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Atomic can help you use the SDK. Ask it to build an integration for your use case.

SDK

The SDK provides programmatic access to atomic’s agent capabilities. Use it to embed atomic in other applications, build custom interfaces, or integrate with automated workflows. Example use cases:
  • Build a custom UI (web, desktop, mobile)
  • Integrate agent capabilities into existing applications
  • Create automated pipelines with agent reasoning
  • Build custom tools that spawn sub-agents
  • Test agent behavior programmatically
See examples/sdk/ for working examples from minimal to full control.

Quick Start

ModelRuntime is the canonical asynchronous provider runtime when an integration wants provider-owned credentials, dynamic catalogs, and native providers in one object:
ModelRuntime.create() accepts custom authPath, modelsPath, credential storage, and runtime auth overrides. ModelRegistry and AuthStorage remain available as Atomic’s synchronous compatibility facades. Use readStoredCredential(provider, authPath?) for a lightweight read of one stored provider credential. Extensions supplied directly to SDK sessions can use the exported InlineExtension type. Extension APIs and event types include native registerProvider(Provider), registerEntryRenderer, entry_appended, before_provider_headers, and agent_settled. The package root also exports buildContextEntries, sessionEntryToContextMessages, and CompactionEntry for converting durable session branches into model context. The equivalent active-session operation is sessionManager.buildContextEntries().

Installation

Install @bastani/atomic as a project dependency with npm, pnpm, or Bun: With npm:
With pnpm:
With Bun:
Atomic does not require package install scripts. If you want to disable dependency lifecycle scripts during the Atomic install, you can add --ignore-scripts to the install command. The SDK is included in the main package. No separate SDK package is needed.

Core Concepts

createAgentSession()

The main factory function for a single AgentSession. createAgentSession() uses a ResourceLoader to supply extensions, skills, prompt templates, themes, and context files. If you do not provide one, it uses DefaultResourceLoader with standard discovery.

AgentSession

The session manages agent lifecycle, message history, model state, compaction, and event streaming.
compact() serializes older context to numbered lines, asks the session model for JSON deleted ranges, validates them, and mechanically reconstructs a durable verbatim transcript string. It appends a compaction entry with details.strategy: "verbatim-lines"; the recent tail remains ordinary messages. The model never authors replacement context text. Session replacement APIs such as new-session, resume, fork, and import live on AgentSessionRuntime, not on AgentSession.

createAgentSessionRuntime() and AgentSessionRuntime

Use the runtime API when you need to replace the active session and rebuild cwd-bound runtime state. This is the same layer used by the built-in interactive, print, and RPC modes. createAgentSessionRuntime() takes a runtime factory plus the initial cwd/session target. The factory closes over process-global fixed inputs, recreates cwd-bound services for the effective cwd, resolves session options against those services, and returns a full runtime result.
AgentSessionRuntime owns replacement of the active runtime across:
  • newSession()
  • switchSession()
  • fork()
  • clone flows via fork(entryId, { position: "at" })
  • importFromJsonl()
Important behavior:
  • runtime.session changes after those operations
  • event subscriptions are attached to a specific AgentSession, so re-subscribe after replacement
  • if you use extensions, call runtime.session.bindExtensions(...) again for the new session
  • creation returns diagnostics on runtime.diagnostics
  • if runtime creation or replacement fails, the method throws and the caller decides how to handle it

Prompting and Message Queueing

PromptOptions controls prompt expansion, queueing behavior while streaming, and prompt preflight notifications:
preflightResult is called once per prompt() invocation:
  • true when the prompt was accepted, queued, or handled immediately
  • false when prompt preflight rejected before acceptance
It fires before prompt() resolves. prompt() still resolves only after the full accepted run finishes, including retries. Failures after acceptance are reported through the normal event and message stream, not through preflightResult(false). The prompt() method handles prompt templates, extension commands, and message sending:
Behavior:
  • Extension commands (e.g., /mycommand): Execute immediately, even during streaming. They manage their own LLM interaction via pi.sendMessage().
  • File-based prompt templates (from .md files): Expanded to their content before sending or queueing.
  • During streaming without streamingBehavior: Throws an error. Use steer() or followUp() directly, or specify the option.
  • preflightResult(true): Means the prompt was accepted, queued, or handled immediately.
  • preflightResult(false): Means preflight rejected before acceptance.
For explicit queueing during streaming:
Both steer() and followUp() expand file-based prompt templates but error on extension commands (extension commands cannot be queued). pauseQueuedMessages() is a synchronous admission gate. It moves existing raw steering/follow-up entries into a hold before an abort boundary and keeps later context-bearing arrivals—including trigger-turn custom messages, batches, interrupts, async job delivery, sendUserMessage(), and ordinary prompt() calls—queued without starting a provider turn. Content blocks, optional data, duplicate identities, raw text, message types, and the existing order within each queue kind are retained. Non-trigger custom messages remain history-only and do not invent a turn. resumeQueuedMessages() releases that hold exactly once but does not itself start or continue a model turn. Its promise resolves to true only when raw held steering/follow-up work was released, and to false when no held raw work existed. The caller must use its existing explicit resume action (for example, the interactive chat submission or workflow resume boundary) to drive execution. clearQueue() clears the paused flag when it explicitly removes the final unowned held item; if a protected or interrupt-owned item remains, the gate stays paused.

Agent and AgentState

The Agent class (from @earendil-works/pi-agent-core) handles the core LLM interaction. Access it via session.agent.

Events

Subscribe to events to receive streaming output and lifecycle notifications.

Options Reference

Directories

Atomic reads primary .atomic locations first and legacy .pi locations for compatibility when multiple config directories are supported. Passing an explicit agentDir makes that directory the user override. cwd is used by DefaultResourceLoader for:
  • Project extensions (.atomic/extensions/, then legacy .pi/extensions/)
  • Project skills:
    • .atomic/skills/, then legacy .pi/skills/
    • .agents/skills/ in cwd and ancestor directories (up to git repo root, or filesystem root when not in a repo)
  • Project prompts (.atomic/prompts/, then legacy .pi/prompts/)
  • Context files (AGENTS.md walking up from cwd)
  • Session directory naming
agentDir is used by DefaultResourceLoader for:
  • Global extensions (extensions/)
  • Global skills:
    • skills/ under agentDir (for example ~/.atomic/agent/skills/; legacy ~/.pi/agent/skills/ is also considered by default)
    • ~/.agents/skills/
  • Global prompts (prompts/)
  • Global context file (AGENTS.md)
  • Settings (settings.json)
  • Custom models (models.json)
  • Credentials (auth.json)
  • Sessions (sessions/)
When you pass a custom ResourceLoader, cwd and agentDir no longer control resource discovery. They still influence session naming and tool path resolution.

Model

ModelRegistry keeps synchronous reads for extension compatibility, while catalog refresh is asynchronous. Extensions should await modelRegistry.refresh() before synchronous getAll(), find(), or getAvailable() reads when a provider may update its catalog. New SDK integrations use ModelRuntime; await modelRuntime.refresh() reports aborted and per-provider errors, and failed providers retain their last-known models. If no model is provided:
  1. Tries to restore from session (if continuing)
  2. Uses default from settings
  3. Falls back to first available model
See examples/sdk/02-custom-model.ts

API Keys and OAuth

ModelRuntime is the asynchronous SDK engine for provider composition, credentials, model catalogs, and requests. ModelRegistry remains a thin synchronous compatibility facade for extensions; new SDK integrations should pass modelRuntime to createAgentSession. Credential resolution combines runtime API-key overrides, stored auth.json credentials, environment variables, and the active models.json provider configuration. OAuth acquisition is provider-owned and runs through ModelRuntime.login().
See the complete ModelRuntime credential and model configuration example.

System Prompt

Use a ResourceLoader to override the system prompt:
See examples/sdk/03-custom-prompt.ts

Tools

Specify which tools to expose by name:
  • Built-in tool names enabled by default: read, bash, edit, write, find, search, ask_user_question, todo
  • find discovers filesystem paths by glob; search searches file contents with regex patterns across files, directories, globs, and internal URLs.
  • tools is an allowlist: when provided, only the listed built-in, extension, and custom tool names are exposed.
  • excludedTools is a blocklist: matching built-in, extension, and custom tool names are omitted from the final registry and active tool set. If both are provided, tools is applied first and excludedTools subtracts from it.
  • noTools: "all" disables all tools
  • noTools: "builtin" disables default built-ins while keeping extension and custom tools enabled, except names listed in excludedTools

Bash tool behavior

Atomic’s built-in bash tool matches upstream pi: when bash is enabled, commands execute through the configured shell with the Atomic process permissions. Use tools, excludedTools, or noTools to decide whether a session exposes the bash tool at all. Atomic no longer provides a command-level allow/deny option for bash; use an operating-system/container sandbox or a custom tool/extension when you need command allowlisting or stronger isolation.

Tools with Custom cwd

When you pass a custom cwd, createAgentSession() builds selected built-in tools for that cwd.
See examples/sdk/05-tools.ts

Custom Tools

Use defineTool() for standalone definitions and arrays like customTools: [myTool]. Inline pi.registerTool({ ... }) already infers parameter types correctly. Custom tools passed via customTools are combined with extension-registered tools. Extensions loaded by the ResourceLoader can also register tools via pi.registerTool(). If you pass tools, include each custom or extension tool name you want enabled, for example tools: ["read", "bash", "my_tool"]. Use excludedTools to remove a custom or extension tool by name from the final exposed set. ToolDefinition.constrainedSampling is part of the public SDK and survives defineTool(), customTools, tool wrappers, session/staged inspection, and isolated execution. Use { type: "json_schema", strict: "prefer" | "require" }, { type: "grammar", variants: { openai_lark?: string, openai_regex?: string } }, or false. prefer can fall back; require fails when the active model cannot enforce strict JSON Schema. Grammar constraints require one required string parameter and capable model metadata. Public inspection preserves optional-property identity exactly: an omitted key stays absent, an explicitly present undefined stays present, and false or a config object remains unchanged. The exported ConstrainedSamplingConfig type and extension reference define the exact shape. Typed RPC clients receive the four model capability flags through optional ModelInfo.compat; see RPC. Factory-created createBashTool() instances receive the same execution-time ATOMIC_SESSION_*/PI_SESSION_* model and session snapshot as the built-in bash tool. Set exposeSessionEnvironment: false only when the subprocess must not receive it. MessageRenderOptions.outputPad is likewise passed to normal and isolated custom message renderers.

Structured output final results

structured_output is not registered in normal agent sessions by default. Add it only when a caller needs a machine-readable final-answer contract by registering the exported factory as a custom tool:
The tool parameters are exactly the supplied schema: with DecisionSchema, the model calls structured_output({ approved, findings }). Array and primitive schemas are also accepted by the factory when the target provider/tool runtime supports them; the captured value is whatever JSON value matches the schema. A successful call stores the params in capture.value, returns them as pretty-printed JSON tool-result text for text print mode, keeps the flat value in tool details, writes the same JSON to the configured output.outputPath when an output file sink is configured, and sets terminate: true so there is no extra follow-up assistant turn. Atomic relies on the tool schema instead of extra structured-output parsing or sidecar validation. Structured-output tool definitions opt out of oversized-result persistence. Custom tool names are supported, and the prompt metadata follows the configured name. If you use a custom name such as final_decision, include that name in any explicit tools allowlist. If the standard structured_output name is required, register the factory with its default name:
See examples/sdk/05-tools.ts

Extensions

Extensions are loaded by the ResourceLoader. DefaultResourceLoader discovers extensions from ~/.atomic/agent/extensions/ and .atomic/extensions/ first, then legacy ~/.pi/agent/extensions/ and .pi/extensions/, plus settings.json extension sources.
Extensions can register tools, subscribe to events, add commands, and more. See Extensions for the full API. Event Bus: Extensions can communicate via pi.events. Pass a shared eventBus to DefaultResourceLoader if you need to emit or listen from outside:
See examples/sdk/06-extensions.ts and Extensions

Skills

See examples/sdk/04-skills.ts

Context Files

See examples/sdk/07-context-files.ts

Slash Commands

See examples/sdk/08-prompt-templates.ts

Session Management

Sessions use a tree structure with id/parentId linking, enabling in-place branching.
SessionManager tree API:
See examples/sdk/11-sessions.ts and Session Format

Settings Management

Static factories:
  • SettingsManager.create(cwd?, agentDir?) - Load from files
  • SettingsManager.inMemory(settings?) - No file I/O
Project-specific settings: Settings load from Atomic-first locations and merge:
  1. Global: ~/.atomic/agent/settings.json, then legacy ~/.pi/agent/settings.json
  2. Project: <cwd>/.atomic/settings.json, then legacy <cwd>/.pi/settings.json
Project overrides global. Nested objects merge keys. Setters modify global settings by default. Persistence and error handling semantics:
  • Settings getters/setters are synchronous for in-memory state.
  • Setters enqueue persistence writes asynchronously.
  • Call await settingsManager.flush() when you need a durability boundary (for example, before process exit or before asserting file contents in tests).
  • SettingsManager does not print settings I/O errors. Use settingsManager.drainErrors() and report them in your app layer.
See examples/sdk/10-settings.ts

ResourceLoader

Use DefaultResourceLoader to discover extensions, skills, prompts, themes, and context files.

Return Value

createAgentSession() returns:

Complete Example

Run Modes

The SDK exports run mode utilities for building custom interfaces on top of createAgentSession():

InteractiveMode

Full TUI interactive mode with editor, chat history, and all built-in commands:

runPrintMode

Single-shot mode: send prompts, output result, exit:

runRpcMode

JSON-RPC mode for subprocess integration:
See RPC documentation for the JSON protocol.

RPC Mode Alternative

For subprocess-based integration without building with the SDK, use the CLI directly:
See RPC documentation for the JSON protocol. The SDK is preferred when:
  • You want type safety
  • You’re in the same Node.js process
  • You need direct access to agent state
  • You want to customize tools/extensions programmatically
RPC mode is preferred when:
  • You’re integrating from another language
  • You want process isolation
  • You’re building a language-agnostic client

Exports

The main entry point exports:
For extension types, see Extensions for the full API.