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Overview

Mengram gives your AI three distinct memory types, inspired by how human memory works:
TypeStoresExample
SemanticFacts, knowledge, preferences”User prefers dark mode and uses Python 3.12”
EpisodicEvents, experiences, interactions”Fixed an OOM bug on Jan 15 by reducing pool size”
ProceduralWorkflows, processes, skills”How to deploy: 1) run tests, 2) build, 3) push to main”
When you call m.add(messages), all three types are extracted automatically from the conversation.

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Semantic Memory

The knowledge graph. Entities with facts, types, and relationships. This is the core memory layer. 
# Search semantic memory
results = m.search("user preferences")
# Returns entities with facts and scores

# Get a specific entity
entity = m.get("PostgreSQL")
# {"name": "PostgreSQL", "type": "technology", "facts": [...]}

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Episodic Memory

Autobiographical events — what happened, when, with whom, and what the outcome was. Each episode has a summary, context, outcome, and participant list. 
# Search episodes
events = m.episodes(query="deployment issues")
# [{"summary": "Fixed OOM on Railway", "outcome": "Resolved by reducing pool", ...}]

# List recent episodes
recent = m.episodes(limit=10)

# Time-range filter
jan_events = m.episodes(after="2026-01-01", before="2026-02-01")

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Procedural Memory

Learned workflows and processes. Mengram extracts step-by-step procedures from conversations and tracks which ones work and which fail. 
# Search procedures
procs = m.procedures(query="deploy")
# [{"name": "Deploy to Railway", "steps": [...], "success_count": 5}]

# Report success/failure — triggers experience-driven evolution
m.procedure_feedback(proc_id, success=True)

# On failure with context, the procedure evolves automatically
m.procedure_feedback(proc_id, success=False,
    context="Step 3 failed: OOM on build",
    failed_at_step=3)

# View how a procedure evolved over time
history = m.procedure_history(proc_id)
# {"versions": [v1, v2, v3], "evolution_log": [...]}
Search all three types at once with a single call: 
results = m.search_all("deployment problems")
# {
#     "semantic": [...],    # knowledge graph entities
#     "episodic": [...],    # related events
#     "procedural": [...]   # relevant workflows
# }