+- Lerim sits above agent traces, compiles the useful signal into cited context, and gives the next agent the operating memory it needs before work begins. + Lerim sits above agent traces, compiles useful signal into cited context and eval assets, and gives future agents the operating memory they need before work begins.
@@ -50,11 +50,11 @@ # Lerim -Lerim is a context compiler for AI agent workflows. +Lerim is a context compiler for repeated AI agent workflows. Agents leave traces everywhere: terminals, tools, tickets, code reviews, support cases, research runs. Most of that history is too noisy to reuse directly. -Lerim filters those traces into evidence-backed context records: the decisions, constraints, facts, preferences, and handoffs future agents should not have to rediscover. +Lerim filters those traces into evidence-backed context records and eval-ready workflow signal: the decisions, constraints, facts, preferences, corrections, and handoffs future agents should not have to rediscover. Instead of replaying raw traces or losing useful context between workflows, Lerim keeps: @@ -62,6 +62,7 @@ Instead of replaying raw traces or losing useful context between workflows, Leri - constraints - preferences - facts +- corrections - handoffs - evidence linked back to the source session @@ -70,7 +71,7 @@ Instead of replaying raw traces or losing useful context between workflows, Leri | Moment | Lerim does | Future agents get | | --- | --- | --- | | A completed agent run lands | Imports a source session from an adapter, MCP submit, or clean custom JSONL | A stable source boundary instead of a transcript paste | -| The trace is noisy | Compacts the run and filters for reusable decisions, constraints, facts, preferences, and handoffs | Durable context, not another log index | +| The trace is noisy | Compacts the run and filters for reusable decisions, constraints, facts, preferences, corrections, and handoffs | Durable context and eval-ready signal, not another log index | | Someone asks later | Retrieves relevant records and answers with citations back to stored evidence | A shorter start with less re-explaining | ## Quick Install @@ -101,7 +102,7 @@ lerim answer "What context should I know before working in this project?" ## Why Lerim -AI agents now triage tickets, investigate incidents, research markets, prepare handoffs, review policies, and change software. +AI agents now triage tickets, investigate incidents, research markets, prepare handoffs, review policies, analyze customers, and change software. Every run leaves a trace. Most traces are too long, too noisy, and too platform-specific for the next agent to reuse directly. @@ -111,16 +112,19 @@ Without a durable context layer: - constraints get rediscovered - preferences get ignored - every new session starts too close to zero +- useful corrections never become eval or training signal -Lerim fixes that by turning raw traces into reusable context records and making them queryable from agent tools and product workflows. +Lerim fixes that by turning raw traces into reusable context records, eval assets, and training-ready workflow signal that remain queryable from agent tools and product workflows. Lerim is meant for any trace-producing agent workflow. Today, native source adapters are strongest for coding agents, and documented custom-trace paths cover -support and incident workflows: +support and incident workflows. Coding is a proof-rich workflow pack, not the +whole product category: - coding agents: repo conventions, architecture decisions, setup facts, failed paths, test lessons, release handoffs - support operations: customer constraints, known fixes, failed fixes, escalation reasons, policy evidence, handoffs - operations and incidents: root causes, mitigations, rejected hypotheses, runbook gaps, incident handoffs, follow-up risks +- research, compliance, security, revenue, and other custom business agents: source trails, assumptions, approvals, rejected paths, policy facts, and workflow-specific handoffs when the source owner handles export, cleaning, and redaction ## Key Capabilities @@ -263,7 +267,19 @@ hardware/runtime metadata, and failure count. - Operations and incidents: documented custom-trace path; preserve root causes, mitigations, rejected hypotheses, runbook gaps, owner decisions, and follow-up risks. - Coding agents: retain architecture decisions, failed paths, repo conventions, setup facts, release handoffs, and constraints. -Research, revenue, security, and other verticals can use the same custom-trace path today when the user owns export, cleaning, and redaction. The first product wedge and strongest examples are coding plus support and incident operations. +Research, revenue, security, compliance, and other verticals can use the same custom-trace path today when the user owns export, cleaning, and redaction. The product wedge is one repeated private workflow with trace access, a workflow owner, privacy constraints, and measurable quality failure. Coding remains a strong proof workflow because the native adapters are mature, but the commercial company should be positioned around private agent improvement for enterprise workflows. + +## Enterprise Readiness To-Do List + +Use this list to keep the repo, website, and pitch aligned without turning the +open-source package into a closed enterprise product: + +- Keep open core useful: CLI, local runtime, MCP server, native adapters, custom trace import, context DB, docs, and benchmarks. +- Sell the production layer: Context Audits, private deployment, workflow evals, governance controls, managed integrations, retention, and enterprise support. +- Prove one workflow first: support escalation, incident/security ops, research intelligence, compliance review, or engineering automation. +- Measure improvement honestly: context reused, false memories rejected, eval pass rate, human acceptance, token budget saved, and repeated work reduced. +- Build training only after proof: approved traces, corrections, and eval assets can become SFT/RL data once the customer workflow and privacy boundary are clear. +- Keep coding agents as a proof pack, not the headline TAM/SAM/SOM story. ## Skill Updates @@ -292,7 +308,43 @@ lerim dashboard See [Skill Updates](docs/guides/skill-updates.md) for the dashboard workflow and [CLI: lerim skill](docs/cli/skill.md) for command details. -## Custom Agent Traces +## Custom & Non-Coding Agents + +Lerim is not only for coding agents. Support, incident/security operations, +research, compliance, revenue, and other custom business agents feed the same +compiler through clean JSONL traces and a signal profile that matches the workflow. + +
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