fix: align ChatGPT OAuth GPT-5 max output token defaults
Update ChatGPT OAuth provider defaults so GPT-5 family models report 128k max output tokens based on current OpenAI model docs, avoiding incorrect 16k values in /v1/models responses.
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* feat: make agent_id optional in conversations list endpoint [LET-7612]
Allow listing all conversations without filtering by agent_id.
**Router changes (conversations.py):**
- Changed agent_id from required (`...`) to optional (`None`)
- Updated description to clarify behavior
- Updated docstring to reflect optional filtering
**Manager changes (conversation_manager.py):**
- Updated list_conversations signature: agent_id: str → Optional[str]
- Updated docstring to clarify optional behavior
- Summary search query: conditionally adds agent_id filter only if provided
- Default list logic: passes agent_id (can be None) to list_async
**How it works:**
- Without agent_id: returns all conversations for the user's organization
- With agent_id: returns conversations filtered by that agent
- list_async handles None gracefully via **kwargs pattern
**Use case:**
- Cloud UI can list all user conversations across agents
- Still supports filtering by agent_id when needed
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* chore: update logs
* chore: update logs
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Co-authored-by: Letta <noreply@letta.com>
When model_settings is sent without max_output_tokens (e.g. only
changing reasoning_effort), the Pydantic default of 4096 was being
applied via _to_legacy_config_params(), silently overwriting the
agent's existing max_tokens.
Use model_fields_set to detect when max_output_tokens was not
explicitly provided and skip overwriting max_tokens in that case.
Only applied to the update path — on create, letting the default
apply is reasonable since there's no pre-existing value.
* feat: add two-way mode control for listener connections
Enable bidirectional permission mode control between letta-cloud UI and letta-code instances.
**Backend:**
- Added ModeChangeMessage and ModeChangedMessage to WebSocket protocol
- Added sendModeChange endpoint (/v1/listeners/:connectionId/mode)
- listenersRouter publishes mode_change via Redis Pub/Sub
- listenersHandler handles mode_changed acknowledgments from letta-code
- Stores current mode in Redis for UI state sync
**Contract:**
- Added sendModeChange contract with PermissionModeSchema
- 4 modes: default, acceptEdits, plan, bypassPermissions
**Frontend:**
- Extended PermissionMode type to 4 modes (was 2: ask/never)
- PermissionModeSelector now shows all 4 modes with descriptions
- Added disabled prop (grayed out when Cloud orchestrator selected)
- PermissionModeContext.sendModeChangeToDevice() calls API
- AgentMessenger sends mode changes to device on mode/device change
- Updated auto-approval logic (only in Cloud mode, only for bypassPermissions)
- Updated inputMode logic (device handles approvals, not cloud)
**Translations:**
- Updated en.json with 4 mode labels and descriptions
- Removed legacy "askAlways" and "neverAsk" keys
**Mode Behavior:**
- default: Ask permission for each tool
- acceptEdits: Auto-approve file edits only
- plan: Read-only exploration (denies writes)
- bypassPermissions: Auto-approve everything
**Lint Fixes:**
- Removed unused imports and functions from trackingMiddleware.ts
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* fix: store mode in connectionData and show approvals for all modes
**Backend:**
- Fixed Redis WRONGTYPE error - store currentMode inside connectionData object
- Changed const connectionData to let connectionData (needs mutation)
- Updated mode_changed handler to reassign entire connectionData object
- Updated ping handler for consistency (also reassigns connectionData)
- Added currentMode field to ListenerConnectionSchema (optional)
**Frontend:**
- Simplified inputMode logic - always show approval UI when toolCallsToApprove.length > 0
- Removed mode-specific approval filtering (show approvals even in bypass/acceptEdits for visibility)
- Users can see what tools are being auto-approved during execution
**Why:**
- Redis key is a JSON string (via setRedisData), not a hash
- Cannot use hset on string keys - causes WRONGTYPE error
- Must update entire object via setRedisData like ping handler does
- Approval visibility helpful for debugging/understanding agent behavior
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* fix: use useMutation hook for sendModeChange instead of direct call
cloudAPI is initialized via initTsrReactQuery, so sendModeChange is a
mutation hook object, not a callable function. Use .useMutation() at the
component level and mutateAsync in the callback.
Co-authored-by: Shubham Naik <4shub@users.noreply.github.com>
* chore: update logs
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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: Shubham Naik <4shub@users.noreply.github.com>
* fix: migrate Anthropic Haiku test model off retired release
Update Anthropic Haiku references in integration and usage parsing tests to a supported model id so test requests stop failing with 404 model not found errors.
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* fix: use canonical Anthropic Haiku handle in tests
Replace dated Anthropic Haiku handle references with the canonical provider handle so handle-based model resolution does not fail in batch and client tests.
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* load default settings instead of loading from agent for summarizer config
* update tests to allow use of get_llm_config_from_handle
* remove nit comment
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Co-authored-by: Amy Guan <amy@letta.com>
* feat: add flattened feedback context fields for PostHog filterability
Add system info (local_time, device_type, cwd), session stats (token counts,
timing), agent info (agent_name, agent_description, model), and account info
(billing_tier) as flat top-level fields to the /v1/metadata/feedback endpoint.
Flat fields allow direct PostHog filtering/breakdown without HogQL.
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* chore: regenerate API specs
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Co-authored-by: Letta <noreply@letta.com>
The Pydantic validator `set_model_specific_defaults` was checking
`values.get("max_tokens") is None`, which matched both "field not
provided" and "field explicitly set to null". This meant users could
not disable the max output tokens limit for GPT-5/GPT-4.1 models -
the validator would always override null with a default value during
request deserialization.
Changed to `"max_tokens" not in values` so that an explicit
`max_tokens: null` is preserved while still applying defaults when
the field is omitted entirely.
list_llm_models_async was constructing LLMConfig without max_tokens,
causing the GET /models/ endpoint to return null for max_tokens.
Now calls typed_provider.get_default_max_output_tokens() for both
base and BYOK provider paths, matching get_llm_config_from_handle.
Add 3.1 model metadata for Google AI and update Gemini tests/examples to use the new handle.
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perf(memfs): delta upload — only push new/modified git objects after commit
Instead of re-uploading the entire .git/ directory after every commit,
snapshot file mtimes before the commit and only upload files that are
new or changed. A typical single-block update creates ~5 new objects
(blob, trees, commit, ref) vs re-uploading all ~30.
Full _upload_repo retained for create_repo and other paths that need it.
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LlmConfig._to_model_settings() for Anthropic built an AnthropicModelSettings
object without passing effort=self.effort, so GET /agents/{id} never returned
the effort field in model_settings even when it was stored on the agent.
The Letta Code CLI derives the reasoning tier displayed in the status bar
from model_settings.effort (canonical source), so the footer always showed
e.g. "Sonnet 4.6" instead of "Sonnet 4.6 (high)" after a model switch.
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* fix(core): always create system message even with _init_with_no_messages
When _init_with_no_messages=True (used by agent import flows), the agent
was created with message_ids=None. If subsequent message initialization
failed, this left orphaned agents that crash when context window is
calculated (TypeError on message_ids[1:]).
Now the system message is always generated and persisted, even when
skipping the rest of the initial message sequence. This ensures every
agent has at least message_ids=[system_message_id].
Fixes Datadog issue 773a24ea-eeb3-11f0-8f9f-da7ad0900000
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* fix(core): clean up placeholder messages during import and add test
Delete placeholder system messages after imported messages are
successfully created (not before), so agents retain their safety-net
system message if import fails. Also adds a test verifying that
_init_with_no_messages=True still produces a valid context window.
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* fix(core): add descriptive error for empty message_ids in get_system_message
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* fix(core): handle ResponseIncompleteEvent in OpenAI Responses API streaming
When reasoning models (gpt-5.x) exhaust their max_output_tokens budget
on chain-of-thought reasoning, OpenAI emits a ResponseIncompleteEvent
instead of ResponseCompletedEvent. This was previously unhandled, causing
final_response to remain None — which meant get_content() and
get_tool_call_objects() returned empty results, silently dropping the
partial response.
Now ResponseIncompleteEvent is handled identically to
ResponseCompletedEvent (extracting partial content, usage stats, and
token details), with an additional warning log indicating the incomplete
reason.
* fix(core): propagate finish_reason for Responses API incomplete events
- Guard usage extraction against None usage payload in
ResponseIncompleteEvent handler
- Add _finish_reason override to LettaLLMAdapter so streaming adapters
can explicitly set finish_reason without a chat_completions_response
- Map incomplete_details.reason="max_output_tokens" to
finish_reason="length" in SimpleLLMStreamAdapter, matching the Chat
Completions API convention
- This allows the agent loop's _decide_continuation to correctly return
stop_reason="max_tokens_exceeded" instead of "end_turn" when the model
exhausts its output token budget on reasoning
* fix(core): handle empty content parts in incomplete ResponseOutputMessage
When a model hits max_output_tokens after starting a ResponseOutputMessage
but before producing any content parts, the message has content=[]. This
previously raised ValueError("Got 0 content parts, expected 1"). Now it
logs a warning and skips the empty message, allowing reasoning-only
incomplete responses to be processed cleanly.
* fix(core): map all incomplete reasons to finish_reason, not just max_output_tokens
Handle content_filter and any future unknown incomplete reasons from the
Responses API instead of silently leaving finish_reason as None.
* fix(core): add OpenAI prompt cache key and model-gated 24h retention (#9492)
* fix(core): apply OpenAI prompt cache settings to request payloads
Set prompt_cache_key using agent and conversation context on both Responses and Chat Completions request builders, and enable 24h retention only for supported OpenAI models while excluding OpenRouter paths.
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* fix(core): prefix prompt cache key with letta tag
Add a `letta:` prefix to generated OpenAI prompt_cache_key values so cache-related entries are easier to identify in provider-side logs and diagnostics.
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* add integration test
* skip test
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Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: Ari Webb <ari@letta.com>
* fix(core): only set prompt_cache_retention, drop prompt_cache_key
Two issues with the original prompt_cache_key approach:
1. Key exceeded 64-char max (agent-<uuid>:conv-<uuid> = 90 chars)
2. Setting an explicit key disrupted OpenAI's default prefix-hash
routing, dropping cache hit rates from 40-45% to 10-13%
OpenAI's default routing (hash of first ~256 tokens) already provides
good cache affinity since each agent has a unique system prompt.
We only need prompt_cache_retention="24h" for extended retention.
Also fixes:
- Operator precedence bug in _supports_extended_prompt_cache_retention
- Removes incorrect gpt-5.2-codex exclusion (it IS supported per docs)
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Co-authored-by: Charles Packer <packercharles@gmail.com>
Co-authored-by: Letta <noreply@letta.com>
Adds a diagnostic log at the streaming chokepoint in LettaAgentV3.stream()
to detect when any LettaMessage chunk is yielded without an id or otid field.
This helps trace the root cause of client-side id/otid inconsistencies.
* auto fixes
* auto fix pt2 and transitive deps and undefined var checking locals()
* manual fixes (ignored or letta-code fixed)
* fix circular import
* remove all ignores, add FastAPI rules and Ruff rules
* add ty and precommit
* ruff stuff
* ty check fixes
* ty check fixes pt 2
* error on invalid
* fix(core): apply OpenAI prompt cache settings to request payloads
Set prompt_cache_key using agent and conversation context on both Responses and Chat Completions request builders, and enable 24h retention only for supported OpenAI models while excluding OpenRouter paths.
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Co-Authored-By: Letta <noreply@letta.com>
* fix(core): prefix prompt cache key with letta tag
Add a `letta:` prefix to generated OpenAI prompt_cache_key values so cache-related entries are easier to identify in provider-side logs and diagnostics.
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* add integration test
* skip test
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Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: Ari Webb <ari@letta.com>
502s and upstream connection errors (envoy proxy failures) from ChatGPT
were not being retried. This classifies them as LLMConnectionError (retryable)
in both the streaming and non-streaming paths, and adds retry handling in
the non-streaming HTTPStatusError handler so 502s get the same exponential
backoff treatment as transport-level connection drops.
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Avoid failing message-list endpoints when historical send_message tool calls are missing the expected message argument by logging and skipping malformed entries during conversion.
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The "Experience the new ADE" page was outdated and no longer useful.
Root path now redirects to /docs (FastAPI Swagger UI) instead.
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LLMError exceptions are already properly formatted errors that should
propagate directly. Without this check, they get unnecessarily wrapped
by handle_llm_error, losing their original error information.
* debug: log statement_timeout + connection pid on every session checkout
Temporary instrumentation to diagnose why some PlanetScale connections
have statement_timeout=5s while others have 0 (disabled).
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* debug: log statement_timeout on every checkout, not just non-zero
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* fix: rollback implicit transaction from debug query
The SELECT implicitly begins a transaction, causing "A transaction is
already begun" errors for code that calls session.begin() explicitly.
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* new prompts for sliding window and all compaction + defaults to corresponding prompt
* regenerate api spec
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Co-authored-by: Amy Guan <amy@letta.com>
fix: update system prompt metadata label from "Memory blocks were last modified" to "System prompt last recompiled"
When git-based memory is enabled, there are no memory blocks, so the label
"Memory blocks were last modified" is inaccurate. Changed to
"System prompt last recompiled" which accurately reflects the timestamp meaning.
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Co-authored-by: Letta <noreply@letta.com>
* fix(core): catch bare openai.APIError in handle_llm_error fallthrough
openai.APIError raised during streaming (e.g. OpenRouter credit
exhaustion) is not an APIStatusError, so it skipped the catch-all
at the end and fell through to LLMError("Unhandled"). Now bare
APIErrors that aren't context window overflows are mapped to
LLMBadRequestError.
Datadog: https://us5.datadoghq.com/error-tracking/issue/7a2c356c-0849-11f1-be66-da7ad0900000🐾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* feat(core): add LLMInsufficientCreditsError for BYOK credit exhaustion
Adds dedicated error type for insufficient credits/quota across all
providers (OpenAI, Anthropic, Google). Returns HTTP 402 with
BYOK-aware messaging instead of generic 400.
- New LLMInsufficientCreditsError class and PAYMENT_REQUIRED ErrorCode
- is_insufficient_credits_message() helper detecting credit/quota strings
- All 3 provider clients detect 402 status + credit keywords
- FastAPI handler returns 402 with "your API key" vs generic messaging
- 5 new parametrized tests covering OpenRouter, OpenAI, and negative case
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