agentsview vs flue: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of agentsview and flue — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
a
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.
f
flue
Astro
Open-source TypeScript agent harness framework for building autonomous agents and AI workflows.
Key features
- Agent Harness: A programmable TypeScript harness giving any model sessions, tools, skills, instructions, and filesystem access.
- Autonomous Agents: Build agents that keep context across conversations and events while working toward a goal.
- Workflows: Run structured automations where your code guides agent reasoning from input to finished result.
- Secure Sandboxes: Give agents an isolated environment to use tools, modify files, and complete real work safely.
- Durable Execution: Agents preserve progress and recover through failures and restarts.
- Subagents and Tools: Delegate tasks to specialized subagents and give agents typed actions for APIs and data.
- MCP and Observability: Connect tools via Model Context Protocol and monitor agents with OpenTelemetry and Braintrust.
Best for
- Building Autonomous Agents: Create Claude Code or Codex-style agents that complete open-ended tasks independently.
- Structured Workflows: Automate multi-step processes where code controls the agent's reasoning path.