refactor: compress console logs for clarity

Console logs now show status only:
- [plan] ✓ 660 chars
- [execute] 1 tool(s) selected
- [1/1] youtube_transcript ✓
- [execute] 1 tools, 1 evidence
- [answer] ✓ 3

Full context saved to _cache/llm_context_*.txt for debugging.

Co-Authored-By: Claude <noreply@anthropic.com>

WORKSPACE.md

@@ -1,8 +1,6 @@
-2026-01-13 15:47:11,653 - httpx - INFO - HTTP Request: HEAD https://huggingface.co/api/telemetry/https%3A/api.gradio.app/gradio-launched-telemetry "HTTP/1.1 200 OK"
-2026-01-13 15:47:11,875 - httpx - INFO - HTTP Request: GET https://api.gradio.app/pkg-version "HTTP/1.1 200 OK"
-2026-01-13 15:47:29,288 - **main** - INFO - UI Config for Full Evaluation: LLM_PROVIDER=HuggingFace
-2026-01-13 15:47:29,290 - **main** - INFO - Initializing GAIAAgent...
-2026-01-13 15:47:29,317 - **main** - INFO - GAIAAgent initialized successfully
+2026-01-13 15:50:59,509 - **main** - INFO - UI Config for Full Evaluation: LLM_PROVIDER=HuggingFace
+2026-01-13 15:50:59,510 - **main** - INFO - Initializing GAIAAgent...
+2026-01-13 15:50:59,535 - **main** - INFO - GAIAAgent initialized successfully
 User logged in: mangubee
 GAIAAgent initializing...
 ✓ All API keys present
@@ -10,101 +8,94 @@ GAIAAgent initializing...
 GAIAAgent initialized successfully
 https://huggingface.co/spaces/mangoobee/Final_Assignment_Template/tree/main
 Fetching questions from: https://agents-course-unit4-scoring.hf.space/questions
-2026-01-13 15:47:29,805 - **main** - WARNING - DEBUG MODE: Targeted 1/20 questions by task_id
+2026-01-13 15:50:59,972 - **main** - WARNING - DEBUG MODE: Targeted 1/20 questions by task_id
 DEBUG MODE: Processing 1 targeted questions (0 IDs not found: set())
 Processing 1 questions.
-2026-01-13 15:47:30,947 - src.utils.ground_truth - INFO - Loading GAIA validation dataset...
-2026-01-13 15:47:31,086 - httpx - INFO - HTTP Request: HEAD https://huggingface.co/datasets/gaia-benchmark/GAIA/resolve/main/README.md "HTTP/1.1 200 OK"
-2026-01-13 15:47:31,279 - httpx - INFO - HTTP Request: HEAD https://huggingface.co/datasets/gaia-benchmark/GAIA/resolve/682dd723ee1e1697e00360edccf2366dc8418dd9/GAIA.py "HTTP/1.1 404 Not Found"
-2026-01-13 15:47:31,650 - httpx - INFO - HTTP Request: HEAD https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/gaia-benchmark/GAIA/gaia-benchmark/GAIA.py "HTTP/1.1 404 Not Found"
-2026-01-13 15:47:31,784 - httpx - INFO - HTTP Request: GET https://huggingface.co/api/datasets/gaia-benchmark/GAIA/revision/682dd723ee1e1697e00360edccf2366dc8418dd9 "HTTP/1.1 200 OK"
-2026-01-13 15:47:31,920 - httpx - INFO - HTTP Request: HEAD https://huggingface.co/datasets/gaia-benchmark/GAIA/resolve/682dd723ee1e1697e00360edccf2366dc8418dd9/.huggingface.yaml "HTTP/1.1 404 Not Found"
-2026-01-13 15:47:32,107 - httpx - INFO - HTTP Request: GET https://datasets-server.huggingface.co/info?dataset=gaia-benchmark/GAIA "HTTP/1.1 200 OK"
-2026-01-13 15:47:32,380 - httpx - INFO - HTTP Request: GET https://huggingface.co/api/datasets/gaia-benchmark/GAIA/tree/682dd723ee1e1697e00360edccf2366dc8418dd9/2023%2Ftest?recursive=false&expand=false "HTTP/1.1 200 OK"
-2026-01-13 15:47:32,689 - httpx - INFO - HTTP Request: GET https://huggingface.co/api/datasets/gaia-benchmark/GAIA/tree/682dd723ee1e1697e00360edccf2366dc8418dd9/2023%2Fvalidation?recursive=false&expand=false "HTTP/1.1 200 OK"
-2026-01-13 15:47:32,821 - httpx - INFO - HTTP Request: HEAD https://huggingface.co/datasets/gaia-benchmark/GAIA/resolve/682dd723ee1e1697e00360edccf2366dc8418dd9/dataset_infos.json "HTTP/1.1 404 Not Found"
-2026-01-13 15:47:32,860 - src.utils.ground_truth - INFO - Loaded 165 ground truth answers
-2026-01-13 15:47:32,861 - **main** - INFO - Ground truth loaded - per-question correctness will be available
-2026-01-13 15:47:32,861 - **main** - INFO - Running agent on 1 questions with 5 workers...
-2026-01-13 15:47:32,862 - **main** - INFO - [1/1] Processing a1e91b78...
-2026-01-13 15:47:32,864 - src.agent.graph - INFO - [plan_node] ========== PLAN NODE START ==========
-2026-01-13 15:47:32,865 - src.agent.graph - INFO - [plan_node] Question: In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?
-2026-01-13 15:47:32,865 - src.agent.graph - INFO - [plan_node] File paths: None
-2026-01-13 15:47:32,865 - src.agent.graph - INFO - [plan_node] Available tools: ['web_search', 'parse_file', 'calculator', 'vision', 'youtube_transcript', 'transcribe_audio']
-2026-01-13 15:47:32,865 - src.agent.graph - INFO - [plan_node] Calling plan_question() with LLM...
-2026-01-13 15:47:32,866 - src.agent.llm_client - INFO - [plan_question] Using provider: huggingface
-2026-01-13 15:47:32,866 - src.agent.llm_client - INFO - Initializing HuggingFace Inference client with model: openai/gpt-oss-120b:scaleway
-2026-01-13 15:47:32,866 - src.agent.llm_client - INFO - [plan_question_hf] Calling HuggingFace (openai/gpt-oss-120b:scaleway) for planning
+2026-01-13 15:51:01,088 - src.utils.ground_truth - INFO - Loading GAIA validation dataset...
+2026-01-13 15:51:02,550 - src.utils.ground_truth - INFO - Loaded 165 ground truth answers
+2026-01-13 15:51:02,551 - **main** - INFO - Ground truth loaded - per-question correctness will be available
+2026-01-13 15:51:02,551 - **main** - INFO - Running agent on 1 questions with 5 workers...
+2026-01-13 15:51:02,551 - **main** - INFO - [1/1] Processing a1e91b78...
+2026-01-13 15:51:02,553 - src.agent.graph - INFO - [plan_node] ========== PLAN NODE START ==========
+2026-01-13 15:51:02,553 - src.agent.graph - INFO - [plan_node] Question: In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?
+2026-01-13 15:51:02,553 - src.agent.graph - INFO - [plan_node] File paths: None
+2026-01-13 15:51:02,554 - src.agent.graph - INFO - [plan_node] Available tools: ['web_search', 'parse_file', 'calculator', 'vision', 'youtube_transcript', 'transcribe_audio']
+2026-01-13 15:51:02,554 - src.agent.graph - INFO - [plan_node] Calling plan_question() with LLM...
+2026-01-13 15:51:02,554 - src.agent.llm_client - INFO - [plan_question] Using provider: huggingface
+2026-01-13 15:51:02,554 - src.agent.llm_client - INFO - Initializing HuggingFace Inference client with model: openai/gpt-oss-120b:scaleway
+2026-01-13 15:51:02,555 - src.agent.llm_client - INFO - [plan_question_hf] Calling HuggingFace (openai/gpt-oss-120b:scaleway) for planning
 GAIAAgent processing question (first 50 chars): In the video https://www.youtube.com/watch?v=L1vXC...
-2026-01-13 15:47:42,465 - httpx - INFO - HTTP Request: POST https://router.huggingface.co/v1/chat/completions "HTTP/1.1 200 OK"
-2026-01-13 15:47:42,476 - src.agent.llm_client - INFO - [plan_question_hf] Generated plan (792 chars)
-2026-01-13 15:47:42,477 - src.agent.graph - INFO - [plan_node] ✓ Plan created successfully (792 chars)
-2026-01-13 15:47:42,478 - src.agent.graph - INFO - [plan_node] ========== PLAN NODE END ==========
-2026-01-13 15:47:42,481 - src.agent.graph - INFO - [execute_node] ========== EXECUTE NODE START ==========
-2026-01-13 15:47:42,481 - src.agent.graph - INFO - [execute_node] Plan: **Execution Plan**
-
-1. **Extract the video transcript** – Use the `youtube_transcript` tool with the URL `https://www.youtube.com/watch?v=L1vXCYZAYYM` to obtain the full spoken transcript of the video.
-
-2. **Locate the relevant passage** – Scan the returned transcript for any sentences that mention “bird species”, “species on camera”, “simultaneously”, or similar wording. Identify the numeric value(s) that are associated with that statement.
-
-3. **Determine the highest number** – If more than one number is mentioned (e.g., “up to 12 species”, “at one point we saw 15 species”), compare them and select the greatest value.
-
-4. **Provide the answer** – Report the highest number of bird species that were on camera at the same time, citing the transcript excerpt that contains the figure.
-   2026-01-13 15:47:42,482 - src.agent.graph - INFO - [execute_node] Question: In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?
-   2026-01-13 15:47:42,483 - src.agent.graph - INFO - [execute_node] Calling select_tools_with_function_calling()...
-   2026-01-13 15:47:42,483 - src.agent.llm_client - INFO - [select_tools] Using provider: huggingface
-   2026-01-13 15:47:42,483 - src.agent.llm_client - INFO - Initializing HuggingFace Inference client with model: openai/gpt-oss-120b:scaleway
-   2026-01-13 15:47:42,484 - src.agent.llm_client - INFO - [select_tools_hf] Calling HuggingFace with function calling for 6 tools, file_paths=None
-   2026-01-13 15:47:44,512 - httpx - INFO - HTTP Request: POST https://router.huggingface.co/v1/chat/completions "HTTP/1.1 200 OK"
-   2026-01-13 15:47:44,514 - src.agent.llm_client - INFO - [select_tools_hf] HuggingFace selected 1 tool(s)
-   2026-01-13 15:47:44,514 - src.agent.graph - INFO - [execute_node] ✓ LLM selected 1 tool(s)
-   2026-01-13 15:47:44,515 - src.agent.graph - INFO - [execute_node] --- Tool 1/1: youtube_transcript ---
-   2026-01-13 15:47:44,515 - src.agent.graph - INFO - [execute_node] Parameters: {'url': 'https://www.youtube.com/watch?v=L1vXCYZAYYM'}
-   2026-01-13 15:47:44,515 - src.agent.graph - INFO - [execute_node] Executing youtube_transcript...
-   2026-01-13 15:47:44,517 - src.tools.youtube - INFO - Processing YouTube video: L1vXCYZAYYM
-   2026-01-13 15:47:44,529 - src.tools.youtube - INFO - Fetching transcript for video: L1vXCYZAYYM
-   2026-01-13 15:47:45,703 - src.tools.youtube - ERROR - YouTube transcript API failed:
+2026-01-13 15:51:13,335 - src.agent.llm_client - INFO - [plan_question_hf] Generated plan (1340 chars)
+2026-01-13 15:51:13,335 - src.agent.graph - INFO - [plan_node] ✓ Plan created successfully (1340 chars)
+2026-01-13 15:51:13,336 - src.agent.graph - INFO - [plan_node] ========== PLAN NODE END ==========
+2026-01-13 15:51:13,337 - src.agent.graph - INFO - [execute_node] ========== EXECUTE NODE START ==========
+2026-01-13 15:51:13,338 - src.agent.graph - INFO - [execute_node] Plan: **Execution Plan**
+
+1. **Extract the video transcript** – Use the `youtube_transcript` tool on the URL `https://www.youtube.com/watch?v=L1vXCYZAYYM` to obtain the full spoken text of the video.
+
+2. **Locate the relevant statement** – Scan the returned transcript for keywords such as “species”, “bird”, “simultaneously”, “on camera”, “different species”, or any numeric value that could represent the count of bird species shown at once.
+
+3. **Identify the highest number mentioned** – If multiple numbers are found, determine which one refers to the “highest number of bird species on camera simultaneously.”
+
+4. **Validate via web search (if needed)** – If the transcript does not contain a clear answer, perform a `web_search` using the video title (or a description of the video) combined with terms like “bird species on camera simultaneously” to find external sources (e.g., articles, forum posts, video description) that state the number.
+
+5. **Extract the answer** – From the transcript (or from the web‑search result), record the exact number of bird species that were on camera at the same time, ensuring it is the highest reported figure.
+
+6. **Provide the final response** – Return the identified number, citing that it comes from the video transcript (or the supporting web source if the transcript was insufficient).
+   2026-01-13 15:51:13,338 - src.agent.graph - INFO - [execute_node] Question: In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?
+   2026-01-13 15:51:13,338 - src.agent.graph - INFO - [execute_node] Calling select_tools_with_function_calling()...
+   2026-01-13 15:51:13,339 - src.agent.llm_client - INFO - [select_tools] Using provider: huggingface
+   2026-01-13 15:51:13,339 - src.agent.llm_client - INFO - Initializing HuggingFace Inference client with model: openai/gpt-oss-120b:scaleway
+   2026-01-13 15:51:13,340 - src.agent.llm_client - INFO - [select_tools_hf] Calling HuggingFace with function calling for 6 tools, file_paths=None
+   2026-01-13 15:51:15,405 - src.agent.llm_client - INFO - [select_tools_hf] HuggingFace selected 1 tool(s)
+   2026-01-13 15:51:15,406 - src.agent.graph - INFO - [execute_node] ✓ LLM selected 1 tool(s)
+   2026-01-13 15:51:15,407 - src.agent.graph - INFO - [execute_node] --- Tool 1/1: youtube_transcript ---
+   2026-01-13 15:51:15,407 - src.agent.graph - INFO - [execute_node] Parameters: {'url': 'https://www.youtube.com/watch?v=L1vXCYZAYYM'}
+   2026-01-13 15:51:15,408 - src.agent.graph - INFO - [execute_node] Executing youtube_transcript...
+   2026-01-13 15:51:15,408 - src.tools.youtube - INFO - Processing YouTube video: L1vXCYZAYYM
+   2026-01-13 15:51:15,420 - src.tools.youtube - INFO - Fetching transcript for video: L1vXCYZAYYM
+   2026-01-13 15:51:16,397 - src.tools.youtube - ERROR - YouTube transcript API failed:
    Could not retrieve a transcript for the video https://www.youtube.com/watch?v=L1vXCYZAYYM! This is most likely caused by:
 
 Subtitles are disabled for this video
 
 If you are sure that the described cause is not responsible for this error and that a transcript should be retrievable, please create an issue at https://github.com/jdepoix/youtube-transcript-api/issues. Please add which version of youtube_transcript_api you are using and provide the information needed to replicate the error. Also make sure that there are no open issues which already describe your problem!
-2026-01-13 15:47:45,708 - src.tools.youtube - INFO - Transcript API failed, trying audio transcription...
-2026-01-13 15:47:45,780 - src.tools.youtube - INFO - Downloading audio from: https://www.youtube.com/watch?v=L1vXCYZAYYM
-
-2026-01-13 15:47:49,192 - src.tools.youtube - INFO - Audio downloaded: /var/folders/05/8vqqybgj751**dmlh3w536dh0000gn/T/youtube_audio_39654.mp3 (1930412 bytes)
-2026-01-13 15:47:49,193 - src.tools.audio - INFO - Transcribing audio: /var/folders/05/8vqqybgj751**dmlh3w536dh0000gn/T/youtube_audio_39654.mp3
-2026-01-13 15:47:49,474 - src.tools.audio - INFO - Loading Whisper model: small
-2026-01-13 15:47:50,776 - src.tools.audio - INFO - Whisper model loaded on cpu
-2026-01-13 15:47:56,765 - src.tools.audio - INFO - Transcription successful: 738 characters
-2026-01-13 15:47:56,766 - src.tools.youtube - INFO - Cleaned up temp file: /var/folders/05/8vqqybgj751\_\_dmlh3w536dh0000gn/T/youtube_audio_39654.mp3
-2026-01-13 15:47:56,768 - src.tools.youtube - INFO - Transcript saved to cache: \_cache/L1vXCYZAYYM_transcript.txt
-2026-01-13 15:47:56,768 - src.tools.youtube - INFO - Transcript retrieved via Whisper: 738 characters
-2026-01-13 15:47:56,768 - src.tools.youtube - INFO - Full transcript: But one challenge stops them in their tracks. A giant petrel. They try to flee, but running isn't an emperor's strong point. A slip is all the petrel needs. The chick is grabbed by his neck feathers. But the down just falls away. They form a defensive circle and prepare to stand their ground. Despite their chick-like appearance, they are close to a metre tall. Quite a size, even for a giant petrel. The chick towers to full height, protecting those behind. His defiance buys time. It's a standoff. Then, as if from nowhere, and a deli, the feistiest penguin in the world. He fearlessly puts himself between the chicks and the petrel. Even petrels don't mess with the delis. Their plucky rescuer accompanies the chicks to the sea. Fair.
-2026-01-13 15:47:56,769 - src.agent.graph - INFO - [execute_node] ✓ youtube_transcript completed successfully
-2026-01-13 15:47:56,769 - src.agent.graph - INFO - [execute_node] Summary: 1 tool(s) executed, 1 evidence items collected
-2026-01-13 15:47:56,769 - src.agent.graph - INFO - [execute_node] ========== EXECUTE NODE END ==========
-2026-01-13 15:47:56,770 - src.agent.graph - INFO - [answer_node] ========== ANSWER NODE START ==========
-2026-01-13 15:47:56,770 - src.agent.graph - INFO - [answer_node] Evidence items collected: 1
-2026-01-13 15:47:56,771 - src.agent.graph - INFO - [answer_node] Errors accumulated: 0
-2026-01-13 15:47:56,771 - src.agent.graph - INFO - ================================================================================
-2026-01-13 15:47:56,771 - src.agent.graph - INFO - [EVIDENCE] Full evidence content being passed to synthesis:
-2026-01-13 15:47:56,771 - src.agent.graph - INFO - ================================================================================
-2026-01-13 15:47:56,771 - src.agent.graph - INFO - [EVIDENCE 1/1]
-2026-01-13 15:47:56,772 - src.agent.graph - INFO - {'text': "But one challenge stops them in their tracks. A giant petrel. They try to flee, but running isn't an emperor's strong point. A slip is all the petrel needs. The chick is grabbed by his neck feathers. But the down just falls away. They form a defensive circle and prepare to stand their ground. Despite their chick-like appearance, they are close to a metre tall. Quite a size, even for a giant petrel. The chick towers to full height, protecting those behind. His defiance buys time. It's a...
-2026-01-13 15:47:56,772 - src.agent.graph - INFO - --------------------------------------------------------------------------------
-2026-01-13 15:47:56,772 - src.agent.graph - INFO - ================================================================================
-2026-01-13 15:47:56,772 - src.agent.graph - INFO - [EVIDENCE] End of evidence content
-2026-01-13 15:47:56,772 - src.agent.graph - INFO - ================================================================================
-2026-01-13 15:47:56,772 - src.agent.graph - INFO - [answer_node] Calling synthesize_answer() with 1 evidence items...
-2026-01-13 15:47:56,773 - src.agent.llm_client - INFO - [synthesize_answer] Using provider: huggingface
-2026-01-13 15:47:56,773 - src.agent.llm_client - INFO - Initializing HuggingFace Inference client with model: openai/gpt-oss-120b:scaleway
-2026-01-13 15:47:56,773 - src.agent.llm_client - INFO - [synthesize_answer_hf] LLM context saved to: \_cache/llm_context_20260113_154756.txt
-2026-01-13 15:47:56,774 - src.agent.llm_client - INFO - [synthesize_answer_hf] Calling HuggingFace for answer synthesis
-2026-01-13 15:47:56,774 - src.agent.llm_client - INFO - ================================================================================
-2026-01-13 15:47:56,774 - src.agent.llm_client - INFO - [LLM CONTEXT] Full synthesis prompt being sent to LLM:
-2026-01-13 15:47:56,774 - src.agent.llm_client - INFO - ================================================================================
-2026-01-13 15:47:56,774 - src.agent.llm_client - INFO - [SYSTEM PROMPT]
+2026-01-13 15:51:16,400 - src.tools.youtube - INFO - Transcript API failed, trying audio transcription...
+2026-01-13 15:51:16,463 - src.tools.youtube - INFO - Downloading audio from: https://www.youtube.com/watch?v=L1vXCYZAYYM
+
+2026-01-13 15:51:19,610 - src.tools.youtube - INFO - Audio downloaded: /var/folders/05/8vqqybgj751**dmlh3w536dh0000gn/T/youtube_audio_40067.mp3 (1930412 bytes)
+2026-01-13 15:51:19,610 - src.tools.audio - INFO - Transcribing audio: /var/folders/05/8vqqybgj751**dmlh3w536dh0000gn/T/youtube_audio_40067.mp3
+2026-01-13 15:51:19,850 - src.tools.audio - INFO - Loading Whisper model: small
+2026-01-13 15:51:21,374 - src.tools.audio - INFO - Whisper model loaded on cpu
+2026-01-13 15:51:27,949 - src.tools.audio - INFO - Transcription successful: 738 characters
+2026-01-13 15:51:27,950 - src.tools.youtube - INFO - Cleaned up temp file: /var/folders/05/8vqqybgj751\_\_dmlh3w536dh0000gn/T/youtube_audio_40067.mp3
+2026-01-13 15:51:27,951 - src.tools.youtube - INFO - Transcript saved to cache: \_cache/L1vXCYZAYYM_transcript.txt
+2026-01-13 15:51:27,951 - src.tools.youtube - INFO - Transcript retrieved via Whisper: 738 characters
+2026-01-13 15:51:27,952 - src.tools.youtube - INFO - Full transcript: But one challenge stops them in their tracks. A giant petrel. They try to flee, but running isn't an emperor's strong point. A slip is all the petrel needs. The chick is grabbed by his neck feathers. But the down just falls away. They form a defensive circle and prepare to stand their ground. Despite their chick-like appearance, they are close to a metre tall. Quite a size, even for a giant petrel. The chick towers to full height, protecting those behind. His defiance buys time. It's a standoff. Then, as if from nowhere, and a deli, the feistiest penguin in the world. He fearlessly puts himself between the chicks and the petrel. Even petrels don't mess with the delis. Their plucky rescuer accompanies the chicks to the sea. Fair.
+2026-01-13 15:51:27,952 - src.agent.graph - INFO - [execute_node] ✓ youtube_transcript completed successfully
+2026-01-13 15:51:27,952 - src.agent.graph - INFO - [execute_node] Summary: 1 tool(s) executed, 1 evidence items collected
+2026-01-13 15:51:27,952 - src.agent.graph - INFO - [execute_node] ========== EXECUTE NODE END ==========
+2026-01-13 15:51:27,953 - src.agent.graph - INFO - [answer_node] ========== ANSWER NODE START ==========
+2026-01-13 15:51:27,954 - src.agent.graph - INFO - [answer_node] Evidence items collected: 1
+2026-01-13 15:51:27,954 - src.agent.graph - INFO - [answer_node] Errors accumulated: 0
+2026-01-13 15:51:27,954 - src.agent.graph - INFO - ================================================================================
+2026-01-13 15:51:27,954 - src.agent.graph - INFO - [EVIDENCE] Full evidence content being passed to synthesis:
+2026-01-13 15:51:27,954 - src.agent.graph - INFO - ================================================================================
+2026-01-13 15:51:27,954 - src.agent.graph - INFO - [EVIDENCE 1/1]
+2026-01-13 15:51:27,954 - src.agent.graph - INFO - {'text': "But one challenge stops them in their tracks. A giant petrel. They try to flee, but running isn't an emperor's strong point. A slip is all the petrel needs. The chick is grabbed by his neck feathers. But the down just falls away. They form a defensive circle and prepare to stand their ground. Despite their chick-like appearance, they are close to a metre tall. Quite a size, even for a giant petrel. The chick towers to full height, protecting those behind. His defiance buys time. It's a...
+2026-01-13 15:51:27,955 - src.agent.graph - INFO - --------------------------------------------------------------------------------
+2026-01-13 15:51:27,955 - src.agent.graph - INFO - ================================================================================
+2026-01-13 15:51:27,955 - src.agent.graph - INFO - [EVIDENCE] End of evidence content
+2026-01-13 15:51:27,955 - src.agent.graph - INFO - ================================================================================
+2026-01-13 15:51:27,955 - src.agent.graph - INFO - [answer_node] Calling synthesize_answer() with 1 evidence items...
+2026-01-13 15:51:27,956 - src.agent.llm_client - INFO - [synthesize_answer] Using provider: huggingface
+2026-01-13 15:51:27,956 - src.agent.llm_client - INFO - Initializing HuggingFace Inference client with model: openai/gpt-oss-120b:scaleway
+2026-01-13 15:51:27,957 - src.agent.llm_client - INFO - [synthesize_answer_hf] LLM context saved to: \_cache/llm_context_20260113_155127.txt
+2026-01-13 15:51:27,957 - src.agent.llm_client - INFO - [synthesize_answer_hf] Calling HuggingFace for answer synthesis
+2026-01-13 15:51:27,958 - src.agent.llm_client - INFO - ================================================================================
+2026-01-13 15:51:27,958 - src.agent.llm_client - INFO - [LLM CONTEXT] Full synthesis prompt being sent to LLM:
+2026-01-13 15:51:27,958 - src.agent.llm_client - INFO - ================================================================================
+2026-01-13 15:51:27,958 - src.agent.llm_client - INFO - [SYSTEM PROMPT]
 You are an answer synthesis agent for the GAIA benchmark.
 
 Your task is to extract a factoid answer from the provided evidence.
@@ -129,27 +120,26 @@ Examples of bad answers (too verbose):
 - "The answer is 42 because..."
 - "Based on the evidence, it appears that..."
 
-2026-01-13 15:47:56,774 - src.agent.llm_client - INFO - --------------------------------------------------------------------------------
-2026-01-13 15:47:56,774 - src.agent.llm_client - INFO - [USER PROMPT]
+2026-01-13 15:51:27,958 - src.agent.llm_client - INFO - --------------------------------------------------------------------------------
+2026-01-13 15:51:27,959 - src.agent.llm_client - INFO - [USER PROMPT]
 Question: In the video https://www.youtube.com/watch?v=L1vXCYZAYYM, what is the highest number of bird species to be on camera simultaneously?
 
 Evidence 1:
 {'text': "But one challenge stops them in their tracks. A giant petrel. They try to flee, but running isn't an emperor's strong point. A slip is all the petrel needs. The chick is grabbed by his neck feathers. But the down just falls away. They form a defensive circle and prepare to stand their ground. Despite their chick-like appearance, they are close to a metre tall. Quite a size, even for a giant petrel. The chick towers to full height, protecting those behind. His defiance buys time. It's a standoff. Then, as if from nowhere, and a deli, the feistiest penguin in the world. He fearlessly puts himself between the chicks and the petrel. Even petrels don't mess with the delis. Their plucky rescuer accompanies the chicks to the sea. Fair.", 'video_id': 'L1vXCYZAYYM', 'source': 'whisper', 'success': True, 'error': None}
 
 Extract the factoid answer from the evidence above. Return only the factoid, nothing else.
-2026-01-13 15:47:56,774 - src.agent.llm_client - INFO - ================================================================================
-2026-01-13 15:47:56,775 - src.agent.llm_client - INFO - [LLM CONTEXT] End of full context
-2026-01-13 15:47:56,775 - src.agent.llm_client - INFO - ================================================================================
-2026-01-13 15:47:59,302 - httpx - INFO - HTTP Request: POST https://router.huggingface.co/v1/chat/completions "HTTP/1.1 200 OK"
-2026-01-13 15:47:59,304 - src.agent.llm_client - INFO - [synthesize_answer_hf] Generated answer: Unable to answer
-2026-01-13 15:47:59,306 - src.agent.llm_client - INFO - [synthesize_answer_hf] Answer appended to context file
-2026-01-13 15:47:59,307 - src.agent.graph - INFO - [answer_node] ✓ Answer generated successfully: Unable to answer
-2026-01-13 15:47:59,307 - src.agent.graph - INFO - [answer_node] ========== ANSWER NODE END ==========
-2026-01-13 15:47:59,309 - **main** - INFO - [1/1] Completed a1e91b78
-2026-01-13 15:47:59,310 - **main** - INFO - Progress: 1/1 questions processed
+2026-01-13 15:51:27,959 - src.agent.llm_client - INFO - ================================================================================
+2026-01-13 15:51:27,959 - src.agent.llm_client - INFO - [LLM CONTEXT] End of full context
+2026-01-13 15:51:27,959 - src.agent.llm_client - INFO - ================================================================================
+2026-01-13 15:51:30,295 - src.agent.llm_client - INFO - [synthesize_answer_hf] Generated answer: Unable to answer
+2026-01-13 15:51:30,296 - src.agent.llm_client - INFO - [synthesize_answer_hf] Answer appended to context file
+2026-01-13 15:51:30,297 - src.agent.graph - INFO - [answer_node] ✓ Answer generated successfully: Unable to answer
+2026-01-13 15:51:30,297 - src.agent.graph - INFO - [answer_node] ========== ANSWER NODE END ==========
+2026-01-13 15:51:30,299 - **main** - INFO - [1/1] Completed a1e91b78
+2026-01-13 15:51:30,300 - **main** - INFO - Progress: 1/1 questions processed
 GAIAAgent returning answer: Unable to answer
 Agent finished. Submitting 1 answers for user 'mangubee'...
 Submitting 1 answers to: https://agents-course-unit4-scoring.hf.space/submit
-2026-01-13 15:48:00,359 - **main** - INFO - Total execution time: 31.07 seconds (0m 31s)
-2026-01-13 15:48:00,361 - **main** - INFO - Results exported to: /Users/mangubee/Documents/Python/16_HuggingFace/Final_Assignment_Template/\_cache/gaia_results_20260113_154800.json
+2026-01-13 15:51:31,493 - **main** - INFO - Total execution time: 31.98 seconds (0m 31s)
+2026-01-13 15:51:31,497 - **main** - INFO - Results exported to: /Users/mangubee/Documents/Python/16_HuggingFace/Final_Assignment_Template/\_cache/gaia_results_20260113_155131.json
 Submission successful.

src/agent/graph.py

@@ -216,55 +216,24 @@ def plan_node(state: AgentState) -> AgentState:
     Returns:
         Updated state with execution plan
     """
-    logger.info(f"[plan_node] ========== PLAN NODE START ==========")
-    logger.info(f"[plan_node] Question: {state['question']}")
-    logger.info(f"[plan_node] File paths: {state.get('file_paths')}")
-    logger.info(f"[plan_node] Available tools: {list(TOOLS.keys())}")
-
     try:
-        # Stage 3: Use LLM to generate dynamic execution plan
-        logger.info(f"[plan_node] Calling plan_question() with LLM...")
         plan = plan_question(
             question=state["question"],
             available_tools=TOOLS,
             file_paths=state.get("file_paths"),
         )
-
         state["plan"] = plan
-        logger.info(f"[plan_node] ✓ Plan created successfully ({len(plan)} chars)")
-        logger.debug(f"[plan_node] Plan content: {plan}")
-
+        logger.info(f"[plan] ✓ {len(plan)} chars")
     except Exception as e:
-        logger.error(f"[plan_node] ✗ Planning failed: {type(e).__name__}: {str(e)}", exc_info=True)
+        logger.error(f"[plan] ✗ {type(e).__name__}: {str(e)}")
         state["errors"].append(f"Planning error: {type(e).__name__}: {str(e)}")
         state["plan"] = "Error: Unable to create plan"
-
-    logger.info(f"[plan_node] ========== PLAN NODE END ==========")
     return state
 
 
 def execute_node(state: AgentState) -> AgentState:
-    """
-    Execution node: Execute tools based on plan.
-
-    Stage 3: Dynamic tool selection and execution
-    - LLM selects tools via function calling
-    - Extracts parameters from question
-    - Executes tools and collects results
-    - Handles errors with retry logic (in tools)
-
-    Args:
-        state: Current agent state with plan
-
-    Returns:
-        Updated state with tool execution results and evidence
-    """
-    logger.info(f"[execute_node] ========== EXECUTE NODE START ==========")
-    logger.info(f"[execute_node] Plan: {state['plan']}")
-    logger.info(f"[execute_node] Question: {state['question']}")
-
+    """Execution node: Execute tools based on plan."""
     # Map tool names to actual functions
-    # NOTE: Keys must match TOOLS registry in src/tools/__init__.py
     TOOL_FUNCTIONS = {
         "web_search": search,
         "parse_file": parse_file,
@@ -274,14 +243,11 @@ def execute_node(state: AgentState) -> AgentState:
         "transcribe_audio": transcribe_audio,
     }
 
-    # Initialize results lists
     tool_results = []
     evidence = []
     tool_calls = []
 
     try:
-        # Stage 3: Use LLM function calling to select tools and extract parameters
-        logger.info(f"[execute_node] Calling select_tools_with_function_calling()...")
         tool_calls = select_tools_with_function_calling(
             question=state["question"],
             plan=state["plan"],
@@ -291,53 +257,39 @@ def execute_node(state: AgentState) -> AgentState:
 
         # Validate tool_calls result
         if not tool_calls:
-            logger.warning(f"[execute_node] ⚠ LLM returned empty tool_calls list - using fallback")
-            state["errors"].append("Tool selection returned no tools - using fallback keyword matching")
-            # MVP HACK: Use fallback keyword-based tool selection
+            logger.warning("[execute] No tools selected, using fallback")
+            state["errors"].append("Tool selection returned no tools - using fallback")
             tool_calls = fallback_tool_selection(
                 state["question"], state["plan"], state.get("file_paths")
             )
-            logger.info(f"[execute_node] Fallback returned {len(tool_calls)} tool(s)")
         elif not isinstance(tool_calls, list):
-            logger.error(f"[execute_node] ✗ Invalid tool_calls type: {type(tool_calls)} - using fallback")
-            state["errors"].append(f"Tool selection returned invalid type: {type(tool_calls)} - using fallback")
-            # MVP HACK: Use fallback
+            logger.error(f"[execute] Invalid type: {type(tool_calls)}, using fallback")
+            state["errors"].append(f"Tool selection returned invalid type: {type(tool_calls)}")
             tool_calls = fallback_tool_selection(
                 state["question"], state["plan"], state.get("file_paths")
             )
         else:
-            logger.info(f"[execute_node] ✓ LLM selected {len(tool_calls)} tool(s)")
-            logger.debug(f"[execute_node] Tool calls: {tool_calls}")
+            logger.info(f"[execute] {len(tool_calls)} tool(s) selected")
 
         # Execute each tool call
         for idx, tool_call in enumerate(tool_calls, 1):
             tool_name = tool_call["tool"]
             params = tool_call["params"]
 
-            logger.info(f"[execute_node] --- Tool {idx}/{len(tool_calls)}: {tool_name} ---")
-            logger.info(f"[execute_node] Parameters: {params}")
-
             try:
-                # Get tool function
                 tool_func = TOOL_FUNCTIONS.get(tool_name)
                 if not tool_func:
                     raise ValueError(f"Tool '{tool_name}' not found in TOOL_FUNCTIONS")
 
-                # Execute tool
-                logger.info(f"[execute_node] Executing {tool_name}...")
                 result = tool_func(**params)
-                logger.info(f"[execute_node] ✓ {tool_name} completed successfully")
-                logger.debug(f"[execute_node] Result: {result[:200] if isinstance(result, str) else result}...")
+                logger.info(f"[{idx}/{len(tool_calls)}] {tool_name} ✓")
 
-                # Store result
-                tool_results.append(
-                    {
-                        "tool": tool_name,
-                        "params": params,
-                        "result": result,
-                        "status": "success",
-                    }
-                )
+                tool_results.append({
+                    "tool": tool_name,
+                    "params": params,
+                    "result": result,
+                    "status": "success",
+                })
 
                 # Extract evidence - handle different result formats
                 if isinstance(result, dict):
@@ -375,38 +327,29 @@ def execute_node(state: AgentState) -> AgentState:
                         "error": str(tool_error),
                         "status": "failed",
                     }
-                )
-
                 # Provide specific error message for vision tool failures
                 if tool_name == "vision" and ("quota" in str(tool_error).lower() or "429" in str(tool_error)):
-                    state["errors"].append(f"Vision analysis failed: LLM quota exhausted. Vision requires multimodal LLM (Gemini/Claude).")
+                    state["errors"].append(f"Vision failed: LLM quota exhausted")
                 else:
-                    state["errors"].append(f"Tool {tool_name} failed: {type(tool_error).__name__}: {str(tool_error)}")
+                    state["errors"].append(f"{tool_name}: {type(tool_error).__name__}")
 
-        logger.info(f"[execute_node] Summary: {len(tool_results)} tool(s) executed, {len(evidence)} evidence items collected")
-        logger.debug(f"[execute_node] Evidence: {evidence}")
+        logger.info(f"[execute] {len(tool_results)} tools, {len(evidence)} evidence")
 
     except Exception as e:
-        logger.error(f"[execute_node] ✗ Execution failed: {type(e).__name__}: {str(e)}", exc_info=True)
+        logger.error(f"[execute] ✗ {type(e).__name__}: {str(e)}")
 
-        # Graceful handling for vision questions when LLMs unavailable
         if is_vision_question(state["question"]) and ("quota" in str(e).lower() or "429" in str(e)):
-            logger.warning(f"[execute_node] Vision question detected with quota error - providing graceful skip")
-            state["errors"].append("Vision analysis unavailable (LLM quota exhausted). Vision questions require multimodal LLMs.")
+            state["errors"].append("Vision unavailable (quota exhausted)")
         else:
-            state["errors"].append(f"Execution error: {type(e).__name__}: {str(e)}")
+            state["errors"].append(f"Execution error: {type(e).__name__}")
 
         # Try fallback if we don't have any tool_calls yet
         if not tool_calls:
-            logger.info(f"[execute_node] Attempting fallback after exception...")
             try:
                 tool_calls = fallback_tool_selection(
                     state["question"], state.get("plan", ""), state.get("file_paths")
                 )
-                logger.info(f"[execute_node] Fallback after exception returned {len(tool_calls)} tool(s)")
 
-                # Try to execute fallback tools
-                # NOTE: Keys must match TOOLS registry in src/tools/__init__.py
                 TOOL_FUNCTIONS = {
                     "web_search": search,
                     "parse_file": parse_file,
@@ -429,7 +372,6 @@ def execute_node(state: AgentState) -> AgentState:
                                 "result": result,
                                 "status": "success"
                             })
-                            # Extract evidence - handle different result formats
                             if isinstance(result, dict):
                                 if "answer" in result:
                                     evidence.append(result["answer"])
@@ -451,86 +393,42 @@ def execute_node(state: AgentState) -> AgentState:
                                 evidence.append(result)
                             else:
                                 evidence.append(str(result))
-                            logger.info(f"[execute_node] Fallback tool {tool_name} executed successfully")
+                            logger.info(f"[execute] Fallback {tool_name} ✓")
                     except Exception as tool_error:
-                        logger.error(f"[execute_node] Fallback tool {tool_name} failed: {tool_error}")
+                        logger.error(f"[execute] Fallback {tool_name} ✗ {tool_error}")
             except Exception as fallback_error:
-                logger.error(f"[execute_node] Fallback also failed: {fallback_error}")
+                logger.error(f"[execute] Fallback failed: {fallback_error}")
 
     # Always update state, even if there were errors
     state["tool_calls"] = tool_calls
     state["tool_results"] = tool_results
     state["evidence"] = evidence
-
-    logger.info(f"[execute_node] ========== EXECUTE NODE END ==========")
     return state
 
 
 def answer_node(state: AgentState) -> AgentState:
-    """
-    Answer synthesis node: Generate final factoid answer.
-
-    Stage 3: Synthesize answer from evidence
-    - LLM analyzes collected evidence
-    - Resolves conflicts if present
-    - Generates factoid answer in GAIA format
-
-    Args:
-        state: Current agent state with evidence from tools
-
-    Returns:
-        Updated state with final factoid answer
-    """
-    logger.info(f"[answer_node] ========== ANSWER NODE START ==========")
-    logger.info(f"[answer_node] Evidence items collected: {len(state['evidence'])}")
-    logger.info(f"[answer_node] Errors accumulated: {len(state['errors'])}")
-
-    # ============================================================================
-    # FULL EVIDENCE LOGGING - Debug what evidence is being passed to synthesis
-    # ============================================================================
-    logger.info("=" * 80)
-    logger.info("[EVIDENCE] Full evidence content being passed to synthesis:")
-    logger.info("=" * 80)
-    for i, ev in enumerate(state['evidence']):
-        logger.info(f"[EVIDENCE {i+1}/{len(state['evidence'])}]")
-        logger.info(f"{ev[:500]}..." if len(ev) > 500 else f"{ev}")
-        logger.info("-" * 80)
-    logger.info("=" * 80)
-    logger.info("[EVIDENCE] End of evidence content")
-    logger.info("=" * 80)
-    # ============================================================================
-
-    logger.debug(f"[answer_node] Evidence: {state['evidence']}")
+    """Answer synthesis node: Generate final factoid answer from evidence."""
     if state["errors"]:
-        logger.warning(f"[answer_node] Error list: {state['errors']}")
+        logger.warning(f"[answer] Errors: {state['errors']}")
 
     try:
-        # Check if we have evidence
         if not state["evidence"]:
-            logger.warning(
-                "[answer_node] ✗ No evidence collected, cannot generate answer"
-            )
-            # Show WHY it failed - include error details
-            error_summary = "; ".join(state["errors"]) if state["errors"] else "No errors logged - check API keys and logs"
-            state["answer"] = f"ERROR: No evidence collected. Details: {error_summary}"
-            logger.error(f"[answer_node] Returning error answer: {state['answer']}")
+            error_summary = "; ".join(state["errors"]) if state["errors"] else "No errors logged"
+            state["answer"] = f"ERROR: No evidence. {error_summary}"
+            logger.error(f"[answer] ✗ No evidence - {error_summary}")
             return state
 
-        # Stage 3: Use LLM to synthesize factoid answer from evidence
-        logger.info(f"[answer_node] Calling synthesize_answer() with {len(state['evidence'])} evidence items...")
         answer = synthesize_answer(
             question=state["question"], evidence=state["evidence"]
         )
-
         state["answer"] = answer
-        logger.info(f"[answer_node] ✓ Answer generated successfully: {answer}")
+        logger.info(f"[answer] ✓ {answer}")
 
     except Exception as e:
-        logger.error(f"[answer_node] ✗ Answer synthesis failed: {type(e).__name__}: {str(e)}", exc_info=True)
+        logger.error(f"[answer] ✗ {type(e).__name__}: {str(e)}")
         state["errors"].append(f"Answer synthesis error: {type(e).__name__}: {str(e)}")
         state["answer"] = f"ERROR: Answer synthesis failed - {type(e).__name__}: {str(e)}"
 
-    logger.info(f"[answer_node] ========== ANSWER NODE END ==========")
     return state
 
 

src/agent/llm_client.py

@@ -1142,30 +1142,13 @@ Extract the factoid answer from the evidence above. Return only the factoid, not
             f.write(ev)
         f.write("\n" + "=" * 80 + "\n")
 
-    logger.info(f"[synthesize_answer_hf] LLM context saved to: {context_file}")
-    # ============================================================================
-
-    logger.info(f"[synthesize_answer_hf] Calling HuggingFace for answer synthesis")
+    logger.info(f"[synthesize_answer_hf] Context saved to: {context_file}")
 
     messages = [
         {"role": "system", "content": system_prompt},
         {"role": "user", "content": user_prompt},
     ]
 
-    # ============================================================================
-    # FULL CONTEXT LOGGING - Debug LLM synthesis failures
-    # ============================================================================
-    logger.info("=" * 80)
-    logger.info("[LLM CONTEXT] Full synthesis prompt being sent to LLM:")
-    logger.info("=" * 80)
-    logger.info(f"[SYSTEM PROMPT]\n{system_prompt}")
-    logger.info("-" * 80)
-    logger.info(f"[USER PROMPT]\n{user_prompt}")
-    logger.info("=" * 80)
-    logger.info("[LLM CONTEXT] End of full context")
-    logger.info("=" * 80)
-    # ============================================================================
-
     response = client.chat_completion(
         messages=messages,
         max_tokens=256,  # Factoid answers are short