B — Some risk signals Graded October 16, 2026Minor concerns worth a quick check.Based on public adoption and transparency signals as of October 16, 2026. Our opinion, not a code audit or security guarantee.
AI agent skill · Developer tools, Memory & knowledge
openrig
Build your own network of agents from Claude Code, Codex and Pi: persistent teams with roles, shared context and owned work.
Bottom line: Signal-based grade (4/7 points): very widely starred on GitHub, public source code. This reflects adoption and transparency, not a code review.
- Popularity score
- 44/100
- Rank
- #230 of 500
- GitHub stars
- 7.5k
What it does
Openrig lets you orchestrate persistent teams of AI agents (Claude, Codex, Pi) with defined roles, shared context, and delegated work assignments. It provides a framework for building multi-agent networks that maintain state and coordinate tasks across different AI models.
Who it’s for
AI builders and teams looking to experiment with multi-agent architectures and persistent agent collaboration. Engineering organizations exploring autonomous team structures powered by different LLM providers.
Copy-paste setup
Check these steps against the project’s own instructions before running anything.
Verify against the repo README at https://github.com/mvschwarz/openrig (opens external site) for current installation instructions. The project appears early-stage; check the repository for npm package availability and configuration examples before attempting setup.
Risks
- Multi-agent credential scope: Multiple agents sharing context increases the attack surface for credential exposure or misuse across the network
- Prompt injection vulnerabilities: Shared context between agents may amplify prompt injection risks if one agent's output feeds untrusted data to others
- State persistence risks: Maintaining shared state across agent teams could lead to data exfiltration or unauthorized state manipulation
- LLM provider coupling: Reliance on external LLM APIs (OpenAI, Anthropic, etc.) introduces third-party data handling and rate-limiting dependencies
- Early-stage maturity: Limited production usage signals suggest security review and hardening may be incomplete
How to stay secure
Least-privilege permissions
- Give each agent only the specific access it needs. Don't share master passwords or admin credentials across your team of agents. Instead, assign each agent a limited role (e.g., "read-only reporter" vs. "data processor").
- Rotate credentials regularly. Change API keys and passwords every 30–90 days, and immediately revoke access for agents no longer in use.
- Separate sensitive data from shared context. Keep confidential information (customer data, financial records, secrets) out of the shared workspace where multiple agents can see it.
Network isolation
- Run agent teams in isolated environments when handling sensitive work. Use separate, dedicated deployments for high-risk tasks rather than mixing them with general-purpose agents.
- Limit external API calls. Restrict which agents can contact outside services (payment processors, third-party APIs) and log all outbound requests for review.
- Use a firewall or network boundary. Prevent agent infrastructure from directly accessing your internal databases or systems without explicit, monitored gateways.
Monitoring
- Log all agent conversations and decisions. Record what each agent does, what data it accesses, and what other agents it communicates with—keep logs for at least 90 days.
- Set up alerts for unusual activity. Flag unexpected credential use, large data transfers, or agents accessing resources they shouldn't, then investigate immediately.
- Audit agent outputs before deployment. Have a human review what agents produce before it reaches customers or affects business-critical systems.
Red flags to watch for
- Agents unexpectedly sharing sensitive information. If you notice credentials, personal data, or confidential instructions appearing in agent outputs or shared context, shut down the team and audit immediately.
- Requests from agents to bypass controls. If an agent asks to skip verification steps, ignore audit logs, or access restricted resources, treat it as a security incident.
- Unusual provider behavior or outages. Sudden rate limits, service degradation, or unexpected changes from your LLM provider (OpenAI, Anthropic, etc.) may signal compromise or third-party interference.
Alternatives
- AutoGen (Microsoft) – mature multi-agent conversation framework with proven enterprise adoption
- Crew AI – agent orchestration platform with role-based task delegation and built-in safety patterns
- LangGraph – LangChain's state-graph framework for building agent workflows with explicit control flow
- Agency Swarm – structured agent teams with defined hierarchies and inter-agent communication protocols