# agentix
Multi-provider LLM client for Rust — streaming, non-streaming, tool calls, agentic loops, and MCP support.
DeepSeek · OpenAI · Anthropic · Gemini — one unified API.
## Installation
```toml
[dependencies]
agentix = "0.9"
# Optional: Model Context Protocol (MCP) tool support
# agentix = { version = "0.9", features = ["mcp"] }
```
---
## Quick Start
```rust
use agentix::{Request, LlmEvent};
use futures::StreamExt;
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let http = reqwest::Client::new();
let mut stream = Request::deepseek(std::env::var("DEEPSEEK_API_KEY")?)
.system_prompt("You are a helpful assistant.")
.user("What is the capital of France?")
.stream(&http)
.await?;
while let Some(event) = stream.next().await {
match event {
LlmEvent::Token(t) => print!("{t}"),
LlmEvent::Done => break,
_ => {}
}
}
println!();
Ok(())
}
```
---
## Providers
Four built-in providers, all using the same API:
```rust
use agentix::Request;
// Shortcut constructors (provider + default model in one call)
let req = Request::deepseek("sk-...");
let req = Request::openai("sk-...");
let req = Request::anthropic("sk-ant-...");
let req = Request::gemini("AIza...");
// Any OpenAI-compatible endpoint (e.g. OpenRouter)
let req = Request::openai("sk-or-...")
.base_url("https://openrouter.ai/api/v1")
.model("openrouter/free");
```
---
## Request API
`Request` is a self-contained value type — it carries provider, credentials, model,
messages, tools, and tuning. Call `stream()` or `complete()` with a shared `reqwest::Client`.
### `stream()` — streaming completion
```rust
let http = reqwest::Client::new();
let mut stream = Request::new(Provider::OpenAI, "sk-...")
.system_prompt("You are helpful.")
.user("Hello!")
.stream(&http)
.await?;
while let Some(event) = stream.next().await {
match event {
LlmEvent::Token(t) => print!("{t}"),
LlmEvent::Reasoning(r) => print!("[think] {r}"),
LlmEvent::ToolCall(tc) => println!("tool: {}({})", tc.name, tc.arguments),
LlmEvent::Usage(u) => println!("tokens: {}", u.total_tokens),
LlmEvent::Error(e) => eprintln!("error: {e}"),
LlmEvent::Done => break,
_ => {}
}
}
```
### `complete()` — non-streaming completion
```rust
let resp = Request::new(Provider::OpenAI, "sk-...")
.user("What is 2+2?")
.complete(&http)
.await?;
println!("{}", resp.content.unwrap_or_default());
println!("reasoning: {:?}", resp.reasoning);
println!("tool_calls: {:?}", resp.tool_calls);
println!("usage: {:?}", resp.usage);
```
### Builder methods
```rust
let req = Request::new(Provider::DeepSeek, "sk-...")
.model("deepseek-reasoner")
.base_url("https://custom.api/v1")
.system_prompt("You are helpful.")
.max_tokens(4096)
.temperature(0.7)
.retries(5, 2000) // max retries, initial delay ms
.user("Hello!") // convenience for adding a user message
.message(msg) // add any Message variant
.messages(vec![...]) // set full history
.tools(tool_defs); // set tool definitions
```
---
## LlmEvent (what you receive from `stream()`)
- `Token(String)` — incremental response text
- `Reasoning(String)` — thinking/reasoning trace (e.g. DeepSeek-R1)
- `ToolCallChunk(ToolCallChunk)` — partial tool call for real-time UI
- `ToolCall(ToolCall)` — completed tool call
- `Usage(UsageStats)` — token usage for the turn
- `Done` — stream ended
- `Error(String)` — provider error
---
## Defining Tools
Two styles are supported: **standalone function** (simpler) and **impl block** (multiple tools in one struct).
### Standalone function
```rust
use agentix::tool;
/// Add two numbers.
/// a: first number
/// b: second number
#[agentix::tool]
async fn add(a: i64, b: i64) -> i64 {
a + b
}
/// Divide a by b.
#[agentix::tool]
async fn divide(a: f64, b: f64) -> Result<f64, String> {
if b == 0.0 { Err("division by zero".into()) } else { Ok(a / b) }
}
// Combine with + operator
let tools = add + divide;
let mut stream = agentix::agent(tools, http, request, history, Some(25_000));
```
The macro generates a unit struct with the same name as the function and implements `Tool` for it.
### Impl block (multiple methods per struct)
```rust
struct Calculator;
#[tool]
impl agentix::Tool for Calculator {
/// Add two numbers.
/// a: first number
/// b: second number
async fn add(&self, a: i64, b: i64) -> i64 {
a + b
}
/// Divide a by b.
async fn divide(&self, a: f64, b: f64) -> Result<f64, String> {
if b == 0.0 { Err("division by zero".into()) } else { Ok(a / b) }
}
}
```
- Doc comment → tool description
- `/// param: description` lines → argument descriptions
- `Result::Err` automatically propagates as `{"error": "..."}` to the LLM
### Streaming tools
Add `#[streaming]` to yield `ToolOutput::Progress` / `ToolOutput::Result` incrementally:
```rust
use agentix::{tool, ToolOutput};
struct ProgressTool;
#[tool]
impl agentix::Tool for ProgressTool {
/// Run a long job and stream progress.
/// steps: number of steps
#[streaming]
fn long_job(&self, steps: u32) {
async_stream::stream! {
for i in 1..=steps {
yield ToolOutput::Progress(format!("{i}/{steps}"));
}
yield ToolOutput::Result(serde_json::json!({ "done": true }));
}
}
}
```
Normal and streaming methods can be freely mixed in the same `#[tool]` block.
---
## MCP Tools
Use external processes as tools via the Model Context Protocol:
```rust
use agentix::McpTool;
use std::time::Duration;
let tool = McpTool::stdio("npx", &["-y", "@playwright/mcp"]).await?
.with_timeout(Duration::from_secs(60));
// Add to a ToolBundle alongside regular tools
let mut bundle = agentix::ToolBundle::new();
bundle.push(tool);
```
### Runtime add / remove
```rust
let mut bundle = agentix::ToolBundle::default();
bundle += Calculator; // AddAssign — add tool in-place
bundle -= Calculator; // SubAssign — remove all functions Calculator provides
let bundle2 = bundle + Calculator - Calculator; // Sub — returns new bundle
```
---
## Structured Output
Constrain the model to emit JSON matching a Rust struct using `Request::json_schema()`.
Derive `schemars::JsonSchema` on your struct and pass the generated schema:
```rust
use schemars::JsonSchema;
use serde::{Deserialize, Serialize};
#[derive(Debug, Deserialize, JsonSchema)]
struct Review {
rating: f32,
summary: String,
pros: Vec<String>,
}
let schema = serde_json::to_value(schemars::schema_for!(Review))?;
let response = Request::openai(api_key)
.system_prompt("You are a film critic.")
.user("Review Inception (2010).")
.json_schema("review", schema, true) // strict=true enforces the schema
.complete(&http)
.await?;
let review: Review = response.json()?;
```
See `examples/08_structured_output.rs` for a runnable example.
**Provider support:**
- **OpenAI** — full `json_schema` support (gpt-4o and later)
- **Gemini** — `responseSchema` + `responseMimeType: application/json` (fully supported)
- **DeepSeek** — `json_object` only; `json_schema` is automatically degraded with a `tracing::warn`
- **Anthropic** — `response_format` is ignored; use prompt engineering instead
---
## Reliability
- **Automatic retries** — exponential backoff for 429 / 5xx responses
- **Usage tracking** — per-request token accounting across all providers; `AgentEvent::Done` contains cumulative totals across all turns
---
## Agent (agentic loop)
`agentix::agent()` drives the full LLM ↔ tool-call loop and yields typed `AgentEvent`s.
Pass it a `ToolBundle`, a base `Request`, and an initial history — it handles
repeated LLM calls, tool execution, and history accumulation automatically.
```rust
use agentix::{AgentEvent, Request, Provider, ToolBundle};
use futures::StreamExt;
#[tokio::main]
async fn main() {
let http = reqwest::Client::new();
let request = Request::new(Provider::DeepSeek, std::env::var("DEEPSEEK_API_KEY").unwrap())
.system_prompt("You are helpful.");
let mut stream = agentix::agent(ToolBundle::default(), http, request, vec![], None);
while let Some(event) = stream.next().await {
match event {
AgentEvent::Token(t) => print!("{t}"),
AgentEvent::ToolCallStart(tc) => println!("→ {}({})", tc.name, tc.arguments),
AgentEvent::ToolResult { name, content, .. } => println!("← [{name}] {content}"),
AgentEvent::Usage(u) => println!("tokens: {}", u.total_tokens),
AgentEvent::Error(e) => eprintln!("error: {e}"),
_ => {}
}
}
}
```
### AgentEvent variants
- `Token(String)` — incremental response text
- `Reasoning(String)` — thinking trace
- `ToolCallChunk(ToolCallChunk)` — streaming partial tool call
- `ToolCallStart(ToolCall)` — complete tool call, about to execute
- `ToolProgress { id, name, progress }` — intermediate tool output
- `ToolResult { id, name, content }` — final tool result
- `Usage(UsageStats)` — token usage per LLM request
- `Done(UsageStats)` — emitted once when the loop finishes normally; contains **cumulative** totals across all turns
- `Warning(String)` — recoverable stream error
- `Error(String)` — fatal error
`agentix::agent()` returns a `BoxStream<'static, AgentEvent>` — drop it to abort.
---
## Changelog
### 0.9.0
- **New `agentix::agent()` free function** — stateless agentic loop: `agent(tools, client, request, history, history_budget)`
- **New `AgentEvent` enum** — `Token`, `Reasoning`, `ToolCallChunk`, `ToolCallStart`, `ToolProgress`, `ToolResult`, `Usage`, `Done`, `Warning`, `Error`
- **`AgentEvent::Done(UsageStats)`** — cumulative token usage across all turns, emitted once on normal completion
- **Concurrent tool execution** — all tool calls in one LLM turn run in parallel via `select_all`; progress events arrive in real time
- **`Request::deepseek/openai/anthropic/gemini(key)`** — shortcut constructors
- **`Request::json_schema(name, schema, strict)`** — structured output with JSON Schema
- **`ToolBundle::remove(name)`** — runtime tool removal
- **`Arc<dyn Tool>` implements `Tool`** — pass a shared bundle without wrapping
- **tracing integration** — `debug!` spans around LLM requests and tool execution (no feature flag needed, uses the `tracing` crate)
### 0.8.0
- **Replaced `LlmClient` with `Request`** — self-contained value type with builder pattern
- **Replaced `Provider` trait with `Provider` enum** — `DeepSeek`, `OpenAI`, `Anthropic`, `Gemini`
- **Removed shared mutable state** — `Request` is `Clone`, `Send`, `Sync`; caller passes `&reqwest::Client`
- **Removed `AgentConfig`** from public API — all config lives in `Request` fields
### 0.7.0
- **Removed `Agent` struct** — `LlmClient` is now the sole entry point; callers own the loop
- **Removed `Memory` trait** — `InMemory`, `SlidingWindow`, `TokenSlidingWindow`, `LlmSummarizer` removed
- **Removed `AgentEvent` / `AgentInput`** — only `LlmEvent` remains
- **New `LlmClient::complete()`** — native non-streaming API for all four providers
- **New `CompleteResponse`** — content, reasoning, tool_calls, usage in one struct
### 0.6.0
- Non-streaming `complete()` method on `Provider` trait
- `post_json` helper for non-streaming HTTP POST with retry
- `CompleteResponse` type
### 0.5.0
- `Agent` API with `chat()`, `send()`, `subscribe()`, `add_tool()`, `abort()`, `usage()`
- Concurrent tool execution via `FuturesUnordered`
- `SlidingWindow` fix for orphaned tool messages
- Default HTTP timeouts (10 s connect, 120 s response)
### 0.4.x
- Initial multi-turn API
- DeepSeek, OpenAI, Anthropic, Gemini providers
- `#[tool]` and `#[streaming]` macros
- Memory backends, MCP tool support
---
## License
MIT OR Apache-2.0