errors, and healthagenttrace critical session list from real local AI coding agent logs
Session detailDiagnostics
agenttrace detail view showing health, cost, tool failures, and next action from a real local sessionagenttrace diagnostics view showing latency, context window, and large parameter calls from real local logs

That local run found:

AGENTTRACE v0.7.1
SignalWhat agenttrace found
Analyzed sessions500
Total tokens4.0B
Estimated cost$2.6K
Tool failure rate2.9%
Critical sessions2
Average health77.7%

Install

Install the latest public release with your package manager. Check the installed version with agenttrace --version.

# macOS and Linux
brew install luoyuctl/tap/agenttrace

# macOS, Linux, and Windows (requires Node.js 18+)
npm install -g @zack78/agenttrace

Windows:

winget install --id Luoyuctl.AgentTrace --exact

The package names above become available once the corresponding release has been published. Manual installs remain available without a package manager:

curl -fsSL https://raw.githubusercontent.com/luoyuctl/agenttrace/master/install.sh | sh
cargo install --git https://github.com/luoyuctl/agenttrace agenttrace
iwr -useb https://raw.githubusercontent.com/luoyuctl/agenttrace/master/install.ps1 | iex

Quickstart

agenttrace

Governance reports

# Audit raw token components, normalized pricing, and fallback confidence.
agenttrace --audit --range 30d -f json

# Optional local model aliases and per-million-token price overrides.
AGENTTRACE_PRICING_FILE=pricing-overrides.json agenttrace --audit -f json

# Rank evidence-backed actions by severity and estimated impact.
agenttrace --recommend --range 30d -f json

# Inspect observed MCP invocations. Loaded-server coverage is intentionally
# reported as unavailable unless the source log actually records it.
agenttrace --mcp-governance --range 30d -f json

# Review cross-session context, cache, repeat-read, and read/write trends.
agenttrace --context-trends --range 30d -f json

# Correlate local Git commit timestamps with sessions (heuristic, read-only).
agenttrace --delivery-evidence --range 30d -f json

pricing-overrides.json accepts aliases plus per-million-token prices:

{"aliases":{"provider/raw-model":"my-model"},"prices":{"my-model":{"input":1,"output":2,"cw":0,"cr":0}}}

--overview now includes scope, parse and pricing confidence, cost audit, prioritized recommendations, MCP governance, context trends, and delivery signals in JSON, Markdown, and HTML output. All cost and delivery fields are explicitly estimates or heuristics; they are not provider billing or proof that a commit reached main.

What you get

Needagenttrace gives you
Historical spend reviewSessions grouped across projects, agents, and models with Today/7d/30d/All ranges
Data confidenceReport scope, per-source coverage, parse skips, cache hits, unknown sources/models, pricing fallbacks, and latest observed session
Cost audit and action planToken component rates, pricing source/status, estimated cost confidence, and prioritized, evidence-backed remediation suggestions
Governance trendsCanonical project grouping, observed MCP invocation governance, cross-session context/cache/read-write trends, and read-only Git delivery correlation
Honest capability levelsDetailed, Aggregate, or Limited per session so missing event-level evidence is never presented as a complete trace
Privacy-safe stepsTool-step metadata and duration when the source provides call IDs and timestamps; no prompt, response, result, or tool-argument body is stored in steps
Slow-task diagnosisLatency stats, long gaps, hanging sessions, retry loops, slow tools, large params, and context pressure
Regression evidenceLocal baseline comparison when supplied, incident timelines, and conservative tool authority categories in reports
First-session triageSort and filter by cost, duration, health, failures, anomalies, model, source, or text search
Shareable evidenceJSON, Markdown, and self-contained HTML reports
Local-first inspectionNo hosted backend required

Docs

Listed in these open source projects:

Contributing

Parser PRs are welcome. A good parser contribution usually includes:

  • a tiny redacted fixture or synthetic sample
  • format detection in crates/agenttrace-core/src/parser.rs
  • role, timestamp, model, token usage, tool call, and tool error extraction
  • tests for successful parsing and malformed input

Run before sending a PR:

cargo test
cargo build --release -p agenttrace
target/release/agenttrace --doctor

See CONTRIBUTING.md for the full contribution flow.

License

MIT © 2026 agenttrace contributors