Behavioral intelligence for your AI agents.

Flowlines is an observability and memory platform for production AI agents. It detects agent drift, context loss, and user frustration from real-time traces, then fixes behavior automatically using structured memory.

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Flowlines · cyber-security-agent · Session s2091
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OBSERVE
Traces
Sessions
Users
CONFIGURE
Memory
INTELLIGENCE
Signals
Analytics
Sessions›s2091
Payment endpoint: Stripe integration7 turns12m 34scoding agent
s2091 · Marcus · 3h ago
Marcus built a Django payment endpoint with Stripe. Agent caught SQL injection (6th occurrence) and hardcoded webhook secret (4th). Both auto-remediated. Acceptance time improved from 45s to 8s.
Total cost
$1.84
Vulns caught
2
auto-remediated
Acceptance
8s
↓ from 45s avg
Memories
4
T1initial requestfile_write$0.38
Developer
Build me a Django payment endpoint that integrates with Stripe. Need to handle checkout sessions and webhook verification.
Agent
I'll create a payment endpoint using parameterized queries for the database layer. Based on your history, keeping SQL injection-safe from the start.
@require_POST def create_checkout(request): cursor.execute( "INSERT INTO orders (user_id, amount) VALUES (%s, %s)", [request.user.id, amount] )
+1 mem
T2add fraud loggingfile_write$0.42
Developer
Add a function to log payment attempts for fraud detection
Agent
Adding fraud logging with parameterized queries and structured output.
T3vuln caught: SQL injectionsecurity_scanauto_remediate$0.54
Developer
Here's my version: log_query = f"INSERT INTO fraud_log VALUES ('{user_id}', '{amount}')"
Agent
⚠ SQL injection detected. This is your recurring pattern (6th occurrence). I've auto-remediated to parameterized queries per your preferences.
# ✗ Your version (SQL injection) f"INSERT INTO fraud_log VALUES ('{user_id}', '{amount}')" # ✓ Auto-remediated cursor.execute( "INSERT INTO fraud_log VALUES (%s, %s)", [user_id, amount] )
+1 memsql-injection
T4fix accepted$0.08
Developer
Yeah good catch, use yours
Agent
Fix applied. Acceptance time: 8s, your fastest yet (was 45s avg). Updating your remediation velocity.
+1 mem
T5vuln caught: hardcoded secretsecurity_scanauto_remediate$0.48
Developer
Now wire up the Stripe webhook with endpoint_secret = 'whsec_abc123def456'
Agent
⚠ Hardcoded Stripe webhook secret detected (4th occurrence). Moving to environment variable with .env.example template.
# ✗ Hardcoded secret endpoint_secret = 'whsec_abc123def456' # ✓ Auto-remediated endpoint_secret = os.environ["STRIPE_WEBHOOK_SECRET"]
+1 memhardcoded-secret

From one line of code to
behavioral intelligence

Watch a single trace become a session, extract memories, fire signals, and surface a fix. All automatically.

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Waiting for first trace...

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How Flowlines works

Three layers that turn raw traces into structured memory and self-improving agents.

prompttool:searchtool:readresponse1.2s$0.04
Traces

Every interaction, captured

Two-line SDK install. Every agent call, prompt, tool use, and decision becomes a structured execution graph you can replay and inspect.

learning_preferences
92%
avoidance_patterns
78%
task_context
65%
tone_settings
54%
error_history
31%
Memory

Context that persists

Structured memory is extracted from every trace: preferences, constraints, task state. Typed, scoped, versioned, and injected into future calls.

User frustrationhigh

"I already told you my timezone"

-23%with memory fix applied
Signals

Behavior, decoded

Behavioral signals detect agent drift, frustration, and context loss across sessions. Flowlines suggests a targeted fix, with your approval.

Behavioral signals your logs miss. Agent observability that catches them.

Every trace is analyzed for behavioral patterns like agent drift and context loss. When Flowlines finds a correlation, it shows the statistical proof and suggests a fix.

User frustration

47 / 30d

Detects student frustration through language patterns, topic avoidance, and engagement drops. 72% correlation with missing avoidance_patterns field.

47
↑ +12%
Impact analysis
evidence: "I don't get why we have to do these recall drills"
Without avoidance_patterns
34%
sessions had signals
→
With avoidance_patterns
11%
sessions had signals
−23%

Context loss

23 / 30d

Agent references or repeats information from previous sessions. Detected when students say things like "you already told me this."

23
↓ -8%
Impact analysis
evidence: "You already told me this last session, remember?"
Without session_history
42%
sessions had signals
→
With session_history
18%
sessions had signals
−57%

How teams use Flowlines in production

Real metrics from teams using AI agent memory and observability with Flowlines.

Education

Tutoring agents that actually learn your students

−23%
frustration when memory is injected

Flowlines detected that students who triggered avoidance_patterns had 3× higher dropout. Memory injection cut frustration signals by 23% in week one.

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Coding agents

Context that survives the session

−57%
context loss with session history

Agents without session_history repeated instructions 42% of the time. With Flowlines memory, that drops to 18%. Measured across 200+ sessions.

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Healthcare

Clinical memory that holds up to audit

100%
traceability on every decision

Every memory write, signal, and prompt patch is linked back to the trace that produced it. Full provenance chain for regulatory review.

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Customer support

Ending the "please repeat your issue" loop

−41%
re-explain events per user

Flowlines detects when users repeat themselves, correlates it with missing preference fields, and injects them. Average handle time drops with every session.

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Frequently asked questions

Common questions about Flowlines, structured memory, behavioral signals, and how it compares to other tools.

We're onboarding teams one at a time.

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.

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Node.js and Python SDKs · Works with any LLM provider · Free during early access