Most AI memory systems store facts and hope for the best. Brain stores facts, tracks where they came from, detects when they contradict each other, and tells your agent before it acts on bad data. It's memory with a built-in immune system.
One bad upload silently replaces ten good sources. Your agent doesn't know the difference.
Source A says revenue is $10M. Source B says $8M. Your agent averages them and moves on.
Correct a fact today. The system doesn't learn which source was wrong. Same mistake, different fact, next week.
Vector stores, key-value memory, and append-only graphs solve storage. They don't solve truth.
| Capability | Vector memory | KV / append-only | Graph RAG | Brain |
|---|---|---|---|---|
| Structured relationships | No — flat embeddings | No — key-value | Yes | Yes — consensus-scored |
| Multi-source tracking | No — one embedding per fact | No — newest wins | Partial — no scoring | Yes — every claim tracked to source |
| Contradiction detection | None | None | None | 13 parallel detectors, proactive |
| Confidence scoring | Cosine similarity only | None | Basic — no source weight | Multi-dimensional: evidence, authority, expert, temporal |
| Self-improvement | Requires re-embedding | None | None | Every resolution trains the model |
| Entity deduplication | None | None | String matching | 4-stage: exact → pattern → semantic → LLM |
| Query-time governance | None | None | None | ALLOW / WARN / BLOCK per query |
| Audit trail | None | Write log only | Basic versioning | Hash-chained, tamper-proof |
Left: what your agent gets from a typical memory system. Right: what it gets from Brain.
Everything a memory system should do — plus everything they don't.
13 parallel detectors continuously scan for contradictions using structural graph queries. Conflicts are found before your agent encounters them — not after.
Every fact carries a confidence score based on evidence weight, source authority, expert validation, and temporal recency. Your agent always knows how much to trust each answer.
Every conflict resolution updates source authority scores across the entire graph. Correct a fact about Source X today — every other fact from Source X recalibrates automatically.
Every claim is tracked to its source document, timestamp, and extraction context. Never wonder "where did this fact come from?" again. Full chain of custody.
One API call before any action. Returns ALLOW, WARN, or BLOCK based on the consensus state of knowledge relevant to that query. Deterministic, not probabilistic.
Exact match → formatting normalization → pattern matching → semantic comparison. Catches "Microsoft Corp" = "MSFT" = "Microsoft" while keeping "Apple Inc" ≠ "apple extract."
Domain-configurable decay rates. Fintech metrics go stale in weeks. Clinical trial data stays relevant for years. Your agent always knows how fresh each fact is.
29 tools exposed via Model Context Protocol. Any MCP-compatible agent runtime can use Brain as its memory layer out of the box. Claude, OpenAI, LangChain — your choice.
Every write, every conflict, every resolution — hash-chained into an immutable ledger. Export to CSV or PDF. EU AI Act Article 12 compliant out of the box.
Add Brain to an existing agent in three steps. No data migration required.
Python, TypeScript, REST, or MCP — pick your interface.
Three lines of config. Brain runs as middleware.
One call returns a verdict with full context.
Brain drops into your existing stack as middleware. No rewrite, no migration. Start in advisory mode — Brain logs everything but blocks nothing. Harden to strict when you're ready.
