Python SDK · Live on PyPI · Patent Pending

Stop shipping agents that forget what works and repeat what fails.

Wisdom Layer adds persistent memory and self-correction to any LLM. Your agents are measurably better next month than today — no fine-tuning required.

pip install wisdom-layer

Calculate Your ROI See How It Works See the Code

2.65× fewer hallucinations 10/10 atomic-fact recall 0 fine-tuning required

See how the loop works →

Built on

Python LangGraph MCP Anthropic OpenAI Gemini Ollama

The state of agent memory · April 2026

The leaders of agent infrastructure say memory is still unsolved.

“We (as an industry) are still figuring out memory. There are not well known or common abstractions for memory.”

— Harrison Chase, CEO, LangChain, April 2026 · source

“The differentiator for enterprise agents will increasingly be what memory they have accumulated rather than which model they call.”

— Databricks AI Research Team, Databricks AI Research, April 2026 · source

“Right now, memory is still very crude, very early.”

— Sam Altman, CEO, OpenAI, Big Technology Podcast, December 2025 · source

Wisdom Layer is the missing layer:
behavior that evolves — not just memory that persists.

See the v1.0 Beta DeepEval scoreboard & methodology

What this looks like in practice

Same prompt. Three architectures. Different agent.

Pick a scenario — or watch them cycle. Hover to pause.

Prompt —

Basic LLM —

—

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+ Memory / RAG —

—

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+ Wisdom Layer —

—

—

The difference isn’t tone — it’s behavior. Memory recalls. Wisdom Layer acts on what it has learned.

Real responses from our v1.0.1 DeepEval suite (April 2026, N=3 mean ± stddev). Customer names, order IDs, and prospect details are synthetic test corpus data. Full transcripts & per-probe scores at /benchmarks.

What Your Agents Gain

Three subsystems running in cycle. No fine-tuning. No retraining. Just architecture.

Remembers

Three-tier memory with semantic search across every prior session.

Reflects

Dream cycles synthesize patterns into self-authored behavioral rules.

Self-corrects

Internal critic evaluates every output against the rules the agent wrote.

See the full architecture & how it differs from RAG →

Pricing

Free agents remember. Pro agents reflect. Enterprise agents reason about themselves.

Start free. Founder rate available for early Pro customers.

See plans →

For Builders

Building agents? See the architecture you’ve been hand-rolling.

The loop, the genome, the directive system, and how it differs from RAG. pip install wisdom-layer — live on PyPI, free tier, full GitHub access. Drops into your existing agent — no rebuild, no migration.

See how it works For Buyers

Will memory pay for itself? Run your numbers.

Plug in your agents, conversations, and incident cost. The math uses our v1.0 Beta DeepEval reduction plus your business reality — no aspirational multipliers, no model-spend hand-waving.

Calculate your ROI

Working on agents that need to get better over time? Read the methodology, talk to the founder, or reach out directly.

Research & Writing rhatigan.ai jeff@rhatigan.ai