letta

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.