Agent Memory

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

Parameter	Purpose	Example
`agent_id`	Scope memories per agent — one API key, many agents	`"support-bot"`, `"devops-agent"`
`run_id`	Track which run produced a memory	`"run-42"`, session IDs
`user_id`	Isolate memories per end-user	`"alice"`, `"customer-123"`
`app_id`	Separate 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

Scenario	Extracts from	How
No `agent_id`	User messages only	Default for chatbots
With `agent_id`	All speakers	Automatic for agents
Override	Your choice	Set `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

Framework	Guide	What it does
CrewAI	CrewAI Integration	5 memory tools for persistent crews
LangChain	LangChain Integration	MengramRetriever for RAG chains
OpenClaw	OpenClaw Plugin	12 tools, auto-recall, auto-capture
Claude Code	Claude Code	Full memory loop with hooks
MCP Server	MCP Server	29 tools for Claude Desktop, Cursor, Windsurf
Claude Managed Agents	Managed Agents	29 MCP tools for Anthropic’s hosted agents
n8n	n8n Integration	HTTP 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.

Getting Started

Concepts

SDKs

Integrations

Reference

Why agents need memory

The agent memory loop

Quick start

Key parameters

Filtering by agent

Procedural learning

Agent mode extraction

Multi-agent systems

Framework integrations

REST API

​Why agents need memory

​The agent memory loop

​Quick start

​Key parameters

​Filtering by agent

​Procedural learning

​Agent mode extraction

​Multi-agent systems

​Framework integrations

​REST API

Why agents need memory

The agent memory loop

Quick start

Key parameters

Filtering by agent

Procedural learning

Agent mode extraction

Multi-agent systems

Framework integrations

REST API