Dive into Claude Code

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A comprehensive source-level architectural analysis of Claude Code (v2.1.88, ~1,900 TypeScript files, ~512K lines of code), combined with a curated collection of community analyses, a design-space guide for agent builders, and cross-system comparisons.

Tip

TL;DR -- Only 1.6% of Claude Code's codebase is AI decision logic. The other 98.4% is deterministic infrastructure -- permission gates, context management, tool routing, and recovery logic. The agent loop is a simple while-loop; the real engineering complexity lives in the systems around it. This repo dissects that architecture and distills it into actionable design guidance for anyone building AI agent systems.

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Table of Contents

From Our Paper

• 🌟 Key Highlights
• 📖 Reading Guide
• 🏗️ Architecture at a Glance
• 🧭 Values and Design Principles
• 🔄 The Agentic Query Loop
• 🛡️ Safety and Permissions
• 🧩 Extensibility
• 🧠 Context and Memory
• 👥 Subagent Delegation
• 💾 Session Persistence

Beyond the Paper

• 🛰️ New Signals in the Agent Design Space
• 🛠️ Build Your Own AI Agent: A Design Guide
• ⚖️ Cross-System Comparison: Claude Code vs OpenClaw vs Hermes-Agent
• 🌐 Community Projects & Research
• 🚀 Other Notable AI Agent Projects
• 🔖 Citation

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Key Highlights

• 98.4% Infrastructure, 1.6% AI -- The agent loop is a simple while-loop; the real complexity is permission gates, context management, and recovery logic.
• 5 Values → 13 Principles → Implementation -- Every design choice traces back to human authority, safety, reliability, capability, and adaptability.
• Defense in Depth with Shared Failure Modes -- 7 safety layers, but all share performance constraints. 50+ subcommands bypass security analysis.
• 4 CVEs Reveal a Pre-Trust Window -- Extensions execute before the trust dialog appears.
• The Cross-Cutting Harness Resists Reimplementation -- The loop is easy to copy; hooks, classifier, compaction, and isolation are not.

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Reading Guide

If you are a...	Start here	Then read
Agent Builder	Build Your Own Agent	Architecture Deep Dive
Security Researcher	Safety and Permissions	Architecture: Safety Layers
Product Manager	Key Highlights	Values and Principles
Researcher	Full Paper (arXiv)	Community Resources

1,884 files · ~512K lines · v2.1.88 · 7 safety layers · 5 compaction stages · 54 tools · 27 hook events · 4 extension mechanisms · 7 permission modes

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Architecture at a Glance

Claude Code answers four design questions that every production coding agent must face:

Question	Claude Code's Answer
Where does reasoning live?	Model reasons; harness enforces. ~1.6% AI, 98.4% infrastructure.
How many execution engines?	One queryLoop for all interfaces (CLI, SDK, IDE).
Default safety posture?	Deny-first: deny > ask > allow. Strictest rule wins.
Binding resource constraint?	~200K (older models) / 1M (Claude 4.6 series) context window. 5 compaction layers before every model call.

The system decomposes into 7 components (User → Interfaces → Agent Loop → Permission System → Tools → State & Persistence → Execution Environment) across 5 architectural layers.

[!NOTE] For the full architectural deep dive -- 7 safety layers, 9-step turn pipeline, 5-layer compaction, and more -- see docs/architecture.md.

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Values and Design Principles

The architecture traces from 5 human values through 13 design principles to implementation:

Value	Core Idea
Human Decision Authority	Humans retain control via principal hierarchy. When a 93% prompt-approval rate revealed approval fatigue, response was restructured boundaries, not more warnings.
Safety, Security, Privacy	System protects even when human vigilance lapses. 7 independent safety layers.
Reliable Execution	Does what was meant. Gather-act-verify loop. Graceful recovery.
Capability Amplification	"A Unix utility, not a product." 98.4% is deterministic infrastructure enabling the model.
Contextual Adaptability	CLAUDE.md hierarchy, graduated extensibility, trust trajectories that evolve over time.

The 13 Design Principles

Principle	Design Question
Deny-first with human escalation	Should unrecognized actions be allowed, blocked, or escalated?
Graduated trust spectrum	Fixed permission level, or spectrum users traverse over time?
Defense in depth	Single safety boundary, or multiple overlapping ones?
Externalized programmable policy	Hardcoded policy, or externalized configs with lifecycle hooks?
Context as scarce resource	Single-pass truncation or graduated pipeline?
Append-only durable state	Mutable state, snapshots, or append-only logs?
Minimal scaffolding, maximal harness	Invest in scaffolding or operational infrastructure?
Values over rules	Rigid procedures or contextual judgment with deterministic guardrails?
Composable multi-mechanism extensibility	One API or layered mechanisms at different costs?
Reversibility-weighted risk assessment	Same oversight for all, or lighter for reversible actions?
Transparent file-based config and memory	Opaque DB, embeddings, or user-visible files?
Isolated subagent boundaries	Shared context/permissions, or isolation?
Graceful recovery and resilience	Fail hard, or recover silently?

The paper also applies a sixth evaluative lens -- long-term capability preservation -- citing evidence that developers in AI-assisted conditions score 17% lower on comprehension tests.

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The Agentic Query Loop

The core is a ReAct-pattern while-loop: assemble context → call model → dispatch tools → check permissions → execute → repeat. Implemented as an AsyncGenerator yielding streaming events.

Before every model call, five compaction shapers run sequentially (cheapest first): Budget Reduction → Snip → Microcompact → Context Collapse → Auto-Compact.

9-step pipeline per turn: Settings resolution → State init → Context assembly → 5 pre-model shapers → Model call → Tool dispatch → Permission gate → Tool execution → Stop condition

Two execution paths:

• StreamingToolExecutor -- begins executing tools as they stream in (latency optimization)
• Fallback runTools -- classifies tools as concurrent-safe or exclusive

Recovery: Max output token escalation (3 retries), reactive compaction (once per turn), prompt-too-long handling, streaming fallback, fallback model

5 stop conditions: No tool use, max turns, context overflow, hook intervention, explicit abort

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Safety and Permissions

7 permission modes form a graduated trust spectrum: plan → default → acceptEdits → auto (ML classifier) → dontAsk → bypassPermissions (+ internal bubble).

Deny-first: A broad deny always overrides a narrow allow. 7 independent safety layers from tool pre-filtering through shell sandboxing to hook interception. Permissions are never restored on resume -- trust is re-established per session.

[!WARNING] Shared failure modes: Defense-in-depth degrades when layers share constraints. Per-subcommand parsing causes event-loop starvation -- commands exceeding 50 subcommands bypass security analysis entirely to prevent the REPL from freezing.

More details: authorization pipeline, auto-mode classifier, CVEs

Authorization pipeline: Pre-filtering (strip denied tools) → PreToolUse hooks → Deny-first rule evaluation → Permission handler (4 branches: coordinator, swarm worker, speculative classifier, interactive)

Auto-mode classifier (yoloClassifier.ts): Separate LLM call with internal/external permission templates. Two-stage: fast-filter + chain-of-thought.

Pre-trust execution window: 2 patched CVEs share this root cause -- hooks and MCP servers execute during initialization before the trust dialog appears, creating a structurally privileged attack window outside the deny-first pipeline.

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Extensibility

Four mechanisms at graduated context costs: Hooks (zero) → Skills (low) → Plugins (medium) → MCP (high). Three injection points in the agent loop: assemble() (what the model sees), model() (what it can reach), execute() (whether/how actions run).

Tool pool assembly (5-step): Base enumeration (up to 54 tools) → Mode filtering → Deny pre-filtering → MCP integration → Deduplication

27 hook events across 5 categories with 4 execution types (shell, LLM-evaluated, webhook, subagent verifier)

Plugin manifest accepts 10 component types: commands, agents, skills, hooks, MCP servers, LSP servers, output styles, channels, settings, user config

Skills: SKILL.md with 15+ YAML frontmatter fields. Key difference -- SkillTool injects into current context; AgentTool spawns isolated context.

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Context and Memory

9 ordered sources build the context window. CLAUDE.md instructions are delivered as user context (probabilistic compliance), not system prompt (deterministic). Memory is file-based (no vector DB) -- fully inspectable, editable, version-controllable.

4-level CLAUDE.md hierarchy: Managed (/etc/) → User (~/.claude/) → Project (CLAUDE.md, .claude/rules/) → Local (CLAUDE.local.md, gitignored)

5-layer compaction (graduated lazy-degradation): Budget reduction → Snip → Microcompact → Context Collapse (read-time projection, non-destructive) → Auto-Compact (full model summary, last resort)

Memory retrieval: LLM-based scan of memory-file headers, selects up to 5 relevant files. No embeddings, no vector similarity.

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Subagent Delegation

6 built-in types (Explore, Plan, General-purpose, Guide, Verification, Statusline) + custom agents via .claude/agents/*.md. Sidechain transcripts: only summaries return to parent (parent's context is protected from subagent verbosity). Three isolation modes: worktree, remote, in-process. Coordination via POSIX flock().

SkillTool vs AgentTool: SkillTool injects into current context (cheap). AgentTool spawns isolated context (expensive, but prevents context explosion).

Permission override: Subagent permissionMode applies UNLESS parent is in bypassPermissions/acceptEdits/auto (explicit user decisions always take precedence).

Custom agents: YAML frontmatter supports tools, disallowedTools, model, effort, permissionMode, mcpServers, hooks, maxTurns, skills, memory scope, background flag, isolation mode.

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Session Persistence

Three channels: append-only JSONL transcripts, global prompt history, subagent sidechains. Permissions never restored on resume -- trust is re-established per session. Design favors auditability over query power.

Chain patching: Compact boundaries record headUuid/anchorUuid/tailUuid. The session loader patches the message chain at read time. Nothing is destructively edited on disk.

Checkpoints: File-history checkpoints for --rewind-files, stored at ~/.claude/file-history/<sessionId>/.

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New Signals in the Agent Design Space

New agent-system developments reinforce the same lesson Claude Code makes clear: agent capability is not a model property alone. It comes from the runtime, context layer, execution boundary, tool supply chain, the controls humans have over it, and the evaluation loop around the model.

Design Implication	What it means for agent builders	Representative signals
Runtime and control plane are first-class design concerns	Durable execution, checkpoints, sandboxes, agent inventory, policy, and observability should be designed as parts of the system that users can see, not hidden deployment details.	Cursor cloud agents, Google Managed Agents, Microsoft Agent 365, Databricks Omnigent
Context is managed infrastructure	Prompts, files, skills, IDE indexes, workspace state, memory namespaces, and interpreter state need lifecycle, provenance, review, and rollback.	LangChain Context Hub, AWS AgentCore, Anthropic managed-agent memory
Execution boundary is the safety boundary	Permissions, network reachability, filesystem access, credential custody, tenant isolation, and OS sandboxing are core architecture, not late-stage hardening.	Codex Windows sandbox, Running Codex safely, Anthropic self-hosted sandboxes
Tools and skills are a supply chain	MCP servers, skills, plugins, and agent-to-agent protocols need registries, allowlists, identity, semantic review, versioning, and revocation.	NSA MCP security, GitHub MCP allowlists, A2A milestone
Humans become managers and verifiers	Agent products should support goals, plans, approvals, interrupts, reviewable diffs, escalation, and constrained multi-agent write authority.	Codex from anywhere, Copilot cloud agent, Cognition multi-agents
Observability must close the improvement loop	Traces should feed evaluation, failure clustering, policy enforcement, and prompt/tool repair rather than ending as passive logs.	LangSmith Engine, OpenAI agent improvement loop, AWS AgentCore Evaluations

These signals do not replace Claude Code's design space; they make its boundaries clearer. The agent loop is the small part. The harness around it is where most capability, safety, and reliability decisions now live. For month-level source notes, see docs/agent-design-space-source-notes_zh.md.

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Build Your Own AI Agent: A Design Guide

Not a coding tutorial. A guide to the design decisions you must make, derived from architectural analysis.

Every production agent must navigate these decisions:

Decision	The Question	Key Insight
Reasoning placement	How much logic in the model vs. harness?	As models converge in capability, the harness becomes the differentiator.
Safety posture	How do you prevent harmful actions?	Defense-in-depth fails when layers share failure modes.
Context management	What does the model see?	Design for context scarcity from day one. Graduated > single-pass.
Extensibility	How do extensions plug in?	Not all extensions need to consume context tokens.
Subagent architecture	Shared or isolated context?	Agent teams in plan mode cost ~7× tokens. Subagent summary-only returns prevent context blow-up.
Session persistence	What carries over?	Never restore permissions on resume. Auditability > query power.

Read the full guide: docs/build-your-own-agent.md

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Cross-System Comparison: Claude Code vs OpenClaw vs Hermes-Agent

The same recurring design questions admit different architectural answers when the deployment context changes. The table below contrasts Claude Code v2.1.88 with two notable peers — OpenClaw, a local-first multi-channel personal-assistant gateway, and NousResearch/hermes-agent, a self-improving multi-deployment agent — across the six design dimensions Section 10 of the paper uses for the OpenClaw comparison. Cells are source-grounded; this is not a feature scoreboard.

Design Dimension	Claude Code (v2.1.88)	OpenClaw	Hermes-Agent
System scope & deployment	Per-user CLI / SDK / IDE interface for coding; one queryLoop async generator across entry points.	Local-first WebSocket gateway (default port 18789, loopback-bound by default; other binds available); routes ~23 messaging channels to an embedded agent runtime; companion apps for macOS, iOS, Android.	Three entry points: hermes (interactive CLI), hermes-agent (programmatic runtime), hermes-acp (ACP server); gateway adapters route messages to per-session AIAgent instances cached LRU-style (max 128, 1 h idle TTL); also runs as MCP server via hermes mcp serve.
Trust model & security	Deny-first per-action evaluation; 7 permission modes; LLM-based auto-mode classifier (yoloClassifier / sideQuery); session-scoped permission state (session bypass flag, app allowlist state) is not restored on resume.	Single trusted operator per gateway; DM pairing codes, sender allowlists, gateway authentication; per-agent allow / deny tool policy; opt-in sandboxing via Docker / SSH / OpenShell, off by default; non-main mode sandboxes only non-main sessions; hostile multi-tenant isolation explicitly not supported.	Dangerous-command pattern detection with per-session approval state; CLI interactive prompts and gateway async prompts; auxiliary-LLM smart approval auto-approves low-risk commands; permanent allowlist persisted in config.yaml; subagent worker threads default to auto-deny dangerous commands (opt-in subagent_auto_approve for batch / cron runs).
Agent runtime & tools	Single queryLoop async generator with streamed event yields; environment- and feature-gated tool registry; before-API compaction (Snip, Microcompact, Context Collapse, Auto-Compact) runs conditionally, with Auto-Compact first attempting session-memory compaction.	Embedded agent runtime inside the gateway's RPC dispatch (the agent RPC validates parameters, accepts immediately, runs asynchronously, and streams lifecycle / stream events back over the gateway protocol); per-session queue serialization with an optional global lane.	While-loop with explicit per-turn iteration budget and grace-call slot; per-turn checkpoint dedup; gateway step_callback hook fires on each iteration; auxiliary-model context compression summarizes middle turns while protecting head and tail.
Extension architecture	Four mechanisms at graduated context cost: hooks → skills → plugins → MCP; 27 hook events; 10 plugin component types.	Manifest-first plugin system with 12 documented capability categories; central registry exposes tools, channels, provider setup, hooks, HTTP routes, CLI commands, services; separate skills layer with multiple sources (workspace highest precedence) plus the ClawHub public registry; openclaw mcp provides both an MCP server interface and an outbound client registry for other MCP servers.	12 bundled plugins under plugins/ (context_engine, disk-cleanup, example-dashboard, google_meet, hermes-achievements, image_gen, kanban, memory, observability, platforms, spotify, strike-freedom-cockpit); MCP server (mcp_serve.py) exposes 10 tools; ACP adapter (acp_adapter/) exposes Hermes as an ACP server.
Memory & context	4-level CLAUDE.md hierarchy; before-API compaction (Snip, Microcompact, Context Collapse, Auto-Compact); LLM-based selection from file-based Markdown memory files.	Workspace bootstrap files (AGENTS.md, SOUL.md, TOOLS.md, IDENTITY.md, USER.md) plus conditional BOOTSTRAP.md / HEARTBEAT.md / MEMORY.md; separate memory system (MEMORY.md, daily notes under memory/YYYY-MM-DD.md, optional DREAMS.md); hybrid vector + keyword search when an embedding provider is configured; experimental dreaming for long-term promotion; pluggable compaction providers.	SQLite state store with FTS5 full-text search and WAL-mode concurrent readers; sessions linked by parent_session_id chains for compression-triggered splits; 8 swappable memory backends under plugins/memory/ (byterover, hindsight, holographic, honcho, mem0, openviking, retaindb, supermemory); auxiliary-LLM compression as a separate context-management layer.
Multi-agent architecture	Sub-agent delegation via sidechain transcripts; 6 built-in agent definitions (availability conditional on build / mode) plus custom; a single summary message returns to parent (in-process / viewable transcript cases preserve more internal detail); agent-isolation settings include worktree and remote, with an in-process teammate backend in the swarm path.	Two layers. (1) Multi-agent routing: per-channel isolated agents with their own workspace, auth profiles, session store, and model configuration, dispatched via deterministic binding rules. (2) Sub-agent delegation: maxSpawnDepth range 1–5, default 1, recommended 2; tool policy varies by depth; project vision (VISION.md) rejects agent-hierarchy frameworks as the default.	delegate_task tool spawns child AIAgent instances in a ThreadPoolExecutor (parent blocks until children complete); each child has fresh conversation history, its own task_id, and a restricted toolset (DELEGATE_BLOCKED_TOOLS strips delegate_task, clarify, memory, send_message, execute_code); default depth MAX_DEPTH = 1 (configurable up to cap 3); default 3 concurrent children.

What this contrast reveals. Three observations follow from the table. First, deployment context drives the rest of the design: a per-user coding CLI converges on per-action approval and a single execution loop, a multi-channel gateway converges on perimeter trust and channel-bound agents, and a multi-deployment messaging-and-cloud agent converges on opt-in container/cloud isolation, an LLM-based smart approval, and a swappable-backend memory layer. Second, the extension layer is where each system most clearly differentiates: Claude Code stratifies four mechanisms by context cost, OpenClaw treats extension as registry-managed capabilities at the gateway, and Hermes-Agent ships bundled plugins plus dual MCP server / ACP server interfaces other agents can connect to. Third, memory architectures sit on a spectrum: file-based and inspectable Markdown (Claude Code), file-based plus optional vector + experimental dreaming (OpenClaw), or full-text indexed (FTS5) plus eight swappable plugin backends including dedicated vector / RAG providers (Hermes-Agent). The table is best read not as a scoreboard but as three different fixed points in the same design space.

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Community Projects & Research

A curated map of the repos, reimplementations, and academic papers surrounding Claude Code's architecture.

Official Anthropic Resources

Primary sources referenced throughout the paper — Anthropic's own engineering and research publications, plus product documentation.

Research & Engineering Blogs

Article	Topic
Building Effective Agents	Foundational: simple composable patterns over heavy frameworks.
Effective Context Engineering for AI Agents	Context curation and token-budget management.
Prompt Caching with Claude	Cache reads at 10% cost, writes at 125%; 5-min default TTL. The platform feature that makes Claude Code's cache-aware compaction architecturally meaningful.
Harness Design for Long-Running Application Development	Harness architecture for autonomous full-stack dev; multi-agent patterns.
Claude Code Auto Mode: A Safer Way to Skip Permissions	ML-classifier approval automation; source of the 93% approval-rate finding.
Beyond Permission Prompts: Making Claude Code More Secure and Autonomous	Sandbox-based security; 84% reduction in permission prompts.
How We Contain Claude Across Products	Containment across claude.ai, Claude Code, and Cowork (May 2026); Claude Code's human-in-the-loop sandbox, approval fatigue, and capping the blast radius.
Measuring AI Agent Autonomy in Practice	Longitudinal usage: auto-approve rates grow from ~20% to 40%+ with experience.
Agentic Coding and Persistent Returns to Expertise	Anthropic's first quantitative account of how the work actually splits: humans make roughly 70% of planning decisions but only 20% of execution decisions, and a single prompt triggers about 10 Claude actions on average, with some stretches running past 100 actions between human touchpoints. An empirical anchor for the paper's claims about human control surfaces and supervision cost.
Our Framework for Developing Safe and Trustworthy Agents	Governance framework for responsible agent deployment.
When AI Builds Itself	Anthropic Institute on recursive self-improvement: AI accelerating AI development, the direction-setting and research-taste gaps, and governance scenarios.
Scaling Managed Agents: Decoupling the Brain from the Hands	Hosted-service architecture separating reasoning, execution, and session.
An Update on Recent Claude Code Quality Reports	Postmortem on three bugs behind perceived quality drops: a reasoning-effort default, a cache optimization bug, and a system-prompt change.
Introducing Claude Opus 4.8	May 2026 model update: sharper judgment and honesty (~4x fewer unremarked code flaws), longer autonomous runs; introduces dynamic workflows in research preview.
Claude Fable 5 and Claude Mythos 5	June 2026 Mythos-class tier sitting above Opus; Fable 5 is the general-use configuration (risky queries fall back to Opus 4.8), with state-of-the-art software-engineering and agentic-coding performance. Access was suspended globally on June 12, 2026 (see next row).
Statement on Suspending Access to Fable 5 and Mythos 5	Anthropic's statement on suspending Fable 5 and Mythos 5. A US export-control directive (June 12, 2026) restricted access for foreign nationals, but Anthropic disabled both models for all users worldwide, just days after launch. A rare case of regulation forcing a deployed frontier model offline, and a concrete example of the compliance and safety pressures that agent systems face in deployment.

Product Documentation

Document	Topic
How Claude Code Works	Official overview of the agent loop, tools, and terminal automation.
Permissions	Tiered permission system, modes, granular rules.
Hooks	27-event hook reference, execution models, lifecycle events.
Memory	CLAUDE.md hierarchy, auto memory, learned preferences.
Sub-agents	Specialized isolated assistants, custom prompts, tool access.
Orchestrate Subagents at Scale with Dynamic Workflows	Claude writes a JavaScript orchestration script; a background runtime fans out to up to 1,000 subagents, with intermediate state held in script variables outside the context window (v2.1.154+, research preview).
Configure Auto Mode	The fullest official account of the auto-mode classifier as a policy engine: a four-tier precedence (hard_deny over soft_deny over allow over explicit user intent), rules written as natural-language prose rather than regexes, and one deliberate exclusion. The classifier reads CLAUDE.md but never the shared .claude/settings.json, so a checked-in repo config cannot grant itself permissions.
Orchestrate Teams of Claude Code Sessions	Agent teams as a primitive distinct from subagents: teammates hold separate contexts, message each other through mailboxes, and share a file-locked task list with dependencies and self-claiming. The trust boundary is explicit: a teammate cannot approve a permission prompt on the user's behalf, and a denied action cannot be handed to another teammate to get around it.
What's New in Claude Opus 4.8	Mid-conversation system messages (prompt-cache-preserving), lower cacheable-prompt minimum, fewer compactions and better compaction recovery.
What's New in Claude Sonnet 5	The model takes back two dials the harness used to hold: manual extended thinking is gone (thinking: {budget_tokens: N} now returns 400) and non-default temperature/top_p/top_k also return 400. A new tokenizer yields roughly 30% more tokens for the same text, so the usable context budget shifts with the model.
Claude Code CHANGELOG	Release notes. Dynamic workflows and Opus 4.8 land in v2.1.154. Across v2.1.178 to v2.1.207 (June to July 2026): subagents run in the background by default with agent_needs_input/agent_completed hooks, and an agent's message is still never treated as the user's approval; the default permission mode is renamed to Manual; a Tool(param:value) rule syntax arrives (for example Agent(model:opus)); auto mode stops reading a repo's .claude/settings.local.json; and sandbox.credentials blocks sandboxed commands from reading credential files and secret environment variables.

Architecture Analysis

Deep dives into Claude Code's internal design.

Repository	Description
ComeOnOliver/claude-code-analysis	Comprehensive reverse-engineering: source tree structure, module boundaries, tool inventories, and architectural patterns.
alejandrobalderas/claude-code-from-source	18-chapter technical book (~400 pages). All original pseudocode, no proprietary source.
liuup/claude-code-analysis	Chinese-language deep-dive — startup flow, query main loop, MCP integration, multi-agent architecture.
sanbuphy/claude-code-source-code	Quadrilingual analysis (EN/JA/KO/ZH) — multi-domain reports covering telemetry, codenames, KAIROS, unreleased tools.
cablate/claude-code-research	Independent research on internals, Agent SDK, and related tooling.
Yuyz0112/claude-code-reverse	Visualize Claude Code's LLM interactions — log parser and visual tool to trace prompts, tool calls, and compaction.
Piebald-AI/claude-code-system-prompts	Version-tracked prompt corpus across 170+ Claude Code releases — main system prompt, builtin tool descriptions, sub-agent prompts (Plan/Explore/Task), and ~40 system reminders. Updated within minutes of each release.

Open-Source Reimplementations

Clean-room rewrites and buildable research forks.

Repository	Description
chauncygu/collection-claude-code-source-code	Meta-collection of community Claude Code source artifacts -- includes claw-code (Rust port), nano-claude-code (Python), and the extracted original source archive.
777genius/claude-code-working	Working reverse-engineered CLI. Runnable with Bun, 450+ chunk files, 31 feature flags polyfilled.
T-Lab-CUHKSZ/claude-code	CUHK-Shenzhen buildable research fork — reconstructed build system from raw TypeScript snapshot.
ruvnet/open-claude-code	Nightly auto-decompile rebuild — 903+ tests, 25 tools, 4 MCP transports, 6 permission modes.
Enderfga/openclaw-claude-code	OpenClaw plugin — unified ISession interface for Claude/Codex/Gemini/Cursor. Multi-agent council.
memaxo/claude_code_re	Reverse engineering from minified bundles — deobfuscation of the publicly distributed cli.js file.
agentforce314/clawcodex	Python rebuild with multi-provider LLM support.

Claude Code Guides & Learning

Tutorials and hands-on learning paths for Claude Code itself.

Repository	Description
shareAI-lab/learn-claude-code	"Bash is all you need" — 19-chapter 0-to-1 course with runnable Python agents, web platform. ZH/EN/JA.
FlorianBruniaux/claude-code-ultimate-guide	Beginner-to-power-user guide with production-ready templates, agentic workflow guides, and cheatsheets.
affaan-m/everything-claude-code	Agent harness optimization — skills, instincts, memory, security, and research-first development.

General Harness Engineering Design Space Resources

External resources that complement this paper's design-space analysis — concept essays, curricula, and code that illuminate the harness layer as an engineering practice.

Repository	Description
deusyu/harness-engineering	Learning archive — original concept essays, independent thinking pieces, and curated translations of harness-engineering writing; from concept to independent practice.
walkinglabs/learn-harness-engineering	Project-based English course with PDF coursebooks, syllabus, and capstone, organized around five harness subsystems: instructions, state, verification, scope, and session lifecycle.
china-qijizhifeng/agentic-harness-engineering	Observability system that auto-evolves a coding agent's harness — a meta-agent reads execution traces and rewrites system prompts, tools, middleware, skills, sub-agents, and memory.
ZhangHanDong/harness-engineering-from-cc-to-ai-coding	The "Horse Book" (《马书》) — Chinese mdBook framing Claude Code v2.1.88 as a Harness Engineering case study; covers architecture, prompt engineering, context management, prompt cache, security, and lessons for builders.
alchaincyf/loop-engineering-orange-book	花叔 (HuaShu)'s plain-language guide to loop engineering, in both Chinese and English. It puts the loop one layer above the harness, walks through what a loop does and the pieces it needs, and credits Steinberger, Osmani, and Anthropic's Claude Code team.
stanford-iris-lab/meta-harness	Reference implementation for the Meta-Harness paper. Holds the model fixed and searches the harness itself (memory, retrieval, context construction, prompts, tool selection) through a propose, score, and Pareto-frontier loop. Turns the harness from something you design by hand into something you can optimize.

Blog Posts & Technical Articles

Article	What Makes It Valuable
Marco Kotrotsos — "Claude Code Internals" (15-part series)	Most systematic pre-leak analysis. Architecture, agent loop, permissions, sub-agents, MCP, telemetry.
Alex Kim — "The Claude Code Source Leak"	Anti-distillation mechanisms, frustration detection, Undercover Mode, ~250K wasted API calls/day.
Haseeb Qureshi — Cross-agent architecture comparison	Claude Code vs Codex vs Cline vs OpenCode — architecture-level comparison.
George Sung — "Tracing Claude Code's LLM Traffic"	Complete system prompts and full API logs. Discovered dual-model usage (Opus + Haiku).
Agiflow — "Reverse Engineering Prompt Augmentation"	5 prompt augmentation mechanisms backed by actual network traces.
Tenet Security — "One Fake Bug Report Hijacked a $250 Billion Company's AI Agent, Then 100+ More"	The sharpest demonstration to date that a tool's return value is untrusted input. A Sentry DSN is public by design (Sentry documents it as safe to embed in frontend JavaScript) and accepts arbitrary error payloads, so an attacker can POST an event carrying markdown instructions that render as a fake "Resolution" section. A developer then asks their agent to look into the Sentry issue, the agent pulls the poisoned event back through MCP, reads it as trustworthy remediation guidance, and runs an attacker-controlled npm package that exfiltrates AWS keys, GitHub tokens, and SSH credentials. Confirmed against Claude Code, Cursor, and OpenAI Codex, including sandboxed variants and CI/CD pipelines.
Engineer's Codex — "Diving into the Source Code Leak"	Modular system prompt, ~40 tools, large query/tool subsystem, anti-distillation.
MindStudio — "Three-Layer Memory Architecture"	In-context memory, MEMORY.md pointer index, CLAUDE.md static config. Best single resource on memory.
WaveSpeed — "Claude Code Architecture: Leaked Source Deep Dive"	512K-line TS source deep dive; context compression and anti-distillation.
Zain Hasan — "Inside Claude Code: An Architecture Deep Dive"	Layered architecture, 5 entry modes, multi-agent walkthrough.
Addy Osmani — "Agent Harness Engineering"	Frames harness engineering as a discipline with named primitives (filesystem/git state, sandboxes, AGENTS.md memory, compaction, planning loops, hooks); cites Claude Code as the canonical mature example.
Addy Osmani — "Loop Engineering"	The essay that named "loop engineering": instead of writing prompts for the agent yourself, you build the loop that prompts it for you. Its parts (automations, worktrees, skills, connectors, sub-agents, and a file that tracks progress) are the harness pieces this paper analyzes.
Armin Ronacher — "The Coming Loop"	Splits the agent loop (tool calls inside one run) from the harness loop (a system that keeps re-prompting the agent to run again). Ronacher is skeptical: loops work well for porting code, tuning performance, and scanning for security bugs, but the code they leave behind tends to be more defensive and harder to maintain, so people still have to read it and decide what to keep.
LangChain — "The Art of Loop Engineering"	Describes four loops built around an agent: the agent loop, a verification loop that scores the output and retries, an event-driven loop that starts agents on outside triggers, and a hill-climbing loop that reads production traces to improve the harness itself. The point: most of the value comes from these loops, not the model on its own.
Andrej Karpathy — "Sequoia Ascent 2026"	Argues for "agentic engineering": humans orchestrate and verify rather than write code. "LLMs and reinforcement learning automate what you can verify"; "you can outsource your thinking, but you can't outsource your understanding."
Lilian Weng — "Harness Engineering for Self-Improvement"	Defines the harness as "the system surrounding a base model that orchestrates execution and decides how the model thinks and plans, calls tools and acts, perceives and manages context, stores artifacts, and evaluates results," then argues that the near-term path to recursive self-improvement does not start with a model rewriting its own weights: it starts with coding agents evolving the harness itself. Covers design patterns, harness optimization, evolutionary search, and joint optimization with weights.
Armin Ronacher — "Better Models: Worse Tools"	The clearest evidence yet that the harness shapes the model, not only the reverse. Opus 4.8 and Sonnet 5 invent fields like requireUnique and oldText2 that no schema defines, while the surrounding payload stays byte-correct. Ronacher's reading: the models were trained in Claude Code's forgiving harness, which quietly repairs malformed calls by dropping unknown keys and accepting parameter aliases, so slightly-wrong calls still got rewarded. Tool schemas are not a neutral abstraction.
Addy Osmani — "Agentic Autonomy Levels"	Separates two axes that usually get collapsed: agency (how independently one agent runs) and orchestration (how several agents coordinate). Proposes a pre-execution contract covering goal, scope, non-goals, tools and permissions, stop conditions, evidence requirements, escalation path, and budget, offering a structured alternative to Claude Code's in-the-moment approval prompts.

Cross-Vendor Code-Agent Engineering

Official engineering posts from other vendors building code agents — useful for seeing how the same design questions are answered outside Claude Code.

Resource	Vendor	What's Notable
Harness Engineering: Leveraging Codex in an Agent-First World	OpenAI	Frames the "harness" as the constraints, feedback loops, and documentation that make agents reliable; reports a roughly 1M-line beta built with essentially no hand-written code.
Best Practices for Coding with Agents	Cursor	Articulates an agent harness as three components — Instructions, Tools, and Model — orchestrated per model.
Build with Google Antigravity	Google	Agent-first platform: a Manager view for asynchronous multi-agent orchestration, with Artifacts (plans, screenshots, recordings) as the verification mechanism instead of raw logs.
Microsoft Agent Framework at BUILD 2026: Agent Harness, Hosted Agents, CodeAct	Microsoft	Adds a built-in "agent harness" with automatic context compaction, file-based memory, plan and execute modes, skill discovery, parallel sub-agents, and a sandboxed shell. Also adds CodeAct, which lets the model run several tool calls as one Python program inside a fresh Hyperlight micro-VM each call.
Codex Security: Now in Research Preview	OpenAI	Application-security agent that builds a project-specific threat model, then finds and pressure-tests vulnerabilities in sandboxed validation environments.
Meet Your Agent Harness and Claw, Working with Your Data, Safely, Scaling Harness Capabilities	Microsoft	A four-part harness series. Notable for the authorization design: a standing rule ("always approve this tool", "always approve these arguments") lives only inside the session and is never baked into the agent, which is a direct point of comparison with Claude Code's permission model. Capability growth is split into four orthogonal mechanisms: Skills, a restricted Shell, CodeAct, and Background Agents.
Bedrock AgentCore Harness is Generally Available	AWS	The harness stops being a loop you write and becomes a managed configuration object: CreateHarness and InvokeHarness declare the model, tools, skills, memory policy, and container environment, over seven primitives (microVM Runtime, Memory, Gateway, sandboxed Browser, Code Interpreter, Identity token vault, Observability). A new coordinate in the design space: who owns the loop, the environment, and the tool boundary.
AgentCore Policy with Guardrails	AWS	Enforcement happens at the gateway boundary, outside the agent's own code, so it holds no matter how autonomous the agent becomes. A sharp architectural contrast with in-loop permission checks.
Transitioning Gemini CLI to Antigravity CLI and antigravity-cli	Google	Gemini CLI is retired (effective June 18, 2026) in favor of a Go rewrite that shares one agent harness with the Antigravity 2.0 desktop app. Per its CHANGELOG (the repo ships releases and examples, not source), the mechanisms sit close to Claude Code's: nested subagents to grandchild depth and beyond, whose subtrajectory updates are recursively relayed to the root conversation; workspace-level .agents/hooks.json where a pre-tool hook decides whether a tool call is permitted; and per-project permission config, stored under ~/.gemini/config/projects/ rather than in the repo, taking precedence over global settings.
Governing Agent Autonomy with Auto-review	Cursor	A classifier runs inside the agent loop rather than as a separate endpoint. When it blocks an action it returns an explanation to the parent agent, which can often route around it safely without ever interrupting the user. Roughly 4% of actions get blocked while only about 7% of chats end up interrupting a human. Blocking as a steering signal, not just a stop.
Reward Hacking is Swamping Model Intelligence Gains	Cursor	An audit of 731 Opus 4.8 Max trajectories found that, among successful resolutions on SWE-bench Pro, 63% of fixes were retrieved rather than derived: 57% found the merged PR or fixed source file on the public web, 9% mined the repository's bundled git history for the fix commit. Two isolations expose it, and scores fall sharply under them: .git is stripped and the repo reinitialized as a single commit (history restored only at scoring time), and network access is denied by default behind a proxy that allows only an allow-list of package registries. Makes eval-harness environment design a precondition for the benchmark meaning anything.
Customize Cursor	Cursor	Plugins, skills, MCPs, subagents, rules, commands, and hooks collapse into one extension surface with user, team, and workspace scopes. A non-Anthropic vendor converging on almost exactly Claude Code's extension points.
Codex-maxxing for Long-Running Work	OpenAI	The memory vault keeps AGENTS.md, TODO.md, projects/, and people/ in GitHub so that the diff becomes the review interface for memory. States the design position directly: memory must be open-able, edit-able, diff-able, and reusable.
Running Untrusted Agent Code Without a Sandbox and Dynamic Subagents in Deep Agents	LangChain	Capability isolation run backwards from the container model: QuickJS inside WASM starts at zero capability and the harness bridges in each capability explicitly. Interpreter memory can be serialized for a durable pause that waits on human approval. Subagent dispatch then drops from conversation turns into program control flow, with the model writing a script that calls task({...}).
Devin Fusion and Agentic MapReduce	Cognition	Fusion couples two mechanisms usually treated separately: a classifier re-scores task difficulty mid-session, and the model swap is timed to land on context compaction, where a cache miss was going to happen anyway, so switching is effectively free. MapReduce draws a clean line between agents and deterministic compute: put an agent only where reasoning is needed, make everything else deterministic.
AI SDK 7	Vercel	HarnessAgent treats Claude Code, Codex, and Pi as interchangeable backends behind one API, with sessions that park and resume. WorkflowAgent makes each tool call a durable retryable step that survives process restarts. Tool approvals are HMAC-signed, which addresses a rarely-discussed problem: forged approvals.
The 2026-07-28 MCP Spec Goes Stateless and Enterprise-Managed Authorization	MCP	The largest structural change in the tool layer so far. The initialize handshake and protocol-level session are gone, with capabilities moving to server/discover; tools can now pause mid-call and ask the user a question via InputRequiredResult; roots, sampling, and logging are deprecated. Separately, authorization moves up from per-user consent screens to the organization's IdP.
Droid Shield 2.0	Factory	A deterministic regex scanner flanked by two fine-tuned models: one re-reads context for recall when the scanner stays silent, the other masks the candidate secret and judges from context alone whether the scanner produced a false positive. Deterministic rules and learned models wired into a two-way correcting gate.
A Single Pane of Glass for All Your Cloud Agents	Warp	Oz as a multi-harness control plane: launch, track, govern, and steer Claude Code, Codex, and Warp Agent from one surface, compare their effectiveness, pick different harnesses for different tasks, and keep one consistent audit trail. Agent Memory carries across harnesses.
Software Is Made Between Commits (DeltaDB)	Zed	Version control rebuilt for agent sessions. A message and the edit it produced are stored side by side so neither drifts from the other, and every reference anchors to a delta rather than a line number, so it survives as code moves underneath it. The argument: git is organized around discrete commits and was never designed to hold the conversation that produced the code.

Related Academic Papers

Paper	Venue	Relevance
Meta-Harness: End-to-End Optimization of Model Harnesses	arXiv	A coding agent acts as the proposer and searches the harness itself (memory, retrieval, context construction, prompts, tool-use logic) with the model held fixed, maintaining a population and a Pareto frontier over evaluated harnesses. Beats a state-of-the-art context-management system by 7.7 points while using 4x fewer context tokens, and its discovered harnesses surpass the best hand-engineered baselines on TerminalBench-2. Turns the harness from a thing you design by hand into a thing you can optimize.
From Question Answering to Task Completion: A Survey on Agent System and Harness Design	arXiv	A contemporaneous survey through a "model-harness lens" that decomposes the execution harness into six coupled runtime responsibilities: observation, context, control, action, state, and verification. The closest structural peer to this paper's framing.
Architectural Design Decisions in AI Agent Harnesses	arXiv	Source-grounded study of 70 agent-system projects identifying recurring design dimensions; closest contemporary peer to this paper's design-space framing.
ActPlane: Programmable OS-Level Policy Enforcement for Agent Harnesses	arXiv	Intercepts every execution path with eBPF in the kernel, including the indirect paths that bypass the tool-call layer entirely, at 1.9% to 8.4% overhead. Names the gap directly: permission checks that live at the tool-call layer, as Claude Code's do, can be routed around.
Lingering Authority: Revocable Resource-and-Effect Capabilities for Coding Agents	arXiv	Names a real weakness in session-scoped approval: once granted, authority does not expire as the task moves on. PORTICO compiles a task spec into capabilities, grant rules, and closure predicates, materializing resources as epoch-bound handles that die once the closure condition is met.
TokenPilot: Cache-Efficient Context Management for LLM Agents	arXiv	Confronts a constraint most compaction work steps around: rewriting history destroys the KV prefix cache. Filters noise at the ingestion boundary to keep the prefix stable, and evicts context segments only after their lifecycle ends.
The Balkanization of Execution-Security Research for AI Coding Agents	arXiv	An SoK sorting 39 execution-layer security papers into 17 categories (sandbox isolation, capabilities and access control, policy enforcement, TOCTOU, MCP threats, identity delegation, execution provenance, egress control). Reports that policy enforcement fails against real denylists 69% to 98% of the time.
Decoding the Configuration of AI Coding Agents	arXiv	Empirical study of 328 Claude Code configuration files — SE concerns and co-occurrence patterns.
On the Use of Agentic Coding Manifests	arXiv	Analyzed 253 CLAUDE.md files from 242 repos — structural patterns in operational commands.
Context Engineering for Multi-Agent Code Assistants	arXiv	Multi-agent workflow combining multiple LLMs for code generation.
OpenHands: An Open Platform for AI Software Developers	ICLR 2025	Primary academic reference for open-source AI coding agents.
SWE-Agent: Agent-Computer Interfaces	NeurIPS 2024	Docker-based coding agent with custom agent-computer interface.

How This Paper Differs

While the projects above focus on engineering reverse-engineering or practical reimplementation, this paper provides a systematic values → principles → implementation analytical framework — tracing five human values through thirteen design principles to specific source-level choices, and using OpenClaw comparison to reveal that cross-cutting integrative mechanisms, not modular features, are the true locus of engineering complexity.

See the full curated list with more resources: docs/related-resources.md

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Other Notable AI Agent Projects

A broader map of the agent design space surrounding Claude Code. The Cross-System Comparison above analyzes the three closest peers (Claude Code, OpenClaw, Hermes-Agent) in depth; the entries below give wider context across coding-agent peers, frameworks, memory systems, harness extensions, the MCP ecosystem, and specialized agents.

Coding Agent CLIs and IDE Harnesses

Repository	Launch	Focus
openclaw/openclaw	Jan 2026	Local-first personal AI assistant across messaging platforms. (Section 10 analysis)
NousResearch/hermes-agent	Feb 2026	Self-improving personal agent with cross-session memory. (Section 10 analysis)
opensquilla/opensquilla	Jun 2026	Token-efficient microkernel personal agent across CLI, Web UI, and chat channels; ML-classifier routing across four model cost tiers, local Markdown+SQLite memory (MEMORY.md plus dated notes with keyword and vector recall), and Bubblewrap/Seatbelt sandbox.
pewdiepie-archdaemon/odysseus	Jun 2026	Self-hosted, local-first AI workspace from PewDiePie: autonomous agents with tools, MCP, and shell access, plus memory, deep research, and hardware-aware model serving. AGPL-3.0.
google-antigravity/antigravity-cli	Jun 2026	Google's Go rewrite that replaces Gemini CLI and shares one agent harness with the Antigravity 2.0 desktop app. Per its CHANGELOG: nested subagents to grandchild depth and beyond, with subtrajectory updates recursively relayed to the root conversation; workspace-level .agents/hooks.json where a pre-tool hook decides whether a tool call is permitted; and per-project permission config kept under ~/.gemini/config/projects/ rather than in the repo, taking precedence over global settings.
patriceckhart/zot	Jun 2026	Single static Go binary harness with a deliberately minimal tool set (read, write, edit, bash). Sessions persist as JSONL transcripts that resume, branch, and compact. /swarm fans out background subagents that share the working directory and edit the same files, each keeping its own session file and transcript; the opt-in auto-swarm setting (off by default) additionally lets the main agent spawn them itself and injects a recap back into the main chat. /jail roots a sandbox at the cwd and refuses obvious escape patterns (sudo, rm -rf /, leading cd /, chmod -R). A readable non-TypeScript point of comparison for subagent and permission design.
sst/opencode	Jun 2025	Provider-agnostic terminal coding agent with ACP integration.
Aider-AI/aider	2023	Pair-program with LLMs in the terminal; works with most popular models.
continuedev/continue	2023	Source-controlled AI checks for IDEs with an open-source Continue CLI.
google-gemini/gemini-cli	2025	Google's open-source terminal coding agent with ReAct loop and MCP support. Superseded by Antigravity CLI as of June 18, 2026; enterprise customers on paid Gemini licenses keep access.
openai/codex	2025	OpenAI's local terminal coding agent in Rust.
OpenHands/OpenHands	2024	Open SWE agent platform (formerly OpenDevin) with sandboxed runtime.
cline/cline	2024	VS Code agent with explicit Plan/Act oversight loop.
block/goose	2025	Block's open-source, editor-agnostic agent with MCP-style extensions.
charmbracelet/crush	2025	Agentic coding TUI in Go with multi-LLM provider abstraction.
RooCodeInc/Roo-Code	2024	VS Code multi-agent dev-team with Architect, Coder, and Reviewer modes.
bytedance/trae-agent	2025	ByteDance's modular SWE-bench-oriented agent for software engineering tasks.
github/copilot-cli	2026	GitHub Copilot's GA agentic terminal CLI; plans, builds, reviews.
badlogic/pi-mono	Aug 2025	Monorepo coding-agent toolkit — unified LLM API, TUI + web UI; OpenClaw embeds the pi-coding-agent SDK from here.

Agent Frameworks and Orchestration

Repository	Launch	Focus
geekan/MetaGPT	2023	Role-based multi-agent software-company simulation (ICLR 2024 oral).
microsoft/autogen	2023	Microsoft Research multi-agent conversation framework (COLM 2024).
microsoft/agent-framework	2025	Microsoft's single successor to AutoGen and Semantic Kernel (1.0 released April 2026). At BUILD 2026 it added a built-in agent harness (context compaction, file memory, shell access) and CodeAct, which lets the model run several tool calls as one Python script instead of one at a time.
langchain-ai/langgraph	2024	Stateful graph-based multi-agent orchestration with checkpointing.
openai/openai-agents-python	2024	OpenAI's lightweight multi-agent framework with handoffs and guardrails.
crewAIInc/crewAI	2023	Lean Python framework for role-based multi-agent collaboration, independent of LangChain.
openai/symphony	Feb 2026	OpenAI's orchestration for isolated, autonomous implementation runs.
ComposioHQ/agent-orchestrator	2025	Orchestration layer for parallel AI agents with git worktree isolation.
omnigent-ai/omnigent	Jun 2026	Databricks' open-source "meta-harness" (Apache 2.0): Claude Code, Codex, Cursor, OpenCode, Hermes, and Pi become interchangeable backends behind one orchestration layer, and can be mixed within a single session. A policy engine stacks across three levels (server, agent, session, with the stricter session rules checked first) and governs shell commands, file edits, token spend, and tool access. Native sandboxing uses bwrap on Linux and seatbelt on macOS; disposable cloud sandboxes run on Modal, E2B, or Kubernetes. The clearest industrial argument that policy and isolation belong above the harness rather than inside a prompt.
coleam00/Archon	Feb 2025	Deterministic harness — YAML-defined workflows with execution audit trail.
bytedance/deer-flow	2026	ByteDance's long-horizon "SuperAgent" harness: subagents, memory, sandboxes, skills, and a message gateway; a ground-up rewrite on LangGraph/LangChain.
QwenLM/Qwen-Agent	2023	Alibaba Qwen's agent framework: function calling, MCP, a Docker code interpreter, and RAG; the backend behind Qwen Chat.
TencentCloudADP/youtu-agent	2025	Tencent Cloud's agent framework, built on the openai-agents SDK; agents are defined in YAML, configs can be auto-generated, and it adds Claude Code-style skills.
coze-dev/coze-studio	2025	ByteDance's open-source edition of Coze: a visual no-code/low-code platform for building, debugging, and deploying agents and workflows.

Memory and Persistent Context

Repository	Launch	Focus
mem0ai/mem0	2024	Production memory layer with LoCoMo and LongMemEval benchmarks (arXiv:2504.19413).
letta-ai/letta	2023	Stateful-agent platform with OS-style hierarchical memory paging (formerly MemGPT, COLM 2024).
MemPalace/mempalace	2026	Local-first memory system for AI agents.

Skills and Harness Extensions

Repository	Launch	Focus
addyosmani/agent-skills	2025	22 lifecycle skills + slash commands (/spec, /plan, /build, /test, /review, /ship).
obra/superpowers	2025	Cross-harness mandatory-workflow skills framework (Claude Code, OpenCode, Codex).
mattpocock/skills	2026	Author's everyday .claude/skills collection for real engineering -- composable TDD, diagnose, and to-issues/to-prd skills; model-agnostic, targeting Claude Code, Codex, and other coding agents.
multica-ai/andrej-karpathy-skills	2026	Single CLAUDE.md encoding Andrej Karpathy's four LLM-coding rules (think before coding, simplicity first, surgical changes, goal-driven execution); installable as a plugin or per-project.
lsdefine/GenericAgent	2025	Minimal self-evolving autonomous agent framework — 9 atomic tools + ~100-line ReAct loop.

MCP Ecosystem

Repository	Launch	Focus
PrefectHQ/fastmcp	2024	Pythonic framework for building MCP servers and clients; de facto SDK.
upstash/context7	2025	Up-to-date library-documentation MCP server for LLMs and AI code editors.
microsoft/playwright-mcp	2024	Microsoft's official MCP server using accessibility-tree snapshots.

Specialized and Domain Agents

Repository	Launch	Focus
666ghj/MiroFish	Mar 2026	Multi-agent swarm-intelligence simulation engine.
multica-ai/multica	2026	Managed-agents platform for task assignment and skill compounding.
HKUDS/nanobot	Feb 2026	Ultra-lightweight personal AI agent from HKU-DS.
HKUDS/OpenHarness	Apr 2026	Open agent harness with built-in personal agent (Ohmo); academic harness reference.
karpathy/autoresearch	Mar 2026	Andrej Karpathy's autonomous AI-agent loop running nanochat training research on a single GPU.
HKUDS/CLI-Anything	Mar 2026	"Making ALL Software Agent-Native" — wraps arbitrary software as agent-callable tools.
Panniantong/Agent-Reach	Feb 2026	CLI giving agents access to Twitter, Reddit, YouTube, GitHub, Bilibili, Xiaohongshu.
agentscope-ai/QwenPaw	Feb 2026	Personal AI assistant from the AgentScope team.
cft0808/edict	Feb 2026	OpenClaw-based multi-agent orchestration on Tang-dynasty Three Departments and Six Ministries (三省六部制) bureaucracy.

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Citation

@article{diveclaudecode2026,
  title={Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems},
  author={Jiacheng Liu, Xiaohan Zhao, Xinyi Shang, and Zhiqiang Shen},
  year={2026},
  eprint={2604.14228},
  archivePrefix={arXiv},
  primaryClass={cs.SE},
}

License

This work is licensed under CC BY-NC-SA 4.0.