The .mem Standard
Version 1.0.0

Structured metadata over lossy fine-tuning. The .mem format is the foundational knowledge graph specification for autonomous enterprise swarms.

Read the Spec
Raw .mem
!state
  task = Evaluate Omni-Memory
  status = active

!rules
  !! [design] No generic AI slop

!files
  /core/router.py::Traffic routing
Parsed Knowledge Graph
Node: State
Task: Evaluate Omni-Memory
Node: Rules
Design: No generic AI slop
Edge: File Reference
/core/router.py → Traffic routing

Reliable Execution.
Total Governance.

Built for enterprise engineering leaders. Deploy fleets of AI agents with centralized governance, persistent memory, and cost-aware orchestration.

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Live Benchmarks Monitor

Retrieval Quality

Recall@1 85.2%
Recall@5 94.1%
MRR 0.89

Retrieval Latency

P50 12ms
P95 34ms
P99 89ms

Resource Efficiency

Avg Token Reduction ~82%
Estimated Cost Savings (Monthly) $4,200

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Zero-hallucination QA pipelines fine-tuned exclusively on your internal documentation and RFCs.

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Implement Agentic Memory Routing (AMR) to reduce RAG token costs by 15x and eliminate context degradation.

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Stress-test your LLM agents with our proprietary AjaxBench framework to eliminate latent hallucinations before production.

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Safely upgrade 100k-file legacy codebases with our Autonomous Refactor Swarm, powered by AST-level validation.

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Our zero-day response swarm ingests CVE reports, scans your monorepo, and submits validated Pull Requests instantly.

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Never let documentation decay. A background swarm reads latest commits to generate perfect, interactive architecture maps.

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Prevent Breaking Production Changes

Deploy a Chief Architect swarm into GitHub/GitLab to review PRs for performance and architectural drift before merging.

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Establish a Single Source of Truth

Connect raw Jira, Slack, and internal PDFs to our pipeline to generate hyper-compressed context files for agent consumption.

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