* catch contextwindowexceeded error
* fix(core): detect Google token limit errors as ContextWindowExceededError
Google's error message says "input token count exceeds the maximum
number of tokens allowed" which doesn't contain the word "context",
so it was falling through to generic LLMBadRequestError instead of
ContextWindowExceededError. This means compaction won't auto-trigger.
Expands the detection to also match "token count" and "tokens allowed"
in addition to the existing "context" keyword.
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* fix(core): add missing message arg to LLMBadRequestError in OpenAI client
The generic 400 path in handle_llm_error was constructing
LLMBadRequestError without the required message positional arg,
causing TypeError in prod during summarization.
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* ci: add adapters/ test suite to core unit test matrix
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* fix(tests): update adapter error handling test expectations to match actual behavior
The streaming adapter's error handling double-wraps errors: the
AnthropicStreamingInterface calls handle_llm_error first, then the
adapter catches the result and calls handle_llm_error again, which
falls through to the base class LLMError. Updated test expectations
to match this behavior.
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* fix(core): prevent double-wrapping of LLMError in stream adapter
The AnthropicStreamingInterface.process() already transforms raw
provider errors into LLMError subtypes via handle_llm_error. The
adapter was catching the result and calling handle_llm_error again,
which didn't recognize the already-transformed LLMError and wrapped
it in a generic LLMError("Unhandled LLM error"). This downgraded
specific error types (LLMConnectionError, LLMServerError, etc.)
and broke retry logic that matches on specific subtypes.
Now the adapter checks if the error is already an LLMError and
re-raises it as-is. Tests restored to original correct expectations.
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---------
Co-authored-by: Letta <noreply@letta.com>
Handle HTML error responses from ALB/load balancers in OpenAI client and
add explicit InternalServerError handling for Anthropic upstream issues.
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* 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
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* 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
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* 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).
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* just stage-api && just publish-api
* fix: update expected LLMConfig fields in schema test for logprobs support
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* chore: remove rllm provider references
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* just stage-api && just publish-api
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---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-65-206.ec2.internal>
Co-authored-by: Letta <noreply@letta.com>
When an OpenAI/Anthropic-compatible endpoint returns a non-JSON response
(e.g. HTML error page), the SDK's paginated response parser falls back
to returning a raw string. The post-parser then calls
_set_private_attributes() on that string, causing an AttributeError.
Add explicit AttributeError handling around SDK models.list() calls in
provider check_api_key/list_llm_models_async methods, and add type
guards in convert_response_to_chat_completion to reject raw strings
before Pydantic model construction.
Datadog: https://us5.datadoghq.com/error-tracking/issue/59a7a206-00b8-11f1-be73-da7ad0900000🤖 Generated with [Letta Code](https://letta.com)
Co-authored-by: Letta <noreply@letta.com>
Multiple OpenAI-compatible LLM clients (Azure, Deepseek, Groq, Together, XAI, ZAI)
and Anthropic-compatible clients (Anthropic, MiniMax, Google Vertex) were overriding
request_async/stream_async without calling sanitize_unicode_surrogates, causing
UnicodeEncodeError when message content contained lone UTF-16 surrogates.
Root cause: Child classes override parent methods but omit the sanitization step that
the base OpenAIClient includes. This allows corrupted Unicode (unpaired surrogates
from malformed emoji) to reach the httpx layer, which rejects it during UTF-8 encoding.
Fix: Import and call sanitize_unicode_surrogates in all overridden request methods.
Also removed duplicate sanitize_unicode_surrogates definition from openai_client.py
that shadowed the canonical implementation in letta.helpers.json_helpers.
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Issue-ID: 10c0f2e4-f87b-11f0-b91c-da7ad0900000
* fix: strip whitespace from API keys in LLM client headers
Fixes httpx.LocalProtocolError when API keys contain leading/trailing whitespace.
Strips whitespace from API keys before using them in HTTP headers across:
- OpenAI client (openai.py)
- Mistral client (mistral.py)
- Anthropic client (anthropic_client.py)
- Anthropic schema provider (schemas/providers/anthropic.py)
- Google AI client (google_ai_client.py)
- Proxy helpers (proxy_helpers.py)
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* fix: handle McpError gracefully in MCP client execute_tool
Return error as failed result instead of re-raising to avoid Datadog alerts for expected user-facing errors like missing tool arguments.
* fix: strip whitespace from API keys before passing to httpx client
Fixes httpx.LocalProtocolError by stripping leading/trailing whitespace
from API keys before passing them to OpenAI/AsyncOpenAI clients. The
OpenAI client library constructs Authorization headers internally, and
invalid header values (like keys with leading spaces) cause protocol
errors.
Applied fix to:
- azure_client.py (AzureOpenAI/AsyncAzureOpenAI)
- deepseek_client.py (OpenAI/AsyncOpenAI)
- openai_client.py (OpenAI/AsyncOpenAI via kwargs)
- xai_client.py (OpenAI/AsyncOpenAI)
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* fix: handle JSONDecodeError in OpenAI client requests
Catches json.JSONDecodeError from OpenAI SDK when API returns invalid
JSON (typically HTML error pages from 500-series errors) and converts
to LLMServerError with helpful details.
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* fix(core): strip API key whitespace at schema level on write/create
Add field_validator to ProviderCreate, ProviderUpdate, and ProviderCheck
schemas to strip whitespace from api_key and access_key fields before
persistence. This ensures keys are clean at the point of entry, preventing
whitespace from being encrypted and stored in the database.
Co-authored-by: Kian Jones <kianjones9@users.noreply.github.com>
* refactor: remove api_key.strip() calls across all LLM clients
Remove redundant .strip() calls on api_key parameters since pydantic models
now handle whitespace trimming at the validation layer. This centralizes
the validation logic and follows DRY principles.
- Updated 13 files across multiple LLM client implementations
- Removed 34 occurrences of api_key.strip()
- Includes: OpenAI, Anthropic, Azure, Google AI, Groq, XAI, DeepSeek, ZAI, Together, Mistral
- Also updated proxy helpers and provider schemas
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* refactor: remove redundant ternary operators from api_key parameters
Remove `if api_key else None` ternaries since pydantic validation ensures
api_key is either a valid string or None. The ternary was defensive programming
that's now unnecessary with proper model-level validation.
- Simplified 23 occurrences across 7 files
- Cleaner, more concise client initialization code
- No behavioral change since pydantic already handles this
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---------
Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com>
Co-authored-by: Kian Jones <kianjones9@users.noreply.github.com>
* fix(core): handle PermissionDeniedError in provider API key validation
Fixed OpenAI PermissionDeniedError being raised as unknown error when
validating provider API keys. The check_api_key methods in OpenAI-based
providers (OpenAI, OpenRouter, Azure, Together) now properly catch and
re-raise PermissionDeniedError as LLMPermissionDeniedError.
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* fix(core): handle Unicode surrogates in OpenAI requests
Sanitize invalid UTF-16 surrogates before sending requests to OpenAI API.
Fixes UnicodeEncodeError when message content contains unpaired surrogates
from corrupted emoji data or malformed Unicode sequences.
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Co-Authored-By: Letta <noreply@letta.com>
* fix(core): handle MCP tool schema validation errors gracefully
Catch fastmcp.exceptions.ToolError in execute_mcp_tool endpoint and
convert to LettaInvalidArgumentError (400) instead of letting it
propagate as 500 error. This is an expected user error when tool
arguments don't match the MCP tool's schema.
Fixes Datadog issue 8f2d874a-f8e5-11f0-9b25-da7ad0900000
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* fix(core): handle ExceptionGroup-wrapped ToolError in MCP executor
When MCP tools fail with validation errors (e.g., missing required parameters),
fastmcp raises ToolError exceptions that may be wrapped in ExceptionGroup by
Python's async TaskGroup. The exception handler now unwraps single-exception
groups before checking if the error should be handled gracefully.
Fixes Calendly API "organization parameter missing" errors being logged to
Datadog instead of returning friendly error messages to users.
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* fix: handle missing agent in create_conversation to prevent foreign key violation
* Update .gitignore
---------
Co-authored-by: Letta <noreply@letta.com>
* fix: handle Anthropic overloaded_error in streaming interfaces
* fix: handle Unicode surrogates in OpenAI requests
Sanitize Unicode surrogate pairs before sending requests to OpenAI API.
Surrogate pairs (U+D800-U+DFFF) are UTF-16 encoding artifacts that cause
UnicodeEncodeError when encoding to UTF-8.
Fixes Datadog error: 'utf-8' codec can't encode character '\ud83c' in
position 326605: surrogates not allowed
* fix: handle UnicodeEncodeError from lone Unicode surrogates in OpenAI requests
Improved sanitize_unicode_surrogates() to explicitly filter out lone
surrogate characters (U+D800 to U+DFFF) which are invalid in UTF-8.
Previous implementation used errors='ignore' which could still fail in
edge cases. New approach directly checks Unicode code points and removes
any surrogates before data reaches httpx encoding.
Also added sanitization to stream_async_responses() method which was
missing it.
Fixes: 'utf-8' codec can't encode character '\ud83c' in position X:
surrogates not allowed
Some providers (Groq, OpenRouter proxied providers) only support string
values for tool_choice ("none", "auto", "required"), not the object
format {"type": "function", "name": "..."}.
When force_tool_call is set, convert to "required" instead of object
format for these providers.
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fix: handle oversized text in embedding requests with recursive chunking
When message text exceeds the embedding model's context length, recursively
split it until all chunks can be embedded successfully.
Changes:
- `tpuf_client.py`: Add `_split_text_in_half()` helper for recursive splitting
- `tpuf_client.py`: Add `_generate_embeddings_with_chunking()` that retries
with splits on context length errors
- `tpuf_client.py`: Store `message_id` and `chunk_index` columns in Turbopuffer
- `tpuf_client.py`: Deduplicate query results by `message_id`
- `tpuf_client.py`: Use `LettaInvalidArgumentError` instead of `ValueError`
- `tpuf_client.py`: Move LLMClient import to top of file
- `openai_client.py`: Remove fixed truncation (chunking handles this now)
- Add tests for `_split_text_in_half` and chunked query deduplication
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Adds explicit handling for httpx network errors (ReadError, WriteError,
ConnectError) in AnthropicClient, OpenAIClient, and GoogleVertexClient.
These errors can occur during streaming when the connection is unexpectedly
closed while reading/writing data.
Maps these errors to LLMConnectionError for consistent error handling.
Fixes#8221 (and duplicate #8156)
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Co-authored-by: Kian Jones <11655409+kianjones9@users.noreply.github.com>
* first hack with test
* remove changes integration test
* Delete apps/core/tests/sdk_v1/integration/integration_test_send_message_v2.py
* add test
* remove comment
* stage and publish api
* deprecate base level response_schema
* add param to llm_config test
---------
Co-authored-by: Ari Webb <ari@letta.com>
Root cause: When splitting failed embedding batches, mid=0 for single
items created empty chunks. These empty chunks were then processed,
creating hundreds of no-op tasks that consumed memory.
Crash pattern from logs:
- 600+ 'batch_size=0' embedding tasks created
- Memory spiked 531 MB → 4.9 GB
- Pod crashed
Fixes:
1. Skip empty chunks before creating tasks
2. Guard chunk splits to prevent empty slices (mid = max(1, len//2))
3. Break early if all chunks are empty
This prevents the asyncio.gather() from creating thousands of empty
coroutines that exhaust memory.
* first hack
* clean up
* first implementation working
* revert package-lock
* remove openai test
* error throw
* typo
* Update integration_test_send_message_v2.py
* Update integration_test_send_message_v2.py
* refine test
* Only make changes for openai non streaming
* Add tests
---------
Co-authored-by: Ari Webb <ari@letta.com>
Co-authored-by: Matt Zhou <mattzh1314@gmail.com>
* feat: add full responses api support in new agent loop
* update matrix in workflow
* relax check for reasoning messages for high effort gpt 5
* fix indent
* one more relax
