{"id":"a4b2f872-040d-4817-998d-51a11fa2efb2","slug":"crewai-loopgain-ai-loopgain","name":"loopgain","description":"Barkhausen stability monitor for AI agent loops. Real-time loop-gain (Aβ) detection of convergence, stalling, oscillation, and divergence — with best-so-far rollback and ETA prediction. Pre-built adapters for LangGraph, CrewAI, AutoGen, LangChain, OpenAI Agents, and Claude Agent SDK; raw API for custom stacks.","canonicalUrl":"https://www.xpersona.co/skill/crewai-loopgain-ai-loopgain","sourceUrl":"https://github.com/loopgain-ai/loopgain","homepage":null,"source":"GITHUB_OPENCLEW","vendor":{"slug":"loopgain-ai","label":"Loopgain Ai","url":"https://github.com/loopgain-ai/loopgain"},"protocols":["OPENCLEW"],"capabilities":["crewai","multi-agent"],"trustScore":null,"trustConfidence":"unknown","artifactCount":0,"benchmarkCount":0,"lastRelease":null,"freshnessAt":"2026-05-31T06:17:55.384Z","freshnessLabel":"May 31, 2026","securityReviewed":true,"openapiReady":false,"stats":[{"label":"Trust score","value":"Unknown"},{"label":"Compatibility","value":"OpenClaw"},{"label":"Freshness","value":"May 31, 2026"},{"label":"Vendor","value":"Loopgain Ai"},{"label":"Artifacts","value":"0"},{"label":"Benchmarks","value":"0"},{"label":"Last release","value":"Unpublished"}],"factsPreview":[{"factKey":"vendor","label":"Vendor","value":"Loopgain Ai","category":"vendor","href":"https://github.com/loopgain-ai/loopgain","sourceUrl":"https://github.com/loopgain-ai/loopgain","sourceType":"profile","confidence":"medium","observedAt":"2026-05-31T06:17:55.385Z","isPublic":true,"metadata":{}},{"factKey":"protocols","label":"Protocol compatibility","value":"OpenClaw","category":"compatibility","href":"https://www.xpersona.co/api/v1/agents/crewai-loopgain-ai-loopgain/contract","sourceUrl":"https://www.xpersona.co/api/v1/agents/crewai-loopgain-ai-loopgain/contract","sourceType":"contract","confidence":"medium","observedAt":"2026-05-31T06:17:55.385Z","isPublic":true,"metadata":{}},{"factKey":"traction","label":"Adoption signal","value":"1 GitHub stars","category":"adoption","href":"https://github.com/loopgain-ai/loopgain","sourceUrl":"https://github.com/loopgain-ai/loopgain","sourceType":"profile","confidence":"medium","observedAt":"2026-05-31T06:17:55.385Z","isPublic":true,"metadata":{}},{"factKey":"handshake_status","label":"Handshake status","value":"UNKNOWN","category":"security","href":"https://www.xpersona.co/api/v1/agents/crewai-loopgain-ai-loopgain/trust","sourceUrl":"https://www.xpersona.co/api/v1/agents/crewai-loopgain-ai-loopgain/trust","sourceType":"trust","confidence":"medium","observedAt":null,"isPublic":true,"metadata":{}}],"highlights":["1 GitHub stars","Trust evidence available"],"agentCard":{"name":"loopgain","description":"Barkhausen stability monitor for AI agent loops. Real-time loop-gain (Aβ) detection of convergence, stalling, oscillation, and divergence — with best-so-far rollback and ETA prediction. Pre-built adapters for LangGraph, CrewAI, AutoGen, LangChain, OpenAI Agents, and Claude Agent SDK; raw API for custom stacks.","source":"GITHUB_OPENCLEW","sourceId":"crewai:1236669709","repository":"https://github.com/loopgain-ai/loopgain","documentation":"https://www.xpersona.co/skill/crewai-loopgain-ai-loopgain/agent/crewai-loopgain-ai-loopgain","protocols":["OPENCLEW"],"capabilities":["crewai","multi-agent"],"languages":["python"],"install":{"command":"git clone https://github.com/loopgain-ai/loopgain.git","ecosystem":"git"},"examples":[{"kind":"example","language":"bash","snippet":"pip install loopgain"},{"kind":"example","language":"python","snippet":"from loopgain import LoopGain\n\nlg = LoopGain(target_error=0.1)\n\nwhile lg.should_continue():\n    errors = verifier.verify(output)\n    lg.observe(errors, output=output)\n    output = reviser.revise(output, errors)\n\nresult = lg.result\nprint(result.outcome)              # \"converged\" | \"oscillating\" | \"diverged\" | \"stalled\" | \"max_iterations\"\nprint(result.best_output)          # the lowest-error iteration's output\nprint(result.iterations_used)\nprint(result.gain_margin)          # 1 / max(Aβ_smooth)\nprint(result.savings_vs_fixed_cap)"}]}}