* 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>
fix: load default provider config when summarizer uses different provider
**Problem:**
Summarization failed when agent used one provider (e.g., Google AI) but
summarizer config specified a different provider (e.g., Anthropic):
```python
# Agent LLM config
model_endpoint_type='google_ai', handle='gemini-something/gemini-2.5-pro',
context_window=100000
# Summarizer config
model='anthropic/claude-haiku-4-5-20251001'
# Bug: Resulting summarizer_llm_config mixed Google + Anthropic settings
model='claude-haiku-4-5-20251001', model_endpoint_type='google_ai', # ❌ Wrong endpoint!
context_window=100000 # ❌ Google's context window, not Anthropic's default!
```
This sent Claude requests to Google AI endpoints with incorrect parameters.
**Root Cause:**
`_build_summarizer_llm_config()` always copied the agent's LLM config as base,
then patched model/provider fields. But this kept all provider-specific settings
(endpoint, context_window, etc.) from the wrong provider.
**Fix:**
1. Parse provider_name from summarizer handle
2. Check if it matches agent's model_endpoint_type (or provider_name for custom)
3. **If YES** → Use agent config as base, override model/handle (same provider)
4. **If NO** → Load default config via `provider_manager.get_llm_config_from_handle()` (new provider)
**Example Flow:**
```python
# Agent: google_ai/gemini-2.5-pro
# Summarizer: anthropic/claude-haiku
provider_name = "anthropic" # Parsed from handle
provider_matches = ("anthropic" == "google_ai") # False ❌
# Different provider → load default Anthropic config
base = await provider_manager.get_llm_config_from_handle(
handle="anthropic/claude-haiku",
actor=self.actor
)
# Returns: model_endpoint_type='anthropic', endpoint='https://api.anthropic.com', etc. ✅
```
**Result:**
- Summarizer with different provider gets correct default config
- No more mixing Google endpoints with Anthropic models
- Same-provider summarizers still inherit agent settings efficiently
👾 Generated with [Letta Code](https://letta.com)
Co-authored-by: Letta <noreply@letta.com>
fix: use shared event + .athrow() to properly set stream_was_cancelled flag
**Problem:**
When a run is cancelled via /cancel endpoint, `stream_was_cancelled` remained
False because `RunCancelledException` was raised in the consumer code (wrapper),
which closes the generator from outside. This causes Python to skip the
generator's except blocks and jump directly to finally with the wrong flag value.
**Solution:**
1. Shared `asyncio.Event` registry for cross-layer cancellation signaling
2. `cancellation_aware_stream_wrapper` sets the event when cancellation detected
3. Wrapper uses `.athrow()` to inject exception INTO generator (not consumer-side raise)
4. All streaming interfaces check event in `finally` block to set flag correctly
5. `streaming_service.py` handles `RunCancelledException` gracefully, yields [DONE]
**Changes:**
- streaming_response.py: Event registry + .athrow() injection + graceful handling
- openai_streaming_interface.py: 3 classes check event in finally
- gemini_streaming_interface.py: Check event in finally
- anthropic_*.py: Catch RunCancelledException
- simple_llm_stream_adapter.py: Create & pass event to interfaces
- streaming_service.py: Handle RunCancelledException, yield [DONE], skip double-update
- routers/v1/{conversations,runs}.py: Pass event to wrapper
- integration_test_human_in_the_loop.py: New test for approval + cancellation
**Tests:**
- test_tool_call with cancellation (OpenAI models) ✅
- test_approve_with_cancellation (approval flow + concurrent cancel) ✅
**Known cosmetic warnings (pre-existing):**
- "Run already in terminal state" - agent loop tries to update after /cancel
- "Stream ended without terminal event" - background streaming timing race
👾 Generated with [Letta Code](https://letta.com)
Co-authored-by: Letta <noreply@letta.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
* make favorite tag a const
* add favorite:user:{userId} for favorites
* favorite agent upon initial creation
* rename const
* add eslint ignore
* expect favorite tag
* test: add comprehensive provider trace telemetry tests
Add two test files for provider trace telemetry:
1. test_provider_trace.py - Integration tests for:
- Basic agent steps (streaming and non-streaming)
- Tool calls
- Telemetry context fields (agent_id, agent_tags, step_id, run_id)
- Multi-step conversations
- Request/response JSON content
2. test_provider_trace_summarization.py - Unit tests for:
- simple_summary() telemetry context passing
- summarize_all() telemetry pass-through
- summarize_via_sliding_window() telemetry pass-through
- Summarizer class runtime vs constructor telemetry
- LLMClient.set_telemetry_context() method
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* test: add telemetry tests for tool generation, adapters, and agent versions
Add comprehensive unit tests for provider trace telemetry:
- TestToolGenerationTelemetry: Verify /generate-tool endpoint sets
call_type="tool_generation" and has no agent context
- TestLLMClientTelemetryContext: Verify LLMClient.set_telemetry_context
accepts all telemetry fields
- TestAdapterTelemetryAttributes: Verify base adapter and subclasses
(LettaLLMRequestAdapter, LettaLLMStreamAdapter) support telemetry attrs
- TestSummarizerTelemetry: Verify Summarizer stores and passes telemetry
- TestAgentAdapterInstantiation: Verify LettaAgentV2 creates Summarizer
with correct agent_id
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* ci: add provider trace telemetry tests to unit test workflow
Add the new provider trace test files to the CI matrix:
- test_provider_trace_backends.py
- test_provider_trace_summarization.py
- test_provider_trace_agents.py
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix: update socket backend test to match new record structure
The socket backend record structure changed - step_id/run_id are now
at top level, and model/usage are nested in request/response objects.
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix: add step_id to V1 agent telemetry context
Pass step_id to set_telemetry_context in both streaming and non-streaming
paths in LettaAgent (v1). The step_id is available via step_metrics.id
in the non-streaming path and passed explicitly in the streaming path.
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
---------
Co-authored-by: Letta <noreply@letta.com>
* feat: add agent_id, run_id, step_id to summarization provider traces
Summarization LLM calls were missing telemetry context (agent_id,
agent_tags, run_id, step_id), making it impossible to attribute
summarization costs to specific agents or trace them back to the
step that triggered compaction.
Changes:
- Add step_id param to simple_summary() and set_telemetry_context()
- Add agent_id, agent_tags, run_id, step_id to summarize_all() and
summarize_via_sliding_window()
- Update Summarizer class to accept and pass telemetry context
- Update LettaAgentV3.compact() to pass full telemetry context
- Update LettaAgentV2.summarize_conversation_history() with run_id/step_id
- Update LettaAgent (v1) streaming methods with run_id param
- Add run_id/step_id to SummarizeParams for Temporal activities
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix: update test mock to accept new summarization params
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
---------
Co-authored-by: Letta <noreply@letta.com>
* 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: enable bedrock for anthropic models
* parallel tool calls in ade
* attempt add to ci
* update tests
* add env vars
* hardcode region
* get it working
* debugging
* add bedrock extra
* default env var [skip ci]
* run ci
* reasoner model update
* secrets
* clean up log
* clean up
* feat: add provider trace backend abstraction for multi-backend telemetry
Introduces a pluggable backend system for provider traces:
- Base class with async/sync create and read interfaces
- PostgreSQL backend (existing behavior)
- ClickHouse backend (via OTEL instrumentation)
- Socket backend (writes to Unix socket for crouton sidecar)
- Factory for instantiating backends from config
Refactors TelemetryManager to use backends with support for:
- Multi-backend writes (concurrent via asyncio.gather)
- Primary backend for reads (first in config list)
- Graceful error handling per backend
Config: LETTA_TELEMETRY_PROVIDER_TRACE_BACKEND (comma-separated)
Example: "postgres,socket" for dual-write to Postgres and crouton
🐙 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* feat: add protocol version to socket backend records
Adds PROTOCOL_VERSION constant to socket backend:
- Included in every telemetry record sent to crouton
- Must match ProtocolVersion in apps/crouton/main.go
- Enables crouton to detect and reject incompatible messages
🐙 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix: remove organization_id from ProviderTraceCreate calls
The organization_id is now handled via the actor parameter in the
telemetry manager, not through ProviderTraceCreate schema. This fixes
validation errors after changing ProviderTraceCreate to inherit from
BaseProviderTrace which forbids extra fields.
🐙 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* consolidate provider trace
* add clickhouse-connect to fix bug on main lmao
* auto generated sdk changes, and deployment details, and clikchouse prefix bug and added fields to runs trace return api
* auto generated sdk changes, and deployment details, and clikchouse prefix bug and added fields to runs trace return api
* consolidate provider trace
* consolidate provider trace bug fix
---------
Co-authored-by: Letta <noreply@letta.com>
* feat: add TypeScript tool support for E2B sandbox execution
This change implements TypeScript tool support using the same E2B path as Python tools:
- Add TypeScript execution script generator (typescript_generator.py)
- Modify E2B sandbox to detect TypeScript tools and use language='ts'
- Add npm package installation for TypeScript tool dependencies
- Add validation requiring json_schema for TypeScript tools
- Add comprehensive integration tests for TypeScript tools
TypeScript tools:
- Require explicit json_schema (no docstring parsing)
- Use JSON serialization instead of pickle for results
- Support async functions with top-level await
- Support npm package dependencies via npm_requirements field
Closes#8793
Co-authored-by: Sarah Wooders <sarahwooders@users.noreply.github.com>
* fix: disable AgentState for TypeScript tools & add letta-client injection
Based on Sarah's feedback:
1. AgentState is a legacy Python-only feature, disabled for TS tools
2. Added @letta-ai/letta-client npm package injection for TypeScript
(similar to letta_client for Python)
Changes:
- base.py: Explicitly set inject_agent_state=False for TypeScript tools
- typescript_generator.py: Inject LettaClient initialization code
- e2b_sandbox.py: Auto-install @letta-ai/letta-client for TS tools
- Added tests verifying both behaviors
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Sarah Wooders <sarahwooders@users.noreply.github.com>
Co-Authored-By: Letta <noreply@letta.com>
* Update core-integration-tests.yml
* fix: convert TypeScript test fixtures to async
The OrganizationManager and UserManager no longer have sync methods,
only async variants. Updated all fixtures to use:
- create_organization_async
- create_actor_async
- create_or_update_tool_async
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix: skip Python AST parsing for TypeScript tools in sandbox base
The _init_async method was calling parse_function_arguments (which uses
Python's ast.parse) before checking if the tool was TypeScript, causing
SyntaxError when running TypeScript tools.
Moved the is_typescript_tool() check to happen first, skipping Python
AST parsing entirely for TypeScript tools.
🤖 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* letta_agent_id
* skip ast parsing for s
* add tool execution test
---------
Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com>
Co-authored-by: Sarah Wooders <sarahwooders@users.noreply.github.com>
Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: Kian Jones <kian@letta.com>
* feat: add conversation_id parameter to context endpoint [LET-6989]
Add optional conversation_id query parameter to retrieve_agent_context_window.
When provided, the endpoint uses messages from the specific conversation
instead of the agent's default message_ids.
👾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* chore: regenerate SDK after context endpoint update [LET-6989]
👾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* feat: add isolated blocks support for conversations
Allows conversations to have their own copies of specific memory blocks (e.g., todo_list) that override agent defaults, enabling conversation-specific state isolation.
👾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* undo
* update apis
* test
* cleanup
* fix tests
* simplify
* move override logic
* patch
---------
Co-authored-by: Letta <noreply@letta.com>
* feat: add tags support to blocks
* fix: add timestamps and org scoping to blocks_tags
Addresses PR feedback:
1. Migration: Added timestamps (created_at, updated_at), soft delete
(is_deleted), audit fields (_created_by_id, _last_updated_by_id),
and organization_id to blocks_tags table for filtering support.
Follows SQLite baseline pattern (composite PK of block_id+tag, no
separate id column) to avoid insert failures.
2. ORM: Relationship already correct with lazy="raise" to prevent
implicit joins and passive_deletes=True for efficient CASCADE deletes.
3. Schema: Changed normalize_tags() from Any to dict for type safety.
4. SQLite: Added blocks_tags to SQLite baseline schema to prevent
table-not-found errors.
5. Code: Updated all tag row inserts to include organization_id.
🐾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* fix: add ORM columns and update SQLite baseline for blocks_tags
Fixes test failures (CompileError: Unconsumed column names: organization_id):
1. ORM: Added organization_id, timestamps, audit fields to BlocksTags
ORM model to match database schema from migrations.
2. SQLite baseline: Added full column set to blocks_tags (organization_id,
timestamps, audit fields) to match PostgreSQL schema.
3. Test: Added 'tags' to expected Block schema fields.
This ensures SQLite and PostgreSQL have matching schemas and the ORM
can consume all columns that the code inserts.
🐾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* revert change to existing alembic migration
* fix: remove passive_deletes and SQLite support for blocks_tags
1. Removed passive_deletes=True from Block.tags relationship to match
AgentsTags pattern (neither have ondelete CASCADE in DB schema).
2. Removed SQLite branch from _replace_block_pivot_rows_async since
blocks_tags table is PostgreSQL-only (migration skips SQLite).
🐾 Generated with [Letta Code](https://letta.com)
Co-Authored-By: Letta <noreply@letta.com>
* api sync
---------
Co-authored-by: Letta <noreply@letta.com>
The `make_post_request` function was removed in commit b1bbf9aabf as
part of cleaning up unused sync code, but the test file still imported
it. This change replaces the removed function with direct `requests.post`
calls.
🐾 Generated with [Letta Code](https://letta.com)
Co-authored-by: Letta <noreply@letta.com>
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)
🤖 Generated with [Letta Code](https://letta.com)
Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com>
Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: Kian Jones <11655409+kianjones9@users.noreply.github.com>
The Anthropic API returns a 413 status code with error type `request_too_large`
when the request payload exceeds the maximum allowed size. This error should
be converted to `ContextWindowExceededError` so the system can handle it
appropriately (e.g., by summarizing the conversation to reduce context size).
Changes:
- Added `request_too_large` and `request exceeds the maximum size` to the
early string-based error detection in `handle_llm_error`
- Added specific handling for HTTP 413 status code in the `APIStatusError`
handler
- Added tests to verify the new error handling behavior
Fixes: #8422🤖 Generated with [Letta Code](https://letta.com)
Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com>
Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: datadog-official[bot] <datadog-official[bot]@users.noreply.github.com>
Co-authored-by: Kian Jones <11655409+kianjones9@users.noreply.github.com>
* 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>
