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Selfship.ai
Summary
You shipped a monitoring dashboard, you triaged the alerts, you filed the tickets — and the same hallucinated policy answer still greeted customer 300 the same way it greeted customer 1. Selfship closes that loop by turning repeated agent failures directly into GitHub pull requests, no ticket required.
The observe-diagnose-fix-verify cycle runs on live traffic without you assigning tasks or writing prompts. When a failure pattern crosses a threshold, Selfship identifies the exact conversation step that breaks, generates a proposed code change with eval evidence attached, and opens a PR in your GitHub. You review, you merge — nothing touches your code without your sign-off. After the merge, it watches real traffic to confirm the fix held; if it didn't, it opens a new PR with what it observed, up to a retry limit, then flags the item for human attention.
Bottom line: Pick this when the same agent failure is showing up in customer complaints week after week and your team keeps deferring the ticket — but plan for a different setup if your agent runs without GitHub or your fix workflow requires regulatory approval chains the PR model cannot accommodate.
Pricing Plans
Usage-Based- Price
- $20/mo base
Base
14-day trial, 6,000 sessions/mo, 120 days retention, unlimited repos
- Failure detection
- PR generation
- Post-merge verification
Enterprise
Custom sessions, retention, compliance (HIPAA, GxP, SOC 2 in progress)
- Audit logs
- Compliance features
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Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Closes the observe-to-fix loop without requiring a ticket, so recurring failures that routinely get deprioritized in sprint planning get addressed on the cycle they appear in traffic.
- Every PR arrives with eval evidence attached — impact scores and sample sessions — so you review a decision with data rather than trusting the agent's judgment blind.
- OpenTelemetry-native instrumentation means it layers onto existing traces from LangSmith, Langfuse, or similar tools rather than forcing you to rip out your current observability stack.
- Post-merge verification on real traffic means you know whether the fix held before closing the item, which eliminates the common pattern of a fix shipping and the same complaint resurfacing two weeks later.
- Provider-agnostic design across OpenAI, Anthropic, Gemini, Mistral, and Bedrock means a model swap does not require reconfiguring your monitoring layer.
Cons
Sign in to edit- The entire fix workflow depends on GitHub — teams whose code lives in GitLab, Bitbucket, or internal version control systems have no PR delivery path, and there is no documented workaround beyond switching to GitHub.
- Novel failure modes that haven't repeated enough to form a detectable traffic pattern produce no action items, which means low-volume but high-severity failures — a hallucination that affects one enterprise customer's critical workflow — stay invisible until they accumulate.
- There is no self-hosted option, so teams under data residency requirements that prohibit sending conversation traces to a third-party SaaS will hit a compliance wall before the trial ends and have no path forward with this tool.
- The verify step retries automatically but only up to a retry limit before marking the item as 'needs attention' — teams whose failure modes require root-cause changes to agent architecture rather than prompt or code-level fixes will find the loop stalls and falls back to manual debugging, at which point the tool's core value proposition disappears and teams running complex multi-agent systems typically revert to LangSmith plus a dedicated engineering sprint.
About
- Platforms
- Web SaaS with GitHub integration
- API Available
- No
- Self-Hosted
- No
- Last Updated
- 2026-09-08T23:26:16.556Z
Best For
Who it's for
- Teams running production AI agents that receive repeated customer complaints
- Organizations seeking automated code fixes backed by real usage data
- Users of LangChain, LlamaIndex, CrewAI or similar frameworks needing closed-loop monitoring
What it does well
- Automatically fixing recurring agent failures from production traffic
- Reducing hallucinated or policy-violating responses in customer-facing agents
- Improving agent success rate and response efficiency without manual debugging
Integrations
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Sign Up to ContributeFrequently Asked Questions
- Is Selfship.ai free?
- Selfship.ai is a paid tool ($20/mo base). A 14-day free trial is available.
- Is Selfship.ai open source?
- No — Selfship.ai is a closed-source tool. Source code is not publicly available.
- What platforms does Selfship.ai support?
- Selfship.ai is available on: Web SaaS with GitHub integration.
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Production AI agents accumulate failure patterns faster than engineering teams can triage them. Selfship addresses this by running a closed observe-diagnose-fix-verify loop on live traffic: it reads what each customer was trying to do and whether they got it, identifies when 300 people hit the same wall in a week, and surfaces a proposed code fix as a GitHub PR with eval evidence — impact scores, sample sessions, and before/after metrics you can audit directly in the PR. The vendor describes setup as roughly 20 minutes of engineer time to first traces, and the tool connects via OpenTelemetry, so it sits alongside existing instrumentation rather than replacing it.
The differentiating claim against pure observability tools is completion. Tools like Langfuse or LangSmith answer “what happened” and hand the alert back to you. Selfship’s positioning is that it answers the question and then acts on it — proposing the fix, verifying it on real traffic, and reopening the loop if verification fails. The vendor states your code never trains a model, which addresses a concern common in enterprise AI procurement about data use.
Selfship fits teams running customer-facing agents on LangChain, LlamaIndex, CrewAI, Vercel AI SDK, or Bedrock who are receiving repeated complaints about the same failure modes and lack the bandwidth to systematically debug them. It breaks down when your repository is not on GitHub, when your deployment pipeline requires approval steps that a PR alone cannot trigger, or when your agent’s failure modes are novel enough that no traffic pattern has yet repeated at the threshold required to generate a diagnosis. There is no self-hosted option and no API, so teams with data residency requirements or those needing to embed the loop into their own tooling will hit a hard wall.
Integration is OpenTelemetry-native and the vendor lists explicit compatibility with OpenAI, Anthropic, Gemini, Mistral, Bedrock, LangChain, LlamaIndex, CrewAI, and the Vercel AI SDK. GitHub is required for the PR workflow — the docs describe connecting GitHub and reviewing a first real fix PR as the primary onboarding path. The fix-verification step runs on real traffic up to a retry limit; if the fix does not hold, the item is marked “needs attention” rather than looped indefinitely.