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Run production-grade AI agents with isolated microVMs, real browsers, real code execution, and persistent memory, directly from your n8n workflows.\n\nListed on n8n at\n**[n8n.io/integrations/amazon-bedrock-agentcore](https://n8n.io/integrations/amazon-bedrock-agentcore/)**.\n\n> Available in the AWS Regions listed in the\n> [AgentCore Regions documentation](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/agentcore-regions.html).\n> Pin this package to a specific version in production and review release notes\n> before upgrading.\n\n## Why this node?\n\nn8n’s native AI Agent node is great for simple agents, but hits walls fast: no cross-session memory, no real browser, no real code execution, and short execution timeouts. AgentCore harness solves all four. This node is the bridge.\n\n|Capability    |Native n8n AI Agent|AgentCore Agent (this node)               |\n|--------------|-------------------|------------------------------------------|\n|Memory        |Per-execution only |Cross-session, semantic, summary, episodic|\n|Code execution|Sandboxed JS only  |Full microVM with Python/Node/etc.        |\n|Browser       |None               |Cloud browser with real navigation        |\n|Session length|Workflow timeout   |Up to 8 hours per session                 |\n|Isolation     |Shared process     |Firecracker microVM per session           |\n\n## Features\n\n- **Auto-provision & reuse** — Leave the Harness ARN blank: the node creates a harness on first run, reuses it across executions, and updates it when configuration changes\n- **Bring your own harness** — Paste a harness ARN (deployed via CLI / CloudFormation / console / Terraform) to invoke it directly, with any config field acting as a per-invocation override\n- **Multi-provider models** (v0.2) — Amazon Bedrock (native), OpenAI, Google Gemini, and LiteLLM, switchable per invocation\n- **Managed memory** (v0.2) — auto-provisioned AgentCore Memory with configurable strategies, or bring your own Memory ARN, or disable it\n- **Inline tool configuration** — AgentCore Browser, Code Interpreter, Gateway (with optional OAuth outbound auth), remote MCP servers, and inline functions\n- **Skills** (v0.2) — AWS curated catalog, Git, S3, and filesystem-path sources\n- **VPC, custom containers, and filesystem mounts** (v0.2) — run in your VPC, bring a linux/arm64 ECR image, mount session storage / EFS / S3 Files\n- **OAuth Bearer invoke** (v0.2) — invoke inbound-OAuth harnesses with a JWT stored on the AgentCore API credential\n- **Versions & endpoints** (v0.2) — list immutable versions, pin named endpoints, invoke by qualifier\n- **Streaming responses** with structured tool-use trace, token usage, and latency metadata\n- **Session persistence** — pass the same session ID across executions for multi-turn conversations\n- **Execution limits** — max iterations, max tokens, timeout\n\n## Quick start\n\nNew here? **[docs/QUICKSTART.md](./docs/QUICKSTART.md)** takes you from install to\na working agent reply in ~5 minutes (install → credential → one prompt → multi-turn).\n\n## Installation\n\nThis node is a **verified community node**, so it works on both **n8n Cloud** and\n**self-hosted n8n** and appears directly in the in-editor nodes panel. Add a node,\nsearch for **Amazon Bedrock AgentCore**, and select it. On n8n Cloud, an instance\nowner does the one-time install.\n\n> New to self-hosting? You can run n8n locally with a single command\n> (`npx n8n`), or with Docker, or on a cloud provider. See the\n> [n8n self-hosting guide](https://docs.n8n.io/hosting/) for all options.\n\n### Via the nodes panel\n\n1. In a workflow, add a node and search for **Amazon Bedrock AgentCore**.\n2. Select it. n8n installs the verified node for you.\n\n### Via settings\n\n1. Go to **Settings → Community Nodes → Install**.\n2. Enter `@aws/n8n-nodes-agentcore`.\n3. Acknowledge the community-node prompt, then choose **Install**.\n\n### Via npm (self-hosted only)\n\n```bash\ncd ~/.n8n\nnpm install @aws/n8n-nodes-agentcore\n# restart n8n\n```\n\n## Prerequisites\n\nYou need:\n\n1. **An AWS account** with Amazon Bedrock and AgentCore access in a supported region (see the [AgentCore Regions documentation](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/agentcore-regions.html))\n2. **AWS credentials** (access key + secret key) with the IAM policy below attached\n3. **An IAM execution role** that the harness assumes when running, with a trust policy and permissions described below\n4. **An enabled foundation model** in the Amazon Bedrock console (Claude Haiku, Sonnet, etc.)\n\n### IAM setup\n\n> **Source of truth:** AWS maintains the canonical, least-privilege harness IAM\n> policies in the\n> [AgentCore harness security guide](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/harness-security.html)\n> and updates them as the service ships features. Grant **only** the statements\n> for the features your workflow actually uses — do not paste a broad policy.\n> This repo intentionally ships only the trust policy (`docs/iam-trust-policy.json`);\n> the permission policies live in the AWS guide so they never drift.\n\nTwo separate IAM principals are involved — keep them distinct:\n\n**1. Caller** — the IAM user/role whose access keys go in the n8n credential.\nGrant the actions in the\n[Required IAM permissions for callers](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/harness-security.html#harness-iam-permissions)\ntable, **scoped to your harness ARN** (`arn:aws:bedrock-agentcore:<region>:<account>:harness/*`;\n`ListHarnesses` requires `*`). That table already lists the paired\n`*AgentRuntime*` / `*Memory` companion actions each harness API needs.\n\n**2. Execution role** — the role AgentCore assumes at runtime. Use the\n[Sample execution role policy](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/harness-security.html#_sample_execution_role_policy),\nplus the trust policy in `docs/iam-trust-policy.json`.\n\n#### Which permissions each feature needs\n\nAdd only the blocks for features you enable. Each maps to a named section in the\nAWS guide's\n[Additional permissions for optional features](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/harness-security.html#_additional_permissions_for_optional_features):\n\n| If your workflow uses… | Add (least-privilege, scope to the named resource) |\n|---|---|\n| **Managed memory** (the v0.2 default) or BYO Memory | Caller: `CreateMemory`/`UpdateMemory`/`DeleteMemory` (per the callers table). Execution role: the *AgentCore Memory* block (`CreateEvent`/`GetEvent`/`ListEvents`/`DeleteEvent`/`RetrieveMemoryRecords`) scoped to your `memory/*`. |\n| **OpenAI / Gemini / LiteLLM models, or MCP header credential refs** | Execution role: the *API key credential provider* block (`bedrock-agentcore:GetResourceApiKey` + the matching `secretsmanager:GetSecretValue` on `…identity!default/apikey/…`). |\n| **OpenAI via Bedrock Mantle** (no API key) | Execution role: `bedrock-mantle:CreateInference`, scoped to `arn:aws:bedrock-mantle:<region>:<account>:project/default`. (Mantle is a separate IAM namespace from `bedrock:`; not in the canned sample — add it explicitly.) |\n| **AgentCore Gateway tool** | Execution role: the *AgentCore Gateway* block (`bedrock-agentcore:InvokeGateway` on your `gateway/<id>`); for OAuth-protected gateways also the *OAuth2 credential provider* block. |\n| **Skills from S3** (or S3 Files data) | Execution role: the *Skill sources in S3* block (`s3:GetObject`/`s3:ListBucket` on your bucket). Private **Git** skills: an API key credential provider holding the PAT. |\n| **Custom container (private ECR)** | Execution role: the *Private ECR access* block — `ecr:GetDownloadUrlForLayer`/`ecr:BatchGetImage` scoped to your `repository/<name>`, and `ecr:GetAuthorizationToken` on `*` (that action can't be resource-scoped). |\n| **EFS filesystem mount** (VPC) | Execution role: `elasticfilesystem:ClientMount`/`ClientWrite` scoped to your access-point ARN. (Session-storage mounts need no extra IAM; no `ClientRootAccess`.) |\n| **Versions / named endpoints** | Caller: `ListHarnessVersions` and the `*HarnessEndpoint` actions, plus the paired `*AgentRuntimeEndpoint` actions (per the callers table). |\n| **VPC networking** | Execution role: the *VPC mode: managed image pull from private ECR* block — in VPC mode the harness pulls its **managed** container from a private ECR repo in-Region (`repository/harness-*`), so add `ecr:BatchGetImage`/`GetDownloadUrlForLayer`/`BatchCheckLayerAvailability` plus `ecr:GetAuthorizationToken`. Your subnets need **no internet access**; create VPC endpoints instead, per [Network configuration](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/harness-security.html#harness-network-config). |\n\n`iam:PassRole` is **not** required: the execution-role ARN is passed to\nCreateHarness as a parameter and assumed by the service, not passed by the caller.\n\n> A future `agentcore iam create-execution-role` CLI command will generate the\n> role automatically (separate workstream).\n\n## Configuring credentials\n\nIn n8n, go to **Credentials → New → Amazon Bedrock AgentCore API** and fill in:\n\n|Field             |Value                                         |\n|------------------|----------------------------------------------|\n|Access Key ID     |From your IAM user                            |\n|Secret Access Key |From your IAM user                            |\n|Session Token     |Optional. Only for temporary STS credentials. |\n|OAuth Bearer Token|Optional. JWT for OAuth-protected harnesses; used only when the node Authentication is set to OAuth Bearer Token. Leave blank for AWS SigV4. |\n|Region            |The AgentCore-supported region you want to use|\n|Execution Role ARN|The role ARN from Step 1                      |\n\nSave.\n\n## How it works\n\nThe node has a single operation. The **Harness ARN** field decides the mode.\n\n### Leave Harness ARN blank — Run Agent\n\nType an Agent Name, set the system prompt and prompt, optionally turn on **Add Tools** to configure tools, and run. The node:\n\n- Creates the harness on first execution (~30 seconds), stores the ARN in workflow static data\n- Reuses the same harness on subsequent runs (~3 seconds)\n- Updates the harness if you change the model, system prompt, tools, or limits\n- Streams the response back\n\n### Fill in Harness ARN — Invoke Existing\n\nFor a harness deployed outside n8n (CLI, console, CloudFormation, Terraform). The node calls `InvokeHarness` directly (~3 seconds). Any config field you fill in — Model, System Prompt, Tools, Max Iterations / Tokens, Timeout, Actor ID — is sent as a **per-invocation override**; leave a field blank to use the harness’s own configuration. The Agent Name, Memory ARN, and Force Recreate fields are hidden in this mode because they only apply when the node owns the harness lifecycle.\n\n**Output shape** (the `operation` field reports the resolved mode, `run` or `invokeExisting`):\n\n```json\n{\n  \"operation\": \"run\",\n  \"agentName\": \"research_agent\",\n  \"harnessId\": \"research_agent-abc1234567\",\n  \"harnessArn\": \"arn:aws:bedrock-agentcore:us-west-2:...\",\n  \"sessionId\": \"a1b2c3d4-...\",\n  \"response\": \"The top 3 quantum computing breakthroughs are ...\",\n  \"stopReason\": \"end_turn\",\n  \"toolUses\": [{ \"name\": \"exa\", \"toolUseId\": \"...\", \"input\": { \"query\": \"...\" } }],\n  \"usage\": { \"inputTokens\": 234, \"outputTokens\": 567 },\n  \"latencyMs\": 4123\n}\n```\n\nWhen versioning/endpoint actions are enabled, the output also carries\n`versions` and/or `endpoint` (+ `endpoints`).\n\n## v0.2 capabilities\n\n### Models\n\nPick a **Model Provider** (Amazon Bedrock, OpenAI, Google Gemini, LiteLLM) and a\n**Model ID**, and expand **Model Options** for API Key ARN, API Base URL, API\nFormat, temperature, top-p/k, and JSON additional params. OpenAI and Gemini\nrequire an API Key ARN (an AgentCore Identity token-vault credential provider).\nTo call OpenAI-style models through **Bedrock Mantle without a key**, pick the\nBedrock provider and set API Format to `Responses` or `Chat Completions` with a\nMantle model id (e.g. `openai.gpt-4o`). You can switch providers between turns of\none session and the conversation continues.\n\n### Memory & sessions\n\n**Memory Mode** (Provisioning Options) chooses **Managed** (auto-provision, the\ndefault — configurable strategies and event expiry), **Bring Your Own ARN**, or\n**Disabled**. A populated **Memory ARN** is always treated as BYO so v0.1\nworkflows are unaffected.\n\nThree related-but-distinct controls govern what the agent remembers — getting\nthese straight prevents the most common \"it didn't remember\" confusion:\n\n- **Session ID** = *continuity of one conversation.* Reuse the same value across\n  runs to continue a conversation; a new/blank value starts a fresh one. **Left\n  blank, every run gets a new random session** and the agent won't recall prior\n  turns — the output's `sessionSource` field reports `generated` (new) vs\n  `provided` (continuing). For multi-turn, set a stable Session ID (e.g. bound to\n  a user/thread id via an expression).\n- **Memory Mode** = *whether anything is persisted/recalled at all.* Managed or\n  BYO = on; Disabled = stateless. This is the on/off switch.\n- **Actor ID** (Additional Options) = *whose memory, within enabled memory.*\n  Memory is scoped by `actorId + sessionId`, so different actors on the same\n  agent get isolated histories. It has no effect when memory is Disabled.\n\nSo: short-term continuity needs a stable **Session ID**; long-term recall needs\n**Memory Mode** on; per-user isolation needs an **Actor ID**.\n\n### Tools & skills\n\nTurn on the **Add Tools** toggle to reveal the Tools section, and **Add Skills**\nto reveal the Skills section (both are hidden by default to keep the node\ncompact). Tools now include **Gateway** with optional OAuth outbound auth and\n**Inline Functions**, alongside Browser, Code Interpreter, and remote MCP.\n**Skills** load domain knowledge on demand from the AWS catalog (glob patterns),\nGit, S3, or a filesystem path. (Need web search? Point a Remote MCP tool at a\nsearch MCP server — a managed `agentcore_web_search` type is documented by\nAgentCore but not yet accepted by the harness API.)\n\n#### Inline-function round-trip\n\nAdd an **Inline Function** tool (name, description, JSON input schema). When the\nagent calls it, the invocation returns `stopReason: \"tool_use\"` and the output's\n`toolUses[]` carries the `toolUseId`, `name`, and parsed `input`. Compute the\nresult in your workflow, then on a second node placement (same Session ID) fill\nthe **Tool Results** field (Tool Use ID, function name, original input, result\ncontent). The node replays the assistant tool-use + your tool-result on the same\nsession and the agent resumes. The Prompt may be blank on that second call.\n\n### VPC, containers, filesystems\n\nSet **Network Mode = VPC** (plus subnets and security groups) on the credential\nto run harnesses in your VPC. Provisioning Options add a **Container Image URI**\n(linux/arm64 ECR) and **Filesystem Mounts** (session storage with no VPC; EFS and\nS3 Files access points require VPC).\n\n> **VPC requirements:** your subnets need **no internet access**. In VPC mode the\n> harness pulls its managed container from a **private ECR repository in the same\n> Region**, so create **VPC endpoints** instead of a NAT gateway: interface\n> endpoints for `com.amazonaws.<region>.ecr.dkr` and\n> `com.amazonaws.<region>.ecr.api`, a gateway endpoint for\n> `com.amazonaws.<region>.s3`, and an interface endpoint for\n> `com.amazonaws.<region>.bedrock-runtime` if the agent calls Bedrock for\n> inference. Without them, sessions fail to start on image-pull timeouts. The\n> execution role also needs private-ECR pull permissions (see\n> [IAM setup](#iam-setup)). AWS keeps the canonical list in\n> [Network configuration](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/harness-security.html#harness-network-config).\n> **First creation of a VPC harness is slow** (network\n> interface provisioning in your subnets can take several minutes); the node waits\n> up to 10 minutes. Subsequent runs reuse the harness and return in seconds.\n\n### OAuth Bearer invoke\n\nFor a harness with an inbound OAuth (JWT) authorizer, set **Authentication =\nOAuth Bearer Token** on the node, and enter your JWT in the **OAuth Bearer\nToken** field of the Amazon Bedrock AgentCore API credential. The node invokes\nInvokeHarness over HTTPS (through n8n's HTTP request helper) with an\n`Authorization: Bearer …` header; provisioning and all other control-plane calls\nstill use the credential's AWS keys with SigV4.\n\n#### Bearer token security (bring-your-own JWT)\n\nThis path is for harnesses configured with an **inbound JWT (OIDC) authorizer**\nonly. The token is a **JWT issued by your identity provider** (e.g. Amazon\nCognito or any OIDC IdP), not an AWS SigV4 / Bedrock API key. The node does\n**not** mint, exchange, or derive it — it is strictly **bring-your-own**: you\nsupply the JWT and the node passes it through verbatim as the\n`Authorization: Bearer …` header. It is stored encrypted in the n8n credential\nvault (never in the workflow JSON), read per-execution, and never logged.\nBecause you own the token, follow these practices:\n\n- **Prefer short-lived JWTs and rotate them.** The token is a static value on\n  the credential, so set a short TTL at your IdP and update the credential when\n  it expires. The node validates the token is a well-formed, unexpired JWT\n  before calling AWS, so an expired token fails fast with a clear message rather\n  than an opaque 401.\n- **Constrain the authorizer.** The inbound JWT authorizer is IdP-agnostic and\n  validates tokens against the discovery URL plus any of: allowed **audiences**\n  (`aud`), allowed **clients** (`client_id`), allowed **scopes**, and required\n  **custom claims** (e.g. `group == Developer`). At least one of these must be\n  configured, and if you set several the authorizer enforces all of them — so\n  scope it tightly so only tokens minted for this harness are accepted. See\n  [inbound JWT authorizer](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/inbound-jwt-authorizer.html).\n- **Plan for revocation and rotation.** Revoke at the IdP (rotate signing keys,\n  revoke the session/refresh token, or disable the client) if a token is\n  exposed, then update or clear the credential.\n- **Keep the token in the credential, not in workflow data.** Storing it on the\n  credential keeps it out of the workflow JSON and execution logs.\n\nFor the full inbound-OAuth setup and how end-user identity threads through to\ndownstream tools, see\n[AgentCore Identity](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/identity.html)\nand the\n[AgentCore harness security guide](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/harness-security.html).\nIf you don't need per-user identity propagation, the default **AWS SigV4** path\n(IAM credentials from the n8n credential vault) is simpler and avoids handling\ntokens entirely.\n\n### Versions & endpoints\n\nEnable **List Versions** to return all immutable versions. Set an **Endpoint\nName** (+ optional **Target Version**) to create or update a named endpoint\npinned to a version. Use **Additional Options ▸ Endpoint (Qualifier)** to invoke\na specific endpoint instead of the latest version.\n\n> **Migrating from v0.1?** v0.2 is additive — existing workflows keep working.\n> The one behavior change to know: memory now defaults to **Managed** for new\n> placements (a populated Memory ARN is still honored as BYO; set Memory Mode =\n> Disabled for the old no-memory behavior). Force Recreate now disassociates\n> managed memory instead of deleting it, and the run-mode default model is\n> Claude Sonnet 4.6.\n\n## Examples\n\nThe `examples/` folder has importable workflows:\n\n1. **`01-mcp-research-agent.json`** — Research agent using Exa search (remote MCP)\n2. **`02-code-interpreter.json`** — Data analyst agent that writes and runs Python\n3. **`03-multiturn-support.json`** — Webhook-triggered support agent with session persistence\n4. **`04-multi-provider-switch.json`** — Bedrock on turn 1, Gemini via LiteLLM on turn 2, same session (v0.2)\n5. **`05-oauth-invoke.json`** — Invoke an OAuth-protected harness with a Bearer token set on the AgentCore API credential (v0.2)\n6. **`06-skills-agent.json`** — Agent loading AWS catalog + Git + S3 skills (v0.2)\n7. **`07-inline-function-roundtrip.json`** — Inline function tool_use → compute result → send it back (v0.2)\n8. **`08-vpc-filesystem.json`** — VPC harness with an EFS access-point mount (v0.2)\n9. **`09-memory-isolation-test.json`** — Three-step check that memory persists across separate executions and that one actor never sees another's memory. Requires only the `agentCoreApi` credential; run the three webhooks in order and read the `pass` field from each verdict node.\n\nImport any of them via **Workflows → Import from File** in n8n. The v0.2 examples\ncontain placeholder ARNs / IDs — replace them with your own.\n\n### End-to-end templates\n\nThe `examples/templates/` folder has fuller workflows that show the agent working\nalongside other n8n nodes in complete, importable automations:\n\n- **`calculate-campaign-statistics-from-a-webhook.json`** — Webhook batch of numbers → code interpreter computes statistics in a microVM → n8n independently recomputes and labels the result → Slack.\n- **`remember-each-customer-across-chat-sessions.json`** — Chat assistant with per-customer long-term memory, scoped by Actor ID. `agentCoreApi` only.\n- **`add-long-term-memory-to-an-n8n-ai-agent.json`** — n8n's own AI Agent with this node attached as a tool, giving it memory that outlives the Simple Memory buffer.\n\nIf you publish one of these (or your own workflow) to the n8n template library,\nrun it through the pre-submission check first. Exporting from the n8n editor bakes\nin your credential IDs and instance fingerprint, which must not appear in a public\ntemplate:\n\n```bash\nnode scripts/check-template-export.mjs path/to/exported-workflow.json\n```\n\n## Local development\n\n### Step 1 — Clone and install\n\n```bash\ngit clone https://github.com/aws/n8n-nodes-agentcore.git\ncd n8n-nodes-agentcore\nnpm install\n```\n\n### Step 2 — Build and test\n\n```bash\nnpm run build\nnpm test\n```\n\n`npm run build` compiles TypeScript to `dist/` and copies the SVG icon. `npm\ntest` runs the unit suite (vitest); it is offline and needs no AWS credentials.\n\n### Step 3 — Link into a local n8n instance\n\n```bash\n# from the repo root\nnpm link\n\n# in the n8n custom directory\nmkdir -p ~/.n8n/custom\ncd ~/.n8n/custom\nnpm init -y           # if package.json doesn't already exist\nnpm link @aws/n8n-nodes-agentcore\n```\n\n### Step 4 — Start n8n\n\n```bash\nnpx n8n start\n```\n\nOpen `http://localhost:5678`. The **Amazon Bedrock AgentCore** node should appear in the node palette under the Development / Utility categories.\n\n### Step 5 — Configure credentials and run an example\n\n1. Add an **Amazon Bedrock AgentCore API** credential with your IAM access keys, region, and execution role ARN\n2. **Workflows → Import from File** → select `examples/01-mcp-research-agent.json`\n3. Attach the credential to the agent node\n4. Click **Execute Workflow**\n\nFirst run takes ~30 seconds (harness creation). Subsequent runs are instant.\n\n### Step 6 — Verify\n\nCheck that a harness was created in your AWS account:\n\n```bash\naws bedrock-agentcore-control list-harnesses --region us-west-2\n```\n\nYou should see `research_agent-<10 char id>` in `READY` state.\n\n## Publishing\n\nReleases are cut through the **Release** GitHub Actions workflow\n(`.github/workflows/release.yml`), which publishes to npm via\n[trusted publishing](https://docs.npmjs.com/trusted-publishers) (OIDC, with\nprovenance) behind a manual approval gate — no tokens.\n\n### Cutting a release\n\n1. Ensure CI is green and `CHANGELOG.md` has an entry for the new version.\n2. **Actions → Release → Run workflow**, and pick the bump type (patch / minor / major / prerelease). The workflow bumps the version and opens a 3release/vX.Y.Z` pull request.\n4. Review and merge that release PR into `main`.\n5. Approve the `npm-publish` environment on the waiting run. The publish job then verifies the version on `main`, builds, publishes `@aws/n8n-nodes-agentcore` with provenance, tags `vX.Y.Z`, and creates the GitHub Release.\n\nThe package name `@aws/n8n-nodes-agentcore` matches n8n’s required\n`@scope/n8n-nodes-*` convention, so n8n’s community node scanner will discover it.\n\n> **First publish only:** trusted publishing can’t create a package that doesn’t\n> exist yet, so the initial `0.1.0` is published manually with a token. Every\n> release after that uses the workflow above.\n\n### Discovery on n8n\n\nn8n has no per-node registry in its docs. Unverified community nodes are\ndiscovered as npm packages and installed on **self-hosted** n8n via\n**Settings → Community Nodes** (or `npm install`) by package name.\n\nAppearing in the in-editor nodes panel, and installing on **n8n Cloud**, both\nrequire the node to be **verified** through the\n[n8n Creator Portal](https://creators.n8n.io/nodes). n8n's verification requires\nan MIT license and no runtime dependencies; this node is MIT-licensed, ships with\nzero runtime dependencies, and **is verified**. It is listed at\n[n8n.io/integrations/amazon-bedrock-agentcore](https://n8n.io/integrations/amazon-bedrock-agentcore/).\n\n### Announce\n\n- Post in the n8n community forum under **Show and Tell**\n- Link to the GitHub repo, the npm package, and one of the example workflows\n\n## Roadmap\n\n|Version           |Capability                                                                                       |\n|------------------|-------------------------------------------------------------------------------------------------|\n|**v0.1**          |Run Agent, Invoke Existing, MCP / Browser / Code Interpreter / Gateway tools, streaming, sessions|\n|**v0.2**          |Multi-provider models, managed memory, VPC, custom containers, filesystem mounts, skills, inline functions, versions & endpoints|\n|**v0.4** (current)|OAuth Bearer Token moved to the credential, Add Tools / Add Skills toggles, AWS calls through n8n's HTTP helper, node `typeVersion` 2|\n|**later**         |ExecuteCommand (shell) with Bearer, custom Browser/Code Interpreter resources, CloudFormation quick-create, Export to Code, Step Functions|\n\n## Limitations (v0.2)\n\n- **No shell ExecuteCommand.** `InvokeAgentRuntimeCommand` (root shell in the\n  microVM, not scoped by `allowedTools`) is intentionally not exposed for\n  security reasons.\n- **OpenAI-via-Mantle without a key** uses the Bedrock provider with API Format\n  `Responses`/`Chat Completions`; the SDK's `openAiModelConfig` requires an API\n  key (Q1).\n- **First run is slow.** ~30 seconds for harness creation; managed memory adds a\n  little more. Subsequent runs are instant.\n- **Workflow static data** holds the harness ARN. Renaming an agent creates a new\n  harness; the old one remains in your account until manually deleted.\n- **Inline functions are stateless across calls.** The tool-result round-trip is\n  a two-node pattern (Tool Results field), not an in-node interactive pause.\n\n## Troubleshooting\n\n| Symptom | Likely cause & fix |\n|---|---|\n| **The agent doesn't remember previous turns** | The **Session ID** was left blank, so each run is a new conversation (output shows `sessionSource: \"generated\"`). Set a stable Session ID and reuse it across runs. See [Memory & sessions](#memory--sessions). |\n| **`AccessDenied` / `not authorized to perform` on the first run** | Either the **Bedrock model isn't enabled** (Bedrock console → Model access), or the **execution role is missing a scoped action** for the feature you used (e.g. `bedrock-mantle:CreateInference` for Mantle models, token-vault read for OpenAI/Gemini keys, `InvokeGateway` for gateways). See [IAM setup](#iam-setup). |\n| **First run hangs for ~30 seconds** | Expected — the node is creating the harness on first use. Subsequent runs reuse it and return in a few seconds. |\n| **\"Harness … did not reach READY within …\" on a VPC harness** | VPC harness creation is slow (ENI provisioning in your subnets). The node now waits up to 10 minutes; if you still see this, the harness is often **still creating** — re-run the node shortly and it will find the now-READY harness and invoke it. A *persistent* failure (or `CREATE_FAILED`) usually means the managed container image could not be pulled: check that the **VPC endpoints** exist (`ecr.dkr` + `ecr.api` interface, `s3` gateway) and that the **execution role has private-ECR pull permissions** on `repository/harness-*`. See [IAM setup](#iam-setup). |\n| **OpenAI / Gemini model errors about a missing key** | Direct OpenAI/Gemini require an **API Key ARN** (a token-vault credential provider) in Model Options. To use OpenAI-style models without a key, pick the **Bedrock** provider with API Format `Responses`/`Chat Completions` and a Mantle model id. |\n| **Two \"Amazon Bedrock AgentCore\" nodes in the palette** | A leftover/older install. Remove the stale package from `~/.n8n/nodes` (or `~/.n8n/custom`), then fully restart n8n and open a fresh browser tab. |\n| **OAuth invoke returns 401 / Unauthorized** | The harness needs an **inbound JWT authorizer** configured, and the Bearer Token must be a valid, unexpired JWT from that IdP whose `aud`/`client_id`/scopes satisfy the authorizer. See [OAuth Bearer invoke](#oauth-bearer-invoke). |\n| **Changed config but the agent behaves the same** | The node updates the harness only when the config hash changes. Confirm the field actually changed; the output `harness.version` increments on each update. |\n| **Node not in the palette after install** | Fully restart the n8n process (not just reload the browser) and open a fresh/incognito tab — n8n caches the node palette and icons. |\n\n## Getting help and contributing\n\n- **Bug reports and feature requests:** [open an issue](https://github.com/aws/n8n-nodes-agentcore/issues)\n- **Security issues:** please do not open a public issue. See [SECURITY.md](./SECURITY.md) for the disclosure process.\n- **Contributing:** see [CONTRIBUTING.md](./CONTRIBUTING.md) for guidelines, development setup, and the Developer Certificate of Origin process.\n- **Code of Conduct:** this project adheres to the [Amazon Open Source Code of Conduct](./CODE_OF_CONDUCT.md).\n\n## Support\n\nThis is a community-maintained project. Maintainers respond to GitHub issues\non a best-effort basis. For production support of Amazon Bedrock AgentCore\nitself, please use [AWS Support](https://aws.amazon.com/support).\n\n## Trademarks\n\nAmazon Web Services, AWS, and Amazon Bedrock are trademarks of Amazon.com, Inc.\nor its affiliates. n8n is a trademark of n8n GmbH. All other trademarks are\nthe property of their respective owners. Use of these names does not imply\nendorsement.\n\n## License\n\nMIT","readmeFilename":"README.md"}