{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-letta","slug":"pypi-letta","name":"letta","type":"framework","url":"https://pypi.org/project/letta/","page_url":"https://unfragile.ai/pypi-letta","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-letta__cap_0","uri":"capability://memory.knowledge.stateful.agent.memory.management.with.conversation.context.persistence","name":"stateful agent memory management with conversation context persistence","description":"Letta implements a core memory architecture that maintains agent state across conversation turns using a structured memory model with core memory (facts about the agent/user), scratch pad (working memory for current reasoning), and message history. The system persists this state server-side, enabling agents to maintain long-term context without re-sending full conversation history on each request. Memory is indexed and retrievable, allowing agents to reference past interactions and learned information.","intents":["Build an agent that remembers user preferences and context across multiple conversation sessions","Implement multi-turn conversations where the agent can reference earlier messages without token bloat","Create agents that learn and update their understanding of users over time","Maintain separate memory contexts for different users or conversation threads"],"best_for":["Teams building long-running conversational AI systems","Developers creating personalized assistant experiences","Applications requiring stateful agent behavior across sessions"],"limitations":["Memory updates are synchronous and block agent response generation","No built-in memory compression or summarization for very long conversations (>10k messages)","Memory retrieval is linear scan by default without semantic indexing","Cross-agent memory sharing requires manual implementation"],"requires":["Python 3.9+","Letta server running (local or remote)","LLM provider API key (OpenAI, Anthropic, or local model)"],"input_types":["text messages","structured memory updates","user metadata"],"output_types":["agent responses with memory context","updated memory state","message history with metadata"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-letta__cap_1","uri":"capability://tool.use.integration.tool.function.calling.with.schema.based.agent.binding","name":"tool/function calling with schema-based agent binding","description":"Letta provides a declarative tool registration system where developers define Python functions with type hints and docstrings, which are automatically converted to JSON schemas and exposed to the LLM for function calling. Tools are bound to specific agent instances, allowing different agents to have different capability sets. The system handles schema generation, parameter validation, and execution with error handling, supporting both synchronous and asynchronous tool implementations.","intents":["Give agents the ability to call external APIs or internal functions","Define custom tools that agents can use to accomplish tasks","Control which tools are available to which agents","Automatically generate LLM-compatible schemas from Python function signatures"],"best_for":["Developers building agents that need to interact with external systems","Teams wanting declarative, type-safe tool definitions","Applications requiring fine-grained control over agent capabilities"],"limitations":["Tool schemas are generated from Python type hints; complex types may not translate cleanly to JSON schema","No built-in retry logic for failed tool calls","Async tools require event loop management in the calling context","Tool execution errors are returned to the LLM as text, not structured error objects"],"requires":["Python 3.9+","Type hints on all tool functions","Letta agent instance to bind tools to"],"input_types":["Python functions","function signatures with type annotations","docstrings for tool descriptions"],"output_types":["JSON schemas","tool execution results","error messages"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-letta__cap_10","uri":"capability://automation.workflow.rate.limiting.and.quota.management.per.agent","name":"rate limiting and quota management per agent","description":"Letta supports configurable rate limiting and quota management at the agent level, allowing developers to control API usage and prevent abuse. Rate limits can be set per agent, per user, or globally. The system tracks token usage, API calls, and other metrics. Quota enforcement is automatic, with configurable behavior on limit exceeded (reject, queue, or degrade). Metrics are exposed for monitoring and billing.","intents":["Control API costs by limiting agent usage","Prevent abuse by rate-limiting requests per agent or user","Track token usage for billing or analytics","Implement fair-share resource allocation across multiple agents"],"best_for":["Multi-tenant systems with cost control requirements","Public APIs exposing agents to external users","Teams managing LLM API budgets"],"limitations":["Rate limiting is enforced at the agent level; no fine-grained per-endpoint limits","Quota tracking is approximate; actual usage may vary due to streaming or retries","No built-in quota reset scheduling; manual reset required","Quota enforcement is synchronous; may add latency to requests"],"requires":["Python 3.9+","Letta server with quota tracking","Metrics storage (Redis, database, etc.)"],"input_types":["rate limit configuration","quota thresholds","enforcement policy"],"output_types":["quota status","usage metrics","enforcement decisions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-letta__cap_11","uri":"capability://automation.workflow.logging.and.observability.with.structured.event.tracking","name":"logging and observability with structured event tracking","description":"Letta provides comprehensive logging and observability through structured event tracking. All agent actions (messages, tool calls, memory updates, errors) are logged with timestamps, metadata, and context. Logs can be queried, filtered, and exported for debugging or auditing. The system supports custom event handlers for integration with external logging systems (e.g., Datadog, ELK). Structured logs enable detailed tracing of agent behavior and performance analysis.","intents":["Debug agent behavior by reviewing detailed action logs","Audit agent decisions and memory updates for compliance","Monitor agent performance and identify bottlenecks","Integrate agent logs with external observability platforms"],"best_for":["Production systems requiring detailed audit trails","Teams debugging complex agent behavior","Organizations with compliance or monitoring requirements"],"limitations":["Structured logging adds overhead; may impact agent latency","Log storage grows quickly with verbose logging; requires retention policies","Custom event handlers must be implemented per external system","Log querying is limited to built-in filters; complex queries require external tools"],"requires":["Python 3.9+","Letta server with logging enabled","Log storage backend (file, database, or external service)"],"input_types":["log level configuration","custom event handlers","filter criteria"],"output_types":["structured log entries","event streams","audit trails"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-letta__cap_12","uri":"capability://automation.workflow.error.handling.and.recovery.with.automatic.retry.logic","name":"error handling and recovery with automatic retry logic","description":"Letta implements error handling and recovery mechanisms for agent operations, including automatic retries for transient failures (API timeouts, rate limits). Developers can configure retry policies (exponential backoff, max attempts) and define fallback behaviors. Errors are categorized (transient vs permanent) and handled accordingly. The system preserves agent state during failures, preventing inconsistencies. Custom error handlers can be registered for specific error types.","intents":["Automatically retry failed API calls with exponential backoff","Handle transient failures gracefully without user intervention","Define custom recovery strategies for specific error types","Prevent agent state corruption during failures"],"best_for":["Production systems requiring high reliability","Applications with external API dependencies","Teams building resilient agent systems"],"limitations":["Retry logic adds latency for failed requests","Exponential backoff may cause long delays for repeated failures","Custom error handlers require manual implementation","Some errors (e.g., authentication failures) cannot be retried"],"requires":["Python 3.9+","Letta agent instance","Configured retry policies"],"input_types":["retry policy configuration","error categorization rules","custom error handlers"],"output_types":["retry attempts","error logs","recovery status"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-letta__cap_2","uri":"capability://tool.use.integration.multi.llm.provider.abstraction.with.unified.agent.interface","name":"multi-llm provider abstraction with unified agent interface","description":"Letta abstracts away provider-specific differences through a unified agent interface that works with OpenAI, Anthropic, Ollama, and other LLM providers. The system handles provider-specific API differences (e.g., message format, function calling syntax, token counting) internally, allowing developers to swap providers without changing agent code. Configuration is provider-agnostic, with credentials managed separately from agent logic.","intents":["Build agents that can work with multiple LLM providers without code changes","Switch between OpenAI, Anthropic, and local models based on cost or latency requirements","Test agents against different models to compare quality and performance","Deploy agents with fallback providers for reliability"],"best_for":["Teams evaluating multiple LLM providers","Cost-conscious developers wanting to optimize provider selection","Organizations with multi-model deployment strategies"],"limitations":["Not all LLM features are available across all providers (e.g., vision support varies)","Provider-specific optimizations (e.g., system prompts) may not translate perfectly","Token counting estimates vary by provider and may not be exact","Streaming behavior differs subtly between providers"],"requires":["Python 3.9+","API keys for at least one LLM provider","Letta server with provider credentials configured"],"input_types":["provider configuration","API credentials","agent prompts and tools"],"output_types":["unified agent responses","token usage statistics","provider-agnostic error messages"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-letta__cap_3","uri":"capability://automation.workflow.agent.lifecycle.management.with.server.side.persistence","name":"agent lifecycle management with server-side persistence","description":"Letta manages agent instances through a server architecture where agents are created, stored, and retrieved from a persistent backend (database or file system). Each agent has a unique ID, configuration, memory state, and tool bindings that persist across server restarts. The system provides CRUD operations for agents and supports multiple concurrent agent instances with isolated state. Agents can be cloned, exported, and imported for reproducibility.","intents":["Create and manage multiple agent instances with separate identities and memories","Persist agent state so conversations survive server restarts","Clone agents for A/B testing or multi-user scenarios","Export and import agent configurations for sharing or backup"],"best_for":["Production deployments requiring agent persistence","Multi-user systems where each user has their own agent","Teams needing reproducible agent configurations"],"limitations":["Agent state is not automatically synchronized across multiple server instances","No built-in versioning of agent configurations","Memory snapshots are not atomic; concurrent updates may lose data","Agent deletion is permanent; no soft-delete or recovery mechanism"],"requires":["Python 3.9+","Letta server running","Persistent storage backend (SQLite, PostgreSQL, etc.)"],"input_types":["agent configuration","system prompts","tool definitions","memory initialization"],"output_types":["agent ID","agent metadata","persisted agent state","configuration exports"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-letta__cap_4","uri":"capability://text.generation.language.streaming.response.generation.with.token.level.control","name":"streaming response generation with token-level control","description":"Letta supports streaming agent responses where tokens are emitted as they are generated by the LLM, enabling real-time feedback to users. The streaming implementation preserves agent memory updates and tool calls, ensuring that streamed responses are fully integrated with the agent's state. Developers can hook into the stream to process tokens, update UI, or implement custom logging. The system handles backpressure and connection management for long-running streams.","intents":["Display agent responses in real-time as they are generated","Implement progressive UI updates while the agent is thinking","Reduce perceived latency by showing tokens immediately","Monitor token generation for debugging or analytics"],"best_for":["Web applications requiring real-time response display","Chat interfaces where immediate feedback improves UX","Developers building custom streaming integrations"],"limitations":["Streaming does not reduce total latency; tokens still require full LLM processing","Memory updates are not streamed; they occur after response completion","Tool calls within streamed responses may cause buffering","Streaming requires persistent client-server connection (WebSocket or HTTP streaming)"],"requires":["Python 3.9+","Letta server with streaming support","Client capable of handling streaming responses"],"input_types":["agent query","streaming configuration"],"output_types":["token stream","metadata about stream (tool calls, memory updates)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-letta__cap_5","uri":"capability://memory.knowledge.semantic.memory.retrieval.with.context.aware.recall","name":"semantic memory retrieval with context-aware recall","description":"Letta provides a memory retrieval system that allows agents to search their conversation history and learned facts using semantic similarity or keyword matching. The system indexes past messages and memory updates, enabling agents to recall relevant context without re-reading entire conversation histories. Retrieval results are ranked by relevance and can be injected into the agent's context window for decision-making. The implementation supports both dense (embedding-based) and sparse (keyword) retrieval strategies.","intents":["Allow agents to search their memory for relevant past interactions","Retrieve context about users or topics without token overhead","Implement context-aware responses based on learned information","Reduce token usage by selectively retrieving relevant memory"],"best_for":["Long-running agents with extensive conversation history","Applications requiring context-aware personalization","Teams building knowledge-intensive agents"],"limitations":["Embedding-based retrieval requires external embedding model (OpenAI, local, etc.)","Keyword retrieval is limited to exact or fuzzy matches; semantic understanding is limited","No built-in deduplication of similar memories","Retrieval latency scales with memory size; no automatic pruning or archival"],"requires":["Python 3.9+","Embedding model (OpenAI, Ollama, or local)","Vector storage or keyword index"],"input_types":["query text","retrieval strategy (semantic or keyword)","memory corpus"],"output_types":["ranked memory results","relevance scores","formatted context for agent"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-letta__cap_6","uri":"capability://planning.reasoning.agent.to.agent.communication.and.delegation","name":"agent-to-agent communication and delegation","description":"Letta supports creating networks of agents that can communicate with each other and delegate tasks. Agents can call other agents as tools, passing context and receiving responses. This enables hierarchical agent architectures where specialized agents handle specific domains or tasks. Communication between agents preserves memory context and allows for complex multi-agent workflows. The system manages agent discovery and routing between instances.","intents":["Build multi-agent systems where agents specialize in different domains","Implement hierarchical task decomposition across multiple agents","Create agent teams that collaborate to solve complex problems","Route requests to the most appropriate agent based on task type"],"best_for":["Complex systems requiring specialized agent roles","Teams building multi-agent orchestration platforms","Applications with domain-specific agent networks"],"limitations":["Agent-to-agent calls add latency (network round trips between agents)","No built-in load balancing across agent instances","Memory context is not automatically shared between agents; must be explicitly passed","Circular dependencies between agents are not detected or prevented"],"requires":["Python 3.9+","Multiple Letta agent instances","Agent discovery mechanism (registry or configuration)"],"input_types":["agent ID or name","task description","context to pass to delegated agent"],"output_types":["delegated agent response","execution status","memory updates from delegated agent"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-letta__cap_7","uri":"capability://text.generation.language.custom.prompt.engineering.with.template.variables.and.system.instructions","name":"custom prompt engineering with template variables and system instructions","description":"Letta allows developers to customize agent behavior through system prompts and instruction templates that support variable substitution. Prompts can include placeholders for agent name, user information, current date, and other context. The system supports prompt versioning and A/B testing of different instruction sets. Prompts are stored with agent configurations and can be updated without redeploying agents. The implementation includes prompt validation and optimization suggestions.","intents":["Customize agent personality and behavior through system prompts","Use dynamic variables in prompts (user name, date, context)","Test different instruction sets to optimize agent performance","Version and track changes to agent prompts over time"],"best_for":["Teams fine-tuning agent behavior without retraining","Applications requiring personalized agent personalities","Developers experimenting with prompt optimization"],"limitations":["Prompt changes require agent restart to take effect","No built-in prompt optimization or automated tuning","Variable substitution is simple string replacement; no conditional logic","Prompt quality depends entirely on developer expertise"],"requires":["Python 3.9+","Letta agent instance","Understanding of LLM prompt engineering"],"input_types":["system prompt text","template variables","instruction sets"],"output_types":["formatted prompt","agent behavior changes","prompt versions"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-letta__cap_8","uri":"capability://data.processing.analysis.structured.data.extraction.with.schema.based.output.validation","name":"structured data extraction with schema-based output validation","description":"Letta supports extracting structured data from agent responses using JSON schemas or Pydantic models. Developers define output schemas, and the system validates agent responses against them, ensuring type safety and consistency. Invalid responses trigger re-prompting or error handling. The system supports nested schemas, optional fields, and custom validation logic. Extracted data is returned as typed Python objects, not raw text.","intents":["Extract structured data from agent responses (e.g., JSON, objects)","Validate agent outputs against expected schemas","Ensure type safety when processing agent responses","Handle schema mismatches with automatic re-prompting"],"best_for":["Applications requiring reliable structured outputs from agents","Systems integrating agent responses with downstream APIs","Teams building data extraction pipelines"],"limitations":["Schema validation adds latency (may require LLM re-prompting on failure)","Complex nested schemas may confuse LLMs, leading to validation failures","No built-in schema inference from examples","Re-prompting on validation failure can consume significant tokens"],"requires":["Python 3.9+","Pydantic or JSON schema definition","Letta agent instance"],"input_types":["output schema (Pydantic model or JSON schema)","agent response text"],"output_types":["validated structured data","typed Python objects","validation errors"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-letta__cap_9","uri":"capability://memory.knowledge.conversation.history.management.with.message.filtering.and.pagination","name":"conversation history management with message filtering and pagination","description":"Letta manages conversation history with support for filtering, pagination, and selective retrieval. Developers can query message history by date range, sender, content, or metadata. The system supports message deletion, archival, and bulk operations. History is indexed for fast retrieval and can be exported in multiple formats. Pagination prevents loading entire conversation histories into memory, enabling efficient handling of long conversations.","intents":["Retrieve specific messages or conversation segments from history","Paginate through long conversations without loading all messages","Filter messages by sender, date, or content","Export conversation history for analysis or backup"],"best_for":["Applications with long conversation histories","Systems requiring conversation analytics or auditing","Teams building conversation management interfaces"],"limitations":["Filtering on message content requires full-text search index (not always available)","Pagination metadata must be tracked by client; no automatic cursor management","Bulk operations (delete, archive) are not atomic; partial failures are possible","Message export formats are limited (JSON, CSV)"],"requires":["Python 3.9+","Letta server with message history storage","Database with indexing support"],"input_types":["filter criteria (date, sender, content)","pagination parameters (limit, offset)","export format"],"output_types":["filtered message list","pagination metadata","exported conversation data"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Letta server running (local or remote)","LLM provider API key (OpenAI, Anthropic, or local model)","Type hints on all tool functions","Letta agent instance to bind tools to","Letta server with quota tracking","Metrics storage (Redis, database, etc.)","Letta server with logging enabled","Log storage backend (file, database, or external service)","Letta agent instance"],"failure_modes":["Memory updates are synchronous and block agent response generation","No built-in memory compression or summarization for very long conversations (>10k messages)","Memory retrieval is linear scan by default without semantic indexing","Cross-agent memory sharing requires manual implementation","Tool schemas are generated from Python type hints; complex types may not translate cleanly to JSON schema","No built-in retry logic for failed tool calls","Async tools require event loop management in the calling context","Tool execution errors are returned to the LLM as text, not structured error objects","Rate limiting is enforced at the agent level; no fine-grained per-endpoint limits","Quota tracking is approximate; actual usage may vary due to streaming or retries","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:24.098Z","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=pypi-letta","compare_url":"https://unfragile.ai/compare?artifact=pypi-letta"}},"signature":"27kKzjWTJQrRwQaD/e1UsCRau/frRfX+pByKRUrdFd7+Ih9AOlG2f1ptkX8E1RD2BvlDSn/P2CSVor6EN/nZDw==","signedAt":"2026-06-21T15:44:36.243Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-letta","artifact":"https://unfragile.ai/pypi-letta","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}