Creating a session requires an environment id. Environments are reusable configuration templates for spinning up containers in Anthropic's infrastructure — you might create different environments for different use cases (e.g. data visualization vs web development, with diff. Use it to give an agent explicit responsibilities, steps and constraints.
Every session has a live trace view in the Anthropic Console at https://platform.claude.com/workspaces/{workspace}/sessions/{session id}. Print this URL immediately after creating a session so the user can watch tool calls and messages stream in real time. {workspace} is th. Use it to ground design choices in named patterns, trade-offs and examples.
Patterns you'll write on the client side when driving a Managed Agent session, grounded in working SDK examples. Use it to ground design choices in named patterns, trade-offs and examples.
All endpoints require x-api-key and anthropic-version: 2023-06-01 headers. Managed Agents endpoints additionally require the anthropic-beta header. Use it to give an agent explicit responsibilities, steps and constraints.
This file contains WebFetch URLs for fetching current information from platform.claude.com and Agent SDK repositories. Use these when users need the latest data that may have changed since the cached content was last updated. Use it to ground design choices in named patterns, trade-offs and examples.
This file documents HTTP error codes returned by the Claude API, their common causes, and how to handle them. For language-specific error handling examples, see the python/ or typescript/ folders. Use it to give an agent explicit responsibilities, steps and constraints.
The ant CLI exposes every Claude API resource as a shell subcommand. Compared to curl: request bodies are built from typed flags or piped YAML instead of hand-written JSON, @path inlines file contents into any string field, --transform extracts fields with a GJSON path (n. Use it to ground design choices in named patterns, trade-offs and examples.
This file covers decision heuristics for building agents on the Claude API: which primitives to reach for, how to design your tool surface, and how to manage context and cost over long runs. For per-tool mechanics and code examples, see tool-use-concepts.md and the language-spe. Use it as a repeatable review, validation or hardening pass.
List files the agent wrote to /mnt/session/outputs/ during a session, then. Use it to ground design choices in named patterns, trade-offs and examples.
For conceptual overview (tool definitions, tool choice, tips), see shared/tool-use-concepts.md. Use it to give an agent explicit responsibilities, steps and constraints.
Use with options() to override client settings for a single call without mutating the client:. Use it as a repeatable review, validation or hardening pass.
This skill helps you build LLM-powered applications with Claude. Choose the right surface based on your needs, detect the project language, then read the relevant language-specific documentation. Use it to make implementation decisions and avoid common dead ends.
A compilation of language agents using the Cognitive Architectures for Language Agents (🐨CoALA) framework. - CoALA Paper (16 pages of main content): https://arxiv.org/abs/2309.02427 - CoALA Tweet (6 threads): https://twitter.com/ShunyuYao12/status/1699396834983362690 - CoALA. Use it to navigate the topic and choose relevant methods, papers or tools.
A practical guide to Claude Code — from your first prompt to multi-agent automation, hooks, MCP, and team workflows. Built around clear mental models and real examples, not marketing. Use it as a repeatable review, validation or hardening pass.
This repository centralizes and summarizes practical and proposed defenses against prompt injection. Use it to navigate the topic and choose relevant methods, papers or tools.
At the core of every agent is a neural network -- a Transformer, an RNN, a trained function -- shaped by billions of gradient updates on sequences of perception, reasoning, and action. Agency was never bestowed by the surrounding code. It was learned during training. Use it to ground design choices in named patterns, trade-offs and examples.
Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on Large Language Models and exploring the boundaries and limits of Generative AI. Use it to build a structured path from fundamentals to hands-on practice.
A developer tool for inspecting Model Context Protocol (MCP) servers. It ships as a single package, @modelcontextprotocol/inspector, that provides three ways to inspect a server:. Use it to ground design choices in named patterns, trade-offs and examples.
Policy enforcement, identity, sandboxing, and SRE for autonomous AI agents. One pip install, any framework. Use it as a repeatable review, validation or hardening pass.
Pick a template, see what it does, get a Dockerfile + docker-compose + bot + README you can run anywhere. Use it as a repeatable review, validation or hardening pass.