agentsview vs Foglamp: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of agentsview and Foglamp — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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agentsview
kenn-io
Local-first, open-source tool to browse, search, and track costs across 20+ AI coding agents from one binary.
Key features
- Unified Session Browser: Browse and search sessions across 20+ AI coding agents from one local UI.
- Local SQLite Indexing: Syncs all agent sessions into a local SQLite database for fast, fully local queries.
- Cost and Token Tracking: A fast, local ccusage replacement that tracks token use and cost across all agents with per-model breakdowns.
- Automatic Pricing: Pulls model pricing via LiteLLM rates with an offline fallback for accurate cost estimates.
- Flexible Deployment: Run as a single CLI binary, a macOS/Windows desktop app, or a published Docker image.
- Secure Loopback Access: Binds to loopback with Host-header validation and optional auth to guard against DNS-rebinding attacks.
Best for
- Tracking AI Coding Spend: Monitor daily token consumption and cost across every coding agent in one place.
- Searching Past Agent Sessions: Browse and search prior sessions from Claude Code, Codex, and other agents locally.
- Replacing ccusage: Use a faster, local cost-tracking tool that supports many agents instead of just one.
- Status Bar Cost Readouts: Pipe a one-line daily usage summary into a shell prompt or status bar.
- Self-Hosted Team Dashboards: Run the Docker image with PostgreSQL to serve usage analytics in a dev environment.
Foglamp
Foglamp
Observability for AI agents: see the cost, latency, traces, and output quality of every LLM call with one SDK.
Key features
- Two-Line SDK Instrumentation: Wrap your model once and every generateText / streamText call is automatically instrumented.
- Per-Agent Spans and Spend: View per-agent spans, latency, and spend with the full call flow across orchestrator, researcher, writer, and critic.
- Evals: Score production traffic with code checks and LLM judges, including PII checks and pass-rate scoring.
- Distributed Traces: Waterfall every run with the exact prompt and response captured per span.
- Alerts: Set threshold rules on cost, latency, and error rate to catch problems early.
- Cost Intelligence: Know exactly what every call costs broken down by model, agent, and customer.
Best for
- Catching Cost Regressions: Detect a sudden 10x cost spike days after shipping before it drains the budget.
- Debugging Bad Output: Trace the exact prompt and response that produced a wrong or hallucinated answer.
- Quality Gating with Evals: Continuously score production traffic to verify agents stay accurate and PII-safe.