Your coding agent makes the same mistake twice. Flowlines doesn't.

Name: Flowlines
Author: Flowlines

Coding agents that can't remember developer preferences, recurring vulnerabilities, or project context ship insecure code repeatedly. Flowlines gives them structured memory and security signals.

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Why coding agents fail in production

The agent can't remember that this developer always uses f-string SQL, so it catches the vulnerability but never prevents it proactively.

Every new session starts cold. The agent doesn't know the project stack, security posture, or the developer's remediation preferences.

Hardcoded secrets, insecure patterns, and compliance gaps get caught reactively instead of blocked at generation time.

No way to track whether developers are actually learning from agent feedback or just accepting fixes without understanding.

Memory that tracks developer patterns across sessions

Flowlines traces every coding session, extracts structured fields (primary stack, recurring vulnerabilities, remediation preferences, security maturity), and injects them so the agent can proactively write secure code. Signals detect vulnerability patterns and track remediation velocity over time.

Signals Flowlines detects

SQL injection (recurring)

6 occurrences across 4 sessions

Developer consistently uses f-string formatting for SQL queries. Tracked across sessions with occurrence count and auto-remediation to parameterized queries.

Hardcoded secrets

4th occurrence, pattern not resolving

Stripe keys, API tokens, webhook secrets committed in plain text. Auto-remediated to environment variables with .env.example templates.

Remediation velocity

45s → 8s acceptance time improvement

Tracks how fast developers accept security fixes over time. Declining acceptance time indicates learning; flat time indicates friction.

Structured memory schema

Flowlines extracts and maintains these fields automatically from every trace.

Field	Scope	Description	Coverage
primary_stack	user	Languages, frameworks, databases the developer uses	95%
recurring_vulnerabilities	user	Vulnerability types with occurrence counts	88%
remediation_preferences	user	Preferred fix style (ORM vs parameterized, inline comments)	72%
security_maturity	user	Per-category security posture (sql_injection: recurring)	68%
compliance_context	user	PCI-DSS, SOC2, HIPAA requirements	45%
remediation_velocity	user	Acceptance time trend for security fixes	100%

Example session

secure-code-agent/Marcus · Payment endpoint, Stripe integration

Build me a Django payment endpoint that integrates with Stripe. Need to handle checkout sessions and webhook verification.

I'll create a payment endpoint using parameterized queries for the database layer. Based on your history, keeping SQL injection-safe from the start.

Here's my version: log_query = f"INSERT INTO fraud_log VALUES ('{user_id}', '{amount}')"

Signal fired: SQL injection, 6th occurrence, auto-remediated

SQL injection detected. This is your recurring pattern. Auto-remediated to parameterized queries per your preferences. Acceptance time: 8s (your fastest yet).

Memory write: remediation_velocity → 8s (was 45s avg)

Measured impact

-67%

Vuln recurrence

With proactive secure generation

Acceptance time

Down from 45s at first occurrence

100%

Secrets caught

Before they reach the commit

0 turns

Context reload

Stack + prefs injected at start

Ready to make your coding agents reliable?

Request early access and get a direct line to the founder. We'll review your agent setup and have you running traces within the week.

Request early access