llm-kernel-0.1.0 has been yanked.

llm-kernel

Foundation library for Rust AI-native apps — provider catalog, LLM client, MCP server, search, telemetry, and safety

Overview

llm-kernel provides the foundational layer for building LLM-powered tools, agents, and servers in Rust:

Provider catalog — 16 built-in providers, 114 models with metadata, pricing, and capabilities
Async client — trait-based client for OpenAI and Anthropic with SSE streaming
Model discovery — dynamic model discovery from models.dev, Ollama, OpenAI-compatible endpoints
Credential vault — dotenv-style API key management with atomic writes
Config loader — TOML config with auto-create from template
Knowledge graph — SQLite-backed graph with FTS5 search, smart recall, BFS traversal, async wrappers
MCP server — JSON-RPC 2.0 server framework with stdio transport and Bearer auth
Embedding — provider trait + cosine similarity, local ONNX (44 models), Qwen3 candle, Nomic V2 MoE candle, OpenAI remote (full model list →)
Search — Reciprocal Rank Fusion for hybrid search result merging
Token estimation — zero-dependency Unicode-script heuristic token counting
Telemetry — enum-gated events with no PII, console and noop sinks
Safety — secret masking, error classification, output sanitization
Install wizard — MCP config generation for Claude Desktop, Cursor, Copilot, OpenCode, Cline

Feature flags

Each module is gated behind a feature flag so you only pay for what you use.

Feature	Description	Default
`provider`	Provider catalog, model descriptors, pricing	✅
`client-async`	Async LLM client (reqwest) with streaming
`discovery`	Dynamic model discovery (models.dev, Ollama, OpenAI-compat)
`secrets`	SecretVault credential management
`store`	SQLite init helpers (WAL, FTS5, schema versioning)
`config`	TOML config loader
`graph`	Knowledge graph — SQLite, FTS5, smart recall, BFS traversal
`graph-async`	Async graph wrappers (requires tokio)
`graph-pool`	Multi-connection async graph pool (`AsyncPoolGraph`, WAL concurrency)
`mcp`	MCP server — JSON-RPC 2.0, stdio transport, Bearer auth
`tokens`	Token estimation with Unicode-script heuristics
`install`	AI tool installation wizard
`search`	Hybrid search with Reciprocal Rank Fusion
`embedding`	Embedding provider trait + cosine similarity
`embedding-openai`	OpenAI text-embedding client (sync HTTP)
`embedding-fastembed`	Local ONNX embedding via fastembed-rs (44 models)
`embedding-fastembed-qwen3`	Qwen3 embedding via candle backend
`embedding-fastembed-nomic-moe`	Nomic V2 MoE embedding via candle backend
`telemetry`	Enum-gated telemetry events, no PII
`safety`	Secret masking, error classification, output sanitization
`full`	All features

Quick start

Add to your Cargo.toml:

[dependencies]
llm-kernel = "0.1.0"

The provider feature is enabled by default. For the async client:

[dependencies]
llm-kernel = { version = "0.1.0", features = ["client-async"] }

For the knowledge graph with async wrappers:

[dependencies]
llm-kernel = { version = "0.1.0", features = ["graph", "graph-async"] }

For local embedding (ONNX, no API key):

[dependencies]
llm-kernel = { version = "0.1.0", features = ["embedding-fastembed"] }

Usage

Provider catalog

The embedded catalog contains 16 providers with 114 models aligned to the models.dev schema.

use llm_kernel::prelude::*;

let catalog = ProviderIndex::embedded();

// List all providers
for id in catalog.ids() {
    let provider = catalog.get(&id).unwrap();
    println!("{}", provider.display_name);
}

// Query models for a provider
for model in catalog.models_for("openai") {
    println!("  {} — ${:.2}/1M in", model.id, model.cost.unwrap().input);
}

// Find a specific model
if let Some(model) = catalog.find_model("claude-sonnet-4-20250514") {
    println!("Context: {} tokens", model.limit.unwrap().context);
}

Async chat completion

use llm_kernel::prelude::*;

let config = ModelConfig {
    provider: "openai".into(),
    model: "gpt-4o".into(),
    api_key_env: "OPENAI_API_KEY".into(),
    base_url: None,
    temperature: 0.7,
    max_tokens: Some(1024),
};

let client = OpenAIClient::new(&config)?;

let response = client.complete(LLMRequest {
    system: Some("You are a helpful assistant.".into()),
    messages: vec![ChatMessage::user("Hello!")],
    temperature: 0.7,
    max_tokens: Some(1024),
    model: None,
}).await?;

println!("{}", response.content);
println!("{} tokens used", response.usage.total_tokens);

Streaming

use llm_kernel::prelude::*;

let config = ModelConfig {
    provider: "anthropic".into(),
    model: "claude-haiku-4-5-20251001".into(),
    api_key_env: "ANTHROPIC_API_KEY".into(),
    base_url: None,
    temperature: 0.7,
    max_tokens: Some(256),
};

let client = AnthropicClient::new(&config)?;
let stream = client.stream_complete(LLMRequest {
    system: Some("Reply concisely.".into()),
    messages: vec![ChatMessage::user("Explain Rust in one paragraph.")],
    temperature: 0.7,
    max_tokens: Some(256),
    model: None,
}).await?;

// Stream yields Delta, Usage, and Done events

Model discovery

use llm_kernel::discovery::{fetch_and_cache, load_cache, fetch_ollama_models};

// Fetch from models.dev (caches to disk)
let payload = fetch_and_cache("~/.cache/llm-kernel/models-dev.json")?;
for model in &payload.models {
    println!("{} — {} (ctx: {:?})", model.id, model.provider_id, model.limits);
}

// Load from cache (no network)
if let Some(cached) = load_cache("~/.cache/llm-kernel/models-dev.json")? {
    println!("{} models cached", cached.models.len());
}

// Discover local Ollama models
let ollama_models = fetch_ollama_models("http://localhost:11434")?;
for name in &ollama_models {
    println!("Ollama: {}", name);
}

Credential vault

use llm_kernel::prelude::*;

let vault = SecretVault::load_from("~/.config/myapp/.env")?;
vault.set("OPENAI_API_KEY", "sk-...");
vault.save_to("~/.config/myapp/.env")?;

// Redact credentials for logging
println!("{}", redact_credential("sk-abcdef1234567890"));
// → "sk-abcd...7890"

TOML config

use llm_kernel::config::load_toml_config;
use serde::Deserialize;

#[derive(Deserialize)]
struct AppConfig {
    model: String,
    temperature: f32,
}

let config: AppConfig = load_toml_config(
    &path,
    Some(&llm_kernel::config::default_config_template("myapp")),
)?;

SQLite store

use llm_kernel::store::init_schema;

let ddl = "CREATE TABLE items (id TEXT PRIMARY KEY, content TEXT);";
let conn = init_schema(&db_path, ddl, 1)?;
// WAL mode, busy timeout, and schema versioning applied automatically

Knowledge graph

use llm_kernel::prelude::*;
use rusqlite::Connection;

let conn = Connection::open_in_memory().unwrap();
init_graph_schema(&conn).unwrap();

// Create nodes
upsert_node(&conn, &GraphNode {
    id: "rust-ownership".into(),
    node_type: "concept".into(),
    title: "Rust Ownership Model".into(),
    body: "Ownership, borrowing, and lifetimes...".into(),
    tags: vec!["rust".into(), "memory-safety".into()],
    projects: vec!["my-project".into()],
    agents: vec![],
    created: "2026-01-01T00:00:00Z".into(),
    updated: "2026-01-01T00:00:00Z".into(),
    importance: 0.8,
    access_count: 0,
    accessed_at: String::new(),
}).unwrap();

// Connect with edges
append_edge(&conn, &GraphEdge {
    id: "e1".into(),
    source: "rust-ownership".into(),
    target: "borrow-checker".into(),
    relation: "related".into(),
    weight: 1.5,
    ts: "2026-01-01T00:00:00Z".into(),
}).unwrap();

// Smart recall with composite scoring
let results = smart_recall(&conn, Some("my-project"), Some("ownership"), 5).unwrap();
for scored in &results {
    println!("{:.2} — {}", scored.score, scored.node.title);
}

// Lifecycle management
decay_importance(&conn, 30, 0.9, 0.05).unwrap();
tag_stale_nodes(&conn, 90).unwrap();
let stats = compute_stats(&conn).unwrap();
println!("{} nodes, {} edges", stats.total_nodes, stats.total_edges);

MCP server

use llm_kernel::mcp::{McpServer, Tool, JsonRpcRequest};
use serde_json::json;

let mut server = McpServer::new("my-server", "1.0.0");
server.register_tool(Tool {
    name: "greet".into(),
    description: "Say hello".into(),
    input_schema: json!({
        "type": "object",
        "properties": { "name": { "type": "string" } },
        "required": ["name"]
    }),
});

// Runs JSON-RPC 2.0 over stdio with Bearer auth
server.run_stdio().await?;

Token estimation

use llm_kernel::tokens::estimate_tokens;

let tokens = estimate_tokens("Hello, world! こんにちは世界 🌍");
println!("Estimated tokens: {}", tokens);

Embedding + search

use llm_kernel::embedding::{EmbeddingProvider, cosine_similarity};
use llm_kernel::search::{SearchResult, rrf_fuse};

// Cosine similarity between vectors
let sim = cosine_similarity(&[0.1, 0.2, 0.3], &[0.4, 0.5, 0.6]);

// Reciprocal Rank Fusion for hybrid search
let bm25 = vec![
    SearchResult { id: "doc-a".into(), score: 0.9, text: "Rust guide".into() },
    SearchResult { id: "doc-b".into(), score: 0.7, text: "Python basics".into() },
];
let vector = vec![
    SearchResult { id: "doc-b".into(), score: 0.95, text: "Python basics".into() },
    SearchResult { id: "doc-c".into(), score: 0.6, text: "Go concurrency".into() },
];
let merged = rrf_fuse(&[bm25, vector], 60);

Local ONNX embedding (fastembed-rs)

44 models via ONNX Runtime — no API key, no network after first download.

use llm_kernel::embedding::{EmbeddingModel, FastembedProvider, EmbeddingProvider};

let provider = FastembedProvider::new(EmbeddingModel::BGESmallENV15, None)?;
let result = provider.embed("hello world")?;
assert_eq!(result.vector.len(), 384);

Qwen3 embedding (candle)

Pure Rust GPU/CPU inference via candle-nn — no ONNX Runtime.

use llm_kernel::embedding::{Qwen3Provider, EmbeddingProvider};

let provider = Qwen3Provider::new("Qwen/Qwen3-Embedding-0.6B")?;
let result = provider.embed("hello world")?;

Nomic V2 MoE embedding (candle)

Lightweight MoE model — 8 experts, top-2 routing, 305M active params.

use llm_kernel::embedding::{NomicMoeProvider, EmbeddingProvider};

let provider = NomicMoeProvider::new()?;
let result = provider.embed("hello world")?;
assert_eq!(result.vector.len(), 768);

Safety utilities

use llm_kernel::safety::{mask_secrets, classify_failure, sanitize_output};

// Mask secrets in logs
let safe = mask_secrets("Authorization: Bearer sk-abcdef123456");
// → "Authorization: Bearer [REDACTED]"

// Classify errors
let category = classify_failure("connection timed out after 30s");
// → ErrorCategory::Timeout

// Sanitize untrusted output
let clean = sanitize_output(user_input)?;

Model metadata

Each model in the catalog includes:

Field	Description
`cost`	Per-million-token pricing (input, output, cache_read, cache_write)
`limit`	Context and output token limits
`modalities`	Input/output modalities (text, image, audio)
`capabilities`	Flags: attachment, reasoning, temperature, tool_call, streaming
`knowledge`	Training data cutoff date

Why llm-kernel?

	llm-kernel	rig	langchain-rust
Provider catalog	✅ 16 providers, 114 models built-in	Manual config	Manual config
Feature gates	✅ 20 independent modules	Monolithic	Monolithic
Local embedding	✅ 44 ONNX + Qwen3 + Nomic MoE	❌	❌
MCP server	✅ JSON-RPC 2.0	❌	❌
Knowledge graph	✅ SQLite + FTS5 + smart recall	❌	❌
Mandatory deps	`serde` only	`reqwest`, `tokio`, …	Many
Chains / agents	❌	✅	✅
RAG pipelines	❌	✅	✅

llm-kernel is a lightweight foundation layer — compose it with rig or langchain-rust when you need chains, agents, or RAG.

Architecture

┌──────────────────────────────────────────┐
│              Your app                    │
├──────────────────────────────────────────┤
│               prelude                    │  ← use llm_kernel::prelude::*;
├───────────────┬──────────┬───────────────┤
│   provider    │  client  │   discovery   │  ← catalog, async LLM, model discovery
│   catalog     │  async   │               │
├───────────────┴──────────┴───────────────┤
│  graph  │  mcp  │  embedding  │  search  │  ← graph, MCP, ONNX/Qwen3/Nomic embed, RRF
├──────────────────────────────────────────┤
│ tokens │ telemetry │ safety │ install    │  ← token est., events, masking, wizard
├──────────────────────────────────────────┤
│    secrets    │   config   │   store     │  ← vault, TOML, SQLite infra
└──────────────────────────────────────────┘

LLMClient trait — unified interface for OpenAIClient and AnthropicClient
EmbeddingProvider trait — unified interface for FastembedProvider (ONNX), Qwen3Provider (candle), NomicMoeProvider (candle), OpenAIEmbeddingClient (remote)
ProviderIndex — zero-copy access to embedded catalog, queryable by provider or model
McpServer — JSON-RPC 2.0 server with stdio transport, Bearer auth, tool registration
SecretVault — HashMap<String, String> with dotenv load/save and symlink guards
graph — SQLite knowledge graph with FTS5 search, composite scoring recall, BFS traversal, importance decay
TelemetryEvent — enum-gated variants for structured observability (no PII)
safety — secret masking, error classification, bidi/ANSI/null sanitization

Benchmarks

Criterion benchmarks under benches/:

cargo bench                          # Run all benchmarks
cargo bench -- graph_bench           # Graph: smart_recall, BFS, neighbors
cargo bench -- compute_bench         # Token estimation, RRF fusion

Examples

# List all providers and models (no API key needed)
cargo run --example provider_list

# OpenAI chat (requires OPENAI_API_KEY)
cargo run --example chat_openai --features client-async

# Anthropic streaming (requires ANTHROPIC_API_KEY)
cargo run --example stream_anthropic --features client-async

llm-kernel 0.1.0