Stop restarting from zero.
Carry forward decisions, constraints, failed paths, approved corrections, and handoffs without replaying every transcript.
Private enterprise agent infrastructure
Make custom business agents improve across runs.
Lerim turns completed agent runs into cited context, eval assets, approved corrections, and private specialization for repeated enterprise workflows.
Self-host the Apache-2.0 core. Hire Lerim's private layer for Context Audits, private deployment, workflow evals, governance, and SFT/RL model specialization.
Capture
Completed runs from CLI, MCP, adapters, or clean JSONL
Compile
Cited context, corrections, eval assets, and evidence
Reuse
Workflow memory, private deployment, and specialization
Open core
Capture, compile, cite, and serve context through CLI, MCP, and briefs
Private layer
Evaluate, correct, specialize models, and tune prompts plus agent harnesses
First step
A focused Context Audit on one repeated workflow
Why it matters
Make agent quality compound.
Raw traces are too long, too noisy, and too platform-specific to replay directly. Lerim compiles the signal future agents, evaluators, and specialization loops actually need.
Keep source evidence attached.
Context records and eval assets stay linked to the run that produced them, so later agents can inspect why the signal exists.
Make learning selective.
Routine chatter, temporary steps, and source-derivable facts should not become permanent workflow memory or training signal.
Enterprise services
Apache-2.0 gives teams the core. We make the improvement loop production-grade.
Teams can self-host Lerim's open core. We sell Lerim's private layer around it: Context Audits, workflow evals, governance, private deployment, and SFT/RL model specialization.
Lerim Trace Integration
Connect customer agent runs, tools, tickets, incidents, research flows, documents, and source traces into Lerim's ingestion layer with clean adapters, redaction, scopes, and retention boundaries.
Context Compiler Optimization
Tune Lerim's compiler for the customer workflow: what becomes cited context, what becomes an eval asset, what gets rejected, and what can become approved training data.
Private Lerim Deployment
Deploy Lerim as the customer's private improvement layer: MCP server, context database, eval workflows, audit logs, retention, monitoring, SSO/RBAC, and production support.
Private Model Specialization
Lerim's private layer specializes models for each workflow: distillation, RL, and prompt plus harness tuning, so agents run cheaper, more accurately, and with customer-specific operating context. Built on the open core.
First paid step
Start with a Context Audit.
Pick one repeated AI-agent workflow. We inspect trace quality, prompts, context flow, evals, privacy boundaries, and training potential, then deliver a concrete deployment roadmap.
Where it fits
Private improvement for repeated business workflows.
The best first deployment is a repeated, evidence-heavy workflow where agents need cited context, measurable quality, privacy controls, and approved correction data.
Research and market intelligence
Source trails, evidence strength, assumptions, rejected leads, brief constraints, and analyst handoffs.
Support and success
Customer constraints, failed fixes, product behavior, escalation reasons, policy references, and next steps.
Security and incidents
Investigation timelines, owner decisions, policy exceptions, remediation evidence, runbook lessons, and unresolved risks.
Engineering automation
Repo conventions, architecture decisions, failed tests, release lessons, and implementation handoffs. Coding stays as one proof pack, not the whole market.
How it works
A compiler pass between completed runs and future improvement.
Lerim is not a broad manual memory bucket. Completed sessions become compact, cited context, eval assets, or approved specialization signal only after the compiler decides the signal is reusable.
01
Connect source sessions
Read native coding-agent histories or import clean custom traces from business workflows.
02
Compile durable context
Extract reusable decisions, constraints, facts, preferences, handoffs, and evidence while rejecting weak signal.
03
Deliver
Serve context and eval assets into the agent harness through CLI answers, startup briefs, MCP tools, and Lerim's private deployment surfaces.
Integration shape
Native capture where possible. MCP everywhere else.
Native adapters are best when an agent has a stable local session store. MCP gives compatible clients recall and completed-session submission without pretending every client has native capture.
Current foundation
Local CLI/runtime, SQLite context store, BAML/LangGraph extraction, native coding-agent adapters, generic trace import, support and incident profiles, MCP server, context briefs, and cited answers.
Commercial path
Context Audits, workflow evals, private MCP, SSO/RBAC, audit logs, retention, managed integrations, and model specialization sit in Lerim's private layer above the Apache-2.0 core.
What a pilot proves
Evidence first. No inflated agent claims.
A serious rollout should prove the actual workflow: does Lerim find useful evidence, improve eval pass rate, reduce repeated context work, and avoid filling memory with noise?
Find the right source run.
Measure whether future agents retrieve the session that contains the decision, constraint, or evidence they need.
Use less context, not less truth.
Track how much transcript text is avoided while preserving the source evidence needed for the next run.
Reject low-value learning.
Review false positives, duplicates, vague notes, weak corrections, and source-derivable facts before scaling the workflow.
Start here
Bring one repeated workflow.
We will connect the traces, tune the compiler, and prove whether Lerim gives future agents better startup context than raw transcripts or manual notes.
Good audit candidates
- Agents keep rediscovering decisions, constraints, policies, or failed paths
- Teams paste old transcripts into new runs because context is not reusable
- Security, platform, or workflow owners need private deployment, audit, eval, and retention controls