* Add log probabilities support for RL training
This enables Letta server to request and return log probabilities from
OpenAI-compatible providers (including SGLang) for use in RL training.
Changes:
- LLMConfig: Add return_logprobs and top_logprobs fields
- OpenAIClient: Set logprobs in ChatCompletionRequest when enabled
- LettaLLMAdapter: Add logprobs field and extract from response
- LettaResponse: Add logprobs field to return log probs to client
- LettaRequest: Add return_logprobs/top_logprobs for per-request override
- LettaAgentV3: Store and pass logprobs through to response
- agents.py: Handle request-level logprobs override
Usage:
response = client.agents.messages.create(
agent_id=agent_id,
messages=[...],
return_logprobs=True,
top_logprobs=5,
)
print(response.logprobs) # Per-token log probabilities
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* Add multi-turn token tracking for RL training via SGLang native endpoint
- Add TurnTokenData schema to track token IDs and logprobs per turn
- Add return_token_ids flag to LettaRequest and LLMConfig
- Create SGLangNativeClient for /generate endpoint (returns output_ids)
- Create SGLangNativeAdapter that uses native endpoint
- Modify LettaAgentV3 to accumulate turns across LLM calls
- Include turns in LettaResponse when return_token_ids=True
* Fix: Add SGLang native adapter to step() method, not just stream()
* Fix: Handle Pydantic Message objects in SGLang native adapter
* Fix: Remove api_key reference from LLMConfig (not present)
* Fix: Add missing 'created' field to ChatCompletionResponse
* Add full tool support to SGLang native adapter
- Format tools into prompt in Qwen-style format
- Parse tool calls from <tool_call> tags in response
- Format tool results as <tool_response> in user messages
- Set finish_reason to 'tool_calls' when tools are called
* Use tokenizer.apply_chat_template for proper tool formatting
- Add tokenizer caching in SGLang native adapter
- Use apply_chat_template when tokenizer available
- Fall back to manual formatting if not
- Convert Letta messages to OpenAI format for tokenizer
* Fix: Use func_response instead of tool_return for ToolReturn content
* Fix: Get output_token_logprobs from meta_info in SGLang response
* Fix: Allow None in output_token_logprobs (SGLang format includes null)
* chore: remove unrelated files from logprobs branch
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix: add missing call_type param to adapter constructors in letta_agent_v3
The SGLang refactor dropped call_type=LLMCallType.agent_step when extracting
adapter creation into conditional blocks. Restores it for all 3 spots (SGLang
in step, SimpleLLM in step, SGLang in stream).
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* just stage-api && just publish-api
* fix: update expected LLMConfig fields in schema test for logprobs support
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* chore: remove rllm provider references
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* just stage-api && just publish-api
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-65-206.ec2.internal>
Co-authored-by: Letta <noreply@letta.com>
* feat: add /agents/{agent_id}/generate endpoint for direct LLM requests
Add new endpoint that makes direct LLM provider requests without agent
context, memory, tools, or state modification. This enables:
- Quick LLM queries without agent overhead
- Testing model configurations
- Simple chat completions using agent's credentials
- Comparing responses across different models
Features:
- Uses agent's LLM config by default
- Supports model override with full provider config resolution
- Non-streaming, stateless operation
- Proper error handling and validation
- Request/response schemas with Pydantic validation
Implementation:
- Add GenerateRequest and GenerateResponse schemas
- Implement generate_completion endpoint handler
- Add necessary imports (LLMError, LLMClient, HandleNotFoundError)
- Include logging and comprehensive error handling
* fix: improve error handling and fix Message construction
- Fix critical bug: use content=[TextContent(text=...)] instead of text=...
- Add explicit error handling for NoResultFound and HandleNotFoundError
- Add error handling for convert_response_to_chat_completion
- Add structured logging for debugging
- Remove unnecessary .get() calls since Pydantic validates messages
* refactor: extract generate logic to AgentCompletionService
Move the generate endpoint business logic out of the endpoint handler
into a dedicated AgentCompletionService class for better code organization
and separation of concerns.
Changes:
- Create new AgentCompletionService in services/agent_completion_service.py
- Service handles all business logic: agent validation, LLM config resolution,
message conversion, LLM client creation, and request/response processing
- Integrate service with SyncServer initialization
- Refactor generate_completion endpoint to use the service
- Endpoint now only handles HTTP concerns (auth, error mapping)
Benefits:
- Cleaner endpoint code (reduced from ~140 lines to ~25 lines)
- Better separation of concerns (HTTP vs business logic)
- Service logic can be reused or tested independently
- Follows established patterns in the codebase (AgentManager, etc.)
* feat: simplify generate API to accept just prompt text
Simplify the client interface by accepting a simple prompt string instead
of requiring clients to format messages.
Changes:
- Update GenerateRequest schema:
- Replace 'messages' array with simple 'prompt' string
- Add optional 'system_prompt' for context/instructions
- Keep 'override_model' for model selection
- Update AgentCompletionService to format messages automatically:
- Accepts prompt and optional system_prompt
- Constructs message array internally (system + user messages)
- Simpler API surface for clients
- Update endpoint documentation with new simplified examples
- Regenerate OpenAPI spec and TypeScript SDK
Benefits:
- Much simpler client experience - just send text
- No need to understand message formatting
- Still supports system prompts for context
- Cleaner API that matches common use cases
Example (before):
{
"messages": [{"role": "user", "content": "What is 2+2?"}]
}
Example (after):
{
"prompt": "What is 2+2?"
}
* test: add comprehensive integration tests for generate endpoint
Add 9 integration tests covering various scenarios:
Happy path tests:
- test_agent_generate_basic: Basic prompt -> response flow
- test_agent_generate_with_system_prompt: System prompt + user prompt
- test_agent_generate_with_model_override: Override model selection
- test_agent_generate_long_prompt: Handle longer prompts
- test_agent_generate_no_persistence: Verify no messages saved to agent
Error handling tests:
- test_agent_generate_empty_prompt_error: Empty prompt validation (422)
- test_agent_generate_invalid_agent_id: Invalid agent ID (404)
- test_agent_generate_invalid_model_override: Invalid model handle (404)
All tests verify:
- Response structure (content, model, usage)
- Proper status codes for errors
- Usage statistics (tokens, counts)
- No side effects on agent state
Tests follow existing test patterns in test_client.py and use the
letta_client SDK (assuming generate_completion method is auto-generated
from the OpenAPI spec).
* openapi
* refactor: rename AgentCompletionService to AgentGenerateCompletionManager
Rename for better clarity and consistency with codebase naming conventions:
- Rename file: agent_completion_service.py → agent_generate_completion_manager.py
- Rename class: AgentCompletionService → AgentGenerateCompletionManager
- Rename attribute: server.agent_completion_service → server.agent_generate_completion_manager
- Update docstrings: 'Service' → 'Manager'
Changes:
- apps/core/letta/services/agent_generate_completion_manager.py (renamed + updated class)
- apps/core/letta/server/server.py (import + initialization)
- apps/core/letta/server/rest_api/routers/v1/agents.py (usage in endpoint)
No functional changes, purely a naming refactor.
* fix: remove invalid Message parameters in generate manager
Remove agent_id=None and user_id=None from Message construction.
The Message model doesn't accept these as None values - only pass
required parameters (role, content).
Fixes validation error:
'Extra inputs are not permitted [type=extra_forbidden, input_value=None]'
This aligns with other Message construction patterns in the codebase
(see tools.py, memory.py examples).
* feat: improve generate endpoint validation and tests
- Add field validator for whitespace-only prompts
- Always include system message (required by Anthropic)
- Use default "You are a helpful assistant." when no system_prompt provided
- Update tests to use direct HTTP calls via httpx
- Fix test issues:
- Use valid agent ID format (agent-{uuid})
- Use available model (openai/gpt-4o-mini)
- Add whitespace validation test
- All 9 integration tests passing
* feat: add usage columns to steps table
Adds denormalized usage fields to the steps table for easier querying:
- model_handle: The model handle (e.g., "openai/gpt-4o-mini")
- cached_input_tokens: Tokens served from cache
- cache_write_tokens: Tokens written to cache (Anthropic)
- reasoning_tokens: Reasoning/thinking tokens
These fields mirror LettaUsageStatistics and are extracted from the
existing prompt_tokens_details and completion_tokens_details JSON columns.
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* chore: regenerate OpenAPI specs and SDK for usage columns
🤖 Generated with [Letta Code](https://letta.com)
Co-authored-by: Sarah Wooders <sarahwooders@users.noreply.github.com>
---------
Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com>
Co-authored-by: Sarah Wooders <sarahwooders@users.noreply.github.com>
* feat: add ID format validation to agent and user schemas
Reuse existing validator types (ToolId, SourceId, BlockId, MessageId,
IdentityId, UserId) from letta.validators to enforce ID format validation
at the schema level. This ensures malformed IDs are rejected with a 422
validation error instead of causing 500 database errors.
Changes:
- CreateAgent: validate tool_ids, source_ids, folder_ids, block_ids, identity_ids
- UpdateAgent: validate tool_ids, source_ids, folder_ids, block_ids, message_ids, identity_ids
- UserUpdate: validate id
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* chore: regenerate API spec and SDK
* fix: override ID validation in AgentSchema for agent file portability
AgentSchema extends CreateAgent but needs to allow arbitrary short IDs
(e.g., tool-0, block-0) for portable agent files. Override the validated
ID fields to use plain List[str] instead of the validated types.
Also fix test_agent.af to use proper UUID-format IDs.
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* chore: regenerate API spec and SDK
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix: revert test_agent.af - short IDs are valid for agent files
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix openapi schema
---------
Co-authored-by: Letta <noreply@letta.com>
* fix: non-streaming conversation messages endpoint
**Problems:**
1. `AssertionError: run_id is required when enforce_run_id_set is True`
- Non-streaming path didn't create a run before calling `step()`
2. `ResponseValidationError: Unable to extract tag using discriminator 'message_type'`
- `response_model=LettaStreamingResponse` but non-streaming returns `LettaResponse`
**Fixes:**
1. Add run creation before calling `step()` (mirrors agents endpoint)
2. Set run_id in Redis for cancellation support
3. Pass `run_id` to `step()`
4. Change `response_model` from `LettaStreamingResponse` to `LettaResponse`
(streaming returns `StreamingResponse` which bypasses response_model validation)
**Test:**
Added `test_conversation_non_streaming_raw_http` to verify the fix.
👾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* api sync
---------
Co-authored-by: Letta <noreply@letta.com>
* feat: add ID format validation to batch request schema
Add ID format validation to LettaBatchRequest using existing validator
types from letta.validators.
Changes:
- LettaBatchRequest.agent_id: str → AgentId
This ensures malformed agent IDs in batch requests are rejected with 422
validation errors instead of causing 500 database errors.
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* chore: regenerate API spec and SDK
---------
Co-authored-by: Letta <noreply@letta.com>
* feat: add ID format validation to identity schemas
Add ID format validation to IdentityCreate, IdentityUpsert, and IdentityUpdate
schemas using existing validator types from letta.validators.
Changes:
- agent_ids: Optional[List[str]] → Optional[List[AgentId]]
- block_ids: Optional[List[str]] → Optional[List[BlockId]]
This ensures malformed IDs are rejected with 422 validation errors instead
of causing 500 database errors.
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* chore: regenerate API spec and SDK
---------
Co-authored-by: Letta <noreply@letta.com>
* feat: add ID format validation to group schemas
Add ID format validation to GroupCreate, GroupUpdate, and manager config
schemas using existing validator types from letta.validators.
Changes:
- GroupCreate/GroupUpdate: agent_ids → List[AgentId], shared_block_ids → List[BlockId]
- SupervisorManager, DynamicManager, SleeptimeManager, VoiceSleeptimeManager:
manager_agent_id → AgentId
- Update variants: manager_agent_id → Optional[AgentId]
This ensures malformed IDs are rejected with 422 validation errors instead
of causing 500 database errors.
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* chore: regenerate API spec and SDK
---------
Co-authored-by: Letta <noreply@letta.com>
* fix: remove deprecation from agent passages endpoints
The client.agent.passages endpoints (list, create, search, delete) were
incorrectly marked as deprecated. This would break significant amounts
of user code and negatively impact developer experience.
Fixes#9116
Co-authored-by: Ari Webb <AriWebb@users.noreply.github.com>
* stage publish api
---------
Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com>
Co-authored-by: Ari Webb <AriWebb@users.noreply.github.com>
Co-authored-by: Ari Webb <ari@letta.com>
* feat: add conversation_id to message search results
Add conversation_id field to all *MessageListResult classes
(SystemMessageListResult, UserMessageListResult, ReasoningMessageListResult,
AssistantMessageListResult) so that conversation IDs are returned from
the /messages/search endpoint alongside agent IDs.
Fixes#9055
Co-authored-by: Charles Packer <cpacker@users.noreply.github.com>
* chore: regenerate SDK and OpenAPI spec
Regenerate autogenerated files after adding conversation_id to
message search result schemas.
Co-authored-by: Sarah Wooders <sarahwooders@users.noreply.github.com>
---------
Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com>
Co-authored-by: Charles Packer <cpacker@users.noreply.github.com>
Co-authored-by: Sarah Wooders <sarahwooders@users.noreply.github.com>
* fix: don't need embedding model for self hosted
* stage publish api
* passes tests
* add test
* remove unnecessary upgrades
* update revision order db migrations
* add timeout for ci
* feat(core): add image support in tool returns [LET-7140]
Enable tool_return to support both string and ImageContent content parts,
matching the pattern used for user message inputs. This allows tools
executed client-side to return images back to the agent.
Changes:
- Add LettaToolReturnContentUnion type for text/image content parts
- Update ToolReturn schema to accept Union[str, List[content parts]]
- Update converters for each provider:
- OpenAI Chat Completions: placeholder text for images
- OpenAI Responses API: full image support
- Anthropic: full image support with base64
- Google: placeholder text for images
- Add resolve_tool_return_images() for URL-to-base64 conversion
- Make create_approval_response_message_from_input() async
🐾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix(core): support images in Google tool returns as sibling parts
Following the gemini-cli pattern: images in tool returns are sent as
sibling inlineData parts alongside the functionResponse, rather than
inside it.
🐾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* test(core): add integration tests for multi-modal tool returns [LET-7140]
Tests verify that:
- Models with image support (Anthropic, OpenAI Responses API) can see
images in tool returns and identify the secret text
- Models without image support (Chat Completions) get placeholder text
and cannot see the actual image content
- Tool returns with images persist correctly in the database
Uses secret.png test image containing hidden text "FIREBRAWL" that
models must identify to pass the test.
Also fixes misleading comment about Anthropic only supporting base64
images - they support URLs too, we just pre-resolve for consistency.
🐾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* refactor: simplify tool return image support implementation
Reduce code verbosity while maintaining all functionality:
- Extract _resolve_url_to_base64() helper in message_helper.py (eliminates duplication)
- Add _get_text_from_part() helper for text extraction
- Add _get_base64_image_data() helper for image data extraction
- Add _tool_return_to_google_parts() to simplify Google implementation
- Add _image_dict_to_data_url() for OpenAI Responses format
- Use walrus operator and list comprehensions where appropriate
- Add integration_test_multi_modal_tool_returns.py to CI workflow
Net change: -120 lines while preserving all features and test coverage.
👾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix(tests): improve prompt for multi-modal tool return tests
Make prompts more direct to reduce LLM flakiness:
- Simplify tool description: "Retrieves a secret image with hidden text. Call this function to get the image."
- Change user prompt from verbose request to direct command: "Call the get_secret_image function now."
- Apply to both test methods
This reduces ambiguity and makes tool calling more reliable across different LLM models.
👾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix bugs
* test(core): add google_ai/gemini-2.0-flash-exp to multi-modal tests
Add Gemini model to test coverage for multi-modal tool returns. Google AI already supports images in tool returns via sibling inlineData parts.
👾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix(ui): handle multi-modal tool_return type in frontend components
Convert Union<string, LettaToolReturnContentUnion[]> to string for display:
- ViewRunDetails: Convert array to '[Image here]' placeholder
- ToolCallMessageComponent: Convert array to '[Image here]' placeholder
Fixes TypeScript errors in web, desktop-ui, and docker-ui type-checks.
👾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
---------
Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: Caren Thomas <carenthomas@gmail.com>
* feat: byok provider models in db also
* make tests and sync api
* fix inconsistent state with recreating provider of same name
* fix sync on byok creation
* update revision
* move stripe code for testing purposes
* revert
* add refresh byok models endpoint
* just stage publish api
* add tests
* reorder revision
* add test for name clashes
feat: update documentation and add new tutorials for memory blocks and agent collaboration
- Updated navigation paths in docs.yml to reflect new tutorial locations.
- Added comprehensive guides on shared memory blocks and attaching/detaching memory blocks.
- Enhanced existing documentation for memory blocks with examples and best practices.
- Corrected API key references in prebuilt tools documentation.
These changes aim to improve user understanding and facilitate multi-agent collaboration through shared memory systems.
* feat: Add conversation_id filtering to message list and search endpoints
Add optional conversation_id parameter to filter messages by conversation:
- client.agents.messages.list
- client.messages.list
- client.messages.search
Changes:
- Added conversation_id field to MessageSearchRequest and SearchAllMessagesRequest schemas
- Added conversation_id filtering to list_messages in message_manager.py
- Updated get_agent_recall_async and get_all_messages_recall_async in server.py
- Added conversation_id query parameter to router endpoints
- Updated Turbopuffer client to support conversation_id filtering in searches
Fixes#8320🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Charles Packer <cpacker@users.noreply.github.com>
* add conversation_id to message and tpuf
* default messages filter for backward compatibility
* add test and auto gen
* fix integration test
* fix test
* update test
---------
Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com>
Co-authored-by: Charles Packer <cpacker@users.noreply.github.com>
Co-authored-by: christinatong01 <christina@letta.com>
