How do I give an AI agent memory across sessions?
Save useful context, then make sure the next session can retrieve it. A project file can hold a few decisions and constraints. A memory system becomes useful when selecting, updating, or sharing that context needs more support.
Start by writing the few project decisions an agent repeatedly forgets into a file it reads. Add a memory system when maintaining that file or finding relevant past work becomes the problem. Before adopting one, check how you inspect, correct, and delete what it remembers.
Surveyed 8 September 2026
Give an agent memory
Open in Tools →- 01Claude-MemAI Agents
Consider it for automatic capture and reuse of coding-session context. The upstream project now calls itself Grok Mem while retaining the claude-mem package name.
Tradeoff: Automatic capture reduces manual note-taking but can preserve irrelevant or sensitive material. Review the capture controls and storage before enabling it.
Alternative: Use a manually maintained project file when the important context is small and changes infrequently.
Product documentation - 02AgentMemoryAI Agents
Consider it when project context needs to persist across coding-agent sessions. Its focus is continuity of coding work.
Tradeoff: Evaluate it with a decision your agent actually forgets: can the next session retrieve the reason, and can you remove an obsolete version?
Alternative: For a human-readable knowledge base organized around research notes, consider arscontexta.
Product documentation - 03ReMeAI Agents
Consider it when you are building an agent application and need a memory-management component you can integrate into that system.
Tradeoff: Integration requires deciding what counts as a useful memory and how to evaluate retrieval. Installing the component does not make those choices for your application.
Alternative: For continuity in an existing coding assistant, start with a session-memory tool or a project instruction file.
Product documentation - 04TencentDB Agent MemoryAI Agents
Consider it for shared team memory. The project organizes chat, skills, wiki knowledge, and code relationships into distinct memory types.
Tradeoff: Shared memory needs ownership and access rules. A fact useful to one person's task may be inappropriate for another person's context.
Alternative: For a single person's research notes, arscontexta has a narrower knowledge-management focus.
Product documentation - 05arscontextaAI Tools
Consider it when the goal is a personal knowledge system built from connected notes, rather than simply carrying a chat into the next session.
Tradeoff: A useful note collection requires decisions about what to keep and how ideas connect. Automatic note creation does not remove that editorial work.
Alternative: For automatic coding-session capture, consider Claude-Mem; for a small project, start with a plain text file.
Product documentation
A curated selection in editorial order. Use the fit and evidence to judge it for your task. Something missing?
What to look for
- 01Can you read and edit the memory directly? Opaque memory that is wrong cannot be corrected.
- 02Does it store why, not just what? Decisions without reasons get reversed by the next session.
- 03Does it re-check stale facts against the code, or repeat them confidently forever?
Common questions
- What should an agent actually remember?
- Keep decisions and their reasons, constraints, and useful preferences. When a memory describes changing code, retain its source and check it against the repository before acting on it.
- Is agent memory the same as RAG?
- Memory persists information for later use. Retrieval-augmented generation brings selected external information into a model request. A memory system can use retrieval to find a relevant past decision instead of loading every saved fact.
More in Work with agents
- Code with an agentWork across a repository from an issue, prompt, or terminal session.
- Control a browserLet an agent navigate, inspect, test, and complete workflows on the web.
- Research with an agentFind sources, verify claims, synthesize evidence, and preserve citations.
- Connect tools with MCPExpose data and actions to agents through Model Context Protocol servers.
- Find agent skillsMarketplaces and indices for discovering, vetting, and installing agent skills.