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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* 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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* 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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* 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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* fix: handle missing agent in create_conversation to prevent foreign key violation
* Update .gitignore
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* mvp
* perfrom type coercion in sandbox
* fix: safely resolve typing annotations on host
Use an AST whitelist for generic annotations to avoid eval while keeping list/dict coercion working.
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Adds validation to the fetch_webpage tool to ensure only HTTP/HTTPS URLs
are accepted. Previously, passing a file:// URL would cause an unhandled
requests.exceptions.InvalidSchema error. Now it raises a clear ValueError
with a helpful error message.
Fixes: requests.exceptions.InvalidSchema: No connection adapters were found for 'file://...'
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Co-authored-by: Kian Jones <11655409+kianjones9@users.noreply.github.com>
MCP tool errors (ToolError, McpError) are expected user-facing errors
from external MCP servers (e.g., "No connected account found"). These
were propagating through @trace_method decorator and being recorded
as errors in Datadog APM.
Changes:
- Add try/except to catch expected MCP errors in ExternalMCPToolExecutor
- Return ToolExecutionResult with status="error" instead of re-raising
- Log expected errors at INFO level instead of letting them trace as ERROR
- Remove stray 'pass' statement that was a no-op
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* 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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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
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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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Co-authored-by: Kian Jones <kian@letta.com>
Problem: When executing a tool that sends messages to many agents matching
tags, the code used asyncio.gather to process all agents concurrently. Each
agent processing creates database operations (run creation, message storage),
leading to N concurrent database connections.
Example: If 100 agents match the tags, 100 simultaneous database connections
are created, exhausting the connection pool and causing errors.
Root cause: asyncio.gather(*[_process_agent(...) for agent in agents])
creates all coroutines and runs them concurrently, each opening a DB session.
Solution: Process agents sequentially instead of concurrently. While this is
slower, it prevents database connection pool exhaustion. The operation is
still async, so it won't block the event loop.
Changes:
- apps/core/letta/services/tool_executor/multi_agent_tool_executor.py:
- Replaced asyncio.gather with sequential for loop
- Added explanatory comment about why sequential processing is needed
Impact: With 100 matching agents:
- Before: 100 concurrent DB connections (pool exhaustion)
- After: 1 DB connection at a time (no pool exhaustion)
Note: This follows the same pattern as PR #6617 which fixed a similar issue
in file attachment operations.
* fix: replace all 'PRODUCTION' references with 'prod' for consistency
Problem: Codebase had 11 references to 'PRODUCTION' (uppercase) that should
use 'prod' (lowercase) for consistency with the deployment workflows and
environment normalization.
Changes across 8 files:
1. Source files (using settings.environment):
- letta/functions/function_sets/multi_agent.py
- letta/services/tool_manager.py
- letta/services/tool_executor/multi_agent_tool_executor.py
- letta/services/helpers/agent_manager_helper.py
All checks changed from: settings.environment == "PRODUCTION"
To: settings.environment == "prod"
2. OTEL resource configuration:
- letta/otel/resource.py
- Updated _normalize_environment_tag() to handle 'prod' directly
- Removed 'PRODUCTION' -> 'prod' mapping (no longer needed)
- Updated device.id check from _env != "PRODUCTION" to _env != "prod"
3. Test files:
- tests/managers/conftest.py
- Fixture parameter changed from "PRODUCTION" to "prod"
- tests/managers/test_agent_manager.py (3 occurrences)
- tests/managers/test_tool_manager.py (2 occurrences)
All test checks changed to use "prod"
Result: Complete consistency across the codebase:
- All environment checks use "prod" instead of "PRODUCTION"
- Normalization function simplified (no special case for PRODUCTION)
- Tests use correct "prod" value
- Matches deployment workflow configuration from PR #6626
This completes the environment naming standardization effort.
* fix: update settings.py environment description to use 'prod' instead of 'PRODUCTION'
The field description still referenced PRODUCTION as an example value.
Updated to use lowercase 'prod' for consistency with actual usage.
Before: "Application environment (PRODUCTION, DEV, CANARY, etc. - normalized to lowercase for OTEL tags)"
After: "Application environment (prod, dev, canary, etc. - lowercase values used for OTEL tags)"
The docstring incorrectly stated that fetch_webpage uses Jina AI reader.
Updated to accurately describe the actual implementation which uses:
1. Exa API (if EXA_API_KEY is available)
2. Trafilatura (fallback)
3. Readability + html2text (final fallback)
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* add anthropic memory tools
* memory view working
* update memory examples
* tools
* feat: some changes (#5003)
* feat: added the ability to modify and add descriptions on creation
* fix: kill dead code & write into core_tool_executor instead
* fix: use block_manager not agent_manager where possible, also turn the return string errors into raising exceptions
* fix: cleanup, get rid of more return string errors replaced with valueerror, also drop deadcode
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