Haystack vs Letta
Side-by-side comparison built from DeepYard's structured catalog. Content updated Aug 21, 2026.
Direct Answer
Haystack suits teams needing open-source access, pipelines focus, and 26,274 GitHub stars in current listing data. Letta suits teams needing open-source access, memory management focus, and 24,339 GitHub stars in current listing data. This summary reflects catalog metadata only for decision support, not independent testing.
What this comparison weighs
- Pricing and free-tier availability
- License model and deployment fit
- GitHub adoption and contributor depth
- Catalog tags, integrations, and supported workflows
| Metric | Haystack | Letta |
|---|---|---|
| GitHub Stars | 26.3K | 24.3K |
| Contributors | 415 | 140 |
| Last Commit | Aug 22, 2026 | Aug 16, 2026 |
| Open Issues | 95 | 42 |
| License | open-source | open-source |
| Pricing | open-source | open-source |
| Free Tier | Yes | Yes |
| Category | frameworks | frameworks |
| Trending | No | No |
Choose Haystack if you need
- • Haystack is the stronger pick when Pipelines matters because that capability is listed only on its profile.
- • Haystack shows broader GitHub adoption with 26,274 stars versus 24,339 for Letta.
- • Haystack has a larger visible contributor base (415 vs 140).
Choose Letta if you need
- • Letta is the stronger pick when Memory Management matters because that capability is listed only on its profile.
- • Letta is the cleaner fit if you specifically need Memory workflows from the catalog tags.
Meaningful differences
- • GitHub adoption: Haystack shows 26,274 stars versus 24,339 for Letta.
- • Contributor count: Haystack lists 415 contributors and Letta lists 140.
Shared capabilities
- • Python
- • Agents
- • Open Source
Shared Tags
Only in Haystack
Only in Letta
Limitations and evidence
- • DeepYard compares structured public metadata; this is not an independent benchmark unless a test record is shown.
- • Signals such as stars, contributors, and last commit indicate public activity, not purchase fit or runtime quality.
- • Pricing and feature coverage reflect the stored listing snapshot and may lag vendor changes between refreshes.
About Haystack
Haystack by deepset is a Python framework for building production-ready LLM applications, RAG pipelines, and AI agents. It provides a composable pipeline architecture where components (retrievers, generators, rankers, agents) connect through a directed graph. Haystack supports all major LLM providers and vector databases, with strong emphasis on evaluation, testing, and production deployment.
View full listingAbout Letta
Letta (formerly MemGPT) is a framework for building stateful AI agents with persistent long-term memory. It implements a virtual context management system that allows agents to maintain conversation history, user preferences, and knowledge across sessions. Provides a server runtime, REST API, and Python SDK for production deployment.
View full listing