Mem0vsLiteLLM

Full side-by-side comparison — features, pricing, platforms, and which one wins in 2026.

Mem0

AI Agent Frameworks

Memory layer for AI agents — persistent, searchable, context-aware

Full review →Website ↗

LiteLLM

LLM APIs & Inference

Unified API proxy for 100+ LLM providers — one interface, any model

Full review →Website ↗
FeatureMem0LiteLLM
CategoryAI Agent FrameworksLLM APIs & Inference
PricingFree (open-source), hosted platform availableFree (open-source), hosted proxy available
GitHub Stars
✓ More stars
25k
16k
PlatformsLinux, macOS, DockerLinux, macOS, Docker
Key Features
  • ✓ Long-term memory
  • ✓ User preferences
  • ✓ Multi-level memory
  • ✓ API
  • ✓ Self-improving
  • ✓ Unified API for 100+ LLM providers
  • ✓ Load balancing across multiple API keys/providers
  • ✓ Automatic fallbacks when providers fail
  • ✓ Spend tracking and budget alerts per team/project
  • ✓ Rate limiting and retry logic built-in
  • ✓ OpenAI SDK compatible — zero code changes
  • ✓ Self-hostable proxy server
  • ✓ Supports streaming, function calling, vision
Pros
  • + Drop-in memory for any AI agent
  • + Automatic relevance scoring
  • + Works with any LLM
  • + Both local and hosted options
  • + One API for 100+ providers
  • + Built-in load balancing and fallbacks
  • + Spend tracking and rate limiting
  • + OpenAI SDK compatible
Cons
  • − Adds complexity to agent architecture
  • − Hosted version has usage limits
  • − Adds a proxy layer (slight latency)
  • − Complex config for advanced features
Tags
memoryagentspersonalizationopen-source
api-gatewaymulti-providerproxyopen-source

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