#agent #agent-workflow #pipeline #workflow #llm

agent-line

A batteries-included Rust library for building agent workflows

3 releases

Uses new Rust 2024

new 0.1.2 Mar 8, 2026
0.1.1 Mar 4, 2026
0.1.0 Mar 3, 2026

#305 in Development tools

MIT license

71KB
1.5K SLoC

agent-line

A batteries-included Rust library for building agent workflows. Sync-only, opinionated, and designed for people getting started with agent patterns.

Define agents, wire them into workflows, and let the runner execute them. Agents communicate through shared context and control flow with outcomes like Continue, Next, Retry, and Done.

Quick Start

use agent_line::{Agent, Ctx, Outcome, Runner, StepResult, Workflow};

#[derive(Clone)]
struct State { n: i32 }

struct AddOne;
impl Agent<State> for AddOne {
    fn name(&self) -> &'static str { "add_one" }
    fn run(&mut self, state: State, _ctx: &mut Ctx) -> StepResult<State> {
        Ok((State { n: state.n + 1 }, Outcome::Done))
    }
}

fn main() {
    let mut ctx = Ctx::new();
    let mut agent = AddOne;
    let (state, _) = agent.run(State { n: 1 }, &mut ctx).unwrap();
    println!("n = {}", state.n); // n = 2
}

Workflows

Agents are registered into a workflow, then wired together with start_at and then. The workflow validates everything at build time.

let wf = Workflow::builder("my-workflow")
    .register(StepA)
    .register(StepB)
    .register(StepC)
    .start_at("step_a")
    .then("step_b")
    .then("step_c")
    .build()
    .unwrap();

let mut runner = Runner::new(wf);
let result = runner.run(initial_state, &mut ctx);

Agents can also route dynamically by returning Outcome::Next("agent_name") instead of Outcome::Continue.

Context (Ctx)

Ctx is shared mutable state passed to every agent. It provides a key-value store and an event log.

let mut ctx = Ctx::new();

// Key-value store
ctx.set("draft", "Hello world");
let val = ctx.get("draft"); // Some("Hello world")
ctx.remove("draft");

// Event log
ctx.log("validator: found 2 errors");
for entry in ctx.logs() {
    println!("{entry}");
}
ctx.clear_logs();

// Reset everything
ctx.clear();

Ctx persists across multiple runner.run() calls, so the log and store accumulate across runs.

LLM Integration

Ctx includes a built-in LLM client that supports Ollama, OpenAI-compatible APIs (OpenRouter, etc.), and the Anthropic API.

let response = ctx.llm()
    .system("You are a helpful assistant.")
    .user("Summarize this text: ...")
    .send()?;

Configuration

Set via environment variables:

Variable Default Description
AGENT_LINE_PROVIDER ollama LLM provider: ollama, openai, or anthropic
AGENT_LINE_LLM_URL http://localhost:11434 LLM API base URL
AGENT_LINE_MODEL llama3.1:8b Model name
AGENT_LINE_NUM_CTX 4096 Context window size
AGENT_LINE_API_KEY (none) API key (required for remote providers)
AGENT_LINE_DEBUG (unset) Set to any value to log config at startup and LLM requests/responses to stderr

Provider examples

Ollama (default, no API key needed):

export AGENT_LINE_MODEL=llama3.1:8b

OpenRouter:

export AGENT_LINE_PROVIDER=openai
export AGENT_LINE_LLM_URL=https://openrouter.ai/api
export AGENT_LINE_MODEL=amazon/nova-lite-v1
export AGENT_LINE_API_KEY=sk-or-...

Anthropic:

export AGENT_LINE_PROVIDER=anthropic
export AGENT_LINE_LLM_URL=https://api.anthropic.com
export AGENT_LINE_MODEL=claude-sonnet-4-20250514
export AGENT_LINE_API_KEY=sk-ant-...

Outcomes

Agents return an Outcome to control what happens next:

Outcome Behavior
Continue Follow the default next step set by .then()
Done Workflow complete, return the final state
Next("name") Jump to a specific agent by name
Retry(hint) Re-run the current agent (counted against max_retries)
Wait(duration) Sleep, then re-run the current agent
Fail(msg) Stop the workflow with an error

Tools

Standalone utility functions for common agent tasks. Import with use agent_line::tools;.

File operations

Function Signature Description
read_file (path: &str) -> Result<String, StepError> Read file contents
write_file (path: &str, content: &str) -> Result<(), StepError> Write to file (creates parent dirs)
append_file (path: &str, content: &str) -> Result<(), StepError> Append to file (creates if missing)
file_exists (path: &str) -> bool Check if a file exists
delete_file (path: &str) -> Result<(), StepError> Delete a file
create_dir (path: &str) -> Result<(), StepError> Create directory (and parents)
list_dir (path: &str) -> Result<Vec<String>, StepError> List directory entries
find_files (path: &str, pattern: &str) -> Result<Vec<String>, StepError> Recursively find files by pattern

Command execution

Function Signature Description
run_cmd (cmd: &str) -> Result<CmdOutput, StepError> Run a shell command
run_cmd_in_dir (dir: &str, cmd: &str) -> Result<CmdOutput, StepError> Run a shell command in a specific directory

CmdOutput has success: bool, stdout: String, and stderr: String.

HTTP

Function Signature Description
http_get (url: &str) -> Result<String, StepError> GET request, returns body as string
http_post (url: &str, body: &str) -> Result<String, StepError> POST with string body
http_post_json (url: &str, body: &Value) -> Result<String, StepError> POST with JSON body

Parsing

Function Signature Description
strip_code_fences (response: &str) -> String Remove markdown code fences from LLM output
parse_lines (response: &str) -> Vec<String> Split LLM response into lines, strip numbering/bullets
extract_json (response: &str) -> Result<String, StepError> Extract first JSON object or array from text

Error Handling

StepError has four variants designed around what the caller can do about them:

Variant Meaning Action
Invalid(String) Bad input or logic error Fix the code
Transient(String) Network/rate limit failure Retry might help
Failed(String) Agent explicitly failed Handle or propagate
Other(String) Everything else Inspect the message

From impls exist for ureq::Error (maps to Transient) and std::io::Error (maps to Other), so you can use ? in tool calls.

Runner Configuration

let mut runner = Runner::new(wf)
    .with_max_steps(10_000)   // default, prevents infinite loops
    .with_max_retries(3);     // default, per-agent consecutive retry limit

Hooks

Runner supports closure-based hooks for observability. Closures are FnMut, so you can use stateful callbacks (counters, accumulators, etc.).

let mut runner = Runner::new(wf)
    .on_step(|e| {
        println!(
            "[step {}] {} -> {:?} ({:.3}s)",
            e.step_number, e.agent, e.outcome, e.duration.as_secs_f64()
        );
    })
    .on_error(|e| {
        eprintln!("[error] {} at step {}: {}", e.agent, e.step_number, e.error);
    });

Or use the built-in tracing shorthand, which prints step transitions and errors to stderr:

let mut runner = Runner::new(wf).with_tracing();

Output looks like:

[step 1] fetch_weather -> Continue (0.001s)
[step 2] fetch_calendar -> Continue (0.000s)
[step 3] fetch_email -> Continue (0.000s)
[step 4] summarize -> Done (2.340s)

Hook event types

StepEvent is passed to on_step after each successful agent step:

Field Type Description
agent &str Name of the agent that ran
outcome &Outcome The outcome the agent returned
duration Duration Wall-clock time for the step
step_number usize Sequential step counter (starts at 1)
retries usize Consecutive retry count for the current agent

ErrorEvent is passed to on_error when an agent errors or a limit is exceeded:

Field Type Description
agent &str Name of the agent that errored
error &StepError The error that occurred
step_number usize Step number where the error happened

Examples

Example Run Description
hello_world cargo run --example hello_world Single agent, no workflow
workflow cargo run --example workflow Linear workflow with chained agents
edit_loop cargo run --example edit_loop Validate/fix loop with retry
newsletter cargo run --example newsletter Multi-phase LLM workflow (needs Ollama)
coder cargo run --example coder Code generation with test loop (needs Ollama)
assistant cargo run --example assistant Personal assistant pipeline with tracing (needs Ollama)
parallel cargo run --example parallel Threaded fan-out/fan-in with researcher/writer/editor pipeline

TODO

  • Rename find_files to glob or add proper glob pattern support

Dependencies

Dependencies

~13–26MB
~419K SLoC