AI Tool Comparison
Agno vs Letta
A side-by-side breakdown to help you pick the right tool for your workflow.
Agno
Build multi-modal agents in plain Python: text, image, audio, and video inputs handled natively. Agno's tool library and memory system handle the infrastructure.
Letta
Build agents that actually remember: facts, preferences, and past interactions persist across every session without manual context management.
Bottom Line
Last reviewed: August 2026
Agno and Letta both compete in Agents, overlapping most directly on coding. Agno runs on a fully free plan while Letta runs on a freemium model, which alone may settle it if budget or a free tier is a hard requirement. Both carry the same 4.4 rating, so the decision comes down to fit, not quality.
Choose Agno if…
Best for python developers building agents that need to reason over images, audio, and video, not just text, and its edge is multi-modal by default, no special handling required for non-text inputs, unlike most agent frameworks. A genuinely readable, Pythonic way to build multi-modal agents, there's no TypeScript SDK if that's your stack.
Choose Letta if…
Best for developers building agents that need to remember and update facts across arbitrarily long interaction histories, and its edge is manages its own memory tiers and decides what to retain or summarize, more reliable than an ad-hoc RAG memory hack. A genuinely more solid approach to agent memory than manual context management, per-step cloud pricing adds up for memory-heavy agents.
| Attribute | Agno | Letta |
|---|---|---|
| Category | Agents | Agents |
| Pricing | free | freemium |
| Pricing Detail | Open source / Free (Agno Cloud in beta) | Open source / Free (Letta Cloud from $0.02/step) |
| Rating |
Key Features
Agno
- Multi-modal agents with native text, image, audio, and video reasoning
- Plain Python class definitions, no framework-specific DSL
- 30+ built-in tools: web search, SQL, file ops, APIs
- Pluggable memory backends including PostgreSQL, MongoDB, and SQLite
- Agent Teams for orchestrating multiple specialized sub-agents
- Structured output support via Pydantic models
Letta
- Three-tier memory architecture: working, recall, and archival
- Self-directed memory management: agents decide what to store
- Cross-session persistence with no manual prompt engineering
- REST API and Python SDK for embedding agents in applications
- Agent builder UI for configuring memory and persona
- Open-source core with managed Letta Cloud option
Pros
Agno
- •Multi-modal by default: no special handling for image or audio inputs
- •Pythonic API makes agents readable to anyone who knows Python
- •Built-in tool library means less boilerplate for common tasks
Letta
- •Solves long-term memory without external memory databases or prompting tricks
- •Open-source core means full control over data and deployment
- •Framework-level memory is more reliable than ad-hoc RAG approaches
Cons
Agno
- Python-only: no TypeScript SDK unlike some competitors
- Cloud observability platform is still early-stage
- Less community content than LangChain or CrewAI at the same maturity
Letta
- Memory management adds latency compared to stateless agents
- Cloud pricing is per-step, complex agents with many memory reads get expensive
- Steeper learning curve than simpler stateless frameworks