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GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace
GitHub - clark-labs-inc/clark-agent: A small, typed, hook...
stan_kirdey · 2026-05-27 · via Hacker News - Newest: "LLM"

A small, typed, hookable agent loop. Provider-agnostic, sandbox-agnostic, tooling-agnostic.

Shape

context → LLM (StreamFn) → tool batch → results appended → repeat

Termination is a tool decision (ToolResult::terminate = true, unanimous across the batch). The runtime owns execution and event emission; tools own semantics; plugins own cross-cutting extension.

Layers

  • typesAgentMessage, content blocks, StopReason. Conversation is Vec<AgentMessage>. Apps extend via AgentMessage::Custom or by wrapping in their own enum.
  • eventAgentEvent enum + EventSink trait. Single sink, typed events. Streamed and final delivery use the same enum. ChannelSink, FanOutSink, NoopSink provided.
  • toolAgentTool trait + ToolRegistry. Tools own their schema, validation, and execution. The loop only dispatches.
  • streamStreamFn trait. Swappable LLM transport: real provider, fixture replay, scripted scenario, remote proxy.
  • pluginPlugin + capability traits (BeforeToolCall, AfterToolCall, ContextTransform, EventObserver, SteeringSource, FollowUpSource, ToolGate). Cross-cutting concerns register here, not inline in the loop.
  • protocolProtocolPolicy. The seam for product-specific tool vocabulary (recovery prose, tool-call alias repair, hidden-tool errors, terminal-tool classification). Default is generic and names no tools.
  • configLoopConfig + AgentBuilder for assembling everything.
  • runrun / run_continue — the canonical loop. Pure functions.
  • exec — tool execution: parallel + sequential dispatch, hook plumbing.
  • budget — default token-budget context transform.
  • error — typed error enums.

Plugin extension points

Trait When it runs
BeforeToolCall After argument validation, before tool.execute. May block with reason.
AfterToolCall After tool.execute. May override result, mark error, vote terminate.
ContextTransform Before each LLM call. Window management, redaction.
EventObserver On every AgentEvent. Logging, telemetry, persistence.
SteeringSource Between batches. Inject extra messages mid-run.
FollowUpSource After natural stop. Re-start the agent if more is queued.

A single struct can implement multiple capability traits — declare the set via Plugin::capabilities() and register once with AgentBuilder::plugin().

Quick start

use std::sync::Arc;
use clark_agent::{AgentBuilder, AgentContext, AgentMessage, ToolRegistry, UserContent};
use tokio_util::sync::CancellationToken;

let registry = ToolRegistry::new()
    .with(Arc::new(my_shell_tool()))
    .with(Arc::new(my_file_tool()));

let config = AgentBuilder::new()
    .stream(Arc::new(my_provider()))
    .tools(registry)
    .before_tool_call(my_security_gate())
    .after_tool_call(my_repeat_detector())
    .context_transform(clark_agent::budget::TokenBudget::default())
    .max_iterations(50)
    .build()?;

let outcome = clark_agent::run(
    vec![AgentMessage::User {
        content: UserContent::Text("List files in /tmp".into()),
        timestamp: None,
    }],
    AgentContext::new("You are a helpful assistant."),
    &config,
    CancellationToken::new(),
).await?;

Examples

Run the smallest possible loop with a scripted transport:

cargo run --example minimal

Run a two-turn loop where the model calls a typed echo tool:

cargo run --example tool_call

Real integrations provide their own StreamFn implementation for an LLM provider and register application tools through AgentTool or TypedAgentTool.

Mid-run steering (steer())

let (steering, handle) = clark_agent::plugin::ChannelSteering::new();
let config = AgentBuilder::new()
    .stream(provider)
    .tools(registry)
    .steering_arc(steering)
    .build()?;

// In another task: inject a message between batches.
handle.steer(AgentMessage::User {
    content: UserContent::Text("actually, focus on /etc instead".into()),
    timestamp: None,
})?;

Design rules

  • One canonical core. run / run_continue are pure functions, not methods on a god-class.
  • Hooks are typed, narrow, side-effect-free. No I/O in BeforeToolCall or AfterToolCall — those belong to the tool's own execute.
  • Failure is a context event. Tool errors become tool result content with is_error: true. The loop appends and continues. Only LoopError (stream transport unrecoverable / aborted) ends the run.
  • Termination requires unanimity. A batch ends the run only when every finalized tool result votes terminate: true. One tool wanting to stop does not stop the batch.
  • Strongly typed contracts. Discriminators are enums; payloads are typed structs; field-name string lookups (obj["role"]) are forbidden in primary contracts. serde_json::Value only at open-by-design leaves (provider extras, custom message payloads, tool arguments).

Open-source boundary

clark-agent is the reusable loop crate: typed history, tool dispatch, provider transport traits, events, and extension hooks. Product wiring belongs in downstream crates.

The core knows no product tool names. The three places that once needed product vocabulary — plain-text recovery prose, model tool-call alias repair, and hidden-tool error messages — now go through a single seam, the ProtocolPolicy trait:

pub trait ProtocolPolicy: Send + Sync + 'static {
    fn terminal_tool_names(&self) -> HashSet<String> { ... }
    fn plain_text_recovery_prompt(&self, ctx: PlainTextRecoveryContext<'_>) -> Option<String> { ... }
    fn normalize_tool_calls(&self, calls: &mut [ToolCall], registry: &ToolRegistry) -> usize { ... }
    fn hidden_tool_error(&self, ctx: HiddenToolContext<'_>) -> Option<HiddenToolError> { ... }
}

The core ships DefaultProtocolPolicy (generic, names no tools). A downstream product installs its own via AgentBuilder::protocol_policy(...) to inject its delivery/ask/plan vocabulary, tool-call aliases, and recovery prose — none of which lives in this crate. New product-specific behavior should be implemented as a ProtocolPolicy, a plugin (ToolGate, ContextTransform, …), or a tool definition rather than added to the core loop.

Release checks

cargo test --all-targets
cargo clippy --all-targets -- -D warnings
RUSTDOCFLAGS="-D warnings" cargo doc --no-deps
cargo publish --dry-run

Citation

Citation authorship: Stanislav Kirdey, Clark Labs Inc. See CITATION.cff for machine-readable citation metadata.

License

Apache-2.0 © Stanislav Kirdey, Clark Labs Inc.


Built by Stanislav Kirdey, Clark Labs Inc. — the team behind Clark, AI-powered web automation and research. If clark-agent is useful to you, a ⭐ on GitHub helps others discover it.