Parallel Swarm

Launch three agents in parallel — each clones a different repo, reads the diffs, and writes its own code review. All three run concurrently in separate sandboxes.

What you'll build

A script that fires off three background tasks with agent.run(), waits for all of them to finish, and downloads three independent code reviews to your machine.

The Script

Create a file called swarm.ts:

import { createClient } from "swarmlord";
import { writeFileSync, mkdirSync } from "fs";

const client = createClient({ apiKey: process.env.SWARMLORD_API_KEY! });

const repos = [
    "https://github.com/pingdotgg/t3code",
    "https://github.com/anthropics/anthropic-cookbook",
    "https://github.com/cloudflare/workers-sdk",
];

mkdirSync("output", { recursive: true });

// launch all three tasks in parallel
const tasks = await Promise.all(
    repos.map(repo =>
        client.agent("build").run(
            `Clone ${repo} (shallow: git clone --depth 20) into /workspace/repo. Read the diffs of the last 5 commits to the default branch using git log -p -5. Write a concise review and summary of the changes to /workspace/summary.md. Focus on architectural decisions and code quality.`,
            { title: `Review: ${repo.split("/").pop()}` }
        )
    )
);

// wait for all agents to finish
const results = await Promise.all(
    tasks.map(task => task.result({ timeoutMs: 300_000 }))
);

// download each summary
for (let i = 0; i < repos.length; i++) {
    const repoName = repos[i].split("/").pop()!;
    const session = client.session(tasks[i].id);

    try {
        const summary = await session.getFile("/workspace/summary.md");
        writeFileSync(`output/${repoName}-review.md`, summary);
        console.log(`${repoName}: ${results[i].status} — $${(results[i].cost ?? 0).toFixed(4)}`);
    } catch {
        console.log(`${repoName}: ${results[i].status} — no summary produced`);
    }

    await session.end();
}

Run It

export SWARMLORD_API_KEY="your-key-here"
bun swarm.ts

Each agent.run() call launches a fire-and-forget task — a background session with its own sandbox. All three start immediately, run in parallel, and task.result() polls with exponential backoff until each one finishes.

t3code: completed — $0.0027
anthropic-cookbook: completed — $0.0029
workers-sdk: completed — $0.0023

Output

Each agent writes its own summary.md. Click to expand the full reviews:

t3code

# Summary of Recent Changes in t3code

This review analyzes the architectural decisions and code quality based on the last 5 commits to the `pingdotgg/t3code` repository.

## Architectural Decisions

### 1. Unified Configuration and Path Management
A significant architectural shift was observed in the most recent commits (e.g., `9e29c9d`), moving from fragmented environment variables and manual path joins toward a central `ServerConfig` service.
- **Base Directory Pattern:** The introduction of `T3CODE_HOME` (aliased as `--home-dir`) provides a single root for all application state, logs, and worktrees.
- **Derived Paths:** The system now dynamically derives subdirectories (e.g., `attachments`, `logs`, `worktrees`) from the base directory using an Effect-based `deriveServerPaths` function. This improves predictability and simplifies testing by allowing easy overrides of the entire environment.

### 2. Adoption of Effect Framework
The project increasingly leverages the **Effect** ecosystem (`@effect/io`, `@effect/platform`).
- **Functional Dependency Injection:** Heavy use of `Layer` for managing services like `GitCore`, `ServerConfig`, and `FileSystem`. This facilitates clean separation of concerns and simplifies mocking in integration tests.
- **Type-Safe Error Handling:** Transitioning from standard `try/catch` to more robust Effect patterns, enhancing error traceability across asynchronous operations.

### 3. State Isolation
Architectural improvements were made to ensure state isolation between development and production environments. The configuration logic now automatically switches the state directory to a `dev` suffix if a `devUrl` is provided, preventing accidental contamination of user data during development sessions.

## Code Quality and Documentation

### 1. Robust Testing Infrastructure
The codebase exhibits a high standard of testing quality:
- **Integration Harnesses:** The use of sophisticated test harnesses (like `OrchestrationEngineHarness`) shows a commitment to simulating real-world scenarios, including Git workspace initialization and temporary directory cleanup using scoped effects.
- **Test Layers:** Refactoring test files to use `Layer.fresh` and `prefix`-based temporary directories ensures that tests remain independent and parallelizable without filesystem collisions.

### 2. Consistency and Refactoring
The recent changes show a consistent effort to clean up "os-jank" and manual filesystem calls. Replacing `fs.mkdirSync` with `FileSystem` services from the Effect platform improves the portability and testability of the code.

### 3. Documentation Alignment
The PRs include updates to `REMOTE.md` and `.docs/scripts.md`, ensuring that CLI help text and developer documentation stay in sync with the underlying codebase changes.

## Conclusion
The repository maintains high code quality with a strong emphasis on functional programming paradigms. The recent architectural move toward a centralized, configurable home directory significantly improves the maintainability and developer experience of the T3 Code CLI.

anthropic-cookbook

# Review of Recent Changes in Anthropic Cookbook

This summary covers the architectural decisions and code quality reflected in the last 5 commits of the `anthropic-cookbook` repository.

## Summary of Changes

### 1. Claude Agent SDK Expansion and Interoperability
A major portion of the recent activity focuses on the `claude_agent_sdk`. 
- **Architectural Shift:** The introduction of a migration guide from OpenAI Agents SDK to Claude Agent SDK highlights a move toward capturing developers from competing ecosystems. It explicitly maps primitives like `@function_tool` to `@tool` with MCP (Model Context Protocol) servers.
- **MCP Integration:** The SDK leans heavily on Model Context Protocol for tool management. Tools are decoupled as in-process or sub-process MCP servers, promoting a modular architecture where the agent’s capabilities can be extended independently of the core logic.
- **Safety and Governance:** Implementation of `UserPromptSubmit` and `PreToolUse` hooks provides a structured way to enforce guardrails (input/output validation) without cluttering the business logic, adhering to a "security-by-design" approach.

### 2. Contextual Embeddings Optimization
A fix was applied to the contextual embeddings guide to prepend context to text chunks rather than appending it.
- **Decision:** This is a performance-oriented heuristic. LLMs often exhibit "lost in the middle" phenomena; placing the context before the chunk ensures the model processes the foundational background before the specific data, likely improving embedding quality or subsequent retrieval relevance.

### 3. Documentation and Link Persistence
- **Cross-Platform Compatibility:** Changes were made to convert relative documentation paths to absolute GitHub URLs. This addresses architectural limitations of the `platform.claude.com` renderer, ensuring that the cookbook remains functional and navigable when hosted outside of a standard Git environment.

## Code Quality and Standards
- **Explicit vs. Implicit:** The SDK emphasizes explicit schemas for tools over implicit type-hint introspection (used in OpenAI's SDK). This reduces "magic" and makes the interface between the LLM and the code more predictable and easier to debug.
- **Dependency Management:** The project uses `uv` for dependency management and enforces strict rules against manual `pyproject.toml` edits, ensuring a reproducible and stable build environment.
- **Observability:** Strong adherence to OpenTelemetry (OTel) standards for tracing and metrics, rather than proprietary dashboards, allows the SDK to integrate into existing enterprise monitoring stacks (Grafana, Datadog).
- **Security:** Strict API key management policies (transitioning to `dotenv` patterns) are enforced via `CLAUDE.md` and linter rules, minimizing the risk of credential leakage.

## Conclusion
The repository is transitioning from a collection of isolated examples into a cohesive ecosystem centered around the Claude Agent SDK and MCP. The code quality is high, with a clear focus on modularity, explicit configuration over implicit magic, and industry-standard observability.

workers-sdk

# Review of Recent Commits in Workers SDK

This summary reviews the last 5 commits to the `cloudflare/workers-sdk` repository, focusing on architectural decisions and code quality.

## Summary of Changes

### 1. [miniflare] Fix mixed pipelines records (#12987)
- **Nature of Change:** Bug fix in Miniflare's configuration schema.
- **Architectural Decision:** Updated the Zod schema for `pipelines` to allow a record containing a mix of simple strings and configuration objects. This improves flexibility for users who may want to use short-hand for some pipelines and detailed config for others.
- **Code Quality:** 
    - Consistent use of Zod for schema validation.
    - Includes a regression test in `packages/miniflare/test/index.spec.ts`.
    - Correctly uses changesets for versioning.

### 2. [opencode] Implementation-first Bonk agent (#12993)
- **Nature of Change:** Documentation/Configuration update for the "Bonk" agent.
- **Architectural Decision:** Refocused the agent's behavior from being descriptive/suggestive to being "implementation-first". This is a significant shift in the expected workflow for automated agents, prioritizing binary pushes/fixes over review comments.
- **Code Quality:** Structured the agent instructions using clear Markdown tags (`<role>`, `<context>`, `<non_negotiable_rules>`, etc.), which likely improves LLM parsing and adherence.

### 3. [miniflare] Fix mixed KV/D1 records (#12986)
- **Nature of Change:** Bug fix, similar to #12987.
- **Architectural Decision:** Standardized the normalization of `kvNamespaces` and `d1Databases` configurations to support mixed string/object entries.
- **Code Quality:** Good reuse of the pattern established for other plugins (like R2). Ensures consistent configuration surface across different Cloudflare services in Miniflare.

### 4. [workflows-shared] Fix waitForEvent stale waiters (#12985)
- **Nature of Change:** Critical bug fix in the Workflows engine.
- **Architectural Decision:** Changed the `waiters` map to store `[cacheKey, resolve]` tuples instead of just resolvers. This allows the engine to identification and remove specific waiters when they timeout.
- **Code Quality:**
    - Significant improvement in reliability by cleaning up internal state.
    - Comprehensive test case added to `packages/workflows-shared/tests/engine.test.ts` that specifically reproduces the "stale waiter" hang scenario.

### 5. [opencode] Add agent persona to Bonk (#12975)
- **Nature of Change:** Initial setup of the Bonk agent persona and GitHub Action update.
- **Architectural Decision:** Integrated a dedicated AI agent persona into the repository's maintenance workflow. 
- **Code Quality:** Established clear "Implementation Conventions" and "Anti-patterns" for the agent to follow, promoting repository-specific best practices (e.g., `pnpm`, strict TypeScript, `node:` prefixes).

## Overall Assessment

### Architectural Decisions
- **Consistency in Configuration:** The project shows a strong trend towards making Miniflare's configuration more flexible and consistent across different plugins (KV, D1, Pipelines).
- **Automation and Tooling:** There is a clear investment in AI-assisted maintenance (`Bonk` agent), with sophisticated instructions designed to enforce repository standards (like the prohibition of `any` and mandatory use of `pnpm`).
- **Critical State Management:** The fix in `workflows-shared` indicates a mature approach to handling asynchronous state and timeouts in complex distributed systems (Durable Objects).

### Code Quality
- **Robust Validation:** Extensive use of Zod for configuration validation ensures type safety at the boundary between user input and internal logic.
- **Test-Driven Fixes:** Almost every bug fix commit is accompanied by a high-quality regression test.
- **Strict Standards:** The repository enforces strict TypeScript rules, mandatory changesets for every user-facing change, and specific architectural constraints (like avoiding runtime dependencies in published packages).
- **Documentation:** The agent persona documentation acts as a Living Style Guide, which is a clever way to maintain code quality at scale.

How It Works

Step	What happens
agent("build").run(prompt)	Launches a fire-and-forget task — a background session with its own sandbox
Promise.all(repos.map(...))	All three tasks start concurrently; each agent clones, reads diffs, and writes a review independently
task.result({ timeoutMs })	Polls each task until it reaches idle or error, with exponential backoff
client.session(task.id)	Reconnects to the task's session to download artifacts
session.getFile(path)	Downloads the review from the sandbox to your machine
session.end()	Cleans up the session and its sandbox

Tasks vs Sessions

Sessions are interactive — you stream messages back and forth. Tasks are fire-and-forget — you launch them, walk away, and collect the result later. Tasks are ideal for parallelism because they don't block your process while the agent works. Add a webhook URL to agent.run() to get notified instead of polling.