Semantic memory infrastructure for agent fleets. One-line integration. Fleet-wide recall.
Every conversation starts from scratch. Sound familiar?
Agents have zero recall between sessions. Each new interaction is a blank slate, wasting all previous context.
Without memory, users must re-explain issues across multiple agents or support sessions. Frustrating for everyone.
Each agent operates in isolation. A decision made by one agent is invisible to another working on the same case.
This isn't a prompt engineering problem. It's a memory infrastructure problem.
From prototype to production in minutes, not months.
Agents automatically store important interactions. No manual embedding or pipeline configuration required.
Find relevant memories using natural language. Vector-based similarity matching returns contextually relevant results.
Agents within a team share context seamlessly. One agent's learning becomes the whole fleet's knowledge.
Automatic importance scoring, retention policies, and archival. Memory degrades gracefully, never floods your storage.
Agent-level isolation by default. Team sharing is opt-in. Full audit logs for compliance and visibility.
Drop into your existing agent pipeline in minutes.
from agentmemory import AgentMemory
memory = AgentMemory(agent_id="support-bot-01")
# Store an interaction
memory.remember(
content="Customer asked about enterprise pricing tiers",
tags=["pricing", "enterprise"],
metadata={"session_id": "abc123", "sentiment": "curious"}
)
# Retrieve relevant context
context = memory.retrieve("What did customers ask about pricing?")
print(context)
# [{'content': 'Customer asked about enterprise pricing tiers', 'score': 0.94, ...}]Start free. Scale as your agent fleet grows.
For solo developers and hobby projects
For growing teams shipping fast
For teams scaling production workloads
For large fleets with custom needs
From zero to production in under 5 minutes. Full SDK reference, tutorials, and example projects.