{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-letta-ai--letta","slug":"letta-ai--letta","name":"letta","type":"agent","url":"https://docs.letta.com/","page_url":"https://unfragile.ai/letta-ai--letta","categories":["ai-agents"],"tags":["ai","ai-agents","llm","llm-agent"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-letta-ai--letta__cap_0","uri":"capability://planning.reasoning.stateful.agent.lifecycle.management.with.persistent.memory.blocks","name":"stateful agent lifecycle management with persistent memory blocks","description":"Letta manages agent instantiation, configuration, and lifecycle through a structured system that persists agent state across sessions via memory blocks (persona, human info, custom context). The Agent Lifecycle and Management subsystem handles agent creation, updates, and deletion while maintaining referential integrity with associated conversations and memory blocks. Unlike stateless chatbots, agents retain structured context that survives server restarts through ORM-backed database persistence.","intents":["Create long-lived agents that remember user preferences and conversation history across sessions","Update agent configuration and memory blocks without losing conversation state","Export and import agent definitions for version control or migration","Manage multiple agent instances with different personas and capabilities"],"best_for":["Teams building customer support agents that need to remember customer history","Developers creating multi-turn conversational systems with evolving context","Organizations requiring agent state persistence for compliance or audit trails"],"limitations":["Memory block updates require explicit API calls — no automatic state synchronization during concurrent requests","Context window summarization adds latency when agents exceed token limits (requires LLM call to compress history)","Agent export/import does not preserve real-time conversation state, only agent configuration and memory blocks"],"requires":["Python 3.9+","PostgreSQL or SQLite database for ORM persistence","REST API server running (SyncServer) or Python SDK client","LLM provider API key (OpenAI, Anthropic, Google Gemini, or compatible)"],"input_types":["agent configuration JSON","memory block definitions (persona, human info, custom context)","conversation messages (text)"],"output_types":["agent state objects","conversation history with metadata","memory block snapshots"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.unified.message.format.transformation","name":"multi-provider llm integration with unified message format transformation","description":"Letta abstracts multiple LLM providers (OpenAI, Anthropic, Google Gemini, Ollama, and 10+ others) through a unified LLM Client Architecture that handles provider-specific message format transformations, model configuration, and error handling. The Provider System maps agent requests to provider-specific APIs while normalizing responses into a consistent schema. Message Format Transformation pipelines convert between Letta's internal message representation and each provider's native format (e.g., OpenAI's function_call vs Anthropic's tool_use).","intents":["Switch between LLM providers without changing agent code","Use reasoning models (o1, Claude Opus) with automatic fallback for unsupported features","Configure model-specific parameters (temperature, max_tokens, top_p) per provider","Handle provider-specific error responses and implement automatic retries with exponential backoff"],"best_for":["Teams wanting to avoid vendor lock-in by supporting multiple LLM providers","Developers building cost-optimized agents that switch providers based on task complexity","Organizations using local LLMs (Ollama) alongside cloud providers for sensitive workloads"],"limitations":["Reasoning models (o1, Claude Opus) do not support tool calling — agents must disable tool execution when using these models","Prompt caching only supported on OpenAI and Anthropic; other providers ignore cache directives","Message format transformation adds ~50-100ms latency per request due to schema conversion overhead","Provider-specific features (e.g., vision capabilities, structured output) require conditional logic in agent code"],"requires":["API keys for at least one LLM provider","Python 3.9+","Network connectivity to provider endpoints or local Ollama instance running on localhost:11434"],"input_types":["agent messages (text, tool calls, system prompts)","model configuration (provider name, model ID, parameters)","tool schemas (for function calling)"],"output_types":["LLM responses (text, tool calls, stop reasons)","token usage metrics","error objects with retry information"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_10","uri":"capability://text.generation.language.voice.agent.support.with.audio.input.output","name":"voice agent support with audio input/output","description":"Letta's Voice Agents subsystem enables agents to process audio input and generate audio responses, supporting real-time voice conversations. The system integrates speech-to-text (STT) and text-to-speech (TTS) providers, handling audio encoding/decoding and streaming. Voice agents maintain the same memory and tool capabilities as text agents, enabling voice-based access to all agent features. This enables use cases like voice assistants, phone-based customer support, and hands-free interaction.","intents":["Build voice assistants that can understand and respond to spoken input","Enable phone-based customer support with voice agents","Create hands-free interfaces for agents using voice input/output","Support multi-modal interactions (text and voice) with the same agent"],"best_for":["Teams building voice assistants or voice-enabled chatbots","Developers creating phone-based customer support systems","Organizations needing hands-free agent interfaces"],"limitations":["Voice quality depends on STT/TTS provider quality — poor audio input leads to recognition errors","Real-time voice processing adds latency (STT + LLM inference + TTS) — typically 2-5 seconds per turn","Voice agents cannot use visual tools or process images — limited to audio and text","Audio streaming requires low-latency network — not suitable for high-latency connections"],"requires":["Python 3.9+","Speech-to-text provider (OpenAI Whisper, Google Cloud Speech-to-Text, etc.)","Text-to-speech provider (OpenAI TTS, Google Cloud Text-to-Speech, etc.)","Audio input device (microphone) and output device (speaker)"],"input_types":["audio streams (WAV, MP3, etc.)","audio configuration (sample rate, encoding)"],"output_types":["audio streams (WAV, MP3, etc.)","transcripts (text from STT)","agent responses (text and audio)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_11","uri":"capability://tool.use.integration.python.sdk.with.type.safe.client.library","name":"python sdk with type-safe client library","description":"Letta's Python SDK provides a type-safe client library for programmatic agent management and interaction. The SDK uses Pydantic models for request/response validation, enabling IDE autocomplete and type checking. The Client Libraries subsystem abstracts REST API calls and provides Pythonic interfaces for common operations (create agent, send message, update memory). The SDK supports both synchronous and asynchronous execution, enabling integration into async applications and frameworks.","intents":["Build Python applications that interact with Letta agents programmatically","Use IDE autocomplete and type checking for agent API calls","Integrate Letta agents into async Python frameworks (FastAPI, asyncio)","Manage agent lifecycle (create, update, delete) via Python code"],"best_for":["Python developers building applications that use Letta agents","Teams using type-safe Python (mypy, Pydantic) for code quality","Developers building async applications that need to interact with agents"],"limitations":["SDK is Python-only — no official support for other languages (JavaScript, Go, etc.)","Async SDK requires Python 3.9+ with asyncio support","SDK does not support all REST API features — some advanced features only available via REST API","Type hints are based on Pydantic models — may not capture all API behavior"],"requires":["Python 3.9+","Letta server running (local or remote)","API key or authentication token"],"input_types":["agent configuration objects","messages (text)","memory block updates"],"output_types":["agent response objects","typed response data (Pydantic models)","streaming responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_12","uri":"capability://automation.workflow.agent.import.export.with.configuration.serialization","name":"agent import/export with configuration serialization","description":"Letta's Agent Import and Export subsystem enables agents to be exported as configuration files (JSON/YAML) and imported into other Letta instances. This enables version control of agent definitions, sharing agents across teams, and migrating agents between environments. The export includes agent configuration, memory blocks, and tool definitions, but not conversation history. Agents can be exported at any point in their lifecycle and imported with the same configuration, enabling reproducible agent deployments.","intents":["Version control agent definitions in git for reproducibility","Share agent configurations across team members or organizations","Migrate agents between development, staging, and production environments","Create agent templates that can be instantiated multiple times"],"best_for":["Teams using version control for infrastructure and configuration","Organizations sharing agent definitions across teams or departments","Developers implementing CI/CD pipelines for agent deployment"],"limitations":["Export does not include conversation history — agents start fresh after import","Export does not include archival memory or indexed documents — requires separate backup","Import does not validate tool availability — imported agents may fail if tools are not registered","Export format is Letta-specific — not compatible with other agent frameworks"],"requires":["Python 3.9+","Letta server with agent to export"],"input_types":["agent ID or name","export format (JSON or YAML)"],"output_types":["agent configuration file (JSON/YAML)","memory block definitions","tool schemas"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_13","uri":"capability://safety.moderation.multi.tenancy.and.role.based.access.control","name":"multi-tenancy and role-based access control","description":"Letta's Multi-Tenancy and Security subsystem enables multiple organizations or users to share a single Letta instance with isolated data and access controls. The system implements role-based access control (RBAC) with roles (admin, agent_creator, user) and permissions (create_agent, read_agent, update_agent, delete_agent). Database-level isolation ensures tenants cannot access each other's agents, conversations, or memory. Authentication is handled via API keys or OAuth, with token-based authorization for REST API calls.","intents":["Host multiple organizations on a single Letta instance with data isolation","Implement fine-grained access control (who can create/modify agents)","Enable multi-user collaboration with different permission levels","Audit access and modifications for compliance"],"best_for":["SaaS platforms hosting multiple customers on shared infrastructure","Enterprise deployments with multiple teams and departments","Organizations requiring fine-grained access control and audit trails"],"limitations":["RBAC is coarse-grained — no per-agent or per-conversation permissions","Multi-tenancy adds database query complexity — may impact performance with many tenants","Audit logging is not built-in — requires external logging system for compliance","Cross-tenant queries are not supported — agents cannot access other tenants' data"],"requires":["Python 3.9+","PostgreSQL for multi-tenancy (SQLite does not support row-level security)","Authentication system (API keys or OAuth provider)"],"input_types":["tenant ID (from authentication token)","user role and permissions","resource access requests"],"output_types":["access control decisions (allow/deny)","audit logs (optional)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_14","uri":"capability://safety.moderation.observability.with.telemetry.logging.and.error.tracking","name":"observability with telemetry, logging, and error tracking","description":"Letta's Observability subsystem provides comprehensive telemetry, logging, and error tracking for monitoring agent behavior and debugging issues. Telemetry and Monitoring collects metrics (token usage, latency, error rates) and exports them to monitoring systems (Prometheus, DataDog). Logging and Error Tracking captures detailed logs of agent execution, LLM calls, and tool execution with configurable log levels. The system integrates with error tracking services (Sentry) for automatic error reporting and alerting.","intents":["Monitor agent performance (latency, token usage, error rates)","Debug agent behavior by reviewing detailed execution logs","Track errors and exceptions across agent instances","Implement alerting for anomalies (high error rates, slow responses)"],"best_for":["Teams running production agents that need monitoring and alerting","Developers debugging agent behavior in complex systems","Organizations requiring observability for compliance and SLAs"],"limitations":["Telemetry collection adds overhead (~5-10% latency) to agent execution","Logging can be verbose — requires careful log level configuration to avoid overwhelming logs","Error tracking requires external service (Sentry) — adds operational complexity","Metrics export is asynchronous — may lose metrics if service crashes"],"requires":["Python 3.9+","Optional: Prometheus or DataDog for metrics collection","Optional: Sentry for error tracking"],"input_types":["agent execution events","LLM calls and responses","tool execution results"],"output_types":["metrics (token usage, latency, error rates)","logs (execution traces)","error reports"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_2","uri":"capability://memory.knowledge.structured.memory.block.management.with.git.backed.versioning","name":"structured memory block management with git-backed versioning","description":"Letta's Memory System provides structured memory blocks (persona, human info, custom context) that agents can read and modify during conversations. The Memory Block Management subsystem stores blocks as ORM entities with optional git-backed versioning, enabling agents to track memory changes over time and revert to previous states. Agents access memory through core memory tools (read_memory, write_memory) that integrate with the message execution pipeline, allowing LLMs to explicitly modify their own context.","intents":["Allow agents to update their own persona or context based on conversation insights","Track memory modifications over time with git-style versioning and diffs","Implement learning mechanisms where agents improve their behavior by modifying memory blocks","Retrieve memory history to audit what an agent learned about a user"],"best_for":["Developers building self-improving agents that learn from interactions","Teams requiring audit trails of agent memory modifications for compliance","Customer support systems that need agents to remember and adapt to user preferences"],"limitations":["Git-backed memory requires external git repository setup — adds operational complexity","Memory block size is limited by context window; large memory blocks require summarization","Concurrent memory writes from multiple agent instances may cause conflicts without explicit locking","Memory tools are not sandboxed — agents can write arbitrary data, requiring validation at application layer"],"requires":["Python 3.9+","PostgreSQL or SQLite for memory block storage","Optional: Git repository for version control (local or remote)"],"input_types":["memory block definitions (JSON with persona, human_info, custom_context fields)","memory update requests from agent tool calls","memory queries (read operations)"],"output_types":["memory block snapshots","memory modification history with timestamps","git diffs (if versioning enabled)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_3","uri":"capability://tool.use.integration.tool.execution.with.sandboxing.and.mcp.integration","name":"tool execution with sandboxing and mcp integration","description":"Letta's Tool System enables agents to execute custom Python tools with sandboxed execution environments and integrates with Model Context Protocol (MCP) for standardized tool definitions. The Tool Management subsystem registers tools, validates schemas, and enforces Tool Rules (execution constraints, rate limits, access controls). Tool Execution and Sandboxing handles function invocation with error isolation, preventing tool failures from crashing the agent. MCP Integration allows agents to discover and use tools defined via the MCP standard, enabling interoperability with external tool ecosystems.","intents":["Allow agents to call custom Python functions (database queries, API calls, file operations)","Enforce execution constraints (rate limits, timeout, resource limits) on tool calls","Integrate with MCP-compliant tools for standardized tool discovery and execution","Isolate tool failures so one broken tool doesn't crash the agent"],"best_for":["Teams building agents that need to interact with external systems (APIs, databases, file systems)","Developers using MCP-compatible tools from the ecosystem (e.g., Claude Desktop tools)","Organizations requiring fine-grained control over agent capabilities and access patterns"],"limitations":["Sandboxing is process-level isolation only — does not prevent resource exhaustion attacks (CPU, memory)","Tool execution timeout is global; individual tools cannot have custom timeout values","MCP integration requires agents to explicitly enable MCP support — not automatic for all tools","Tool schemas must be manually defined; no automatic schema inference from Python function signatures"],"requires":["Python 3.9+","Tool functions must be registered with Letta before agent execution","Optional: MCP server running for MCP-integrated tools","Tool schemas defined in JSON Schema format"],"input_types":["tool definitions (Python functions or MCP tool specs)","tool schemas (JSON Schema)","tool call requests from LLM (function name + arguments)"],"output_types":["tool execution results (JSON-serializable)","error messages with stack traces","execution metadata (latency, token usage)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_4","uri":"capability://search.retrieval.archival.memory.with.semantic.search.over.documents.and.codebases","name":"archival memory with semantic search over documents and codebases","description":"Letta's Archival Memory and Passages subsystem enables agents to store and search over large document collections using semantic search. The File Processing Pipeline handles OCR, chunking, and embedding generation for documents and codebases. Vector Database Integration (Qdrant, Pinecone, or in-memory) stores embeddings and enables similarity search. Agents can retrieve relevant passages from archival memory during conversations, enabling RAG-style knowledge augmentation without loading entire documents into context.","intents":["Index and search over large codebases or documentation without fitting everything in context","Enable agents to answer questions by retrieving relevant passages from archival memory","Process documents with OCR to extract text from PDFs and images","Implement semantic search over conversation history to find relevant past interactions"],"best_for":["Teams building documentation assistants or code search agents","Developers creating customer support agents with access to large knowledge bases","Organizations needing to augment agents with proprietary documents or codebases"],"limitations":["Semantic search quality depends on embedding model quality — poor embeddings lead to irrelevant results","Chunking strategy is fixed (no per-document customization) — may split important context across chunks","Vector database synchronization is eventual consistent — newly indexed documents may not appear in search immediately","OCR accuracy depends on document quality — scanned PDFs with poor image quality produce unreliable text"],"requires":["Python 3.9+","Vector database (Qdrant, Pinecone, or in-memory)","Embedding model (OpenAI, Anthropic, or local)","Optional: OCR library (pytesseract) for document processing"],"input_types":["documents (PDF, text, code files)","search queries (text)","chunking configuration (chunk size, overlap)"],"output_types":["retrieved passages (text with metadata)","similarity scores","document metadata (source, page number)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_5","uri":"capability://memory.knowledge.conversation.history.management.with.search.and.persistence","name":"conversation history management with search and persistence","description":"Letta's Conversation History and Search subsystem persists all agent-user interactions in a structured message format with full-text and semantic search capabilities. The Message System stores messages with metadata (timestamp, sender, message type) in the ORM database. Message Persistence and Retrieval enables agents to access conversation history for context, while Message Conversion Pipeline normalizes messages between internal representation and provider-specific formats. Agents can search conversation history to find relevant past interactions without loading entire conversations into context.","intents":["Retrieve conversation history for context without manually managing message lists","Search past conversations to find relevant interactions or decisions","Implement conversation summarization by querying message history","Audit agent-user interactions for compliance or debugging"],"best_for":["Teams building multi-turn conversational agents with long interaction histories","Developers implementing conversation search or summarization features","Organizations requiring audit trails of all agent-user interactions"],"limitations":["Full conversation history is not automatically included in agent context — requires explicit retrieval","Search performance degrades with very large conversation histories (millions of messages)","Message deletion is not supported — only logical deletion via soft deletes","Conversation export does not include vector embeddings — requires re-embedding for semantic search"],"requires":["Python 3.9+","PostgreSQL or SQLite for message persistence","Optional: Vector database for semantic search over conversation history"],"input_types":["messages (text, tool calls, system messages)","search queries (text or semantic)","conversation filters (date range, sender, message type)"],"output_types":["message objects with metadata","conversation summaries","search results with relevance scores"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_6","uri":"capability://memory.knowledge.context.window.management.with.automatic.summarization","name":"context window management with automatic summarization","description":"Letta's Context Window Management and Summarization subsystem automatically manages token limits by summarizing conversation history when agents approach context window limits. The system monitors token usage across messages, memory blocks, and tool schemas, and triggers LLM-based summarization to compress conversation history while preserving key information. This enables agents to maintain long conversations without manual context management or conversation truncation.","intents":["Maintain long conversations without hitting LLM context window limits","Automatically compress conversation history while preserving important context","Handle different context window sizes across LLM providers (e.g., GPT-4 vs Claude)","Implement progressive context compression as conversations grow"],"best_for":["Teams building long-running conversational agents (customer support, tutoring)","Developers working with smaller context window models (e.g., older GPT-3.5)","Organizations needing to minimize token usage for cost optimization"],"limitations":["Summarization adds latency (~1-2 seconds) when triggered, causing noticeable delays in agent responses","Summarization quality depends on LLM capability — important details may be lost in compression","Summarization is not reversible — original conversation details cannot be recovered after compression","Summarization triggers are heuristic-based (token count) — may not account for semantic importance of messages"],"requires":["Python 3.9+","LLM provider with sufficient context window for summarization prompts","Token counting library (tiktoken for OpenAI models)"],"input_types":["conversation messages","memory blocks","tool schemas","context window size (from model configuration)"],"output_types":["summarized conversation history","compression ratio metrics","token usage estimates"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_7","uri":"capability://automation.workflow.rest.api.with.streaming.and.background.job.execution","name":"rest api with streaming and background job execution","description":"Letta's REST API Structure provides full-featured endpoints for agent management, messaging, and streaming. The Streaming Architecture enables real-time message streaming using Server-Sent Events (SSE) or WebSockets, allowing clients to receive agent responses as they are generated. Job and Run Management handles asynchronous task execution with background job queues, enabling long-running operations (batch processing, file indexing) without blocking API responses. The SyncServer and Service Layer abstracts database operations and provides a consistent interface for both REST API and Python SDK clients.","intents":["Build web applications with real-time agent responses using streaming","Execute long-running tasks (batch processing, file indexing) asynchronously","Manage agents and conversations via REST API without Python SDK","Monitor job execution status and retrieve results when complete"],"best_for":["Teams building web applications with streaming agent responses","Developers integrating Letta into non-Python applications via REST API","Organizations running batch processing jobs (document indexing, agent evaluation)"],"limitations":["Streaming adds complexity to client implementations — requires SSE or WebSocket support","Background jobs are not persistent across server restarts — requires external job queue (Redis, RabbitMQ) for production","REST API does not support all Python SDK features — some advanced capabilities only available via SDK","Job status polling adds latency — no push notifications for job completion"],"requires":["Python 3.9+","REST API server running (FastAPI-based)","Optional: Redis or RabbitMQ for persistent job queues","HTTP client library (requests, httpx, fetch API)"],"input_types":["HTTP requests (JSON payloads)","agent configuration","messages","file uploads"],"output_types":["HTTP responses (JSON)","streaming responses (SSE or WebSocket)","job status objects"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_8","uri":"capability://planning.reasoning.multi.agent.orchestration.with.agent.groups.and.coordination.patterns","name":"multi-agent orchestration with agent groups and coordination patterns","description":"Letta's Multi-Agent Systems and Groups subsystem enables coordination of multiple agents with different roles and capabilities. Agents can be organized into groups with defined coordination patterns (e.g., sequential, parallel, hierarchical). The system manages message routing between agents, enables inter-agent communication, and provides mechanisms for agents to delegate tasks to specialized agents. This enables complex workflows where different agents handle different aspects of a problem.","intents":["Build multi-agent systems where different agents specialize in different tasks","Implement hierarchical workflows where agents delegate to specialized sub-agents","Coordinate parallel agent execution for tasks that can be parallelized","Enable agents to communicate and share context across agent boundaries"],"best_for":["Teams building complex AI systems with multiple specialized agents","Developers implementing hierarchical task decomposition workflows","Organizations needing to scale agent capabilities by composing multiple agents"],"limitations":["Inter-agent communication adds latency — each agent-to-agent message requires LLM inference","Coordination patterns are predefined — custom patterns require code changes","Agent groups do not have built-in load balancing — all agents in a group share the same resource pool","Debugging multi-agent systems is complex — requires tracing message flow across multiple agents"],"requires":["Python 3.9+","Multiple agent instances configured with different roles/capabilities","Coordination pattern definition (sequential, parallel, hierarchical)"],"input_types":["agent group configuration","coordination pattern specification","inter-agent messages"],"output_types":["final agent response","execution trace (all inter-agent messages)","resource usage metrics"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-letta-ai--letta__cap_9","uri":"capability://automation.workflow.batch.processing.and.human.in.the.loop.workflows","name":"batch processing and human-in-the-loop workflows","description":"Letta's Batch Processing subsystem enables agents to process large datasets asynchronously, with results stored for later retrieval. Human-in-the-Loop Workflows allow agents to pause execution and request human feedback before proceeding, enabling collaborative AI systems where humans and agents work together. The Job and Run Lifecycle manages batch job execution, tracking progress and handling failures. This enables use cases like document processing, data labeling, and decision workflows that require human oversight.","intents":["Process large datasets (documents, images, records) with agents asynchronously","Implement approval workflows where agents propose actions and humans approve","Enable agents to request clarification or feedback from humans during execution","Track batch job progress and retrieve results when complete"],"best_for":["Teams building document processing pipelines with human review","Developers implementing approval workflows for agent-generated content","Organizations needing to combine AI automation with human oversight"],"limitations":["Batch processing requires external job queue for production — in-memory queues lose jobs on restart","Human-in-the-loop workflows require manual UI implementation — no built-in approval interface","Batch job results are not automatically cleaned up — requires manual deletion or TTL configuration","Human feedback integration is asynchronous — agents cannot block waiting for feedback without timeout"],"requires":["Python 3.9+","Job queue (Redis, RabbitMQ) for production batch processing","Human feedback interface (custom UI or integration with approval system)"],"input_types":["batch job configuration (dataset, agent, parameters)","human feedback (approval/rejection, corrections)"],"output_types":["batch job results (per-item outputs)","job status and progress metrics","human feedback history"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PostgreSQL or SQLite database for ORM persistence","REST API server running (SyncServer) or Python SDK client","LLM provider API key (OpenAI, Anthropic, Google Gemini, or compatible)","API keys for at least one LLM provider","Network connectivity to provider endpoints or local Ollama instance running on localhost:11434","Speech-to-text provider (OpenAI Whisper, Google Cloud Speech-to-Text, etc.)","Text-to-speech provider (OpenAI TTS, Google Cloud Text-to-Speech, etc.)","Audio input device (microphone) and output device (speaker)","Letta server running (local or remote)"],"failure_modes":["Memory block updates require explicit API calls — no automatic state synchronization during concurrent requests","Context window summarization adds latency when agents exceed token limits (requires LLM call to compress history)","Agent export/import does not preserve real-time conversation state, only agent configuration and memory blocks","Reasoning models (o1, Claude Opus) do not support tool calling — agents must disable tool execution when using these models","Prompt caching only supported on OpenAI and Anthropic; other providers ignore cache directives","Message format transformation adds ~50-100ms latency per request due to schema conversion overhead","Provider-specific features (e.g., vision capabilities, structured output) require conditional logic in agent code","Voice quality depends on STT/TTS provider quality — poor audio input leads to recognition errors","Real-time voice processing adds latency (STT + LLM inference + TTS) — typically 2-5 seconds per turn","Voice agents cannot use visual tools or process images — limited to audio and text","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7461800854459038,"quality":0.5,"ecosystem":0.52,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:11.504Z","last_commit":"2026-04-12T20:54:54Z"},"community":{"stars":22419,"forks":2383,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=letta-ai--letta","compare_url":"https://unfragile.ai/compare?artifact=letta-ai--letta"}},"signature":"vo/AN6J7df/aysQLRs9Gm1UuuWFyqp//ISddzvuYGuuHziaulwQLZjirgt5pjuNTZ/kSwBADOfmq5M0syQqsAQ==","signedAt":"2026-06-21T13:00:57.507Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/letta-ai--letta","artifact":"https://unfragile.ai/letta-ai--letta","verify":"https://unfragile.ai/api/v1/verify?slug=letta-ai--letta","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"}}