create-agent-adapter

name	create-agent-adapter
description	Technical guide for creating a new Bedlam agent adapter. Use when building a new adapter package, adding support for a new AI coding tool (e.g. a new CLI agent, API-based agent, or custom process), or when modifying the adapter system. Covers the required interfaces, module structure, registration points, and conventions derived from the existing claude-local and codex-local adapters.

Creating a Bedlam Agent Adapter

An adapter bridges Bedlam's orchestration layer to a specific AI agent runtime (Claude Code, Codex CLI, a custom process, an HTTP endpoint, etc.). Each adapter is a self-contained package that provides implementations for three consumers: the server, the UI, and the CLI.

---

1. Architecture Overview

packages/adapters/<name>/
  src/
    index.ts            # Shared metadata (type, label, models, agentConfigurationDoc)
    server/
      index.ts          # Server exports: execute, sessionCodec, parse helpers
      execute.ts        # Core execution logic (AdapterExecutionContext -> AdapterExecutionResult)
      parse.ts          # Stdout/result parsing for the agent's output format
    ui/
      index.ts          # UI exports: parseStdoutLine, buildConfig
      parse-stdout.ts   # Line-by-line stdout -> TranscriptEntry[] for the run viewer
      build-config.ts   # CreateConfigValues -> adapterConfig JSON for agent creation form
    cli/
      index.ts          # CLI exports: formatStdoutEvent
      format-event.ts   # Colored terminal output for `bedlam run --watch`
  package.json
  tsconfig.json

Three separate registries consume adapter modules:

Registry	Location	Interface
Server	server/src/adapters/registry.ts	ServerAdapterModule
UI	ui/src/adapters/registry.ts	UIAdapterModule
CLI	cli/src/adapters/registry.ts	CLIAdapterModule

---

2. Shared Types (@bedlam/adapter-utils)

All adapter interfaces live in packages/adapter-utils/src/types.ts. Import from @bedlam/adapter-utils (types) or @bedlam/adapter-utils/server-utils (runtime helpers).

Core Interfaces

// The execute function signature — every adapter must implement this
interface AdapterExecutionContext {
  runId: string;
  agent: AdapterAgent;          // { id, companyId, name, adapterType, adapterConfig }
  runtime: AdapterRuntime;      // { sessionId, sessionParams, sessionDisplayId, taskKey }
  config: Record<string, unknown>;  // The agent's adapterConfig blob
  context: Record<string, unknown>; // Runtime context (taskId, wakeReason, approvalId, etc.)
  onLog: (stream: "stdout" | "stderr", chunk: string) => Promise<void>;
  onMeta?: (meta: AdapterInvocationMeta) => Promise<void>;
  authToken?: string;
}

interface AdapterExecutionResult {
  exitCode: number | null;
  signal: string | null;
  timedOut: boolean;
  errorMessage?: string | null;
  usage?: UsageSummary;           // { inputTokens, outputTokens, cachedInputTokens? }
  sessionId?: string | null;      // Legacy — prefer sessionParams
  sessionParams?: Record<string, unknown> | null;  // Opaque session state persisted between runs
  sessionDisplayId?: string | null;
  provider?: string | null;       // "anthropic", "openai", etc.
  model?: string | null;
  costUsd?: number | null;
  resultJson?: Record<string, unknown> | null;
  summary?: string | null;        // Human-readable summary of what the agent did
  clearSession?: boolean;         // true = tell Bedlam to forget the stored session
}

interface AdapterSessionCodec {
  deserialize(raw: unknown): Record<string, unknown> | null;
  serialize(params: Record<string, unknown> | null): Record<string, unknown> | null;
  getDisplayId?(params: Record<string, unknown> | null): string | null;
}

Module Interfaces

// Server — registered in server/src/adapters/registry.ts
interface ServerAdapterModule {
  type: string;
  execute(ctx: AdapterExecutionContext): Promise<AdapterExecutionResult>;
  testEnvironment(ctx: AdapterEnvironmentTestContext): Promise<AdapterEnvironmentTestResult>;
  sessionCodec?: AdapterSessionCodec;
  supportsLocalAgentJwt?: boolean;
  models?: { id: string; label: string }[];
  agentConfigurationDoc?: string;
}

// UI — registered in ui/src/adapters/registry.ts
interface UIAdapterModule {
  type: string;
  label: string;
  parseStdoutLine: (line: string, ts: string) => TranscriptEntry[];
  ConfigFields: ComponentType<AdapterConfigFieldsProps>;
  buildAdapterConfig: (values: CreateConfigValues) => Record<string, unknown>;
}

// CLI — registered in cli/src/adapters/registry.ts
interface CLIAdapterModule {
  type: string;
  formatStdoutEvent: (line: string, debug: boolean) => void;
}

---

2.1 Adapter Environment Test Contract

Every server adapter must implement testEnvironment(...). This powers the board UI "Test environment" button in agent configuration.

type AdapterEnvironmentCheckLevel = "info" | "warn" | "error";
type AdapterEnvironmentTestStatus = "pass" | "warn" | "fail";

interface AdapterEnvironmentCheck {
  code: string;
  level: AdapterEnvironmentCheckLevel;
  message: string;
  detail?: string | null;
  hint?: string | null;
}

interface AdapterEnvironmentTestResult {
  adapterType: string;
  status: AdapterEnvironmentTestStatus;
  checks: AdapterEnvironmentCheck[];
  testedAt: string; // ISO timestamp
}

interface AdapterEnvironmentTestContext {
  companyId: string;
  adapterType: string;
  config: Record<string, unknown>; // runtime-resolved adapterConfig
}

Guidelines:

• Return structured diagnostics, never throw for expected findings.
• Use error for invalid/unusable runtime setup (bad cwd, missing command, invalid URL).
• Use warn for non-blocking but important situations.
• Use info for successful checks and context.

Severity policy is product-critical: warnings are not save blockers.
Example: for claude_local, detected ANTHROPIC_API_KEY must be a warn, not an error, because Claude can still run (it just uses API-key auth instead of subscription auth).

---

3. Step-by-Step: Creating a New Adapter

3.1 Create the Package

packages/adapters/<name>/
  package.json
  tsconfig.json
  src/
    index.ts
    server/index.ts
    server/execute.ts
    server/parse.ts
    ui/index.ts
    ui/parse-stdout.ts
    ui/build-config.ts
    cli/index.ts
    cli/format-event.ts

package.json — must use the four-export convention:

{
  "name": "@bedlam/adapter-<name>",
  "version": "0.0.1",
  "private": true,
  "type": "module",
  "exports": {
    ".": "./src/index.ts",
    "./server": "./src/server/index.ts",
    "./ui": "./src/ui/index.ts",
    "./cli": "./src/cli/index.ts"
  },
  "dependencies": {
    "@bedlam/adapter-utils": "workspace:*",
    "picocolors": "^1.1.1"
  },
  "devDependencies": {
    "typescript": "^5.7.3"
  }
}

3.2 Root index.ts — Adapter Metadata

This file is imported by all three consumers (server, UI, CLI). Keep it dependency-free (no Node APIs, no React).

export const type = "my_agent";        // snake_case, globally unique
export const label = "My Agent (local)";

export const models = [
  { id: "model-a", label: "Model A" },
  { id: "model-b", label: "Model B" },
];

export const agentConfigurationDoc = `# my_agent agent configuration
...document all config fields here...
`;

Required exports:

• type — the adapter type key, stored in agents.adapter_type
• label — human-readable name for the UI
• models — available model options for the agent creation form
• agentConfigurationDoc — markdown describing all adapterConfig fields (used by LLM agents configuring other agents)

Writing agentConfigurationDoc as routing logic:

The agentConfigurationDoc is read by LLM agents (including Bedlam agents that create other agents). Write it as routing logic, not marketing copy. Include concrete "use when" and "don't use when" guidance so an LLM can decide whether this adapter is appropriate for a given task.

export const agentConfigurationDoc = `# my_agent agent configuration

Adapter: my_agent

Use when:
- The agent needs to run MyAgent CLI locally on the host machine
- You need session persistence across runs (MyAgent supports thread resumption)
- The task requires MyAgent-specific tools (e.g. web search, code execution)

Don't use when:
- You need a simple one-shot script execution (use the "process" adapter instead)
- The agent doesn't need conversational context between runs (process adapter is simpler)
- MyAgent CLI is not installed on the host

Core fields:
- cwd (string, required): absolute working directory for the agent process
...
`;

Adding explicit negative cases improves adapter selection accuracy. One concrete anti-pattern is worth more than three paragraphs of description.

3.3 Server Module

server/execute.ts — The Core

This is the most important file. It receives an AdapterExecutionContext and must return an AdapterExecutionResult.

Required behavior:

1. Read config — extract typed values from ctx.config using helpers (asString, asNumber, asBoolean, asStringArray, parseObject from @bedlam/adapter-utils/server-utils)
2. Build environment — call buildBedlamEnv(agent) then layer in BEDLAM_RUN_ID, context vars (BEDLAM_TASK_ID, BEDLAM_WAKE_REASON, BEDLAM_WAKE_COMMENT_ID, BEDLAM_APPROVAL_ID, BEDLAM_APPROVAL_STATUS, BEDLAM_LINKED_ISSUE_IDS), user env overrides, and auth token
3. Resolve session — check runtime.sessionParams / runtime.sessionId for an existing session; validate it's compatible (e.g. same cwd); decide whether to resume or start fresh
4. Render prompt — use renderTemplate(template, data) with the template variables: agentId, companyId, runId, company, agent, run, context
5. Call onMeta — emit adapter invocation metadata before spawning the process
6. Spawn the process — use runChildProcess() for CLI-based agents or fetch() for HTTP-based agents
7. Parse output — convert the agent's stdout into structured data (session id, usage, summary, errors)
8. Handle session errors — if resume fails with "unknown session", retry with a fresh session and set clearSession: true
9. Return AdapterExecutionResult — populate all fields the agent runtime supports

Environment variables the server always injects:

Variable	Source
BEDLAM_AGENT_ID	agent.id
BEDLAM_COMPANY_ID	agent.companyId
BEDLAM_API_URL	Server's own URL
BEDLAM_RUN_ID	Current run id
BEDLAM_TASK_ID	context.taskId or context.issueId
BEDLAM_WAKE_REASON	context.wakeReason
BEDLAM_WAKE_COMMENT_ID	context.wakeCommentId or context.commentId
BEDLAM_APPROVAL_ID	context.approvalId
BEDLAM_APPROVAL_STATUS	context.approvalStatus
BEDLAM_LINKED_ISSUE_IDS	context.issueIds (comma-separated)
BEDLAM_API_KEY	authToken (if no explicit key in config)

server/parse.ts — Output Parser

Parse the agent's stdout format into structured data. Must handle:

• Session identification — extract session/thread ID from init events
• Usage tracking — extract token counts (input, output, cached)
• Cost tracking — extract cost if available
• Summary extraction — pull the agent's final text response
• Error detection — identify error states, extract error messages
• Unknown session detection — export an is<Agent>UnknownSessionError() function for retry logic

Treat agent output as untrusted. The stdout you're parsing comes from an LLM-driven process that may have executed arbitrary tool calls, fetched external content, or been influenced by prompt injection in the files it read. Parse defensively:

• Never eval() or dynamically execute anything from output
• Use safe extraction helpers (asString, asNumber, parseJson) — they return fallbacks on unexpected types
• Validate session IDs and other structured data before passing them through
• If output contains URLs, file paths, or commands, do not act on them in the adapter — just record them

server/index.ts — Server Exports

export { execute } from "./execute.js";
export { testEnvironment } from "./test.js";
export { parseMyAgentOutput, isMyAgentUnknownSessionError } from "./parse.js";

// Session codec — required for session persistence
export const sessionCodec: AdapterSessionCodec = {
  deserialize(raw) { /* raw DB JSON -> typed params or null */ },
  serialize(params) { /* typed params -> JSON for DB storage */ },
  getDisplayId(params) { /* -> human-readable session id string */ },
};

server/test.ts — Environment Diagnostics

Implement adapter-specific preflight checks used by the UI test button.

Minimum expectations:

1. Validate required config primitives (paths, commands, URLs, auth assumptions)
2. Return check objects with deterministic code values
3. Map severity consistently (info / warn / error)
4. Compute final status:
   • fail if any error
   • warn if no errors and at least one warning
   • pass otherwise

This operation should be lightweight and side-effect free.

3.4 UI Module

ui/parse-stdout.ts — Transcript Parser

Converts individual stdout lines into TranscriptEntry[] for the run detail viewer. Must handle the agent's streaming output format and produce entries of these kinds:

• init — model/session initialization
• assistant — agent text responses
• thinking — agent thinking/reasoning (if supported)
• tool_call — tool invocations with name and input
• tool_result — tool results with content and error flag
• user — user messages in the conversation
• result — final result with usage stats
• stdout — fallback for unparseable lines

export function parseMyAgentStdoutLine(line: string, ts: string): TranscriptEntry[] {
  // Parse JSON line, map to appropriate TranscriptEntry kind(s)
  // Return [{ kind: "stdout", ts, text: line }] as fallback
}

ui/build-config.ts — Config Builder

Converts the UI form's CreateConfigValues into the adapterConfig JSON blob stored on the agent.

export function buildMyAgentConfig(v: CreateConfigValues): Record<string, unknown> {
  const ac: Record<string, unknown> = {};
  if (v.cwd) ac.cwd = v.cwd;
  if (v.promptTemplate) ac.promptTemplate = v.promptTemplate;
  if (v.model) ac.model = v.model;
  ac.timeoutSec = 0;
  ac.graceSec = 15;
  // ... adapter-specific fields
  return ac;
}

UI Config Fields Component

Create ui/src/adapters/<name>/config-fields.tsx with a React component implementing AdapterConfigFieldsProps. This renders adapter-specific form fields in the agent creation/edit form.

Use the shared primitives from ui/src/components/agent-config-primitives:

• Field — labeled form field wrapper
• ToggleField — boolean toggle with label and hint
• DraftInput — text input with draft/commit behavior
• DraftNumberInput — number input with draft/commit behavior
• help — standard hint text for common fields

The component must support both create mode (using values/set) and edit mode (using config/eff/mark).

3.5 CLI Module

cli/format-event.ts — Terminal Formatter

Pretty-prints stdout lines for bedlam run --watch. Use picocolors for coloring.

import pc from "picocolors";

export function printMyAgentStreamEvent(raw: string, debug: boolean): void {
  // Parse JSON line from agent stdout
  // Print colored output: blue for system, green for assistant, yellow for tools
  // In debug mode, print unrecognized lines in gray
}

---

4. Registration Checklist

After creating the adapter package, register it in all three consumers:

4.1 Server Registry (server/src/adapters/registry.ts)

import { execute as myExecute, sessionCodec as mySessionCodec } from "@bedlam/adapter-my-agent/server";
import { agentConfigurationDoc as myDoc, models as myModels } from "@bedlam/adapter-my-agent";

const myAgentAdapter: ServerAdapterModule = {
  type: "my_agent",
  execute: myExecute,
  sessionCodec: mySessionCodec,
  models: myModels,
  supportsLocalAgentJwt: true,  // true if agent can use Bedlam API
  agentConfigurationDoc: myDoc,
};

// Add to the adaptersByType map
const adaptersByType = new Map<string, ServerAdapterModule>(
  [..., myAgentAdapter].map((a) => [a.type, a]),
);

4.2 UI Registry (ui/src/adapters/registry.ts)

import { myAgentUIAdapter } from "./my-agent";

const adaptersByType = new Map<string, UIAdapterModule>(
  [..., myAgentUIAdapter].map((a) => [a.type, a]),
);

With ui/src/adapters/my-agent/index.ts:

import type { UIAdapterModule } from "../types";
import { parseMyAgentStdoutLine } from "@bedlam/adapter-my-agent/ui";
import { MyAgentConfigFields } from "./config-fields";
import { buildMyAgentConfig } from "@bedlam/adapter-my-agent/ui";

export const myAgentUIAdapter: UIAdapterModule = {
  type: "my_agent",
  label: "My Agent",
  parseStdoutLine: parseMyAgentStdoutLine,
  ConfigFields: MyAgentConfigFields,
  buildAdapterConfig: buildMyAgentConfig,
};

4.3 CLI Registry (cli/src/adapters/registry.ts)

import { printMyAgentStreamEvent } from "@bedlam/adapter-my-agent/cli";

const myAgentCLIAdapter: CLIAdapterModule = {
  type: "my_agent",
  formatStdoutEvent: printMyAgentStreamEvent,
};

// Add to the adaptersByType map

---

5. Session Management — Designing for Long Runs

Sessions allow agents to maintain conversation context across runs. The system is codec-based — each adapter defines how to serialize/deserialize its session state.

Design for long runs from the start. Treat session reuse as the default primitive, not an optimization to add later. An agent working on an issue may be woken dozens of times — for the initial assignment, approval callbacks, re-assignments, manual nudges. Each wake should resume the existing conversation so the agent retains full context about what it has already done, what files it has read, and what decisions it has made. Starting fresh each time wastes tokens on re-reading the same files and risks contradictory decisions.

Key concepts:

• sessionParams is an opaque Record<string, unknown> stored in the DB per task
• The adapter's sessionCodec.serialize() converts execution result data to storable params
• sessionCodec.deserialize() converts stored params back for the next run
• sessionCodec.getDisplayId() extracts a human-readable session ID for the UI
• cwd-aware resume: if the session was created in a different cwd than the current config, skip resuming (prevents cross-project session contamination)
• Unknown session retry: if resume fails with a "session not found" error, retry with a fresh session and return clearSession: true so Bedlam wipes the stale session

If the agent runtime supports any form of context compaction or conversation compression (e.g. Claude Code's automatic context management, or Codex's previous_response_id chaining), lean on it. Adapters that support session resume get compaction for free — the agent runtime handles context window management internally across resumes.

Pattern (from both claude-local and codex-local):

const canResumeSession =
  runtimeSessionId.length > 0 &&
  (runtimeSessionCwd.length === 0 || path.resolve(runtimeSessionCwd) === path.resolve(cwd));
const sessionId = canResumeSession ? runtimeSessionId : null;

// ... run attempt ...

// If resume failed with unknown session, retry fresh
if (sessionId && !proc.timedOut && exitCode !== 0 && isUnknownSessionError(output)) {
  const retry = await runAttempt(null);
  return toResult(retry, { clearSessionOnMissingSession: true });
}

---

6. Server-Utils Helpers

Import from @bedlam/adapter-utils/server-utils:

Helper	Purpose
asString(val, fallback)	Safe string extraction
asNumber(val, fallback)	Safe number extraction
asBoolean(val, fallback)	Safe boolean extraction
asStringArray(val)	Safe string array extraction
parseObject(val)	Safe Record<string, unknown> extraction
parseJson(str)	Safe JSON.parse returning Record or null
renderTemplate(tmpl, data)	{{path.to.value}} template rendering
buildBedlamEnv(agent)	Standard BEDLAM_* env vars
redactEnvForLogs(env)	Redact sensitive keys for onMeta
ensureAbsoluteDirectory(cwd)	Validate cwd exists and is absolute
ensureCommandResolvable(cmd, cwd, env)	Validate command is in PATH
ensurePathInEnv(env)	Ensure PATH exists in env
runChildProcess(runId, cmd, args, opts)	Spawn with timeout, logging, capture

---

7. Conventions and Patterns

Naming

• Adapter type: snake_case (e.g. claude_local, codex_local)
• Package name: @bedlam/adapter-<kebab-name>
• Package directory: packages/adapters/<kebab-name>/

Config Parsing

• Never trust config values directly — always use asString, asNumber, etc.
• Provide sensible defaults for every optional field
• Document all fields in agentConfigurationDoc

Prompt Templates

• Support promptTemplate for every run
• Use renderTemplate() with the standard variable set
• Default prompt: "You are agent {{agent.id}} ({{agent.name}}). Continue your Bedlam work."

Error Handling

• Differentiate timeout vs process error vs parse failure
• Always populate errorMessage on failure
• Include raw stdout/stderr in resultJson when parsing fails
• Handle the agent CLI not being installed (command not found)

Logging

• Call onLog("stdout", ...) and onLog("stderr", ...) for all process output — this feeds the real-time run viewer
• Call onMeta(...) before spawning to record invocation details
• Use redactEnvForLogs() when including env in meta

Bedlam Skills Injection

Bedlam ships shared skills (in the repo's top-level skills/ directory) that agents need at runtime — things like the bedlam API skill and the bedlam-create-agent workflow skill. Each adapter is responsible for making these skills discoverable by its agent runtime without polluting the agent's working directory.

The constraint: never copy or symlink skills into the agent's cwd. The cwd is the user's project checkout — writing .claude/skills/ or any other files into it would contaminate the repo with Bedlam internals, break git status, and potentially leak into commits.

The pattern: create a clean, isolated location for skills and tell the agent runtime to look there.

How claude-local does it:

1. At execution time, create a fresh tmpdir: mkdtemp("bedlam-skills-")
2. Inside it, create .claude/skills/ (the directory structure Claude Code expects)
3. Symlink each skill directory from the repo's skills/ into the tmpdir's .claude/skills/
4. Pass the tmpdir to Claude Code via --add-dir <tmpdir> — this makes Claude Code discover the skills as if they were registered in that directory, without touching the agent's actual cwd
5. Clean up the tmpdir in a finally block after the run completes

// From claude-local execute.ts
async function buildSkillsDir(): Promise<string> {
  const tmp = await fs.mkdtemp(path.join(os.tmpdir(), "bedlam-skills-"));
  const target = path.join(tmp, ".claude", "skills");
  await fs.mkdir(target, { recursive: true });
  const entries = await fs.readdir(BEDLAM_SKILLS_DIR, { withFileTypes: true });
  for (const entry of entries) {
    if (entry.isDirectory()) {
      await fs.symlink(
        path.join(BEDLAM_SKILLS_DIR, entry.name),
        path.join(target, entry.name),
      );
    }
  }
  return tmp;
}

// In execute(): pass --add-dir to Claude Code
const skillsDir = await buildSkillsDir();
args.push("--add-dir", skillsDir);
// ... run process ...
// In finally: fs.rm(skillsDir, { recursive: true, force: true })

How codex-local does it:

Codex has a global personal skills directory ($CODEX_HOME/skills or ~/.codex/skills). The adapter symlinks Bedlam skills there if they don't already exist. This is acceptable because it's the agent tool's own config directory, not the user's project.

// From codex-local execute.ts
async function ensureCodexSkillsInjected(onLog) {
  const skillsHome = path.join(codexHomeDir(), "skills");
  await fs.mkdir(skillsHome, { recursive: true });
  for (const entry of entries) {
    const target = path.join(skillsHome, entry.name);
    const existing = await fs.lstat(target).catch(() => null);
    if (existing) continue;  // Don't overwrite user's own skills
    await fs.symlink(source, target);
  }
}

For a new adapter: figure out how your agent runtime discovers skills/plugins, then choose the cleanest injection path:

1. Best: tmpdir + flag (like claude-local) — if the runtime supports an "additional directory" flag, create a tmpdir, symlink skills in, pass the flag, clean up after. Zero side effects.
2. Acceptable: global config dir (like codex-local) — if the runtime has a global skills/plugins directory separate from the project, symlink there. Skip existing entries to avoid overwriting user customizations.
3. Acceptable: env var — if the runtime reads a skills/plugin path from an environment variable, point it at the repo's skills/ directory directly.
4. Last resort: prompt injection — if the runtime has no plugin system, include skill content in the prompt template itself. This uses tokens but avoids filesystem side effects entirely.

Skills as loaded procedures, not prompt bloat. The Bedlam skills (like bedlam and bedlam-create-agent) are designed as on-demand procedures: the agent sees skill metadata (name + description) in its context, but only loads the full SKILL.md content when it decides to invoke a skill. This keeps the base prompt small. When writing agentConfigurationDoc or prompt templates for your adapter, do not inline skill content — let the agent runtime's skill discovery do the work. The descriptions in each SKILL.md frontmatter act as routing logic: they tell the agent when to load the full skill, not what the skill contains.

Explicit vs. fuzzy skill invocation. For production workflows where reliability matters (e.g. an agent that must always call the Bedlam API to report status), use explicit instructions in the prompt template: "Use the bedlam skill to report your progress." Fuzzy routing (letting the model decide based on description matching) is fine for exploratory tasks but unreliable for mandatory procedures.

---

8. Security Considerations

Adapters sit at the boundary between Bedlam's orchestration layer and arbitrary agent execution. This is a high-risk surface.

Treat Agent Output as Untrusted

The agent process runs LLM-driven code that reads external files, fetches URLs, and executes tools. Its output may be influenced by prompt injection from the content it processes. The adapter's parse layer is a trust boundary — validate everything, execute nothing.

Secret Injection via Environment, Not Prompts

Never put secrets (API keys, tokens) into prompt templates or config fields that flow through the LLM. Instead, inject them as environment variables that the agent's tools can read directly:

• BEDLAM_API_KEY is injected by the server into the process environment, not the prompt
• User-provided secrets in config.env are passed as env vars, redacted in onMeta logs
• The redactEnvForLogs() helper automatically masks any key matching /(key|token|secret|password|authorization|cookie)/i

This follows the "sidecar injection" pattern: the model never sees the real secret value, but the tools it invokes can read it from the environment.

Network Access

If your agent runtime supports network access controls (sandboxing, allowlists), configure them in the adapter:

• Prefer minimal allowlists over open internet access. An agent that only needs to call the Bedlam API and GitHub should not have access to arbitrary hosts.
• Skills + network = amplified risk. A skill that teaches the agent to make HTTP requests combined with unrestricted network access creates an exfiltration path. Constrain one or the other.
• If the runtime supports layered policies (org-level defaults + per-request overrides), wire the org-level policy into the adapter config and let per-agent config narrow further.

Process Isolation

• CLI-based adapters inherit the server's user permissions. The cwd and env config determine what the agent process can access on the filesystem.
• dangerouslySkipPermissions / dangerouslyBypassApprovalsAndSandbox flags exist for development convenience but must be documented as dangerous in agentConfigurationDoc. Production deployments should not use them.
• Timeout and grace period (timeoutSec, graceSec) are safety rails — always enforce them. A runaway agent process without a timeout can consume unbounded resources.

---

9. TranscriptEntry Kinds Reference

The UI run viewer displays these entry kinds:

Kind	Fields	Usage
init	model, sessionId	Agent initialization
assistant	text	Agent text response
thinking	text	Agent reasoning/thinking
user	text	User message
tool_call	name, input	Tool invocation
tool_result	toolUseId, content, isError	Tool result
result	text, inputTokens, outputTokens, cachedTokens, costUsd, subtype, isError, errors	Final result with usage
stderr	text	Stderr output
system	text	System messages
stdout	text	Raw stdout fallback

---

10. Testing

Create tests in server/src/__tests__/<adapter-name>-adapter.test.ts. Test:

1. Output parsing — feed sample stdout through your parser, verify structured output
2. Unknown session detection — verify the is<Agent>UnknownSessionError function
3. Config building — verify buildConfig produces correct adapterConfig from form values
4. Session codec — verify serialize/deserialize round-trips

---

11. Minimal Adapter Checklist

• packages/adapters/<name>/package.json with four exports (., ./server, ./ui, ./cli)
• Root index.ts with type, label, models, agentConfigurationDoc
• server/execute.ts implementing AdapterExecutionContext -> AdapterExecutionResult
• server/test.ts implementing AdapterEnvironmentTestContext -> AdapterEnvironmentTestResult
• server/parse.ts with output parser and unknown-session detector
• server/index.ts exporting execute, testEnvironment, sessionCodec, parse helpers
• ui/parse-stdout.ts with StdoutLineParser for the run viewer
• ui/build-config.ts with CreateConfigValues -> adapterConfig builder
• ui/src/adapters/<name>/config-fields.tsx React component for agent form
• ui/src/adapters/<name>/index.ts assembling the UIAdapterModule
• cli/format-event.ts with terminal formatter
• cli/index.ts exporting the formatter
• Registered in server/src/adapters/registry.ts
• Registered in ui/src/adapters/registry.ts
• Registered in cli/src/adapters/registry.ts
• Added to workspace in root pnpm-workspace.yaml (if not already covered by glob)
• Tests for parsing, session codec, and config building