{"type":"ScholarlyArticle","title":"TraceBrain: An Open-Source Framework for Agentic Trace Management","authors":[{"name":"Quy Minh Le","slug":"quy-minh-le","given":"Quy Minh","family":"Le","affiliation":""},{"name":"Oscar Cao","slug":"oscar-cao","given":"Oscar","family":"Cao","affiliation":""},{"name":"Hoang Quoc Viet Pham","slug":"hoang-quoc-viet-pham","given":"Hoang Quoc Viet","family":"Pham","affiliation":""},{"name":"Hoang Thanh Lam","slug":"hoang-thanh-lam","given":"Hoang Thanh","family":"Lam","affiliation":""},{"name":"Hoang D. Nguyen","slug":"hoang-d-nguyen","given":"Hoang D.","family":"Nguyen","affiliation":""}],"abstract":"As Large Language Model (LLM) agents scale toward real-world deployment, they generate large volumes of fragmented, non-standardized execution traces. Many existing observability platforms treat these traces primarily as passive logging artifacts, lacking the unified infrastructure to operationalize them for active governance and agent adaptation across heterogeneous single-agent and multi-agent workflows. To address this gap, we introduce TraceBrain, an open-source infrastructure for autonomous agent trace management. TraceBrain adopts a framework-agnostic architecture built on a delta-based OpenTelemetry (OTLP) schema, which mitigates context explosion and supports on-demand reconstruction of long-horizon execution trajectories. For runtime governance, TraceBrain implements uncertainty-driven supervision, where an internal Trace Evaluator prioritizes ambiguous trajectories for human review, thereby reducing manual annotation workload. Moving beyond passive observation, the platform incorporates a hybrid semantic-lexical retrieval engine that combines dense vector similarity and exact keyword matching for operational memory retrieval. Furthermore, an automated curriculum mechanism continuously synthesizes failure patterns into structured training artifacts. Empirical evaluations demonstrate a ~100x reduction in storage overhead together with high precision in uncertainty-guided trace supervision. Ultimately, TraceBrain transforms the execution history into a reusable operational memory substrate, bridging runtime observability with retrieval-driven agent adaptation. The system is publicly available at https://github.com/ToolBrain/TraceBrain.","doi":"10.24963/ijcai.2026/975","url":"https://ijcai.org/proceedings/2026/975","pdf":"https://ijcai.org/proceedings/2026/0975.pdf","markdown":"https://ijcai.org/proceedings/2026/975.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/975","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Demo Track","track":"Demo Track","number":975,"pages":{"first":8441,"last":8444},"keywords":["AI: Agent-based and Multi-agent Systems","AI: Knowledge Representation and Reasoning","AI: Search","AI: Uncertainty in AI"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}