Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.mengram.io/llms.txt

Use this file to discover all available pages before exploring further.

​
Why agents need memory

Your agents run thousands of tasks but start from scratch every time. Customer context is lost between sessions. Proven workflows are forgotten. Mistakes get repeated. Mengram gives agents persistent memory across 3 dimensions:
  • Facts (semantic) — what the agent knows about users, systems, policies
  • Events (episodic) — what happened in past runs, with outcomes
  • Workflows (procedural) — learned step-by-step procedures with success/failure tracking

​
The agent memory loop

Agent starts run
  → search() recalls relevant context from past runs
  → Agent executes task with full context
  → add() saves the conversation
  → Mengram auto-extracts facts, events, and procedures
  → Next run starts smarter

​
Quick start

from mengram import Mengram

m = Mengram(api_key="om-your-key")

# 1. Before the agent acts — recall relevant context
context = m.search_all("customer refund request", agent_id="support-bot")
# Returns: {"semantic": [...], "episodic": [...], "procedural": [...]}

# Inject into your agent's system prompt
system_prompt = f"""You are a support agent.
Relevant memories:
{context}
"""

# 2. Agent handles the task (your agent framework here)
response = call_llm(system_prompt, user_message)

# 3. After the agent acts — save the conversation
m.add([
    {"role": "user", "content": "Customer wants refund for order #1234"},
    {"role": "assistant", "content": "Processed refund, applied loyalty discount."},
], agent_id="support-bot", run_id="run-42")

​
Key parameters

ParameterPurposeExample
agent_idScope memories per agent — one API key, many agents"support-bot", "devops-agent"
run_idTrack which run produced a memory"run-42", session IDs
user_idIsolate memories per end-user"alice", "customer-123"
app_idSeparate environments"prod", "staging"
All parameters work with add(), search(), search_all(), and other endpoints.

​
Filtering by agent

Search only memories from a specific agent: 
# Only support-bot memories
results = m.search("refund policy", agent_id="support-bot")

# Only memories from a specific run
results = m.search("deployment", run_id="run-42")

# Combine with user isolation
results = m.search("preferences", agent_id="support-bot", user_id="customer-123")

​
Procedural learning

Mengram extracts step-by-step workflows from agent conversations and tracks which ones succeed. Agents get better over time without retraining. 
# Search for relevant procedures
procs = m.procedures(query="deploy to Railway")
# [{"name": "Railway Deployment", "steps": [...], "success_count": 8, "fail_count": 1}]

# Report outcome after agent completes a task
m.procedure_feedback(proc_id, success=True)

# On failure, the procedure evolves automatically
m.procedure_feedback(proc_id, success=False,
    context="OOM error at build step",
    failed_at_step=3)

# Next time, the agent gets the updated procedure

​
Agent mode extraction

By default, Mengram only extracts facts from the user role — assistant responses are treated as context only. When you pass agent_id, Mengram automatically switches to full extraction — remembering both what the user asked and what the agent did: 
# agent_id triggers full extraction automatically
m.add([
    {"role": "user", "content": "Deploy to staging"},
    {"role": "assistant", "content": "Deployed v2.15 to staging. All health checks passed."},
], agent_id="devops-agent")
# Extracts from BOTH user request and assistant actions

await m.add(messages, { agentId: 'devops-agent' });
// Full extraction — no extra flags needed
ScenarioExtracts fromHow
No agent_idUser messages onlyDefault for chatbots
With agent_idAll speakersAutomatic for agents
OverrideYour choiceSet agent_mode=False to force user-only even with agent_id

​
Multi-agent systems

Multiple agents can share the same memory pool or be isolated: 
# Agent A discovers something
m.add([...], agent_id="researcher")

# Agent B finds what A learned
results = m.search("findings", agent_id="researcher")

# Search across ALL agents (omit agent_id)
results = m.search("findings")

​
Framework integrations

FrameworkGuideWhat it does
CrewAICrewAI Integration5 memory tools for persistent crews
LangChainLangChain IntegrationMengramRetriever for RAG chains
OpenClawOpenClaw Plugin12 tools, auto-recall, auto-capture
Claude CodeClaude CodeFull memory loop with hooks
MCP ServerMCP Server29 tools for Claude Desktop, Cursor, Windsurf
Claude Managed AgentsManaged Agents29 MCP tools for Anthropic’s hosted agents
n8nn8n IntegrationHTTP nodes for any workflow

​
REST API

For agents built with raw HTTP calls: 
# Save agent conversation
curl -X POST https://mengram.io/v1/add \
  -H "Authorization: Bearer om-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "Deploy to staging"},
      {"role": "assistant", "content": "Deployed v2.15. All tests passed."}
    ],
    "agent_id": "devops-agent",
    "run_id": "run-77"
  }'

# Recall before next run
curl -X POST https://mengram.io/v1/search \
  -H "Authorization: Bearer om-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "staging deployment issues",
    "agent_id": "devops-agent",
    "limit": 5
  }'
For the full API reference with all endpoints and parameters, see API Reference.