MemGPT
AI & LLMsLetta is the platform for building stateful agents: open AI with advanced memory that can learn and self-improve over time.
Release History
View all versions →0.16.82 fixesThis release primarily focuses on security improvements by changing the serialization method for tool results and includes minor workflow updates.
0.16.71 fixThis patch release focuses on security by fixing a bypass vulnerability related to local filesystem access in ImageContent. It also includes documentation updates.
0.16.66 fixes7 featuresThis release significantly expands Conversations API functionality, introducing default conversation mode and immediate system message compilation upon creation. It also includes important fixes for model configuration overrides and improved compatibility with various LLM providers.
0.16.5This release bumps the version to 0.16.5. It appears to be a maintenance or chore release.
0.16.41 fixThis release primarily consists of maintenance updates, including updating GitHub templates and bumping the version from 0.16.2 to 0.16.4.
0.16.2This release primarily focuses on documentation updates, including corrections to the README and contributing guides, and bumps the version to 0.16.2.
0.16.11 fixThis patch release (v0.16.1) primarily fixes an issue with the provider name configuration for openai-proxy in LLMConfig.
0.16.01 fixThis release bumps the version to v0.16.0 and includes an update to the readme and architecture-specific OTEL installation logic.
0.15.1This release bumps the version to 0.15.1.
0.15.01 featureVersion 0.15.0 introduces context window support for grok-4 models. This release focuses on expanding model capabilities.
0.14.0This release bumps the version number to 0.14.0.
0.13.01 featureVersion 0.13.0 introduces Haiku 4.5 as a new reasoning model and includes documentation cleanup.
0.12.1Breaking2 fixes11 featuresThis release introduces the major `letta_v1_agent` architecture, offering broader provider compatibility and simpler control flow, alongside new features like Human-in-the-Loop and Parallel Tool Calling. It also deprecates and renames several older API endpoints.
0.12.0No release notes provided.
0.11.72 fixes17 featuresThis release introduces significant Human-in-the-Loop (HITL) support for tool execution and upgrades the Agent File schema to v2. It also brings substantial improvements to archival memory search capabilities and enhances model support for GPT-5, DeepSeek, and Anthropic.
0.11.6No release notes provided.
0.11.51 featureThis release introduces background mode support for message streaming, enhancing asynchronous operations.
0.11.42 featuresThis release deprecates legacy paths for azure and together, introduces an asyncio shield for stream timeouts, and adds step metrics recording.
0.11.31 featureVersion 0.11.3 primarily involves refactoring by moving dictconfig out of the getlogger function.
0.11.22 fixes1 featureThis release introduces a new max_steps parameter for agent export and fixes two bugs related to the Ollama provider, specifically concerning the embeddings endpoint URL and model type returns.
0.11.1Breaking1 fix2 featuresThis release introduces support for new LLM models like Claude Opus 4.1 and GPT-5, and enhances built-in tools by improving memory reliability and paginating the file grep tool.
0.11.0Breaking1 fix2 featuresThis release fully removes legacy clients, introduces Signoz tracing integration, optimizes Jinja templating performance, and raises the minimum required Python version to 3.11.
0.10.0Breaking5 fixes8 featuresThis release introduces the `LettaPing` message for stable long streaming connections and adds support for OAuth MCP providers. It also defaults agents to the new `memgpt_v2_agent` architecture and includes various performance improvements and bug fixes.
0.9.11 fix1 featureThis release focuses on bug fixes for the filesystem feature and introduces asynchronous rendering for jinja templates in core routes, alongside adding an agent tag reverse index.
0.9.03 featuresVersion 0.9.0 introduces the Letta Filesystem for enhanced document management and context control, along with new options for document parsing via Mistral OCR.
Common Errors
ModuleNotFoundError6 reportsThe "ModuleNotFoundError" in MemGPT typically indicates a missing Python package required by the application. To fix it, identify the missing module (e.g., asyncpg, mcp) and install it using pip: `pip install <missing_module>`. Ensure you're installing packages within the correct environment (e.g., venv) to avoid conflicts.
NotImplementedError4 reportsThe "NotImplementedError" in memgpt often arises when attempting to use a feature, like a specific embedding model, that hasn't been fully implemented or properly configured within the memgpt codebase, especially in archival memory or agent configurations. To fix it, ensure that the desired embedding model is correctly specified in both the agent config (`~/.memgpt/agents/<agent_name>/config.json`) and global config (`~/.memgpt/config`), and that the corresponding embedding function is implemented in the memgpt code (e.g., `memgpt/embeddings/openai_embeddings.py` or equivalent for other providers); if a custom model is needed, implement the embedding logic and correctly wire it into the agent's archival memory tools.
UniqueViolationError3 reportsUniqueViolationError usually arises when attempting to insert data with a primary key or unique constraint that already exists in the database. To fix it, either update the existing record instead of inserting a new one if the data represents an update, or ensure your application logic prevents duplicate insertions by checking for existing records before creating new ones. Consider using `upsert` functionality provided by your database ORM if updating behavior is intended; otherwise, implement a `get_or_create` or similar pattern.
BadRequestError3 reportsBadRequestError in memgpt usually arises from malformed API requests to the LLM provider, such as exceeding token limits, incorrect formatting of the request body, or providing invalid arguments to a function call. To fix this, carefully inspect the request being sent to the LLM, ensure it adheres to the provider's API specifications (including token limits and data types), and validate that tool call arguments are correctly formatted and complete before submission. If using a proxy, confirm it's correctly configured and forwarding requests without modifications that cause errors.
NotFoundError2 reportsThe "NotFoundError" in MemGPT usually arises from incorrect file paths or missing files during data ingestion or when accessing specific resources. To fix this, double-check the file paths specified in your configuration and code, ensuring they accurately point to the intended files or directories. Verify that the files actually exist in the specified locations and that MemGPT has the necessary permissions to access them.
NameError2 reportsNameError usually arises when a variable or function is used without being defined in the current scope. To fix this, ensure the variable or function is defined (imported or declared) before its usage. Alternatively, check for typos or incorrect capitalization in the variable or function name and correct them to match the definition.
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