atomr-agents

A native Rust agentic framework built as a layered actor / strategy / harness substrate on top of atomr and atomr-infer. atomr-agents gives you a single mental model — pluggable strategies that resolve under shared budgets, channelled state with first-class checkpointing, tool-call orchestration with parallel dispatch, and durable harness loops — that scales from a one-off chatbot to a multi-tenant production agent platform.

use atomr_agents::prelude::*;

// One Pipeline composes prompt → model → parser like LCEL.
let pipeline = Pipeline::from(prompt)
    .then(model)
    .then(parser)
    .build();

let answer = pipeline.call(input, ctx).await?;

Python parity wave

The Python facade in 0.3 catches up to the Rust surface. The native extension atomr_agents._native is now split into hierarchical submodules — errors, core, observability, registry, tool, skill, persona, agent, workflow, harness, eval, guest — mirroring atomr-infer/inference-py-bindings. The top-level package re-exports the full surface, ships a PEP 561 py.typed marker, and exposes async coroutines / async iterators over pyo3-async-runtimes.

Install

pip install atomr-agents

For an editable workflow against the local checkout:

pip install maturin
maturin develop --features python -m crates/py-bindings/Cargo.toml
pip install -e ".[dev]"

Host-mode async event stream

EventBus.stream() returns an EventStream that implements the Python async iterator protocol. Drive a producer on the same loop and consume events as they fire:

import asyncio
from atomr_agents.observability import EventBus


async def main() -> None:
    bus = EventBus()
    stream = bus.stream()

    bus.emit_tool_invoked("calc", args_hash=0, elapsed_ms=5, ok=True)
    bus.emit_tool_invoked("search", args_hash=1, elapsed_ms=12, ok=True)

    async for ev in stream:
        print(ev.kind, ev.timestamp_ms)
        if ev.kind == "tool_invoked" and ev.tool == "search":
            break


asyncio.run(main())

Async registry publish

Registry.publish_async returns a Python awaitable backed by a tokio future, so version pins land without blocking the event loop:

import asyncio
from atomr_agents.registry import Registry


async def main() -> None:
    registry = Registry()
    record = await registry.publish_async(
        "tool_set", "calc", "0.1.0", {"name": "calc"}
    )
    print(record.kind, record.id, record.version)


asyncio.run(main())

Guest-mode @tool decorator

atomr_agents.guest exposes real decorators wired through _native.guest.register_*_factory. A guest tool is a class with an async def invoke(self, args, ctx) method:

from atomr_agents.guest import tool


@tool(toolset="calc")
class Add:
    name = "add"

    async def invoke(self, args: dict, ctx) -> dict:
        return {"sum": args["a"] + args["b"]}

Mirror decorators are available for @strategy, @persona, @skill, @parser, @scorer, @memory_store, and @embedder.

Where things live

The hierarchical layout is reflected in the Python facade — every submodule has a one-to-one .py mirror under atomr_agents/:

from atomr_agents.errors import RegistryError
from atomr_agents.core import TokenBudget, AgentId
from atomr_agents.agent import AgentSpec, AgentBudgets
from atomr_agents.tool import ToolDescriptor, ToolCallParser
from atomr_agents.observability import EventBus, RunTreeBuilder
from atomr_agents.registry import Registry

The top-level package keeps the 0.2.x convenience names — so from atomr_agents import EventBus, Registry still works.

Roadmap

Agent.run_turn, Harness.run, and WorkflowRunner.run are not yet exposed as Python coroutines. The Rust types are generic over four-plus strategy traits, so PyO3 cannot construct them from a stable #[pyclass] shape; they need a Boxed* adapter (BoxedAgent / BoxedHarness / BoxedWorkflow) under crates/agent / crates/harness / crates/workflow. Until that adapter lands, host code drives the loop in Rust and observes progress over the (already async-iterable) EventBus. See docs/python-api.md for the full module map.

Why an agentic framework, in Rust, on actors

Agentic systems don't fail because the models aren't good enough — they fail because the substrate underneath them treats context, composition, and persistence as afterthoughts. Glue-code retry policies, opaque memory, hand-rolled tool loops, brittle handoff between agents — that's where 3 a.m. pages come from.

Composition is the unit of work. A real agent is a Pipeline of prompts, models, parsers, and tools — each with its own retry, fallback, timeout, cache, and trace policy. atomr-agents makes every component a Callable with the same composition surface, so with_retry, with_fallbacks, and with_config apply uniformly to prompts, models, retrievers, and parsers alike.

State is channelled, durable, and forkable. Long-running agents need more than chat history. They need typed channels with reducers (AppendMessages, MergeMap, LastWriteWins, MaxByTimestamp), per-super-step checkpoints keyed by (workflow, run, step), and fork-with-edit so an operator can branch a divergent run from any prior state. atomr-agents ships LangGraph's state model verbatim in atomr's actor idiom.

Tool calls are parallel and provider-agnostic. When a model emits five tool calls in one turn, atomr-agents fans them into a JoinSet and aggregates in original order. The streaming tool_call_delta parser handles OpenAI and Anthropic deltas natively; new providers plug in behind the same Provider enum. Per-call deltas are also surfaced as Event::ToolCallStreamed so tracers and UIs see tool intent in real time, distinct from the post-call Event::ToolInvoked. RichTool returns ToolReturn::{Content, ContentAndArtifact, Command} so a tool can also drive graph control flow.

Provider runtimes are opt-in feature flags. Enable provider-anthropic, provider-openai, or provider-gemini on the umbrella to pull the corresponding atomr-infer-runtime-* crate and re-export its *Config / *Pricing / *Runner via atomr_agents::agent::providers::{anthropic, openai, gemini}. Cost reports include cached_tokens (Anthropic prompt-cache, OpenAI cached input) and reasoning_tokens (o1-style) automatically.

Granular efficiency. Rust gives us deterministic resource use, zero-cost abstractions, and ownership-as-concurrency-safety. Strategy trait generics monomorphize the per-turn pipeline; Box<dyn> opt-in exists for config-driven loading. The whole 26-crate workspace builds under cargo check --workspace in seconds and ships zero runtime overhead beyond what the actor crate already pays.

What's in the box

Plus a Python facade — pip install atomr-agents — that exposes the host-mode Registry / EventBus and the guest-mode @tool / @strategy / @persona decorators.

Quick start (Rust)

The umbrella crate is published on crates.io as atomr-agents:

[dependencies]
atomr-agents = { version = "0.2", features = ["agent", "harness", "eval"] }
atomr-infer  = { version = "0.6", features = ["openai"] }   # or any provider

Or, to pull a provider runtime through the umbrella so Agent / LocalRunnerClient / OpenAiRunner come from one crate:

atomr-agents = { version = "0.2", features = ["agent", "provider-openai"] }
# or features = ["agent", "provider-anthropic"], ["agent", "provider-gemini"]

A minimal agent against MockRunner (good for tests; swap for any ModelRunner in production):

use std::sync::Arc;
use atomr_agents::prelude::*;
use atomr_agents::agent::{Agent, AgentBudgets, InferenceClient, LocalRunnerClient, Provider};
use atomr_agents::tool::{StaticToolStrategy, DynTool};
use atomr_agents::memory::{InMemoryStore, RecencyMemoryStrategy};
use atomr_agents::skill::StaticSkillStrategy;
use atomr_agents::persona::StaticPersonaStrategy;
use atomr_agents::instruction::{
    ComposedInstructionStrategy, StaticBehaviorStrategy, StaticTaskStrategy,
};
use atomr_agents::observability::EventBus;
use atomr_infer_testkit::{MockRunner, MockScript};

let runner = MockRunner::new(MockScript::from_text(["the answer is ", "42"]));
let inference: Arc<dyn InferenceClient> =
    Arc::new(LocalRunnerClient::new(runner, Provider::OpenAi));

let agent = Agent {
    id: AgentId::from("a-1"),
    model: "mock".into(),
    instructions: ComposedInstructionStrategy::new(
        StaticPersonaStrategy::new("You are a helpful assistant."),
        StaticTaskStrategy("Answer arithmetic questions.".into()),
        StaticBehaviorStrategy("Reply tersely.".into()),
    ),
    tools: StaticToolStrategy::new(Vec::<DynTool>::new()),
    memory: RecencyMemoryStrategy::new(Arc::new(InMemoryStore::new()), 5, 30),
    skills: StaticSkillStrategy::new(vec![]),
    inference,
    bus: EventBus::new(),
    max_tool_iterations: 3,
};

let r = agent
    .run_turn("what's 1+2".into(), AgentBudgets::default())
    .await?;
println!("{}", r.text);

Add tools, switch the MockRunner to a real ModelRunner (OpenAI, Anthropic, vLLM, …), and the same code runs unchanged.

Quick start (Python)

pip install atomr-agents

from atomr_agents import EventBus, Registry

bus = EventBus()
bus.subscribe(lambda ev: print(ev.kind, ev.timestamp_ms))

registry = Registry()
registry.publish("tool_set", "ts", "0.1.0", {"tools": ["calc"]})
print(registry.latest("tool_set", "ts"))

See docs/python.md for the full host/guest model and the subinterpreter-pool dispatcher pattern inherited from atomr's pycore.

Documentation map

• docs/index.md — documentation hub
• docs/architecture.md — runtime layout, crate stack, where each layer slots in
• docs/state-and-checkpointing.md — channels, reducers, Checkpointer, fork/replay
• docs/agent-pipeline.md — the per-turn pipeline + tool-call loop + middleware
• docs/workflows-and-hitl.md — DAG, Send-API, dynamic interrupts, breakpoints
• docs/retrieval-and-ingestion.md — retriever zoo, LongStore, loaders, splitters
• docs/observability.md — EventBus, RunTree, tracers
• docs/eval.md — eval suites, judge / pairwise / rubric scorers, regression gate
• docs/multi-agent-patterns.md — supervisor / swarm / network / hierarchical
• docs/feature-matrix.md — every feature flag, what it pulls in
• docs/python.md — Python bindings + subinterpreter-pool guest mode
• docs/python-api.md — Python API reference: submodule map, async surfaces, 0.2 → 0.3 migration
• docs/migrating-from-langgraph.md — concept-mapping table + concrete code translations
• ai-skills/ — Claude Code / Agent SDK skills for AI-assisted coding against atomr-agents

License

Apache-2.0.