Swiftide
Fast, streaming indexing, query, and agentic LLM applications in Rust
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What is Swiftide?
Swiftide is a Rust library for building LLM applications, specifically designed for Retrieval Augmented Generation (RAG). It allows developers to ingest, transform, and index large amounts of data quickly, then query that data to inject into LLM prompts. The library is built with Rust's async and streaming features, making it fast, safe, and efficient for production use.
Key features include fast streaming indexing pipelines with async parallel processing, an experimental query pipeline built specifically for RAG, an experimental agent framework for building autonomous agents, built-in transformers for code and text using tree-sitter, customizable Jinja-like prompt templating via Tera, and integrations with OpenAI, Groq, Gemini, Anthropic, AWS Bedrock, Ollama, Qdrant, Redis, LanceDB, and FastEmbed. Swiftide supports sparse vector hybrid search, pipeline evaluation with RAGAS, Langfuse tracing, and MCP tool support for agents.
Swiftide is designed for Rust developers building production LLM applications, AI engineers working on RAG systems, data engineers creating data pipelines for AI, and teams who want to move from experimentation to production without rewriting code. It is part of Bosun.ai and is actively used in production.
Swiftide pricing
Pricing model: Freemium
Swiftide is 100% open source under the MIT License and completely free to use. There are no paid tiers or subscription plans. The library itself is free, but users must pay for their chosen LLM API providers (OpenAI, Groq, Anthropic, etc.) and storage services (Qdrant, Redis, LanceDB, etc.) separately. No feature restrictions exist in the open source version.
Swiftide pros
- Written in Rust for maximum performance and safety
- Blazing fast streaming indexing with async parallel processing
- Real-time RAG systems enabled by real-time indexing
- Minimal abstractions with modular, extendable API
- Simple traits make it easy to extend with custom loaders and transformers
- Built-in code transformers using tree-sitter for semantic chunking
- Supports both dense and sparse vectors for hybrid search
- Extensive LLM provider integrations (OpenAI, Groq, Anthropic, Gemini, Ollama, AWS Bedrock)
- Multiple storage backends (Qdrant, Redis, LanceDB, Postgres, DuckDB)
- Experimental query pipeline purpose-built for RAG
- Full agent framework with tool support and MCP integration
- Streaming agents with delta responses
- Jinja-style prompt templating with Tera
- Langfuse tracing and observability support
- 100% open source under MIT License
- Pipeline evaluation with RAGAS
- Resume agents from existing history
- Filter cached nodes to avoid redundant processing
Swiftide cons
- Still under heavy development toward 1.0 with breaking changes
- Documentation may be outdated or fall short of all features
- No Python bindings yet (only Rust native)
- Query pipeline is still experimental
- Agent framework is still experimental
- No integrations enabled by default (must cherry-pick features)
- Some integrations are code heavy
- Rate limit errors on OpenAI can occur quickly with fast models during chunking
Frequently asked questions about Swiftide
What is Swiftide?
Swiftide is a Rust library for building LLM applications, specifically for Retrieval Augmented Generation (RAG). It allows you to ingest, transform, and index large amounts of data fast, then query that data to inject into prompts. Written in Rust with async and streaming features, it is designed to be fast, modular, and production-ready.
What problem does Swiftide solve?
In other solutions, the experimental phase is often done in Python and then either rewritten from scratch or deployed distributedly. Swiftide brings experimentation directly to production in Rust. It is so fast that it enables real-time indexing before querying, opening up real-time RAG systems, and stretches the limits before needing distributed setups like Kafka.
How does Swiftide work?
With Swiftide, you define a sequence of steps from ingesting to processing to indexing. Under the hood, it uses Rust's async and streaming features to speed things up drastically. For indexing, you start with a Loader that emits Nodes, then apply transformers, chunkers, and storage. For querying, you build a pipeline that transforms queries, retrieves documents, and generates answers.
What LLM providers does Swiftide support?
Swiftide supports OpenAI (including Azure), Anthropic, Gemini, OpenRouter, AWS Bedrock (Anthropic and Titan), Groq (all models), and Ollama (all models). Each provider must be enabled via feature flags in Cargo.toml as no integrations are enabled by default.
What storage backends are supported?
Swiftide supports Qdrant for vector storage and retrieval, Redis as an indexing cache and storage, LanceDB for persistence and querying, Postgres with pgvector, DuckDB, and Redb as an embeddable node cache. Each storage backend is enabled via its corresponding feature flag.
Can I use Swiftide with Python?
Currently Swiftide is a Rust-native library with no Python bindings. Python bindings have been mentioned as coming soon in announcements, but as of the latest information, Swiftide requires the Rust toolchain and is used within Rust projects via Cargo.
How do I install Swiftide?
First, ensure you have the Rust toolchain installed via rustup. Then set up a new Rust project, add Swiftide with 'cargo add swiftide', enable the feature flags for integrations you need in your Cargo.toml, and write your pipeline. No integrations are enabled by default as some are code heavy, so you must cherry-pick what you need.
Is Swiftide production-ready?
Swiftide is part of Bosun.ai and is actively used in production. However, it is still under heavy development toward version 1.0 and can have breaking changes. Documentation might fall short of all features and be slightly outdated, so it is recommended to keep an eye on the GitHub repo and API documentation on docs.rs.
Can I build AI agents with Swiftide?
Yes, Swiftide provides an experimental agent framework for building autonomous agents. Agents can use tools (including MCP tools), loop over LLM calls and tool calls, hook into lifecycle hooks, stream their output with deltas, resume from existing history, and call other agents. Tools are defined via a simple trait or macros for async functions and structs.
How do I extend Swiftide with custom transformers?
Swiftide is designed to be easily extended by implementing simple traits. You can write your own loaders, transformers, and storages by extending the straightforward traits (Loader, Transformer, BatchTransformer, ChunkerTransformer, Storage). Closures also implement these traits, allowing you to use closures for custom transformations without writing boilerplate.