Python SDK

Installation

pip install mengram-ai

Initialize

from mengram import Mengram

# Pass API key directly
m = Mengram(api_key="om-your-key")

# Or use environment variable
# export MENGRAM_API_KEY=om-your-key
m = Mengram()

Core methods

add(messages, ...)

Add memories from a conversation. Automatically extracts entities, facts, episodes, and procedures.

result = m.add([
    {{"role": "user", "content": "We fixed the OOM with Redis cache"}},
    {{"role": "assistant", "content": "Noted the Redis cache fix."}},
])
# Returns: {{"status": "accepted", "job_id": "job-..."}}

Parameter	Type	Default	Description
messages	list[dict]	required	Chat messages with role and content
user_id	str	"default"	User identifier for multi-user isolation
agent_id	str	None	Agent identifier
run_id	str	None	Session/run identifier
app_id	str	None	Application identifier
expiration_date	str	None	ISO datetime — facts auto-expire

add_text(text, ...)

Add memories from plain text instead of chat messages.

m.add_text("Meeting notes: decided to migrate to PostgreSQL 16")

search(query, ...)

Semantic search across the knowledge graph.

results = m.search("database preferences", limit=10)
for r in results:
    print(f"{{r['entity']}} — score: {{r['score']:.2f}}")
    for fact in r.get("facts", []):
        print(f"  • {{fact}}")

Parameter	Type	Default	Description
query	str	required	Natural language search query
limit	int	5	Max results
graph_depth	int	2	Knowledge graph traversal depth
filters	dict	None	Metadata filters

search_all(query, ...)

Unified search across all 3 memory types.

results = m.search_all("deployment")
print(results["semantic"])     # entities
print(results["episodic"])     # events
print(results["procedural"])   # workflows

get_all() / get(name) / delete(name)

memories = m.get_all()           # list all entities
entity = m.get("PostgreSQL")     # get specific entity
m.delete("PostgreSQL")           # delete entity

get_profile(...)

Generate a Cognitive Profile. See Cognitive Profile docs.

episodes(...)

Search or list episodic memories.

events = m.episodes(query="auth bug", limit=5)
recent = m.episodes(limit=20)
jan = m.episodes(after="2026-01-01", before="2026-02-01")

procedures(...)

Search or list procedural memories.

procs = m.procedures(query="deploy")
all_procs = m.procedures(limit=50)

procedure_feedback(id, ...)

Report success/failure. Triggers experience-driven evolution on failure with context.

m.procedure_feedback(proc_id, success=True)
m.procedure_feedback(proc_id, success=False,
    context="Build OOM", failed_at_step=3)

Memory management

m.dedup()                    # find and merge duplicates
m.merge("src", "target")    # merge two entities
m.archive_fact("Entity", "old fact")  # archive a fact
m.run_agents()               # run curator, connector, digest agents
m.stats()                    # usage statistics

Webhooks

m.create_webhook(url="https://example.com/hook",
    event_types=["memory_add", "memory_update"])
hooks = m.get_webhooks()

Import data

# Import ChatGPT export
m.import_chatgpt("~/Downloads/chatgpt-export.zip")

# Import Obsidian vault
m.import_obsidian("~/Documents/MyVault")

# Import text/markdown files
m.import_files(["notes.md", "journal.txt"])