{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"letta-memgpt","slug":"letta-memgpt","name":"Letta (MemGPT)","type":"framework","url":"https://github.com/letta-ai/letta","page_url":"https://unfragile.ai/letta-memgpt","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"letta-memgpt__cap_0","uri":"capability://memory.knowledge.virtual.context.window.management.with.automatic.summarization","name":"virtual context window management with automatic summarization","description":"Implements a sliding-window context management system that maintains unlimited conversation history by automatically summarizing older messages and archiving them when the LLM's context window approaches capacity. Uses a tiered memory architecture where recent messages stay in the active context, mid-range messages are compressed via LLM summarization, and older messages are moved to archival storage with vector embeddings for semantic retrieval. The system tracks token counts per message and dynamically decides what to keep in-context vs. archive based on configurable thresholds and message importance scoring.","intents":["Build agents that maintain coherent long-term conversations without losing context or hitting token limits","Automatically compress conversation history while preserving semantic meaning for retrieval","Configure context window strategies per LLM provider (OpenAI, Anthropic, etc.) with different token budgets"],"best_for":["Teams building conversational AI agents that need multi-session memory","Developers requiring unlimited conversation history without manual pruning","Applications where context window size varies by LLM provider"],"limitations":["Summarization adds latency (~1-3s per compression cycle depending on message volume and LLM speed)","Archived messages require vector search for retrieval — not guaranteed to surface all relevant context","Summarization quality depends on underlying LLM capability; lossy compression may miss nuanced details"],"requires":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, Google Gemini, etc.)","Vector database for archival storage (Chroma, Pinecone, or local SQLite with embeddings)","Sufficient compute for periodic summarization tasks"],"input_types":["conversation messages (text)","token count metadata","context window size configuration"],"output_types":["trimmed message list for LLM context","compressed summary messages","archived message embeddings"],"categories":["memory-knowledge","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_1","uri":"capability://memory.knowledge.structured.memory.block.system.with.self.editing.capabilities","name":"structured memory block system with self-editing capabilities","description":"Provides a multi-block memory architecture where agents maintain distinct, editable memory sections: persona (agent identity/instructions), human (user profile/preferences), and custom context blocks. Each block is independently versioned, searchable, and can be modified by the agent itself through dedicated memory-editing tools (core_memory_append, core_memory_replace). The system uses a Git-backed storage model for memory versioning, allowing rollback and audit trails. Memory blocks are injected into the system prompt at runtime, and the agent can introspect and modify its own memory based on conversation context.","intents":["Enable agents to learn and adapt by modifying their own memory during conversations","Maintain persistent agent personality and user context across sessions without manual updates","Audit and version-control agent memory changes for compliance and debugging","Allow agents to self-correct misconceptions by editing their stored knowledge"],"best_for":["Developers building adaptive agents that improve through interaction","Teams requiring audit trails for agent behavior and memory modifications","Applications where agent personality must evolve based on user feedback"],"limitations":["Memory block size is limited by context window — very large memory blocks reduce space for conversation","Self-editing requires careful prompt engineering to prevent agents from corrupting their own memory","Git-backed storage adds complexity; requires database setup and migration management","No built-in conflict resolution if multiple agents or processes modify memory simultaneously"],"requires":["Python 3.9+","PostgreSQL or SQLite database for memory persistence","Git repository or Git-compatible backend for version control","LLM provider with function-calling support (OpenAI, Anthropic, etc.)"],"input_types":["memory block content (text)","memory operation commands (append, replace, search)","agent instructions and context"],"output_types":["updated memory blocks","memory edit history/diffs","versioned memory snapshots"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_10","uri":"capability://memory.knowledge.conversation.message.persistence.and.retrieval.with.full.text.search","name":"conversation message persistence and retrieval with full-text search","description":"Implements a message persistence layer that stores all agent-user conversations in a database with support for full-text search, filtering, and retrieval. Messages are stored with metadata (timestamp, sender, message type, tool calls, etc.) and indexed for efficient querying. Supports searching conversations by content, date range, sender, or message type. Provides APIs for retrieving conversation history, exporting conversations, and analyzing conversation patterns. Integrates with the archival memory system to automatically extract and index important passages from conversations.","intents":["Retrieve full conversation history for agents across sessions","Search conversations by content or metadata for debugging and analysis","Export conversations for compliance, auditing, or analysis","Analyze conversation patterns and agent behavior over time"],"best_for":["Teams requiring conversation audit trails for compliance","Applications needing to analyze agent behavior and conversation patterns","Systems where agents need to reference past conversations"],"limitations":["Full-text search performance degrades with very large conversation volumes (millions of messages)","Storing all messages increases database size significantly","Message retrieval requires database queries; not suitable for real-time high-frequency access","Privacy concerns with storing sensitive user information in conversations"],"requires":["Python 3.9+","Database with full-text search support (PostgreSQL with pg_trgm, Elasticsearch, etc.)","Message schema and ORM models"],"input_types":["agent messages (text, tool calls, metadata)","search queries and filters","conversation ID or date range"],"output_types":["message records with metadata","search results with relevance ranking","conversation exports (JSON, CSV)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_11","uri":"capability://automation.workflow.batch.processing.and.scheduled.agent.execution","name":"batch processing and scheduled agent execution","description":"Provides batch processing capabilities for running agents on large datasets or executing agents on schedules. Supports batch job submission with input data (CSV, JSON, etc.), parallel execution across multiple agent instances, and result aggregation. Integrates with job scheduling systems (APScheduler, Celery) to enable periodic agent execution (e.g., daily reports, periodic data processing). Batch jobs can be monitored for progress, paused/resumed, and results can be exported or streamed to external systems.","intents":["Process large datasets by running agents in parallel on batches of data","Schedule agents to run periodically (daily, hourly, etc.) for automated tasks","Monitor batch job progress and handle failures with retries","Aggregate and export results from batch processing"],"best_for":["Teams running agents on large datasets or periodic schedules","Applications requiring automated agent-based data processing","Systems needing to parallelize agent execution for performance"],"limitations":["Batch processing requires careful resource management to avoid overloading system","Parallel execution adds complexity; debugging batch jobs is harder than single-agent execution","Result aggregation may be memory-intensive for very large batches","Scheduled execution requires reliable job scheduler infrastructure"],"requires":["Python 3.9+","Job scheduler (APScheduler, Celery, or similar)","Database for batch job state and results","Message queue for job distribution (optional, for distributed execution)"],"input_types":["batch data (CSV, JSON, database records)","batch job configuration","schedule specification (cron, interval)"],"output_types":["batch job status and progress","aggregated results","error logs and retry information"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_12","uri":"capability://planning.reasoning.human.in.the.loop.workflows.with.approval.gates.and.feedback.loops","name":"human-in-the-loop workflows with approval gates and feedback loops","description":"Implements human-in-the-loop (HITL) workflows where agents can request human approval before executing sensitive operations, and humans can provide feedback to improve agent behavior. The system pauses agent execution at designated checkpoints, routes requests to human reviewers, and resumes execution based on approval/rejection. Supports feedback collection (ratings, corrections, suggestions) that can be used to fine-tune agent behavior or update memory. Integrates with the tool execution system to gate sensitive tool calls, and with the memory system to incorporate human feedback.","intents":["Require human approval for sensitive agent actions before execution","Collect human feedback to improve agent behavior over time","Implement compliance workflows where human review is required","Enable agents to learn from human corrections and suggestions"],"best_for":["Teams deploying agents in regulated industries requiring human oversight","Applications where agent mistakes have significant consequences","Systems where human feedback is needed to improve agent quality"],"limitations":["HITL workflows add significant latency (humans may take hours/days to respond)","Requires human reviewer availability; bottleneck for high-volume operations","Feedback incorporation requires careful prompt engineering to avoid overfitting to individual feedback","Managing approval workflows at scale requires robust infrastructure"],"requires":["Python 3.9+","User interface for human reviewers (web dashboard, email, etc.)","Database for approval requests and feedback storage","Notification system to alert reviewers of pending approvals"],"input_types":["approval requests from agents","human feedback (approval/rejection, ratings, corrections)","agent context for human review"],"output_types":["approval decisions","feedback incorporated into agent memory","audit trail of human decisions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_13","uri":"capability://text.generation.language.voice.agent.support.with.audio.streaming.and.transcription","name":"voice agent support with audio streaming and transcription","description":"Provides voice interaction capabilities for agents with audio input/output streaming and automatic speech-to-text transcription. Agents can receive audio streams, transcribe them to text using speech recognition services, process the text, and generate audio responses using text-to-speech. Supports streaming audio for low-latency voice interactions and integrates with voice providers (OpenAI Whisper, Google Speech-to-Text, etc.). Handles audio format conversion and quality management.","intents":["Build voice-based agent interfaces for hands-free interaction","Enable agents to process audio input and generate spoken responses","Support real-time voice conversations with low latency","Integrate voice agents with existing agent infrastructure"],"best_for":["Teams building voice assistants or conversational voice interfaces","Applications requiring hands-free agent interaction","Systems where voice is the primary interaction modality"],"limitations":["Speech recognition accuracy varies by language, accent, and audio quality","Audio streaming adds latency compared to text-based interaction","Text-to-speech quality may not match human speech","Requires audio input/output hardware and network bandwidth"],"requires":["Python 3.9+","Speech-to-text service (OpenAI Whisper, Google, Azure, etc.)","Text-to-speech service (Google, Azure, ElevenLabs, etc.)","Audio streaming library (pyaudio, sounddevice, etc.)","Audio codec support for streaming"],"input_types":["audio streams (WAV, MP3, etc.)","audio configuration (sample rate, channels, codec)"],"output_types":["transcribed text","audio responses (WAV, MP3, etc.)","speech recognition confidence scores"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_14","uri":"capability://safety.moderation.multi.tenancy.and.role.based.access.control","name":"multi-tenancy and role-based access control","description":"Implements multi-tenant architecture where multiple organizations/users can use the same Letta instance with isolated data and access control. Each tenant has isolated agents, conversations, and data. The system implements role-based access control (RBAC) with roles like admin, agent-creator, viewer, etc., and fine-grained permissions for agent management, conversation access, and tool execution. Supports API key-based authentication and OAuth integration. Tenant isolation is enforced at the database and API levels.","intents":["Deploy Letta as a multi-tenant SaaS platform","Manage access control for different user roles and organizations","Isolate data between tenants for security and compliance","Support API key-based authentication for programmatic access"],"best_for":["Teams building multi-tenant SaaS platforms with Letta","Organizations requiring role-based access control","Systems with multiple users/organizations sharing infrastructure"],"limitations":["Multi-tenancy adds complexity to database schema and queries","Tenant isolation requires careful implementation to prevent data leaks","RBAC enforcement adds latency to permission checks","Cross-tenant operations (e.g., sharing agents) are complex to implement"],"requires":["Python 3.9+","Database with support for row-level security (PostgreSQL recommended)","Authentication system (OAuth, API keys, etc.)","Tenant context management in request handling"],"input_types":["tenant ID and user context","role and permission definitions","API key or OAuth token"],"output_types":["tenant-isolated data and agents","access control decisions","audit logs of access"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.message.format.transformation","name":"multi-provider llm abstraction with unified message format transformation","description":"Provides a unified LLM client interface that abstracts over 10+ LLM providers (OpenAI, Anthropic, Google Gemini, Ollama, local models, etc.) with automatic message format transformation. The system implements a provider-agnostic message schema internally, then transforms messages to each provider's specific format (OpenAI's chat completion format, Anthropic's native format, etc.) at request time. Handles provider-specific features like prompt caching (OpenAI), thinking tokens (o1), tool-use schemas, and reasoning models. Includes built-in retry logic, error handling, and fallback mechanisms for provider failures.","intents":["Switch between LLM providers without changing agent code","Leverage provider-specific features (caching, reasoning, tool-use) while maintaining portability","Handle provider-specific errors and rate limits with automatic retries","Support both cloud and local LLM deployments through a unified interface"],"best_for":["Teams wanting to avoid vendor lock-in with a single LLM provider","Developers building cost-optimized agents that switch providers based on task complexity","Organizations running hybrid cloud + local LLM deployments"],"limitations":["Message format transformation adds ~50-100ms overhead per request","Provider-specific features (e.g., o1 thinking tokens) require custom handling and may not be fully portable","Error handling is provider-agnostic — some provider-specific errors may be masked","Prompt caching and optimization strategies vary by provider; not all features available on all providers"],"requires":["Python 3.9+","API keys for desired LLM providers (OpenAI, Anthropic, Google, etc.)","For local models: Ollama, vLLM, or compatible inference server","Network connectivity to provider APIs or local inference endpoints"],"input_types":["provider configuration (model name, API key, endpoint)","messages in Letta's internal schema","tool/function definitions"],"output_types":["LLM responses in provider-native format","transformed to unified Letta message schema","tool calls with provider-specific schemas"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_3","uri":"capability://tool.use.integration.tool.execution.with.sandboxing.and.rule.based.access.control","name":"tool execution with sandboxing and rule-based access control","description":"Provides a tool management and execution system where agents can call custom Python tools with configurable sandboxing and access control rules. Tools are registered with schemas (input/output types, descriptions) and executed in isolated environments with resource limits (CPU, memory, execution time). The system includes a rule engine that evaluates tool-use policies before execution — agents can be restricted from calling certain tools, tools can be rate-limited, and execution can require human approval. Supports both synchronous and asynchronous tool execution with streaming support for long-running operations.","intents":["Allow agents to execute custom Python code safely without compromising system security","Enforce tool-use policies (e.g., 'agent can only call read tools, not write tools')","Require human approval for sensitive operations before agent execution","Monitor and log all tool executions for audit and debugging"],"best_for":["Teams deploying agents in production with security requirements","Applications requiring human-in-the-loop approval for sensitive agent actions","Developers building multi-agent systems with different capability levels"],"limitations":["Sandboxing adds execution overhead (~100-500ms per tool call depending on isolation level)","Resource limits may cause legitimate long-running tools to timeout","Rule evaluation is synchronous — complex policies may add latency","Sandboxing is process-level; determined attackers could potentially escape isolation"],"requires":["Python 3.9+","Tool definitions with proper type hints and docstrings","For advanced sandboxing: Docker or container runtime","Database for rule storage and execution logging"],"input_types":["tool definitions (Python functions with schemas)","tool-use policies (rule definitions)","tool invocation requests from agents"],"output_types":["tool execution results","execution logs and audit trails","approval requests for human review"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_4","uri":"capability://tool.use.integration.mcp.model.context.protocol.integration.with.native.tool.binding","name":"mcp (model context protocol) integration with native tool binding","description":"Integrates with the Model Context Protocol (MCP) standard, allowing agents to discover and use tools exposed via MCP servers. The system implements native MCP client bindings that communicate with MCP servers over stdio or HTTP transports, automatically translating MCP tool schemas into Letta's internal tool format. Agents can dynamically load tools from MCP servers at runtime, and the system handles MCP-specific features like resource management and sampling. Supports both local MCP servers and remote MCP endpoints.","intents":["Connect agents to tools exposed via MCP servers without manual integration","Dynamically discover and load new tools at runtime from MCP endpoints","Leverage the growing MCP ecosystem of pre-built tool integrations","Build agent systems that can adapt to new tool availability without code changes"],"best_for":["Teams building agents that need access to diverse external tools","Developers wanting to leverage MCP ecosystem without custom integration code","Organizations standardizing on MCP for tool distribution"],"limitations":["MCP server availability and reliability directly impact agent capability","Tool discovery and schema translation adds startup latency (~500ms-2s per MCP server)","Error handling for MCP server failures requires careful implementation","MCP protocol overhead may be slower than direct function calls for high-frequency tool use"],"requires":["Python 3.9+","MCP servers running and accessible (local or remote)","MCP client library (included in Letta)","Network connectivity to MCP endpoints"],"input_types":["MCP server configuration (endpoint, transport type)","MCP tool schemas","tool invocation requests"],"output_types":["translated tool schemas in Letta format","tool execution results from MCP servers","MCP resource metadata"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_5","uri":"capability://memory.knowledge.archival.memory.with.semantic.search.and.passage.based.retrieval","name":"archival memory with semantic search and passage-based retrieval","description":"Implements a vector-backed archival storage system for long-term memory that stores conversation passages, documents, and knowledge with semantic embeddings. When agents need to retrieve relevant information, the system performs vector similarity search across archived passages and returns the most relevant results. Passages are automatically chunked from documents and conversations, embedded using configurable embedding models, and stored in a vector database (Chroma, Pinecone, etc.). The system supports hybrid search (semantic + keyword) and can rank results by relevance, recency, or custom scoring functions.","intents":["Store and retrieve relevant historical information from unlimited conversation history","Build knowledge bases from documents and allow agents to search them semantically","Implement long-term learning by archiving and retrieving past interactions","Support multi-agent systems where agents can share knowledge through archival memory"],"best_for":["Teams building knowledge-intensive agents that need to reference past interactions","Applications requiring document-based knowledge bases with semantic search","Multi-agent systems where agents need to share learned information"],"limitations":["Vector search is approximate — may miss relevant information if embedding quality is poor","Embedding generation adds latency (~100-500ms per document depending on size and model)","Vector database scaling requires careful index management for large knowledge bases","Relevance ranking is heuristic-based; may require tuning for specific use cases"],"requires":["Python 3.9+","Vector database (Chroma, Pinecone, Weaviate, or similar)","Embedding model (OpenAI, Sentence Transformers, local models)","Document processing pipeline for chunking and embedding"],"input_types":["documents (text, PDF, code files)","conversation passages","search queries (text)"],"output_types":["ranked passage results with relevance scores","passage metadata (source, timestamp, context)","embedding vectors"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_6","uri":"capability://planning.reasoning.multi.agent.orchestration.with.agent.groups.and.coordination.patterns","name":"multi-agent orchestration with agent groups and coordination patterns","description":"Provides a multi-agent system framework where agents can be organized into groups and coordinated through different patterns (sequential, parallel, hierarchical, broadcast). Agents within a group can communicate through shared memory, message passing, or tool calls. The system manages agent lifecycle (creation, activation, sleep/wake), handles inter-agent communication, and provides coordination primitives like barriers and message queues. Supports 'sleeptime agents' that wake up based on time or event triggers, enabling long-running multi-agent workflows.","intents":["Build multi-agent systems where agents collaborate on complex tasks","Coordinate agent execution with different patterns (sequential, parallel, hierarchical)","Implement long-running workflows with agents that activate/deactivate based on triggers","Enable agents to share knowledge and context through group memory"],"best_for":["Teams building complex AI systems requiring agent collaboration","Applications with long-running workflows that need periodic agent activation","Organizations building hierarchical agent systems (manager agents, worker agents)"],"limitations":["Inter-agent communication adds latency and complexity to agent coordination","Shared memory consistency requires careful synchronization; race conditions possible","Debugging multi-agent systems is significantly more complex than single-agent","Sleeptime agents require background job infrastructure (scheduler, message queue)"],"requires":["Python 3.9+","Database for agent state and group membership","Job scheduler (APScheduler, Celery, or similar) for sleeptime agents","Message queue or event system for inter-agent communication"],"input_types":["agent definitions and configurations","group membership and coordination patterns","inter-agent messages and events"],"output_types":["agent execution results","group-level outputs and coordination state","inter-agent communication logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_7","uri":"capability://tool.use.integration.rest.api.with.streaming.job.management.and.background.execution","name":"rest api with streaming, job management, and background execution","description":"Exposes a comprehensive REST API for agent management, messaging, and streaming with support for long-running operations through a job/run system. The API supports streaming responses (Server-Sent Events) for real-time agent output, background job execution with status tracking, and webhook callbacks for job completion. Implements a SyncServer abstraction layer that handles request routing, database persistence, and service orchestration. The job system decouples request handling from execution, allowing agents to run asynchronously with status polling or webhook notifications.","intents":["Build web applications that interact with agents through REST endpoints","Stream agent responses in real-time to clients","Execute long-running agent tasks asynchronously without blocking client connections","Monitor agent execution status and receive notifications on completion"],"best_for":["Teams building web/mobile applications that need agent backends","Applications requiring real-time streaming of agent responses","Systems with long-running agent tasks that need async execution"],"limitations":["REST API adds network latency compared to direct Python SDK usage","Streaming requires persistent connections; not suitable for all client types","Job system adds complexity; requires background worker infrastructure","Webhook callbacks require client-side endpoint availability for notifications"],"requires":["Python 3.9+","FastAPI or similar web framework (included in Letta)","Database for job/run persistence","Background job worker (Celery, APScheduler, or similar)","HTTP client library for webhook callbacks"],"input_types":["HTTP requests (JSON payloads)","agent configuration and messages","job parameters"],"output_types":["JSON responses","Server-Sent Events (streaming)","job status and results","webhook callbacks"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_8","uri":"capability://data.processing.analysis.file.processing.pipeline.with.ocr.chunking.and.semantic.indexing","name":"file processing pipeline with ocr, chunking, and semantic indexing","description":"Provides an end-to-end file processing pipeline that ingests documents (PDF, text, code, images) and makes them searchable by agents. The pipeline includes OCR for image-based PDFs, intelligent chunking strategies (semantic, fixed-size, sliding-window), and automatic embedding generation. Processed documents are stored in a vector database with metadata (source, page number, chunk boundaries) and indexed for semantic search. Agents can query documents through the archival memory system or directly through file-based tools. Supports batch processing of large document collections.","intents":["Enable agents to search and reference documents without manual preprocessing","Build document-based knowledge bases that agents can query semantically","Process large document collections (codebases, manuals, research papers) for agent access","Extract information from images and scanned documents using OCR"],"best_for":["Teams building agents that need to reference documents or codebases","Applications requiring document-based knowledge bases","Organizations processing large volumes of documents for agent access"],"limitations":["OCR quality varies by document type and image quality; may require manual correction","Chunking strategies are heuristic-based; may split semantic units incorrectly","Processing large documents is computationally expensive (~1-5s per document depending on size)","Embedding generation requires external service; adds cost and latency"],"requires":["Python 3.9+","PDF processing library (PyPDF2, pdfplumber, or similar)","OCR engine (Tesseract, EasyOCR, or cloud-based)","Embedding model (OpenAI, Sentence Transformers, local models)","Vector database for indexed documents"],"input_types":["documents (PDF, text, code, images)","chunking strategy configuration","embedding model selection"],"output_types":["processed chunks with embeddings","document metadata and indexing","searchable document store"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__cap_9","uri":"capability://automation.workflow.agent.import.export.with.configuration.serialization","name":"agent import/export with configuration serialization","description":"Provides serialization and deserialization of agent configurations, memory state, and conversation history for backup, migration, and sharing. Agents can be exported to JSON/YAML with full state (memory blocks, tools, LLM configuration, conversation history) and imported to recreate the agent in a new environment. Supports partial exports (e.g., memory only, configuration only) and selective import (e.g., restore memory but keep new conversation history). Enables version control of agent configurations and facilitates agent cloning and templating.","intents":["Backup and restore agent state for disaster recovery","Migrate agents between environments (dev, staging, production)","Share agent configurations and templates across teams","Version control agent configurations in Git"],"best_for":["Teams managing multiple agent instances across environments","Organizations requiring agent backup and recovery procedures","Teams sharing agent templates and configurations"],"limitations":["Large conversation histories can produce very large export files","Export/import is not atomic; partial failures may leave agents in inconsistent state","Sensitive data (API keys, user information) must be handled carefully during export","Imported agents may not work identically if LLM provider or model versions differ"],"requires":["Python 3.9+","JSON/YAML serialization libraries (included in Letta)","Database access for agent state"],"input_types":["agent ID or configuration object","export options (what to include/exclude)","export format (JSON, YAML)"],"output_types":["serialized agent configuration","memory blocks and conversation history","tool and LLM configuration"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"letta-memgpt__headline","uri":"capability://memory.knowledge.stateful.ai.agent.framework.with.long.term.memory","name":"stateful ai agent framework with long-term memory","description":"Letta is a framework for building stateful AI agents that maintain long-term memory, enabling unlimited conversation history and advanced tool use, making it ideal for developers looking to create intelligent, context-aware applications.","intents":["best stateful AI agent framework","AI agents with long-term memory for conversational applications","stateful AI agents for tool integration","how to build AI agents with memory","top frameworks for AI agents"],"best_for":["developers building intelligent agents","applications requiring context retention"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, Google Gemini, etc.)","Vector database for archival storage (Chroma, Pinecone, or local SQLite with embeddings)","Sufficient compute for periodic summarization tasks","PostgreSQL or SQLite database for memory persistence","Git repository or Git-compatible backend for version control","LLM provider with function-calling support (OpenAI, Anthropic, etc.)","Database with full-text search support (PostgreSQL with pg_trgm, Elasticsearch, etc.)","Message schema and ORM models","Job scheduler (APScheduler, Celery, or similar)"],"failure_modes":["Summarization adds latency (~1-3s per compression cycle depending on message volume and LLM speed)","Archived messages require vector search for retrieval — not guaranteed to surface all relevant context","Summarization quality depends on underlying LLM capability; lossy compression may miss nuanced details","Memory block size is limited by context window — very large memory blocks reduce space for conversation","Self-editing requires careful prompt engineering to prevent agents from corrupting their own memory","Git-backed storage adds complexity; requires database setup and migration management","No built-in conflict resolution if multiple agents or processes modify memory simultaneously","Full-text search performance degrades with very large conversation volumes (millions of messages)","Storing all messages increases database size significantly","Message retrieval requires database queries; not suitable for real-time high-frequency access","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.692Z","last_scraped_at":null,"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=letta-memgpt","compare_url":"https://unfragile.ai/compare?artifact=letta-memgpt"}},"signature":"xEJrOoHSmDFiscobErKklLHc4KkhLWJS3/d0kY7ANPKw//UwbGZ3ZSfxBRPRh9/CPTeqLmpDthLv6USfQz14BQ==","signedAt":"2026-06-20T16:11:46.005Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/letta-memgpt","artifact":"https://unfragile.ai/letta-memgpt","verify":"https://unfragile.ai/api/v1/verify?slug=letta-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"}}