* add gpu runners and prod memory_repos
* add lmstudio and vllm in model_settings
* fix llm_configs and change variable name in reusable workflow and change perms for memory_repos to admin in tf
* fix: update self-hosted provider tests to use SDK 1.0 and v2 tests
- Update letta-client from ==0.1.324 to >=1.0.0
- Switch ollama/vllm/lmstudio tests to integration_test_send_message_v2.py
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Co-Authored-By: Letta <noreply@letta.com>
* fix: use openai provider_type for self-hosted model settings
ollama/vllm/lmstudio are not valid provider_type values in the SDK
model_settings schema - they use openai-compatible APIs so provider_type
should be openai. The provider routing is determined by the handle prefix.
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Co-Authored-By: Letta <noreply@letta.com>
* fix: enable redis for ollama/vllm/lmstudio tests
Background streaming tests require Redis. Add use-redis: true to
self-hosted provider test workflows.
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* prep for lmstudio and vllm
* used lmstudio_openai client
* change tool call parser from hermes to qwen3_xml
* qwen3_xmlk -> qwen3_coder
* revert to hermes (incompatible with parallel tool calls?) and skipping vllm tests on parallel tool calls
* install uv redis extra
* remove lmstudio
* create lmstudio test
* qwen3-14b on lmstudio
* try with qwen3-4b
* actually update the model config json to use qwen3-4b
* add test_providers::test_lmstudio
* bump timeout from 60 to 120 for slow lmstudio on cpu model
* misc vllm changes
---------
Co-authored-by: Letta <noreply@letta.com>
* fix(core): handle git memory label prefix collisions in filesystem view
Prevent context window preview crashes when a block label is both a leaf and a prefix (e.g. system/human and system/human/context) by rendering a node as both file and directory. Add regression test.
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Co-Authored-By: Letta <noreply@letta.com>
* fix(core): parse git-backed core memory in context window preview
ContextWindowCalculator.extract_system_components now detects git-backed memory rendering (<memory_filesystem> and <system/...> tags) when <memory_blocks> wrapper is absent, so core_memory is populated in the context preview. Add regression tests.
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---------
Co-authored-by: Letta <noreply@letta.com>
* add gpu runners and prod memory_repos
* add lmstudio and vllm in model_settings
* fix llm_configs and change variable name in reusable workflow and change perms for memory_repos to admin in tf
* fix: update self-hosted provider tests to use SDK 1.0 and v2 tests
- Update letta-client from ==0.1.324 to >=1.0.0
- Switch ollama/vllm/lmstudio tests to integration_test_send_message_v2.py
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix: use openai provider_type for self-hosted model settings
ollama/vllm/lmstudio are not valid provider_type values in the SDK
model_settings schema - they use openai-compatible APIs so provider_type
should be openai. The provider routing is determined by the handle prefix.
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Co-Authored-By: Letta <noreply@letta.com>
* fix: use openai_compat_base_url for ollama/vllm/lmstudio providers
When reconstructing LLMConfig from a model handle lookup, use the
provider's openai_compat_base_url (which includes /v1) instead of
raw base_url. This fixes 404 errors when calling ollama/vllm/lmstudio
since OpenAI client expects /v1/chat/completions endpoint.
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* fix: enable redis for ollama/vllm/lmstudio tests
Background streaming tests require Redis. Add use-redis: true to
self-hosted provider test workflows.
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Co-Authored-By: Letta <noreply@letta.com>
* add memfs-py in prod bucket access
* change ollama
* change packer model defaults
* self-hosted provider support
* diasble reasoner to match the number of messages in test case, enable parallel tool calls, and pass embedding configs
* remove reasoning setting not supported for ollama
* add qwen3 to extra assistant message case
* lower temp
* prep for lmstudio and vllm
* used lmstudio_openai client
* skip parallel tool calls on cpu ran provider lmstudio
* revert downgrade since it's so slow already
* add reuired flags for tool call parsing etc.
* change tool call parser from hermes to qwen3_xml
* qwen3_xmlk -> qwen3_coder
* upgrade vllm to latest container
* revert to hermes (incompatible with parallel tool calls?) and skipping vllm tests on parallel tool calls
* install uv redis extra
* remove lmstudio
---------
Co-authored-by: Letta <noreply@letta.com>
* 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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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
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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>
The A2A messaging tools were instructing receiving agents to use the
send_message tool to reply, but that tool is often not attached to
agents anymore. This caused agents confusion when they couldn't find
the required tool.
For synchronous functions (send_message_to_agent_and_wait_for_reply,
send_message_to_agents_matching_tags, send_message_to_all_agents_in_group),
the system already captures AssistantMessage automatically, so agents
just need to respond normally.
For the async/fire-and-forget function (send_message_to_agent_async),
updated to indicate it's a one-way notification and hint that messaging
tools exist without requiring a specific one.
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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>
* fix(core): use INSERT ON CONFLICT DO NOTHING for provider model sync
Replaces try/except around model.create_async() with pg_insert()
.on_conflict_do_nothing() to prevent UniqueViolationError from being
raised at the asyncpg driver level during concurrent model syncs.
The previous approach caught the exception in Python but ddtrace still
captured it at the driver level, causing Datadog error tracking noise.
Fixes Datadog issue d8dec148-d535-11f0-95eb-da7ad0900000
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* cleaner impl
* fix
---------
Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: Ari Webb <ari@letta.com>
* Fix Anthropic ValueError for long-running operations
Adds proper error handling for Anthropic SDK's streaming requirement.
When operations may exceed 10 minutes, the SDK raises a ValueError.
Changes:
- Catch ValueError in sync request() method
- Provide user-friendly error directing to async API
- Async version already had this fix with streaming fallback
Fixes Datadog issue 955d10b4-ed95-11f0-a5a5-da7ad0900000
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* fix: use LLMBadRequestError instead of ValueError for Anthropic streaming constraint
ValueError maps to HTTP 400 which incorrectly implies a bad client request.
LLMBadRequestError maps to HTTP 502 (Bad Gateway) which correctly signals
that the downstream provider (Anthropic) rejected the proxied request due
to its own constraints.
Co-authored-by: Kian Jones <kianjones9@users.noreply.github.com>
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---------
Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com>
Fixes Datadog issue a47619fa-d5b8-11f0-9fd7-da7ad0900000
Handle empty content in Anthropic responses gracefully by replacing RuntimeError with LLMServerError. Now logs detailed debugging information (response ID, model, stop_reason) and returns a user-friendly error instead of crashing.
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Adds automatic retry with exponential backoff for PostgreSQL deadlock
errors (40P01) in all ORM write methods: create_async, update_async,
batch_create_async, hard_delete_async, and bulk_hard_delete_async.
For update_async, column values are snapshotted before the commit
attempt so they can be restored after rollback clears them.
Also adds DatabaseDeadlockError to _handle_dbapi_error as a fallback
when retries are exhausted.
Datadog: https://us5.datadoghq.com/error-tracking/issue/53ccdd7a-f0cc-11f0-8969-da7ad0900000🤖 Generated with [Letta Code](https://letta.com)
Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com>
The google.genai.errors.ClientError with code 499 (CANCELLED) indicates the
client disconnected, not a server-side failure. Previously this fell through
to the generic ClientError handler and was classified as LLMServerError,
causing false 500s in Datadog error tracking.
- Add explicit 499 handling in handle_llm_error: log at info level, return
LLMConnectionError instead of LLMServerError
- Catch 499 during stream iteration in stream_async and end gracefully
instead of propagating the error
Datadog: https://us5.datadoghq.com/error-tracking/issue/c8453aaa-d559-11f0-81c6-da7ad0900000🤖 Generated with [Letta Code](https://letta.com)
Co-authored-by: Letta <noreply@letta.com>
* fix(core): preserve Gemini thought_signature on function calls in non-streaming path
The Google Gemini API requires thought_signature to be echoed back on
function call parts in multi-turn conversations. In the non-streaming
request path, the signature was only captured for subsequent function
calls (else branch) but dropped for the first/only function call (if
branch) in convert_response_to_chat_completion. This caused 400
INVALID_ARGUMENT errors on the next turn.
Additionally, when no ReasoningContent existed to carry the signature
(e.g. Gemini 2.5 Flash with include_thoughts=False), the signature was
lost in the adapter layer. Now it falls through to TextContent.
Datadog: https://us5.datadoghq.com/error-tracking/issue/17c4b114-d596-11f0-bcd6-da7ad0900000🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix(core): preserve Gemini thought_signature in non-temporal agent path
Carry reasoning_content_signature on TextContent in letta_agent.py
at both locations where content falls through from reasoning (same
fix already applied to the adapter and temporal activity paths).
Co-authored-by: Kian Jones <kianjones9@users.noreply.github.com>
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Co-Authored-By: Letta <noreply@letta.com>
---------
Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com>
MCP server connection failures were raising Python's builtin ConnectionError,
which bypassed the LettaMCPConnectionError FastAPI exception handler and hit
Datadog as unhandled 500 errors. Now all MCP client classes convert
ConnectionError to LettaMCPConnectionError at the source, which the existing
exception handler returns as a user-friendly 502.
Datadog: https://us5.datadoghq.com/error-tracking/issue/93db4a82-fe5a-11f0-85f0-da7ad0900000🐛 Generated with [Letta Code](https://letta.com)
Co-authored-by: Letta <noreply@letta.com>
Fixes Datadog issue 5efbb1d4-eec5-11f0-8f8e-da7ad0900000
Add ExceptionGroup unwrapping in OAuth stream exception handler.
The bug was caused by ExceptionGroup not being caught by the general
`except Exception` handler, since ExceptionGroup is a subclass of
BaseException, not Exception. This caused TaskGroup errors to escape
as unhandled ExceptionGroups in Datadog.
The fix adds an explicit ExceptionGroup handler before the general
Exception handler, following the same unwrapping pattern used in
other parts of the codebase (mcp_tool_executor.py, base_client.py).
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Models (especially Opus) take this instruction literally and re-call
the memory edit tool in a loop — one user saw 96 consecutive rethink
calls. Dropping the sentence stops the feedback loop while still
asking the agent to review the result.
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Co-authored-by: Letta <noreply@letta.com>
* feat(core): store block metadata as YAML frontmatter in .md files
Block .md files in git repos now embed metadata (description, limit,
read_only, metadata dict) as YAML frontmatter instead of a separate
metadata/blocks.json file. Only non-default values are rendered.
Format:
---
description: "Who I am"
limit: 5000
---
Block value content here...
Changes:
- New block_markdown.py utility (serialize_block / parse_block_markdown)
- Updated all three write/read paths: manager.py, memfs_client.py,
memfs_client_base.py
- block_manager_git.py now passes description/limit/read_only/metadata
through to git commits
- Post-push sync (git_http.py) parses frontmatter and syncs metadata
fields to Postgres
- Removed metadata/blocks.json reads/writes entirely
- Backward compat: files without frontmatter treated as raw value
- Integration test verifies frontmatter in cloned files and metadata
sync via git push
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Co-Authored-By: Letta <noreply@letta.com>
* fix: derive frontmatter defaults from BaseBlock schema, not hardcoded dict
Remove _DEFAULTS dict from block_markdown.py. The core version now
imports BaseBlock and reads field defaults via model_fields. This
fixes the limit default (was 5000, should be CORE_MEMORY_BLOCK_CHAR_LIMIT=20000).
Also:
- memfs-py copy simplified to parse-only (no serialize, no letta imports)
- All hardcoded limit=5000 fallbacks replaced with CORE_MEMORY_BLOCK_CHAR_LIMIT
- Test updated: blocks with all-default metadata correctly have no frontmatter;
frontmatter verified after setting non-default description via API
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Co-Authored-By: Letta <noreply@letta.com>
* fix: always include description and limit in frontmatter
description and limit are always rendered in the YAML frontmatter,
even when at their default values. Only read_only and metadata are
conditional (omitted when at defaults).
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Co-Authored-By: Letta <noreply@letta.com>
* fix: resolve read_only from block_update before git commit
read_only was using the old Postgres value instead of the update value
when committing to git. Also adds integration test coverage for
read_only: true appearing in frontmatter after API PATCH, and
verifying it's omitted when false (default).
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Co-Authored-By: Letta <noreply@letta.com>
* test: add API→git round-trip coverage for description and limit
Verifies that PATCH description/limit via API is reflected in
frontmatter after git pull. Combined with the existing push→API
test (step 6), this gives full bidirectional coverage:
- API edit description/limit → pull → frontmatter updated
- Push frontmatter with description/limit → API reflects changes
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Co-Authored-By: Letta <noreply@letta.com>
---------
Co-authored-by: Letta <noreply@letta.com>
When enable_git_memory_for_agent is called on an agent that already has
the git-memory-enabled tag, it was returning early if the repo existed,
even if the repo was missing blocks.
Now checks if all blocks are present in the repo and backfills any
missing ones.
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Co-authored-by: Letta <noreply@letta.com>
Prevents ForeignKeyViolationError when attaching files to agents where
the file has been deleted between listing and attachment (race condition).
Now validates file IDs exist in the files table before inserting, and
skips any missing files with a warning log.
Fixes Datadog issue a1768774-d691-11f0-9330-da7ad0900000
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Co-authored-by: Letta <noreply@letta.com>
Check if a block is already attached to an agent before appending to
core_memory. Prevents asyncpg UniqueViolationError on the
unique_agent_block constraint when attach_block_async is called twice
with the same (agent_id, block_id) pair.
Fixes Datadog issue d8dec148-d535-11f0-95eb-da7ad0900000
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Co-authored-by: Letta <noreply@letta.com>
Extends the ExceptionGroup unwrapping fix from mcp_tool_executor to the
base MCP client implementations (AsyncBaseMCPClient, AsyncFastMCPSSEClient,
AsyncFastMCPStreamableHTTPClient). When ToolError exceptions are wrapped
in ExceptionGroup by Python's async TaskGroup, the exception handler now
unwraps single-exception groups before checking class names.
This prevents wrapped ToolError exceptions from being logged to Datadog
as unexpected errors instead of being handled as expected validation failures.
Related to commit 1cbf1b231 which fixed the same issue in mcp_tool_executor.
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Co-authored-by: Letta <noreply@letta.com>
* fix(core): handle ExceptionGroup-wrapped ToolError and McpError in MCP tool execution
Fixes 3 related Datadog bugs (all fastmcp.exceptions.ToolError):
- 75d43daa-ff04-11f0-81b2-da7ad0900000
- 7af6373e-0080-11f1-9855-da7ad0900000
- a322edc8-fffa-11f0-b26c-da7ad0900000
These errors were caused by ToolError and McpError exceptions bubbling up
unhandled from the MCP REST endpoint. This fix combines the approaches from
PRs #9320 and #9321:
1. Handle ExceptionGroup wrapping (Python 3.11+ async TaskGroup)
2. Check for ToolError by class name to handle module variations
3. Convert ToolError to LettaInvalidArgumentError for proper client response
4. Catch McpError and return HTTP 500 with proper error message
Issue-IDs: 75d43daa-ff04-11f0-81b2-da7ad0900000, 7af6373e-0080-11f1-9855-da7ad0900000, a322edc8-fffa-11f0-b26c-da7ad0900000
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Co-Authored-By: Letta <noreply@letta.com>
* fix: return 422 instead of 500 for McpError (user config issue)
* fix: use LettaMCPConnectionError instead of HTTPException for McpError
---------
Co-authored-by: Letta <noreply@letta.com>
When git push completes, the webhook fires immediately but GCS upload
may still be in progress. This causes KeyError when trying to read
commit objects that haven't been uploaded yet.
Add retry with exponential backoff (1s, 2s, 4s) to handle this race.
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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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Co-Authored-By: Letta <noreply@letta.com>
Issue-ID: 10c0f2e4-f87b-11f0-b91c-da7ad0900000
PR #9309 changed the block storage from blocks/ to memory/ directory.
Update memfs_client.py and memfs_client_base.py to match.
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Co-authored-by: Letta <noreply@letta.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>
* try to fix
* revert random stuff
* revert some stuff
---------
Co-authored-by: Letta <noreply@letta.com>
* 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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Co-Authored-By: Letta <noreply@letta.com>
* 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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Co-Authored-By: Letta <noreply@letta.com>
* 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>
* feat: add memfs-py service
* add tf for bucket access and secrets v2 access
* feat(memfs): add helm charts, deploy workflow, and bug fixes
- Add dev helm chart (helm/dev/memfs-py/) with CSI secrets pattern
- Update prod helm chart with CSI secrets and correct service account
- Add GitHub Actions deploy workflow
- Change port from 8284 to 8285 to avoid conflict with core's dulwich sidecar
- Fix chunked transfer encoding issue (strip HTTP_TRANSFER_ENCODING header)
- Fix timestamp parsing to handle both ISO and HTTP date formats
- Fix get_head_sha to raise FileNotFoundError on 404
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Co-Authored-By: Letta <noreply@letta.com>
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Co-authored-by: Kian Jones <kian@letta.com>
Co-authored-by: Letta <noreply@letta.com>
* fix: handle const keyword in google genai tool schemas
* fix: handle pydantic ValidationError in Google GenAI client
Fixes Datadog error tracking issue where pydantic_core.ValidationError
was raised when tool schemas contained unsupported fields (e.g., 'const',
'default', 'additionalProperties').
Changes:
- Add error handling for pydantic ValidationError in request(), request_async(), and stream_async()
- Convert validation errors to LLMBadRequestError with helpful error message
- Deep copy tool parameters before cleaning to avoid modifying shared objects
- Add imports for pydantic_core and copy module
This prevents unhandled exceptions and provides better diagnostics when
tool schemas contain fields not supported by Google AI API.
* 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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Co-Authored-By: Letta <noreply@letta.com>
* 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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Co-Authored-By: Letta <noreply@letta.com>
* 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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Co-Authored-By: Letta <noreply@letta.com>
* fix: handle missing agent in create_conversation to prevent foreign key violation
* Update .gitignore
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Co-authored-by: Letta <noreply@letta.com>
