Agent Memory

Definition: Agent memory is the ability of an AI agent to retain and recall information across conversations, tasks, and time. Memory transforms agents from stateless responders into intelligent collaborators that build context and improve over time.

Why Agent Memory Matters

Without memory, every interaction with an AI agent starts from zero. The agent doesn't know your preferences, previous decisions, or accumulated context. Memory changes this:

• Continuity — Pick up where you left off without re-explaining context
• Personalization — Agents learn your preferences, communication style, and workflows
• Accuracy — Access to historical context reduces hallucinations and improves relevance
• Efficiency — No need to repeat instructions or background information

Types of Agent Memory

Short-Term Memory (Context Window)

The conversation history within a single session. Limited by the model's context window — typically 128K-200K tokens for frontier models. Information here is immediately accessible but lost when the session ends.

Long-Term Memory (Persistent)

Information stored across sessions. Taskade agents use workspace projects as persistent memory — facts, preferences, and decisions are saved and retrievable in future conversations.

Workspace Memory (RAG)

Access to the entire workspace's knowledge through retrieval-augmented generation. Agents query your projects, documents, and databases using multi-layer search (full-text + semantic + OCR) to find relevant context.

Episodic Memory

Records of specific past interactions — what was discussed, what decisions were made, what outcomes resulted. Helps agents learn from experience and avoid repeating mistakes.

Memory Types at a Glance

Type	Duration	Scope	How It Works in Taskade
Short-Term	Single session	Current conversation	Model context window (128K-200K tokens)
Long-Term	Persistent	Across sessions	Workspace projects as persistent storage
Workspace (RAG)	Query-time	Entire workspace	Multi-layer search (full-text + semantic + OCR)
Episodic	Historical	Past interactions	Conversation logs and decision records

How Memory Works in Taskade

Taskade implements agent memory through the Workspace DNA architecture:

1. Memory (Projects) — All workspace data is accessible to agents as knowledge
2. Intelligence (Agents) — Agents reason over stored context to provide relevant responses
3. Execution (Automations) — Actions create new data that feeds back into memory

This creates the Living Memory Loop — each interaction compounds the agent's contextual intelligence.

Memory Configuration

• Agent Knowledge — Upload documents, connect projects, and define knowledge sources each agent can access
• Custom Instructions — Persistent system prompts that define agent behavior across all interactions
• 22+ Built-in Tools — Agents actively search and retrieve information from your workspace

Memory Best Practices

• Scope memory appropriately — Give agents access to relevant projects, not everything
• Keep knowledge sources current — Outdated information in memory produces outdated responses
• Use structured data — Tables and structured formats are easier for agents to retrieve and reason about
• Review and prune — Periodically review what agents have access to and remove irrelevant context

Further Reading:

• How to Train AI Agents with Your Knowledge — Configure agent memory in Taskade
• What Is RAG? — The retrieval technology powering workspace memory

Frequently Asked Questions About Agent Memory

Do AI agents remember previous conversations?

It depends on the platform. Taskade AI agents have persistent memory through workspace knowledge — they can access project data, documents, and previous interaction context across sessions. Standard chatbots like ChatGPT have limited session-based memory.

How is agent memory different from a context window?

The context window is the amount of text an AI model can process in a single interaction (128K-200K tokens). Agent memory extends beyond this through persistent storage and RAG, giving agents access to effectively unlimited historical context.

Can I control what an agent remembers?

Yes. In Taskade, you configure agent knowledge by selecting which projects, documents, and data sources each agent can access. You can add or remove knowledge sources at any time.

Related Wiki Pages: Agent Knowledge, Multi-Agent Teams, Agent Orchestration