Memory
A cross-session memory mechanism for AI agents that establishes a persistent storage layer outside the model. It saves user preferences, correction history, and project experience as retrievable notes, enabling agents to accumulate personalized knowledge across different sessions.
Core Facts
The Agent's so-called "getting smarter with use" is based on rule induction and context memory retrieval, not model weight updates, so accumulated experience will be lost if the account is changed or memory is cleared.
The memory module enables an agent to remember context and historical interactions, enabling continuous decision-making.
LangChain introduced two paradigms—chain invocation (Chain) and agents (Agent)—where the former is suited for fixed sequential process tasks and the latter is suited for open-ended tasks that dynamically infer the next step.
Timeline (last 90 days)
Common strategies of LangChain's Memory component include retaining the full history, retaining only the most recent N turns, summarizing and compressing the history, and storing long-term facts in external storage for on-demand retrieval.
LangChain maintains message history at the application layer through components such as Memory to achieve coherent multi-turn interaction, but does not change the inherently stateless nature of the model itself.
AWS AgentCore comprises nine capabilities, of which Memory, Runtime and Identity support cross-session memory scenarios.
Forecall, on 88 tools across 4 servers (Memory, Notion, Playwright, Chrome DevTools), generated 8 single-step requests per tool and tested model selection accuracy with Claude Sonnet 5.5 and GPT-6.1 Sol respectively, with accuracy across all servers ranging from 91% to 99%.
Cognition's Devin has a mechanism called Memory that can retain awareness of the user's working habits across sessions.
Devin's Memory belongs to the semantic layer of long-term memory, while Dreaming is closer to metadata management of long-term memory.
Devin's memory system consists of two mechanisms: Memory and Dreaming.
Historical conversations are usually truncated by turns or by a token-count window, to avoid the prompt growing indefinitely and exceeding the model's context length limit or driving up call costs.
When OpenAI's ChatGPT launched the Memory feature, it went through controversy, as the system automatically memorizing user information across sessions caused some users to worry about privacy leakage or information cross-contamination
OpenAI is attempting to build cross-session user profiles through the Memory feature and GPT Actions.
26 more timeline events
All Facts (20)
The Agent's so-called "getting smarter with use" is based on rule induction and context memory retrieval, not model weight updates, so accumulated experience will be lost if the account is changed or memory is cleared.
65%VerifiedThe memory module enables an agent to remember context and historical interactions, enabling continuous decision-making.
65%VerifiedLangChain introduced two paradigms—chain invocation (Chain) and agents (Agent)—where the former is suited for fixed sequential process tasks and the latter is suited for open-ended tasks that dynamically infer the next step.
65%UnverifiedCommon strategies of LangChain's Memory component include retaining the full history, retaining only the most recent N turns, summarizing and compressing the history, and storing long-term facts in external storage for on-demand retrieval.
50%UnverifiedLangChain maintains message history at the application layer through components such as Memory to achieve coherent multi-turn interaction, but does not change the inherently stateless nature of the model itself.
50%UnverifiedAWS AgentCore comprises nine capabilities, of which Memory, Runtime and Identity support cross-session memory scenarios.
50%UnverifiedForecall, on 88 tools across 4 servers (Memory, Notion, Playwright, Chrome DevTools), generated 8 single-step requests per tool and tested model selection accuracy with Claude Sonnet 5.5 and GPT-6.1 Sol respectively, with accuracy across all servers ranging from 91% to 99%.
50%UnverifiedDevin's memory system consists of two mechanisms: Memory and Dreaming.
50%UnverifiedDevin's Memory belongs to the semantic layer of long-term memory, while Dreaming is closer to metadata management of long-term memory.
50%UnverifiedCognition's Devin has a mechanism called Memory that can retain awareness of the user's working habits across sessions.
50%UnverifiedHistorical conversations are usually truncated by turns or by a token-count window, to avoid the prompt growing indefinitely and exceeding the model's context length limit or driving up call costs.
50%UnverifiedWhen OpenAI's ChatGPT launched the Memory feature, it went through controversy, as the system automatically memorizing user information across sessions caused some users to worry about privacy leakage or information cross-contamination
50%UnverifiedOpenAI is attempting to build cross-session user profiles through the Memory feature and GPT Actions.
50%UnverifiedContent recorded by Memory is saved as brief notes and is exclusive to individual users.
50%UnverifiedSession ID is used to distinguish different conversation contexts, so that when multiple users are conversing simultaneously, each user's message history is stored separately.
50%UnverifiedClaude's memory entries can be manually managed by the user or automatically generated and updated by Claude during conversations.
50%UnverifiedAccording to Claude's official documentation, enabling Incognito Chat does not invoke Claude's existing memories.
50%UnverifiedThe content of Incognito Chat will not be incorporated into future memory entries.
50%UnverifiedClaude's Memory feature allows Claude to retain information about the user across multiple conversations, such as occupation, preferences, and habits.
50%UnverifiedWithout a Memory node, each reply from the Agent is independent and cannot be associated with previous conversation content.
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