Interstrata vs Letta
THE SHORT VERSION
Letta is a stateful agent runtime where agents manage their own memory, learn from experience, and persist state across sessions. Interstrata is a cross-runtime accountability layer that reconstructs what all your agents did — regardless of which runtime they use — proves it with evidence, and measures what it cost. Letta makes agents smarter; Interstrata makes agents accountable.
At a glance
Feature comparison
Agent-managed memory: agents can read, write, and edit their own memory blocks — true self-improvement
MemGPT research lineage: pioneered self-editing memory for LLMs at UC Berkeley
Model agnostic: works with any LLM provider — not locked to OpenAI or Anthropic
Cross-runtime accountability: Interstrata works across Letta, LangGraph, CrewAI, and custom agents
Provenance, not just persistence: Interstrata hash-chains every claim to its source evidence
Cost attribution at the action level: Interstrata tracks what every agent action cost
Incident binders: one-click exportable postmortems with timeline, receipts, and decision trail
Who should use which
Choose Letta if:
→ You're building agents from scratch and want a runtime with built-in memory management
→ Agent self-improvement and continuous learning are core requirements
→ You want an open-source foundation you can self-host and customize
Choose Interstrata if:
→ You run agents on multiple runtimes and need unified accountability
→ You need to answer 'what did our agents do this week across all platforms?'
→ Hash-verified provenance and audit-ready exports are required