Memory Types

Overview

Mengram gives your AI three distinct memory types, inspired by how human memory works:

Type	Stores	Example
Semantic	Facts, knowledge, preferences	"User prefers dark mode and uses Python 3.12"
Episodic	Events, experiences, interactions	"Fixed an OOM bug on Jan 15 by reducing pool size"
Procedural	Workflows, 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.

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": [...]}}

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")

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": [...]}}

Unified Search

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
# }}