Ship AI agents on your data — the right way
A visual platform to design, test and run AI agents and data pipelines with best practices built in — secure by default, scalable as you grow, and small enough for one engineer to own.
Launching September 14
The first 50 sign-ups get free credits on launch day — enough to build, scan and run real agents before you spend anything.
One engineer, a whole agent platform
Convolution Labs is a visual platform to design, test and run AI agents and data pipelines on your own data — with the patterns and guardrails a senior team would set up, already in place. You bring the use case and your LLM keys; the platform handles the wiring, security and scale.
Visual Canvas Editor
Design agent and data workflows by dragging nodes — no boilerplate
Custom Python Tools
Extend agents with your own sandboxed Python code
Built-in Testing
Debug runs with real-time execution traces
Bring Your Own Keys
Your LLM keys are encrypted; you pay your provider directly
Proven Agent & Pipeline Patterns
Don't reinvent the wheel. Start from well-known agent architectures and customize them for your use case — the structure and best practices come built in.
ReAct Agent
A reasoning and acting loop where the agent thinks, decides on an action, executes it, observes the result, and repeats.
Use this patternChain of Thought
Agent breaks down complex problems into sequential reasoning steps before producing final output.
Use this patternSupervisor
A supervisor agent analyzes tasks and delegates to specialized worker agents.
Use this patternMap-Reduce
Split input into chunks, process in parallel, then aggregate results.
Use this patternSelf-Reflection
Agent produces initial output, critiques it, and iteratively refines based on the critique.
Use this patternPlan and Execute
Agent first creates a detailed plan, then executes each step sequentially.
Use this patternFrom Idea to Production in Minutes
Our visual canvas makes it easy to design complex agent workflows. Connect LLMs, tools, and data sources with drag-and-drop simplicity. The platform guides you with best practices at every step.
Choose a Pattern
Start with a pre-built template or design from scratch
Configure & Connect
Add LLMs, tools, and define your workflow logic
Test & Deploy
Debug with traces, then deploy with one click
Start from a working agent, not a blank page
The marketplace will let you publish agents you've built and pull in ones others have shared — for research, coding, content and data tasks. Clone one, point it at your data and keys, and adapt it instead of starting from scratch.
Best Practices Built Into the Platform
The hard parts of running agents in production — isolation, secret handling, access control — are handled for you, so a small team can ship safely.
Encrypted Key Storage
LLM keys are encrypted at rest and never leave your account — you bring your own and pay your provider directly.
Sandboxed Execution
Custom Python tools run in an isolated sandbox, so untrusted code can't reach the rest of the system.
Access Control & Teams
Role-based teams and per-key API access keep runs and data scoped to who should see them.
Proven Patterns
Start from patterns like ReAct, Supervisor and Map-Reduce instead of wiring agents together by hand.
Observable Runs
Every run produces a real-time execution trace and token usage, so you can debug and control cost.
Scales With You
A queue-backed worker runs your agents and pipelines, so workloads grow without re-architecting.
Get your data where the agents are
An agent is only as useful as the data it can reach. Move data between databases on a schedule — sources, destinations and connections, running on the same worker infrastructure as your agents.
Reusable sources & destinations
Configure a database once, sync as many tables from it as you need
Full refresh or incremental
Incremental syncs resume from the last successful run — no skipped or double-read rows
Credentials encrypted at rest
AES-256-GCM, decrypted only inside the worker, with connector hosts validated before every connection
A configured connector instance — where data comes from
A source and destination pair: what to sync, how, and when
Where the rows land
Postgres → Postgres today, with more connectors on the way
Find the holes before attackers do
Agents are a new attack surface — prompt injection, data exfiltration, SSRF through tools, sandbox escapes. Convolution Labs scans your agents for these, scores their attack surface, and lets you run real attack probes — mapped to the OWASP LLM Top 10 and NIST.
Static scans
Analyze prompts, tools and data flow for risks — instantly, no run needed
Live attack probes
Run prompt-injection, jailbreak, prompt-leak and SSRF tests against your own agent
Security score & remediation
Get a 0–100 score, fix findings, and watch it improve over time
What Can You Build?
Research Agents
Autonomous web research with citations
Code Assistants
PR reviews, bug detection, refactoring
Data Pipelines
ETL, transformations, analytics
Content Writers
SEO articles, social media, emails
Customer Support
Ticket triage, FAQ bots, escalation
Sales Agents
Lead qualification, outreach, CRM sync
Document Processing
Extraction, summarization, Q&A
Custom Workflows
Build anything you can imagine
See it on your own data
Walk through the platform with us — pipelines, agents, and the security scans — using a setup that looks like yours. Bring your questions about the parts that matter for your stack.
A live walkthrough
Not a recorded video — a real session where you can interrupt
Your use case
Tell us what you're moving and building, and we'll aim the demo at it
Straight answers
Including what the platform doesn't do yet
Ready to Build Your First Agent?
Bring your data and your LLM keys. Stand up a secure, scalable agent without a platform team. The platform opens September 14 — the first 50 on the list start with free credits.