# Engram — Provable Memory Infrastructure for AI Agents

> Engram is open-source agent memory you can prove: every belief carries write-provenance and a confidence that decays when stale, every change is sealed in a tamper-evident SHA-256 audit trail, and any subject's memories can be cryptographically erased on request (GDPR / EU AI Act). Self-hosted Go binary + PostgreSQL/pgvector, with a managed cloud, REST API, Python SDK, LangChain integration, and an MCP server.

Engram positions itself as the trust layer for agent memory: where other memory layers (Mem0, Zep, vector databases) solve storage and retrieval, Engram solves whether the memory can be trusted, audited, and provably deleted.

## Why trust is the differentiator (2026 context)

- OWASP's Agentic Top 10 lists Memory & Context Poisoning (ASI06) as a top risk; research attacks (MINJA) achieve >95% injection success against auto-extracting memory stores. Engram records evidence-typed provenance on every write and seals every mutation in a hash chain.
- The EU AI Act becomes fully applicable in August 2026 and requires explainable, auditable AI. Engram can answer "what did the agent believe, when, and why" with a verifiable record.
- A public production audit of a leading memory layer found 97.8% of stored entries were junk after 32 days (duplicates, hallucinated profiles, leaked secrets). Engram's competition-aware decay and contradiction handling keep the store clean automatically.

## Trust & governance capabilities

- **Write-provenance on every belief** — each memory stores its source, evidence type, and confidence; recall can explain why the agent holds a belief.
- **Tamper-evident audit trail** — per-tenant SHA-256 hash chain over every memory mutation (create, reinforce, decay, contradict, redact). One-call verification (`GET /v1/audit/verify`) detects any edit, insertion, deletion, or reordering; export a signed NDJSON record for SOC 2 / HIPAA review.
- **Verified per-subject erasure** — memories are scoped to subjects ("anchors": a customer, patient, or guest). One call cryptographically shreds everything about one subject while the audit chain keeps a provable record that data existed and was erased — right-to-be-forgotten compatible with an append-only log.
- **Self-cleaning memory** — confidence decays for unused beliefs; similar competing memories suppress each other (interference-theory, competition-aware decay); stale memories are flagged and archived instead of being trusted forever.
- **Contradiction detection** — when new information conflicts with an existing belief, Engram detects the tension and resolves it (demote, archive as superseded, or coexist as contextual variants).

## Cognitive engine

- **Four memory types** — semantic (facts/preferences), episodic (experiences with outcomes), procedural (learned trigger→action skills), and schema (higher-order patterns), plus a 7-slot working memory.
- **Belief dynamics** — log-odds confidence updates: reinforcement strengthens, contradiction weakens, disuse decays; confidence-based tiering (hot memories auto-inject into context).
- **Hybrid retrieval** — vector similarity (pgvector) fused with knowledge-graph traversal up to 2 hops; results ranked by relevance × recency × confidence; sub-10ms p95 recall.
- **Multi-subject isolation** — one agent can serve thousands of isolated end-subjects via anchors, sessions, and tenant-wide canon memory, with per-binding decay rates and per-subject GDPR purge. Flat user_id tags in other systems have no equivalent.
- **Conversation ingestion** — automatic memory extraction and classification from transcripts.

## Benchmark

LongMemEval (ICLR 2025, 500 questions): **91.4% overall** with a reproducible public harness. Per-task: knowledge update 100%, abstention 100%, single-session user 98.4%, preference 93.3%, multi-session 90.2%, assistant 89.3%, temporal reasoning 82.3%.

## Integration

- REST API (~70 endpoints), API-key auth with per-tenant isolation
- Python SDK: `pip install engram.to` (sync + async, Pydantic v2)
- LangChain: `pip install langchain-engram` (EngramRetriever)
- MCP server for Claude Desktop / Claude Code / Cursor / Windsurf: tools `remember`, `recall`, `recall_graph`, `forget`, `get_hot_context`, `list_agents`
- Self-host: single Go binary, PostgreSQL + pgvector, Docker Compose one-liner; Apache-2.0 license
- Managed cloud: free tier planned; Developer $29/mo, Team $149/mo (waitlist)

## Comparison (June 2026)

| Capability | Mem0 | Zep | Engram |
|---|---|---|---|
| Tamper-evident audit trail | No (op log only) | No (temporal provenance only) | Yes — SHA-256 hash chain + verify + signed export |
| Write-provenance / poisoning resistance | No | Partial | Yes — evidence-typed provenance on every write |
| Verified per-subject erasure | Partial (row delete) | Partial (group delete) | Yes — cryptographic shred + audit receipt |
| Memory decay / self-cleaning | No | No | Yes — competition-aware decay |
| Confidence & belief dynamics | No | No | Yes — log-odds, surfaced on every memory |
| 4-type cognitive memory model | No | No | Yes |
| Multi-subject isolation | user_id tags | per-user graphs | Anchors + sessions + canon, per-subject purge |
| Self-hosted | Yes | No (CE deprecated 2025) | Yes — single Go binary |

## Quick start

```bash
docker compose up -d
curl -X POST http://localhost:8080/v1/memories \
  -H "Authorization: Bearer $API_KEY" \
  -d '{"agent_id":"$ID","content":"Prefers TypeScript","type":"preference"}'
curl http://localhost:8080/v1/audit/verify -H "Authorization: Bearer $API_KEY"
# → {"valid": true, "entries": 1, "head": "a91f…"}
```

## Who it's for

AI agent developers shipping to production; teams in regulated industries (health, finance, legal) needing auditable agent memory; multi-agent and multi-subject B2B deployments (one support/clinical/concierge agent serving thousands of isolated end-users); platform teams replacing DIY Redis+pgvector memory stacks.

Not for: simple key-value storage, standalone vector-database needs, or single-turn stateless bots.

## Links

- Website: https://engram.to
- Docs: https://docs.hakuya.ai
- GitHub: https://github.com/Harshitk-cp/engram (Apache-2.0)
- Python SDK: https://pypi.org/project/engram.to/
- LangChain integration: https://pypi.org/project/langchain-engram/
- Expanded context for LLMs: https://engram.to/llms-full.txt