* 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
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* 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
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* 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.
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* letta_agent_id
* skip ast parsing for s
* add tool execution test
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The import was `from datetime import datetime` but the code used
`datetime.timezone.utc` which is incorrect - timezone is a sibling
class in the datetime module, not an attribute of the datetime class.
This caused the error: "type object 'datetime.datetime' has no
attribute 'timezone'"
PR #8682 added code that references settings.use_clickhouse_for_provider_traces
but never added the field to Settings, causing AttributeError in prod.
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feat(core): add run_id filter to internal runs endpoint
Adds the ability to filter runs by a specific run ID in the
internal runs list endpoint.
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* chore: deprecate identities/groups APIs and remove from SDK
- Mark all /v1/identities/* endpoints as deprecated
- Mark all /v1/groups/* endpoints as deprecated
- Remove identities, groups, and batches resources from stainless.yml
- Batch API remains active but hidden from SDK
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* chore: update autogenerated SDK files
* chore: regenerate SDK and OpenAPI spec
Run `just stage-api` and `just publish-api` to sync generated files.
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* chore: remove schedule API from stainless SDK
Remove schedule subresource from stainless.yml to hide scheduled messages
endpoints from the SDK generation.
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* feat: add /v1/runs/{run_id}/trace endpoint for OTEL traces
- Add new endpoint to retrieve filtered OTEL spans for a run
- Filter to only return UI-relevant spans (agent_step, tool executions, root span, TTFT)
- Skip Postgres writes when ClickHouse is enabled for provider traces
- Add USE_CLICKHOUSE_FOR_PROVIDER_TRACES env var to helm/justfile
- Move typecheck CI to self-hosted runners
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* fix: add missing clickhouse_provider_traces.py
The telemetry_manager.py imports ClickhouseProviderTraceReader from
this module, but the file was not included when splitting the PR.
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* autogen
* fix: add trace.retrieve to stainless.yml for SDK generation
Adds the runs.trace.retrieve method mapping so Stainless generates
the useRunsServiceRetrieveTraceForRun hook.
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* 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.
Fixes#8073🤖 Generated with [Letta Code](https://letta.com)
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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"
Fixes#8516🤖 Generated with [Letta Code](https://letta.com)
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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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