Agentbase Memory

AgentBase Memory Service

Manage memory stores for AI agents on the GreenNode AgentBase platform — conversation history (short-term events) and semantic fact extraction (long-term memory records). Parse the user's arguments to determine the operation and optional name/ID.

Base URLs

• Memory: https://agentbase.api.vngcloud.vn/memory
• Console (Memory): https://aiplatform.console.vngcloud.vn/memory

Authentication & Endpoints

Read the shared auth setup reference at /agentbase skill's references/auth-setup.md for full IAM credential configuration. In brief: run bash .claude/skills/agentbase/scripts/check_credentials.sh iam to verify credentials are configured, then use TOKEN=$(bash .claude/skills/agentbase/scripts/get_token.sh) to obtain a token. NEVER read .greennode.json or .env directly — always use the helper scripts. On 401: re-run with --force. If check_credentials.sh iam returns MISSING, STOP — you MUST read the "If Credentials Are Not Found" section in /agentbase skill's references/auth-setup.md and follow it exactly. Do NOT skip this or provide your own credential setup instructions.

IMPORTANT: Before constructing any API URL, read /agentbase skill's references/endpoints.md for the domain validation whitelist. Only use domains listed there.

Interaction Guidelines

• Never assume API response structure — always inspect the actual response first before extracting or filtering data. Do not guess field names.
• Guide first, act only when asked — if the user asks "how to" create memory or persist conversations, respond with instructions and guidance only. Do NOT execute API calls or create resources unless they explicitly ask you to do it for them.
• Confirm before executing (HARD GATE) — before performing any action (create, update, delete, retrieve, generate, search), present a clear summary of what will be done (including all parameters and values) and ask the user to confirm. Do NOT auto-execute. Only proceed when the user responds with an explicit confirmation keyword: yes, confirm, ok, approve, proceed, go ahead, do it, ship it, lgtm, or equivalent affirmative. If the user responds with ANYTHING ELSE (parameter changes, questions, corrections, additional info, or ambiguous text), treat it as adjustment input — update the plan and re-present the full summary for confirmation again. NEVER interpret a non-confirmation response as approval. For destructive operations (delete memory, delete event, delete memory record), additionally warn that the action is irreversible.
• Never auto-decide parameters — when an action requires parameters (e.g., memory name, strategy type, event expiry, namespace, actorId, sessionId), always ask the user for each required value. You may recommend sensible defaults or examples, but never auto-select or impose values without the user's explicit agreement.
• Present options, let user choose — when there are multiple choices (e.g., strategy type, operations), list the available options and let the user pick. Do not make the choice for them.
• Dry-run support: When user requests --dry-run or preview, show the exact API request (method, URL, headers, payload) and explain the expected outcome WITHOUT executing. Let user review before proceeding.
• Always read full API response body — when calling platform APIs, capture and read the full JSON response (not just status codes). This avoids misidentifying field names or data structures, ensures correct field extraction, and enables better error handling and debugging.

Runtime Auto-Injection

When an agent is deployed on AgentBase Runtime, the IAM service account and Agent Identity are managed by the runtime system and automatically injected into the container as GREENNODE_CLIENT_ID, GREENNODE_CLIENT_SECRET, and GREENNODE_AGENT_IDENTITY. The SDK automatically uses these — no manual credential configuration needed in agent code. Memory operations and LangGraph bridge integrations all work automatically.

The IAM credentials and memory management described in this skill are for local development and platform management (creating/listing/updating resources from outside the runtime). See /agentbase-deploy runtime for details on runtime environment management.

Memory Service

The Memory Service provides conversation history (short-term events) and semantic fact extraction (long-term memory records) for AI agents.

Core Concepts

Strategy Types

• SEMANTIC - General semantic fact extraction
• USER_PREFERENCE - Extract user preferences and habits
• CUSTOM - Custom fact extraction with a user-defined prompt (customFactExtractionPrompt). When using customFactExtractionPrompt, the type MUST be CUSTOM — using SEMANTIC or USER_PREFERENCE with a custom prompt will silently ignore the prompt without any error. Always validate: if user provides a custom prompt, auto-correct type to CUSTOM and warn the user about the change. The custom prompt is prepended to the built-in extraction prompt, not a replacement.

Namespace Template

Controls how memory records are partitioned. Default: /strategies/{memoryStrategyId}/actors/{actorId}

Available variables: {memoryStrategyId}, {actorId}, {sessionId}

Note on actorId: The actorId represents the end-user (the person interacting with the agent), not the agent itself. Use any string that uniquely identifies the user (e.g. a user ID like alice, user-123). This allows the memory system to partition and recall facts per user. Do not confuse actorId with the agent's identity — for agent identity management, see /agentbase-identity.

Memory Operations Summary

Note: Memory Service uses 1-indexed pagination (page=1 is first page). This differs from Identity Service which uses 0-indexed pagination.

Read references/memory-ops.md for full API details, SDK examples, curl commands, and framework integration guides.

Memory Instructions

1. Parse the user's argument to determine the operation.
2. If credentials are not configured, present the user with the two options (Auto create / I already have) as described in the Authentication section above.
3. For create: ask for each parameter individually — name, description, eventExpiryDuration, and strategy details (name, type, namespaceTemplate, enableAutomaticMemoryRecordGeneration, optionally customFactExtractionPrompt). If user provides customFactExtractionPrompt, always set type to CUSTOM.
4. For get: always fetch both basic info (GET /memories/{id}) and strategies (GET /memories/{id}/long-term-memory-strategies) to present a complete view.
5. For operations requiring actorId, sessionId, or namespace, ask the user for these values.
6. Show SDK examples by default. Show curl examples if user specifically asks or if working outside Python.
7. For framework integration (LangChain/LangGraph checkpointer, long-term memory tools): Quick start — for LangChain, use AgentBaseMemoryEvents as checkpointer via create_agent(checkpointer=...). For LangGraph, use builder.compile(checkpointer=...). For complete examples, read references/memory-ops.md for the integration section, and references/langchain.md / references/langgraph.md for complete examples. Do NOT provide integration code without reading these first.

Troubleshooting