{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-memgpt","slug":"memgpt","name":"MemGPT","type":"repo","url":"https://github.com/cpacker/MemGPT","page_url":"https://unfragile.ai/memgpt","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-memgpt__cap_0","uri":"capability://memory.knowledge.hierarchical.memory.management.with.tiered.storage","name":"hierarchical-memory-management-with-tiered-storage","description":"MemGPT implements a multi-tier memory architecture that separates short-term context (in-context window), working memory (editable state), and long-term storage (persistent vector embeddings). The system uses a sliding window approach where older messages are automatically summarized and moved to vector-indexed long-term memory, while maintaining a compact working memory buffer that fits within LLM token limits. This enables conversations that span thousands of messages without exceeding context windows.","intents":["I need my LLM agent to remember context from conversations weeks ago without hitting token limits","I want to build a stateful chatbot that can recall and reference past interactions across sessions","I need to manage conversation history efficiently while keeping recent context immediately accessible"],"best_for":["developers building long-running conversational agents","teams implementing persistent AI assistants with multi-session memory","builders creating customer support bots that need historical context"],"limitations":["summarization of old messages introduces lossy compression — fine details may be lost during tier transitions","vector similarity search for long-term memory retrieval is probabilistic, not deterministic — may miss relevant context","memory tier transitions add latency (~100-500ms per promotion depending on summarization model)","requires external vector database for production use — no built-in persistence layer"],"requires":["Python 3.8+","LLM API access (OpenAI, Anthropic, or local Ollama)","Vector database (Pinecone, Weaviate, or Chroma for embeddings storage)","sufficient disk space for conversation logs and embeddings"],"input_types":["text messages","conversation history (JSON/structured logs)","user metadata and context"],"output_types":["summarized memory blocks","vector embeddings","retrieved context chunks","conversation state snapshots"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-memgpt__cap_1","uri":"capability://memory.knowledge.core.memory.editing.with.structured.state.management","name":"core-memory-editing-with-structured-state-management","description":"MemGPT provides a structured 'core memory' system where the LLM can explicitly read and edit a JSON-like state object representing facts about the user, conversation goals, and system state. This differs from implicit memory (embeddings) by allowing deterministic, editable state that persists across turns. The LLM can call dedicated functions to update core memory fields, and these updates are immediately reflected in subsequent context windows.","intents":["I want my agent to explicitly track and update user preferences, goals, or facts in a structured way","I need the LLM to maintain editable state that it can reason about and modify during conversation","I want to inspect and manually edit the agent's understanding of the user or task state"],"best_for":["developers building task-oriented agents with explicit state tracking","teams implementing personalization systems where user preferences must be updatable","builders creating debugging-friendly agents where state is inspectable and modifiable"],"limitations":["core memory size is limited by token budget — typically 500-2000 tokens for practical use","no built-in schema validation — malformed updates can corrupt state if not carefully handled","updates are synchronous and blocking — large state modifications may add latency to response generation","no versioning or rollback mechanism — state changes are permanent unless manually reverted"],"requires":["Python 3.8+","LLM with function-calling capability (OpenAI, Anthropic, or compatible)","JSON serialization support in the LLM's function schema"],"input_types":["JSON state objects","function calls with state update payloads","user instructions for state modification"],"output_types":["updated JSON state","state change logs","confirmation of memory edits"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-memgpt__cap_10","uri":"capability://automation.workflow.debugging.and.introspection.tools","name":"debugging-and-introspection-tools","description":"MemGPT provides tools for inspecting and debugging agent behavior including memory state viewers, message logs, function call traces, and memory access patterns. Developers can inspect core memory, view long-term memory retrieval results, and trace the execution of agent functions. The framework logs all memory operations and provides APIs to query these logs for debugging and analysis.","intents":["I want to inspect what my agent is remembering and why it made certain decisions","I need to debug memory retrieval failures or understand why certain context wasn't retrieved","I want to trace the execution of my agent to understand its behavior"],"best_for":["developers building and debugging agents","teams troubleshooting agent behavior in production","builders creating agent monitoring and observability systems"],"limitations":["logging all memory operations adds overhead (~5-10% latency increase)","logs can grow very large for long-running agents — requires log rotation or archival","introspection tools are CLI/API-based — no built-in UI for visualization","debugging distributed multi-agent systems is complex — logs must be aggregated across agents"],"requires":["Python 3.8+","access to agent state and logs (local or remote)"],"input_types":["agent identifiers","time ranges or message IDs for log queries","filter criteria (function names, memory operations, etc.)"],"output_types":["memory state snapshots","message logs and traces","function call records","memory access patterns"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-memgpt__cap_11","uri":"capability://text.generation.language.prompt.engineering.and.system.message.management","name":"prompt-engineering-and-system-message-management","description":"MemGPT provides a system for managing and versioning system prompts and instructions that guide agent behavior. Prompts can include dynamic variables (user context, memory state, current goals) that are filled in at runtime. The framework supports prompt templates, versioning, and A/B testing of different prompts. System messages are automatically augmented with memory context (core memory, retrieved long-term memories) before being sent to the LLM.","intents":["I want to manage and version system prompts for my agents","I need to dynamically inject memory context into prompts at runtime","I want to A/B test different prompts to optimize agent behavior"],"best_for":["developers fine-tuning agent behavior through prompt engineering","teams managing multiple agent configurations and prompt versions","builders creating prompt optimization systems"],"limitations":["prompt engineering is more art than science — no built-in optimization algorithm","A/B testing requires manual setup and analysis — no built-in statistical testing","dynamic variable injection can fail silently if variables are missing — requires careful error handling","prompt versioning is manual — no automatic rollback or version management"],"requires":["Python 3.8+","understanding of prompt engineering and LLM behavior"],"input_types":["prompt templates (text with variable placeholders)","dynamic variables (user context, memory state, etc.)","prompt metadata (version, description, tags)"],"output_types":["rendered prompts with variables filled in","augmented system messages with memory context","prompt versions and history"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-memgpt__cap_2","uri":"capability://memory.knowledge.automatic.context.compression.via.summarization","name":"automatic-context-compression-via-summarization","description":"MemGPT automatically summarizes conversation segments when they exceed token budgets or age thresholds, using the LLM itself or a dedicated summarization model to compress multi-turn exchanges into concise semantic summaries. These summaries are then stored in long-term memory (as embeddings) while the original messages are archived. The system uses configurable policies to determine when summarization triggers (e.g., every N messages, when context window fills, or on time-based intervals).","intents":["I want old conversation history automatically compressed so it doesn't bloat the context window","I need to preserve the semantic meaning of past exchanges without storing every message verbatim","I want configurable policies for when and how aggressively to compress conversation history"],"best_for":["developers building long-running agents that need to handle unbounded conversation length","teams implementing cost-optimized LLM systems where token usage is a constraint","builders creating archival systems that need to preserve conversation semantics efficiently"],"limitations":["summarization is lossy — specific details, exact quotes, and nuanced context may be lost","summarization adds latency (typically 1-5 seconds per summary depending on segment length and model)","quality of summaries depends on the summarization model — poor summaries degrade downstream retrieval","no built-in mechanism to detect and prevent over-summarization (cascading summaries of summaries)"],"requires":["Python 3.8+","LLM API access for summarization (can use same model as main agent or separate)","vector database for storing summary embeddings"],"input_types":["conversation message sequences","summarization policy configuration","token budget thresholds"],"output_types":["compressed summary text","summary embeddings","metadata (original message count, compression ratio, timestamp)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-memgpt__cap_3","uri":"capability://memory.knowledge.vector.embedding.based.context.retrieval","name":"vector-embedding-based-context-retrieval","description":"MemGPT integrates with vector databases to store and retrieve conversation segments and summaries based on semantic similarity. When the agent needs context from long-term memory, it generates an embedding of the current query/context and performs a similarity search to retrieve the most relevant archived messages or summaries. This enables the agent to selectively pull relevant historical context without scanning the entire conversation history.","intents":["I want my agent to automatically find and retrieve relevant past context based on semantic similarity","I need efficient similarity-based search over thousands of archived messages","I want to augment the current context window with semantically relevant historical information"],"best_for":["developers building agents that need to search large conversation histories","teams implementing semantic search over archived interactions","builders creating RAG-enhanced agents that combine current context with historical retrieval"],"limitations":["vector similarity is probabilistic — may retrieve irrelevant results or miss relevant context depending on embedding quality","requires tuning of similarity threshold and retrieval count — no one-size-fits-all configuration","embedding generation adds latency (~50-200ms per query depending on model)","vector database must be kept in sync with conversation state — no built-in consistency guarantees"],"requires":["Python 3.8+","vector database (Pinecone, Weaviate, Chroma, Milvus, or compatible)","embedding model (OpenAI, Sentence Transformers, or local)","API keys and network access to vector database"],"input_types":["query text or context snippets","embedding vectors","similarity threshold parameters"],"output_types":["retrieved message/summary chunks","similarity scores","metadata (timestamps, source conversation IDs)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-memgpt__cap_4","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.function.calling","name":"multi-provider-llm-abstraction-with-function-calling","description":"MemGPT provides a unified interface for interacting with multiple LLM providers (OpenAI, Anthropic, local Ollama, etc.) with consistent function-calling semantics. The framework abstracts away provider-specific API differences, allowing agents to be written once and run against different backends. Function calling is implemented via a schema registry that maps agent functions to provider-specific formats (OpenAI tools, Anthropic tool_use, etc.).","intents":["I want to build an agent that works with multiple LLM providers without rewriting code","I need to switch between OpenAI, Anthropic, and local models without changing agent logic","I want a consistent function-calling interface across different LLM backends"],"best_for":["developers building portable agents that should work across multiple LLM providers","teams evaluating different LLM backends and wanting to avoid vendor lock-in","builders creating open-source agents that should work with both commercial and local models"],"limitations":["abstraction layer adds ~50-100ms latency per LLM call due to request/response transformation","not all LLM providers support identical function-calling semantics — some features may be unavailable for certain backends","local models (Ollama) may have limited function-calling support compared to commercial APIs","requires maintaining compatibility shims as provider APIs evolve"],"requires":["Python 3.8+","API keys for desired LLM providers (OpenAI, Anthropic, etc.) or local Ollama instance","network access to LLM endpoints"],"input_types":["agent function definitions","LLM provider configuration","conversation messages and context"],"output_types":["normalized LLM responses","function call invocations","provider-agnostic completion tokens"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-memgpt__cap_5","uri":"capability://planning.reasoning.agent.orchestration.with.message.passing","name":"agent-orchestration-with-message-passing","description":"MemGPT provides a message-passing architecture for orchestrating multi-agent systems where agents communicate via a shared message bus. Agents can send messages to each other, and the framework handles routing, queuing, and state synchronization. Each agent maintains its own memory (core memory and long-term storage) and can be independently configured with different LLM backends, memory policies, and function schemas.","intents":["I want to build a multi-agent system where agents collaborate and share information","I need agents to communicate asynchronously without tight coupling","I want each agent to have independent memory while sharing a conversation context"],"best_for":["developers building complex multi-agent systems with specialized roles","teams implementing hierarchical agent architectures (e.g., manager + worker agents)","builders creating collaborative AI systems where agents need to coordinate"],"limitations":["message passing adds latency for inter-agent communication (~100-500ms per message depending on queue depth)","no built-in consensus mechanism — agents may have conflicting views of shared state","debugging multi-agent systems is complex — message ordering and timing issues are hard to reproduce","requires careful design to avoid circular message dependencies or infinite loops"],"requires":["Python 3.8+","message queue or bus implementation (in-memory or external like Redis)","LLM API access for each agent"],"input_types":["agent definitions with roles and capabilities","message payloads (text, structured data)","routing rules and agent topology"],"output_types":["routed messages to target agents","agent responses and state updates","message logs and audit trails"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-memgpt__cap_6","uri":"capability://memory.knowledge.persistent.agent.state.serialization.and.recovery","name":"persistent-agent-state-serialization-and-recovery","description":"MemGPT provides serialization and deserialization of agent state (core memory, conversation history, embedding indices) to enable agent persistence across sessions and recovery from failures. Agent state can be saved to disk or external storage and restored to resume conversations from a checkpoint. This includes serialization of vector indices, conversation logs, and core memory snapshots.","intents":["I want to save an agent's state and resume it later without losing context","I need to implement fault tolerance so agents can recover from crashes","I want to create agent snapshots for debugging or auditing purposes"],"best_for":["developers building production agents that need to survive restarts","teams implementing fault-tolerant systems with agent state recovery","builders creating debugging tools that need to inspect and replay agent state"],"limitations":["serialization of large vector indices can be slow (seconds to minutes for million-scale embeddings)","no built-in compression — serialized state can be very large (GBs for long conversations)","deserialization requires compatible versions of dependencies — schema migrations are manual","vector index serialization may not be portable across different vector database backends"],"requires":["Python 3.8+","disk space or external storage (S3, GCS, etc.) for state snapshots","compatible vector database for index restoration"],"input_types":["agent state objects (core memory, conversation history, embeddings)","serialization format configuration (JSON, pickle, etc.)","storage location (local disk, S3, etc.)"],"output_types":["serialized state files","checkpoint metadata (timestamp, version, size)","restored agent state objects"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-memgpt__cap_7","uri":"capability://memory.knowledge.configurable.memory.policies.and.eviction.strategies","name":"configurable-memory-policies-and-eviction-strategies","description":"MemGPT allows fine-grained configuration of memory management policies including eviction strategies (LRU, LFU, time-based), summarization triggers, and tier promotion rules. Policies are specified declaratively and can be adjusted at runtime without restarting agents. The framework supports different policies for different memory tiers and can apply custom scoring functions to determine which memories to evict or promote.","intents":["I want to customize how my agent manages memory (e.g., aggressive summarization vs. retention)","I need different memory policies for different conversation types or user segments","I want to tune memory behavior based on token budget, latency, or cost constraints"],"best_for":["developers building agents with specific memory requirements or constraints","teams optimizing agent behavior for cost, latency, or quality trade-offs","builders creating configurable agent systems where memory behavior is a tunable parameter"],"limitations":["policy configuration is complex — requires understanding of memory tiers and eviction semantics","no built-in policy optimization — finding good policies requires manual tuning or experimentation","policies are applied globally or per-agent — no per-conversation or per-user granularity","policy changes may not apply retroactively to existing agent state"],"requires":["Python 3.8+","understanding of memory management concepts (eviction, promotion, summarization)"],"input_types":["policy configuration (YAML, JSON, or Python objects)","eviction strategy parameters (thresholds, scoring functions)","memory tier definitions"],"output_types":["applied policies","memory management logs","policy effectiveness metrics"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-memgpt__cap_8","uri":"capability://memory.knowledge.conversation.branching.and.alternative.path.exploration","name":"conversation-branching-and-alternative-path-exploration","description":"MemGPT supports conversation branching where agents can explore alternative responses or conversation paths without losing the original context. Branches are tracked separately with their own memory state, and users can switch between branches or merge them. This enables agents to reason about multiple possible futures or allow users to explore 'what-if' scenarios without affecting the main conversation thread.","intents":["I want my agent to explore multiple response options and let me choose the best one","I need to support conversation branching for interactive storytelling or decision trees","I want agents to reason about alternative paths without committing to one"],"best_for":["developers building interactive agents with branching narratives","teams implementing decision-support systems where users explore alternatives","builders creating agents that need to reason about multiple futures"],"limitations":["branching multiplies memory storage requirements — each branch maintains separate state","merging branches is non-trivial — conflicting state changes require manual resolution","UI/UX for branch visualization and navigation is complex — not built-in","no built-in mechanism to prune or garbage-collect abandoned branches"],"requires":["Python 3.8+","storage for multiple conversation branches (disk or database)","UI layer to visualize and navigate branches (not provided by MemGPT)"],"input_types":["conversation state to branch from","alternative response options","branch metadata (name, description)"],"output_types":["branch identifiers","branched conversation states","branch history and metadata"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-memgpt__cap_9","uri":"capability://memory.knowledge.user.context.and.metadata.management","name":"user-context-and-metadata-management","description":"MemGPT provides a system for managing user-specific context and metadata that persists across conversations. This includes user profiles, preferences, conversation history across sessions, and custom attributes. User context is automatically included in agent prompts and can be updated by the agent or external systems. The framework supports user segmentation and allows different memory policies or agent configurations per user.","intents":["I want my agent to remember user preferences and personalize responses across sessions","I need to track user-specific metadata and make it available to agents","I want to segment users and apply different agent behaviors based on user attributes"],"best_for":["developers building personalized conversational agents","teams implementing customer support systems with user history","builders creating multi-user systems where user context must be isolated and managed"],"limitations":["user context adds tokens to every prompt — must be carefully sized to avoid bloat","no built-in privacy/security controls — user data is stored in plaintext by default","user context updates are not transactional — concurrent updates may conflict","no built-in user authentication or authorization — must be implemented separately"],"requires":["Python 3.8+","user database or storage system (in-memory, SQL, or document store)","user identification mechanism (session IDs, user IDs, etc.)"],"input_types":["user identifiers","user profile data (JSON/structured)","user preferences and attributes","custom metadata"],"output_types":["user context objects","personalized prompts","updated user profiles"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","LLM API access (OpenAI, Anthropic, or local Ollama)","Vector database (Pinecone, Weaviate, or Chroma for embeddings storage)","sufficient disk space for conversation logs and embeddings","LLM with function-calling capability (OpenAI, Anthropic, or compatible)","JSON serialization support in the LLM's function schema","access to agent state and logs (local or remote)","understanding of prompt engineering and LLM behavior","LLM API access for summarization (can use same model as main agent or separate)","vector database for storing summary embeddings"],"failure_modes":["summarization of old messages introduces lossy compression — fine details may be lost during tier transitions","vector similarity search for long-term memory retrieval is probabilistic, not deterministic — may miss relevant context","memory tier transitions add latency (~100-500ms per promotion depending on summarization model)","requires external vector database for production use — no built-in persistence layer","core memory size is limited by token budget — typically 500-2000 tokens for practical use","no built-in schema validation — malformed updates can corrupt state if not carefully handled","updates are synchronous and blocking — large state modifications may add latency to response generation","no versioning or rollback mechanism — state changes are permanent unless manually reverted","logging all memory operations adds overhead (~5-10% latency increase)","logs can grow very large for long-running agents — requires log rotation or archival","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=memgpt","compare_url":"https://unfragile.ai/compare?artifact=memgpt"}},"signature":"N4U6FXQb38l0+sZ+AGTt9pSehWRGmiZKh8T8LjS9H0qy0/nQw4uLaMLDS9dA/Th01fj0WuIk52ien6idHRvNCg==","signedAt":"2026-06-21T21:01:56.496Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/memgpt","artifact":"https://unfragile.ai/memgpt","verify":"https://unfragile.ai/api/v1/verify?slug=memgpt","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}