* 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.
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* chore: regenerate SDK after context endpoint update [LET-6989]
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* 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.
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* undo
* update apis
* test
* cleanup
* fix tests
* simplify
* move override logic
* patch
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This commit addresses the httpx.ReadTimeout error detected in production
by adding explicit timeout configurations to several httpx client usages:
1. MCP SSE client: Pass mcp_connect_to_server_timeout (30s) to sse_client()
2. MCP StreamableHTTP client: Pass mcp_connect_to_server_timeout (30s) to streamablehttp_client()
3. OpenAI model list API: Add 30s timeout with 10s connect timeout
4. Google AI model list/details API: Add 30s timeout with 10s connect timeout
Previously, these httpx clients were created without explicit timeouts,
which could cause ReadTimeout errors when remote servers are slow to respond.
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When the Anthropic SDK detects a request may exceed 10 minutes, it
raises a ValueError requiring streaming mode. This fix catches that
specific error in request_async and automatically falls back to
streaming mode, accumulating the response into the same format as
non-streaming.
This resolves the production error:
"ValueError: Streaming is required for operations that may take
longer than 10 minutes"
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The FastMCP clients were not properly wrapping exceptions from
`connect_to_server()`, causing raw RuntimeErrors (like DNS resolution
failures with "[Errno -2] Name or service not known") to propagate
up unchanged.
Changes:
- Both `AsyncFastMCPSSEClient` and `AsyncFastMCPStreamableHTTPClient`
now properly catch all exceptions and wrap them in `ConnectionError`
- Added warning-level logging for failed connections
- Provides user-friendly error messages with the server URL
Fixes#8568
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This fixes a 400 INVALID_ARGUMENT error from Google's Gemini API where
function calls were missing required thought_signature in functionCall parts.
Changes:
- Allow signatures when self.model is None (backwards compatibility for
older messages that may not have had their model field set)
- Only add thought_signature to the FIRST function call for parallel
tool calls, per Google's docs
- Take the first non-None signature found (don't keep overwriting)
Reference: https://ai.google.dev/gemini-api/docs/thought-signaturesCloses#8589🤖 Generated with [Letta Code](https://letta.com)
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The handle_db_timeout decorator only caught SQLAlchemy's TimeoutError
(for pool/connection timeouts) but not asyncpg's QueryCanceledError
which is thrown when PostgreSQL's statement_timeout kills a long-running
query.
This fix:
- Import asyncpg.exceptions.QueryCanceledError
- Update handle_db_timeout decorator to catch QueryCanceledError and wrap
it in DatabaseTimeoutError
- Update _handle_dbapi_error method to also handle wrapped QueryCanceledError
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When multiple concurrent transactions try to upsert the same tools,
they can deadlock if they acquire row locks in different orders.
This fix sorts tools by name before the bulk INSERT to ensure all
transactions acquire locks in a consistent order, preventing deadlocks.
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Add ToolError to exception handling alongside McpError in MCP client classes.
ToolError is raised by fastmcp for input validation errors (e.g., missing
required properties like 'filename'). Both error types are expected user-facing
errors from external MCP servers and should be logged at warning/debug level
to avoid triggering production alerts.
Fixes issue with production error: "fastmcp.exceptions.ToolError: Input
validation error: 'filename' is a required property"
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The MCP library internally uses TaskGroup for async operations, which can
raise ExceptionGroup when cleanup fails. This was causing unhandled errors
to propagate in production.
Changes:
- Update cleanup() method in AsyncBaseMCPClient to catch ExceptionGroup
using except* syntax and log errors at debug level (best-effort cleanup)
- Remove redundant try/except blocks in mcp_manager.py and
mcp_server_manager.py that incorrectly re-raised cleanup exceptions
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- Fix typo "upate" -> "update" in TODO comments (mcp_manager.py, mcp_server_manager.py)
- Improve comments in OAuth callback handler to explain why MCPOAuthSession
is used directly (callback is unauthenticated, manager requires actor)
- Clean up variable naming in callback handler
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* 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.
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* 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.
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* 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).
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* api sync
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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.
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* Revert "chore: temp revert to public ddtrace (#8462)"
This reverts commit 09e541b7732fdc03be4cd9c00cc2c8518300acf1.
* Update ddtrace version requirement in pyproject.toml
Removed specific ddtrace versions for profiling and updated to a minimum version requirement.
* fix: wrap turbopuffer vector writes in thread pool
Turbopuffer library does CPU-intensive base64 encoding of vectors
synchronously in async functions (_async_transform_recursive →
b64encode_vector), blocking the event loop during file uploads.
Solution: Created _run_turbopuffer_write_in_thread() helper that runs
turbopuffer writes in an isolated event loop within a worker thread.
Applied to all vector write operations:
- insert_tools()
- insert_archival_memories()
- insert_messages()
- insert_file_passages()
This prevents pybase64.b64encode_as_string() from blocking the main
event loop during vector encoding.
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* fix: wrap all turbopuffer operations in thread pool
Extended the thread pool wrapping to ALL turbopuffer write operations,
including delete operations, for complete isolation from the main event loop.
All turbopuffer namespace.write() calls now run in isolated event loops
within worker threads, preventing any potential CPU work from blocking.
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MarkItDown.convert() does blocking file I/O and CPU-intensive PDF parsing.
This was blocking the event loop during file uploads.
Now wraps the entire markitdown pipeline (tempfile write, convert, cleanup)
in asyncio.to_thread() to run in thread pool.
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Critical fixes:
- llm_client_base.send_llm_request() now calls await self.request_async() instead of self.request()
- Remove unused sync get_openai_embedding() that used sync OpenAI client
- Remove deprecated compile_in_thread_async() from Memory
These were blocking the event loop during LLM requests and embeddings.
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Remove commented-out sync_value_and_value_enc model validator and
unused imports (traceback, model_validator, logger). This code was
disabled and replaced with async decryption via from_orm_async methods.
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.
* remove enable cache
* trigger CI
* remove extra with paramters which I believe to be unecessary
* try installing uv manuallly to avoid post install step
* should be fixed by manually installing and not using the action
* remove comment to trigger
