When the LLM returns ontology attributes as plain strings instead of
dicts, set_ontology() crashes with "TypeError: string indices must be
integers, not 'str'" at attr_def["name"].
Two-layer fix:
1. ontology_generator.py: normalize string attrs to {"name", "type",
"description"} dicts during validation, so downstream code always
receives well-formed structures.
2. graph_builder.py: add isinstance guard as a safety net in
set_ontology() for both entity and edge attribute loops.
Co-Authored-By: Octopus <liyuan851277048@icloud.com>
When the LLM generates a <tool_call> block followed by a self-generated
<tool_result> block in the same response, the fake result must be stripped
before appending to message history. The real tool result will be injected
separately by the system.
This fixes a React Hallucination bug where models could fabricate tool
results that didn't actually come from tool invocations.
The API retrieval layer hardcoded reddit as the default platform in 11+ locations. When a Twitter-only simulation was run, all data retrieval APIs silently returned empty results because they looked for reddit_simulation.db which did not exist. This commit reads the simulation enable_twitter/enable_reddit config to determine the correct default platform.
Fixes#150
Background threads (graph building, simulation prep, report generation,
profile generation) now inherit the requesting user's locale preference.
Previously these fell back to 'zh' because Flask request context was
unavailable in spawned threads.
Ensure poster_type stays PascalCase English and stance stays English enum
values regardless of language setting. Only natural language fields follow
the user's language preference.
The language instruction was causing LLM to change entity/relation naming
conventions. Now explicitly enforce PascalCase/UPPER_SNAKE_CASE for technical
identifiers while only applying language preference to description fields.
- Decreased the maximum tool calls per section from 8 to 5.
- Reduced the maximum iterations in the ReACT loop from 8 to 5, streamlining the report generation process.
- Reduced maximum tool calls per chat from 5 to 2 for improved efficiency.
- Simplified system prompt to focus on concise responses and report content.
- Implemented report content retrieval with length limitation to prevent context overflow.
- Adjusted tool call execution to limit to one call per iteration, enhancing clarity in responses.
- Updated user message prompts to encourage concise answers based on retrieved data.
- Increased the maximum tool calls per section from 4 to 8, enhancing the agent's capabilities.
- Raised the maximum reflection rounds from 2 to 3 to allow for deeper analysis.
- Adjusted the maximum tool calls per chat from 3 to 5 for improved interaction.
- Expanded the maximum agents for interviews from 5 to 20, facilitating more comprehensive data gathering.
- Increased the maximum iterations for ReACT loops from 5 to 8 and from 3 to 5 in different contexts, optimizing the report generation process.
- Updated the `to_text` method in the `PanoramaResult` class to provide complete outputs for current facts, historical facts, and involved entities, improving data visibility.
- Modified the `to_text` method in the `AgentInterview` class to display the full agent bio without truncation.
- Adjusted the `ZepToolsService` class to retrieve all related entity details and facts without limiting the output, ensuring comprehensive data representation.
666ghj
octo-patch
ygh1254
IagoMelo01
MiroFish Contributor
karesansui
Sami Rusani
ghostubborn