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docs: marketing-grade home + Quickstart polish (#35)
Home (introduction.mdx):
- New hero: 'Build LLM agents in Rust without the duct tape.' Three lines
of pitch with concrete numbers (six providers, six vector stores, one
umbrella crate) instead of a 47-word run-on.
- Shields.io badges row across the top: GitHub stars, crates.io version,
docs.rs, Rust 1.75+, MIT, CI status. Same set the README ships, just
surfaced where first-time visitors land.
- New 'Real apps you can ship today' strip with three Pattern cards
(research assistant, code Q&A over a repo, streaming UI). Patterns
was buried in 'Where to next'; now it's the second thing visitors
scroll to.
- 'What you can build' cards re-tightened. Stateful graphs in
particular went from a 7-feature run-on to a value-led one-liner.
- 'How Cognis is organized' (workspace map) removed from the home —
it's internal architecture and lives in /reference/api/cognis.
- New 'What's not here yet' section, honest about LangSmith / OTel
gaps, missing managed runtime, the smaller provider matrix. Builds
trust faster than 'we have everything.'
- New 'Like what you see? Star the repo.' card at the bottom — explicit
CTA where visitors finish reading.
Quickstart:
- Description: 'Build a tool-calling agent in five minutes' →
'From zero to a running agent in five minutes.' (also updates the
Quickstart card on the home.)
- New 'What you just built' section between the run command and
'How it works'. Closes the loop — visitor finishes the tutorial
feeling they understood it, not just typed it.
Copy file name to clipboardExpand all lines: docs/mintlify/get-started/quickstart.mdx
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---
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title: "Quickstart"
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description: "Build a tool-calling agent in five minutes."
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description: "From zero to a running agent in five minutes."
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sidebarTitle: "Quickstart"
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---
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You should see something like `23 * 17 + 4 = 395`.
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## What you just built
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In about 11 lines, you ran an agent that:
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-**Decided when to call a tool** (the calculator) and when to answer directly.
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-**Handled the round-trip** from prompt → model → tool call → tool result → final reply.
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-**Works against any of six providers** with the same code — flip `COGNIS_PROVIDER` and rerun.
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-**Compiled to one binary** with everything you imported. No runtime, no Python, no shim.
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That's the whole V2 surface in its smallest form. Every [Pattern](/patterns/research-assistant) on this site builds on this shape — same `AgentBuilder`, same `with_*` chain, just more parts wired in.
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## How it works
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Behind the scenes, `AgentBuilder` compiled a small `Graph<AgentState>` and `agent.run` walked it:
description: "The Rust-native way to build LLM agents, RAG pipelines, and stateful graph workflows."
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description: "Build LLM agents in Rust without the duct tape. Typed Runnables, an agent loop, a stateful graph engine, and production-grade RAG — all in one workspace."
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sidebarTitle: "Overview"
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---
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Cognis is the easiest way to build LLM-powered apps in Rust. The shapes are familiar — typed `Runnable`s you compose with `pipe`, an agent loop with tools and memory, a stateful graph engine with checkpoints and interrupts, a RAG pipeline with pluggable splitters and stores — all translated into idiomatic Rust with type safety, ownership, and zero-cost composition.
It's a rebuild, not a port. The intent is to take the patterns the Python LLM ecosystem proved out and recast them around Rust's strengths.
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# Build LLM agents in Rust without the duct tape.
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Typed `Runnable<I, O>` flows from your prompt to your parsed struct. Tool schemas are checked at compile time. Memory, retries, fallbacks, rate limits, prompt caching, evals — all in the box. **Six providers behind one client. Six vector stores behind one trait. One umbrella crate.**
That's a real, working tool-calling agent. Swap `COGNIS_PROVIDER` between `openai`, `anthropic`, `google`, `ollama`, `azure`, or `openrouter` — same code.
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A real, working tool-calling agent in 11 lines. Swap `COGNIS_PROVIDER` between `openai`, `anthropic`, `google`, `ollama`, `azure`, or `openrouter` — same code.
`axum` SSE endpoint streaming agent tokens, tool starts, and tool results to the browser.
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</Card>
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</CardGroup>
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Five more in the [Patterns gallery](/patterns/research-assistant) — multi-agent debate, long-context summarization, HITL approval, stateful chat with memory, and a fully local Ollama setup.
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## What you can build
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A model with tools, memory, and a loop that knows when to stop. Add middleware for retry, fallback, rate limits, PII redaction, and human approval.
Sequential pipelines, supervisor routers, parallel-vote ensembles, round-robin debates. Or wire your own handoff strategy.
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Sequential pipelines, supervisor routers, parallel-vote ensembles, round-robin load balancing, hierarchical trees. Or wire your own `HandoffStrategy`.
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</Card>
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<Cardtitle="RAG pipelines"icon="database">
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Documents → splitters → embeddings → vector store → retriever → prompt. Six vector store backends, eight splitters, and an indexing pipeline that only re-embeds what changed.
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Documents → splitters → embeddings → vector store → retriever → prompt. Six vector store backends. An indexing pipeline that only re-embeds what changed.
`Graph<S>` with typed state, reducers, conditional edges, checkpoints, time-travel, interrupts, and seven stream modes. Pregel-style execution; built around Rust's type system.
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`Graph<S>` with typed state and per-field reducers. Time-travel through checkpoints, pause for human approval, fan out in parallel — all type-checked at compile time.
LLM-aware tracing into Langfuse out of the box. Token counts, USD cost, prompt versioning, and evaluation scores — opt in with one feature flag.
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LLM-aware tracing into Langfuse out of the box. Token counts, USD cost per run, prompt versioning, evaluation scores — one feature flag.
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</Card>
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<Cardtitle="Local-first apps"icon="laptop">
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Run entirely against Ollama with no API keys. Swap to a hosted provider later — the agent code doesn't change.
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-**Compile-time guarantees.** Tool schemas, message types, and graph state transitions are checked before your code runs. No "unknown variant at runtime" surprises.
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-**Pay only for what you use.** Every external integration is feature-gated. Your binary doesn't include OpenAI code if you only use Anthropic.
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-**Async-native.** Built on `tokio` and `futures::Stream`. Streaming tokens, events, and graph state updates uses one consistent API.
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-**One umbrella, full stack.**`cognis` re-exports the foundation, LLM, RAG, and graph layers. Most apps need a single `use cognis::prelude::*;` and a few specific imports.
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-**One umbrella, full stack.**`cognis` re-exports the foundation, LLM, RAG, and graph layers. Most apps need a single `use cognis::prelude::*;`.
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## How Cognis is organized
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## What's not here yet
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The workspace is six libraries — one foundation, four sibling capabilities, one umbrella — plus a proc-macro crate.
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Honest about the gaps so you can decide whether to wait or contribute:
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| Crate | What's in it |
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|---|---|
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|`cognis-core`| The `Runnable` trait, messages, prompts, output parsers, callbacks, composition primitives. Zero internal dependencies. |
|`cognis`| Umbrella + agent layer. The crate most apps depend on. |
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-**LangSmith and OpenTelemetry exporters** — Langfuse is the supported production backend; OTel + others are on the roadmap. The `TraceExporter` trait is one async method, so building your own is fast.
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-**A hosted gateway / managed deployment** — bring your own infra. Production patterns are documented; managed runtime isn't shipping today.
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-**A wider provider matrix** — six are battle-tested; others (Groq, Together, Bedrock direct, Cohere) are open issues looking for owners.
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You don't need to think about crate boundaries while building — `cognis` re-exports the things you reach for daily.
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[See the full roadmap →](https://github.com/0xvasanth/cognis/issues)
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