FML injects your team's prior work into every agent run, making every agent more productive and recovering up to 40% of AI coding spend.
Your engineers work with models all day, and every session creates valuable context: how the codebase fits together, what was tried, which tickets shipped last week. Then the session ends, and that context disappears or never leaves one laptop.
FML keeps it. Everything your models have learned becomes available in every session, for everyone: prior sessions, review comments, Git history, incidents, docs, and setup patterns that carry forward between engineers and tools.
~32%
of this team's AI spend went to repeated discovery and known dead ends
600
sessions
3
engineers
30 days
captured
Repeated discovery means the model would have acted differently with information already in the team's history, as opposed to a logic failure. Across customers and models the avoidable share runs up to ~40%; the share recovered at install time, before any tuning, is ~25–30%.
This is a retrospective classification, not realized savings. The pilot measures the before-and-after directly: a baseline with injection off, then injection on, compared on cost per shipped outcome with an agreed quality guardrail.
Install the FML plugin wherever you're coding and the context graph will be built automatically.
Before an agent writes a line of code, FML hands it the institutional knowledge from across the team: prior engineering sessions on the files, connected PRs, code review issues that were flagged, and Sentry reports related to the feature. Retrospective analysis of captured sessions finds up to 40% of AI coding spend goes to repeated discovery — work the team already did.
How we measure it →Works with
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.
FML helps teams better understand AI activity, from individual session breakdowns to model performance on their codebase.
Last 30 days
Your team ran 128 sessions across 9 active repos. Checkout has the richest setup, 7 skills were used in sessions, and estimated spend is $3.8k.
Large-context sessions
6 sessions exceeded 150k tokens before the first useful edit.
Top models · 8.5M tokens
Skills, commands, hooks, MCP
Signals attached to sessions
Sessions connect to the work they produced — PRs, deploys, incidents, product events — and the relevant history goes back to the next agent. Repo history seeds the graph on day one.
The same task, three runs in
illustrative
What feeds the graph
FML sits locally on the machine and saves the useful parts of every coding session: prompts, session state, files touched, tool calls, and outcomes.
A trace can stay local, be queried over MCP, be injected into the next run, or be exported to a customer-owned pipeline.
Saved on this device
Claude Code, Codex, and Gemini sessions become local trace records.
Each trace keeps
Prompt and session state
Tools, files, diffs, and test output
Outcome, review result, and cost
Then use it to
Query over MCP
Find related sessions and prior attempts without leaving the coding harness.
Inject into the next run
Give Claude or Codex compact context before it starts exploring.
Export the trace
Send records to eval, annotation, audit, or governance pipelines.
Ask what the team shipped, what a session cost, or why a change landed. FML answers from the work record, posts a morning digest, and sends alerts from connected sources like Sentry and Linear.
Install FML wherever your team codes. Claude Code, Codex, and Gemini sessions land in a local trace store, with optional team sync.
Sessions
128
last 30 days
Spend
$3.8k
model usage
Recent sessions
Checkout retry fix
Claude Code · $4.18 · shipped
Auth migration notes
Codex · reused as context
As engineers work, FML shows sessions, spend, stuck runs, reusable workflows, and the context graph forming across the team.
Use MCP, Slack, terminal, or the coding agent to recover prior work and inject context into the next run.
The same captured work record improves the next agent run, the next engineer handoff, and the next finance review.
Shared context
Spend attribution
Onboarding
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: