Skip to content

Lerim

Lerim turns completed agent traces into reusable operating context.

It filters noisy execution history into evidence-backed context records: decisions, preferences, constraints, facts, references, and compact episode history.

Summary

Lerim sits after trace systems and before future agents. Observability shows what happened; Lerim decides what was worth learning from it.

The strongest native capture path today is coding agents. Support operations and operations/incidents have documented custom-trace paths and source profiles, not separate pipelines.

If you are evaluating Lerim, start with the workflow: traces in, durable context out, cited answers and startup context for future agents.

Public support and benchmark claims are intentionally artifact-backed. The integration matrix separates config support from native trace capture, and the benchmark pages name the raw report.json evidence behind each number.

The operating model is simple:

  • capture traces from supported agent work
  • expose context to MCP-compatible agents
  • filter noisy execution history into durable signal
  • curate overlap so context stays compact
  • link related context into a navigable graph
  • answer questions and compile startup context for future agents
  • propose updates to registered agent skills and instruction files

Main phases

  • ingest extracts durable records from supported traces
  • curate merges and archives low-value records so memory stays selective
  • context_graph links curated records into a sparse context graph during curate cycles
  • answer retrieves records and answers a question
  • skill registers instruction targets and manages evidence-backed update proposals

Focused workflows

  • Coding agents preserve repo conventions, architecture decisions, setup facts, failed commands, test lessons, and release handoffs.
  • Support operations preserve customer constraints, known fixes, failed fixes, escalation reasons, policy-backed facts, and handoffs.
  • Operations and incidents preserve root causes, mitigations, rejected hypotheses, runbook gaps, incident handoffs, and follow-up risks.

Start here