Pricing
Start locally. Add Pro when you want the team record: sessions, PRs, costs, and reusable context across every agent run.
Everything in Free, plus:
For orgs that need the full platform at scale.
Everything in Pro, plus:
Everything in Free, plus:
For orgs that need the full platform at scale.
Everything in Pro, plus:
Estimate how much of your AI spend is avoidable
Enter last month's AI coding model bill from your OpenAI or Anthropic dashboard. FML classifies captured sessions and identifies the share spent re-answering questions your team already answered.
Monthly AI coding spend
$50,000
Avoidable share
up to 40%
Monthly avoidable spend
$20,000
Annual avoidable spend
$240,000
Retrospective classification of captured sessions — spend where the model would have acted differently with the team's history. Across customers and models this runs up to 40%; our measured team came in at ~32%. Not a savings guarantee; the pilot measures realized results against your own baseline.
Questions
FML Observe captures AI coding sessions: prompts, tool calls, file operations, model responses, token counts, costs, and generated session summaries. Everything starts local in SQLite on the developer's machine. You control what syncs to FML.
FML starts from coding-agent sessions, then connects them to PRs, reviews, issues, incidents, docs, setup patterns, cost, and stuck work. The team can see what agents touched and what context should be reused next.
FML builds compact context packages from the current codebase, changed files, related PRs, prior agent sessions, and team patterns. Instead of making the agent rediscover everything through broad file reads and repeated prompts, FML injects relevant context up front. Retrospective classification of captured sessions finds up to 40% of AI coding spend goes to repeated discovery — spend the model would have avoided with the team's history already in context.
FML works with Claude Code, Codex CLI, Gemini CLI, and Pi, with access through the CLI, Slack, and MCP. GitHub is the first source for repo memory, and you can connect Slack, Linear, Sentry, PostHog, Notion, and Stripe from Settings → Integrations.
A seat is an FML organization member. Billing starts with the members in your FML workspace and updates as invited teammates accept access.
A developer can start locally in a few minutes: run the FML install command, register the coding-tool hooks, and link the org. Teams can then add sync, dashboards, Slack, and shared context rules.
FML starts local-first, and teams control what syncs. Most team views are built from summaries, setup patterns, costs, stuck sessions, and links to PRs or incidents. Raw prompts can be governed by policy instead of treated as an all-or-nothing feed.
Better prompts help one session. FML captures the work, indexes repo history, compares setup patterns, and packages the right context at task time. The next prompt gets better because the codebase has memory.
Yes. FML can find skills, hooks, permission rules, MCP servers, local docs, repeated workflows, cost patterns, and stuck sessions. The useful patterns become reviewable instead of staying trapped on one laptop.
Reach out to us at hi@fml.inc and we'll get back to you.