B

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

Sources