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The world's first Autonomous Product Engine (APE): AI agents research your market, generate features, and ship code as PRs. Convoy mode, crash recovery, cost tracking, 80+ API endpoints. Self-hosted via OpenClaw Gateway.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
Autopilot now has a product-level Repo Setup tab that verifies a repository is ready before agents create PR-bound work. It checks authenticated git access, default branch confirmation, GitHub API and PR metadata access, GitHub Actions status, workflow token permissions, PR workflow secrets, and PR workflow variables.
When setup is blocked, users can fix supported GitHub configuration from the UI: set workflow token permissions to read/write, add missing Actions secrets, and add missing Actions variables. Secret values are written directly to GitHub and are not stored in Autensa.
Build Queue tasks with GitHub PRs now include a PR checks recovery panel. Failed checks are classified as retryable, repo setup, or external provider failures, with actions to rerun failed GitHub Actions jobs or rerequest external checks where GitHub supports it.
Product creation and settings now validate private repos with authenticated git access, detect the remote default branch, and require user confirmation before Autopilot starts. Workspace isolation preflights the selected branch and can use the detected default branch, avoiding main clone failures on repos that use master.
Each dispatched task now gets its own OpenClaw conversation session. Previously, all tasks assigned to the same agent shared one session, causing context to accumulate across tasks until the model's context window was exhausted and the agent stalled. The openclaw_sessions table already had a task_id column — dispatch now uses it for session lookup, session ID generation, and insert. Parallel tasks on the same agent work independently.
Agent ID fields now accept both standard UUID format (8-4-4-4-12) and 32-character hex identifiers from the OpenClaw gateway. Previously, Zod's strict .uuid() validation rejected gateway-format agent IDs, causing "Invalid UUID" errors when assigning imported agents to tasks.
The task delete button now shows a loading state ("Deleting..."), disables during the request, and displays inline error messages when deletion fails. Previously, the button had no feedback — if the API request failed or was slow, users saw no response and assumed the button was broken.
The Autopilot product settings modal now includes a Status dropdown (Active / Paused) and a Danger Zone section with an Archive button. Paused products stop automated research and ideation cycles. Archived products are hidden from the dashboard but data is preserved. The main product listing now filters out archived products.
openclaw/default with the original model in x-openclaw-model, fixing 404 errors on OpenClaw deployments. (@Ahmedkasmi-dev, #109)AUTOPILOT_MODEL config is respected. (@aaronmeza, #116)Autensa v2 is a ground-up expansion from task orchestration dashboard to the world's first autonomous product improvement engine. It researches your market, generates feature ideas, lets you decide with a swipe, and builds them — automatically.
The headline feature. Point Autensa at any product (repo + live URL) and it runs a continuous improvement loop:
Autonomous Research — AI agents analyze your codebase, scan your live site, and research your market: competitors, user intent, conversion patterns, SEO gaps, technical opportunities. Runs on configurable schedules — daily, weekly, or on-demand.
AI-Powered Ideation — Research feeds into ideation agents that generate concrete, scored feature ideas. Each idea includes an impact score, feasibility score, size estimate, technical approach, and a direct link to the research that inspired it.
Swipe to Decide — Ideas appear as cards in a Tinder-style interface. Four actions:
Automated Build → PR — Approved ideas flow through the full agent pipeline: Build agent implements the feature → Test agent runs the suite → Review agent inspects the diff → Pull request created on GitHub with full context.
Your only job is the swipe. Everything else is automated.
Inspired by Andrej Karpathy's AutoResearch architecture. Each product has a Product Program — a living document that instructs research and ideation agents on what to look for, what matters, and what to ignore. The program evolves as swipe data accumulates: the system learns your taste, not just patterns.
Large features get decomposed into subtasks with a visual dependency graph (DAG). Multiple agents (3–5) work simultaneously with dependency-aware scheduling:
Don't wait for a PR to give feedback. Two communication modes:
Full chat history preserved per task — every message, note, and response.
Granular spend visibility across every dimension:
A dedicated Learner agent captures lessons from every build cycle — what worked, what failed, what patterns emerged. Knowledge entries are injected into future dispatches so agents don't repeat mistakes.
Before any build starts, agents run a structured planning phase:
Agent progress is saved at configurable checkpoints:
Every swipe trains a per-product preference model:
Ideas you're not sure about don't disappear:
Real-time SSE stream of everything happening across all products:
Choose your comfort l