News Agent

Day 1 (Apr 15)

• Project Initialization: Finalized project discussion and system architecture approval.
• Initial Setup: Established repository, technical requirements, and core folder structure.
• Documentation: Created base README.md.

Day 2 (Apr 16)

• Core Integration: Setup MCP Server/Client and integrated DuckDuckGo (DDG) and Tavily tool catalogs.
• Refinement: Optimized prompts and restricted scope for better local model performance.
• Infrastructure: Introduced a Rate Limiter (IP and Session-based) and a delegation script (run.py) for managing dual servers.
• Workflow: Established the base API setup with node-based logic and tool-calling methods.

Day 3 (Apr 17)

• Memory & Storage: Integrated ChromaDB for vector operations and established a News Memory Client.
• Logic Gates: Added a planner_node with new conditional logic and refactored search nodes for better modularity.
• Session Management: Built a robust Session Manager to handle input, synthesis, and session state within the API.
• UX Enhancements: Resolved "double texting" bugs and initiated Phase 1 of UX Streams.
• Health Checks: Established system metrics and health monitoring endpoints.

Day 4 (Apr 20)

• Advanced Querying: Refactored search logic to utilize a k-NN (k-Nearest Neighbors) approach for more accurate querying.
• Multimodal Expansion: Introduced a lightweight Base Voice Model to handle audio interactions.

Day 5 (Apr 21)

• Validation Layer: Implemented a new Validator module and patched validation logic within the synthesis node.
• Configuration: Centralized settings into a dedicated Config File with subsequent patching for stability.
• Agent Intelligence: Integrated advanced Agent Tools and established a ReAct integration flow using Gemini for complex reasoning.
• Caching & Sessions: Added Redis CLI integration and enhanced Session Manager with cache-backed workflows.
• Infrastructure Updates: Introduced /query/sync cache handling and applied Redis patches.
• Release: Tagged v1.0.1 milestone.
• Maintenance: Merged development branch into main and performed final synthesis node patches.
• Docker: Initialized docker scripts for future deployment and scalability.

Day 6 (Apr 22)

• Model Enhancements: Updated Gemini integration with time-aware modifications and improved response validation (handling rate limits like 429 errors).

• Clustering System:

  • Added Cluster Schema Endpoint.
  • Introduced Smart Clustering (initial version; performance improvements pending).

• State Management:

  • Implemented Base Checkpointing System.
  • Added State Versioning for better traceability and rollback.
  • Improved Tracing in checkpoint and observation layers.

• Caching Layer:

  • Introduced Semantic Cache Layer.
  • Implemented Semantic Caching Mechanisms for improved query efficiency.

• Memory Intelligence:

  • Built Memory Router for dynamic routing.
  • Added Memory Update Loop.
  • Introduced Memory Intelligence Layer for adaptive learning.
  • Implemented Memory Consolidation for long-term coherence.

Day 7 (Apr 23 – Apr 28)

Debug + Trace System (Section 6)

• Trace Viewer API – GET /trace/{session_id} endpoint showing:
  • Node execution flow
  • Latency per node
  • Cache hits/misses
  • Memory state (snapshot)
• Replay Debugger – full pipeline replay from any checkpoint:
  • replay_engine.py with ability to re‑execute the graph from a saved state
  • Diff comparison between original and replayed runs
  • REST endpoints: /replay/{session_id}/rerun, /replay/diff/{session_id}
• Checkpoint Middleware – automatically captures state at each node, stores in Redis, and creates trace events with memory snapshots.

Evaluation System (Section 4)

• Regression Test Suite – run_regression.py script that:
  • Executes a golden dataset of queries
  • Measures relevance (word‑overlap Jaccard similarity, 0–10 scale)
  • Measures hallucination rate (presence of key facts in answer, 0.0 or 0.5)
  • Measures latency per query (ms)
• Golden Dataset – golden_dataset.json storing queries, expected answers, key facts, and pass thresholds.
• Baseline Comparison – saves results as baseline.json and latest.json, supports --compare to show quantitative changes:
  • Relevance change
  • Hallucination change
  • Latency change
  • Pass rate change
• Heuristic Evaluator – pure Python (no external LLM) to avoid quota issues and ensure deterministic metrics.

Other improvements

• Fixed AgentState attribute access across all nodes (replaced dict subscripting with getattr).
• Added mock search for regression (avoids MCP dependency).
• Improved synthesis node with factual answer extraction for simple queries.
• Removed Gemini dependency from evaluator, relying on heuristics.

Day 8 (Apr 28 – Apr 29)

Frontend & API Expansion

• Built initial Frontend Interface for interacting with the agent.
• Added structured User Routes + API Routes for better separation of concerns.
• Fixed critical issues in main.py and improved overall API stability.
• Performed multiple frontend bug fixes and usability improvements.

LLM + MCP Improvements

• Refined LLM response formatting and cleanup for more consistent outputs.
• Reworked Search Node integration with MCP, improving reliability and modularity.
• Fixed bugs in MCP interaction layer.

Event System Introduction

• Designed and initialized Event Schema.
• Implemented Event Logger inside API routes for better observability and debugging.

Data & Graph Layer (RedisGraph Phase)

• Introduced RedisGraph CLI tooling.
• Added graph-based experimentation layer for relationships and structured querying.

Embeddings Pipeline

• Built Embedding + Builder pipeline for semantic processing and indexing.

Dev Workflow

• Merged dev=>frontend and stabilized integration across branches.

Day 9 (Apr 30)

Answerable Node (Major Feature)

• Introduced Answerable Node to determine if a query can be directly answered.
• Integrated node into execution graph with proper routing logic.
• Added streaming support for Answerable Node responses.
• Updated routes.py to support real-time streaming outputs.

Graph Patching & Stability

• Patched node graph to properly include Answerable Node in flow.
• Improved orchestration between planner=>answerable=>synthesis.

Infrastructure Progress

• Began migration toward Redis Stack (partially completed).
• Applied multiple fixes to stabilize transition.

Day 10 (May 5)

Graph Layer Migration (Major Refactor)

• Migrated from RedisGraph=>Neo4j for improved stability and scalability.
• Updated Docker configuration to support Neo4j setup.
• Refactored graph-related logic to align with new backend.

Core Runtime Refactor

• Refactored run.py (multi-part update):
  • Improved server orchestration
  • Cleaner execution flow
  • Better maintainability for dual-service architecture

System Direction

• Shift from experimental graph layer=>production-ready graph architecture (Neo4j).
• Continued focus on stability, streaming, and modular execution graph.
• Load Balancer & Containerisation: Added Nginx reverse proxy, scaled API with Docker Compose, and containerised the graph builder service.

Day 11 (May 6)

Router & Classification System

• Built Router Dataset Generator for automated dataset creation and routing experiments.
• Introduced initial Routing Model pipeline for query classification and execution control.
• Added Decomposer + Classifier Nodes into the execution graph for better intent decomposition and routing.
• Established early-stage query decomposition workflows for multi-intent handling.

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Day 12 (May 7)

Model Training + Validation

• Built complete training + validation pipeline for routing and decomposition models.
• Added automated data generation workflows for training samples and evaluation datasets.
• Refactored Classifier Node into the input layer with fallback routing logic for improved efficiency.
• Refined Decomposer Node using lexical-rule based decomposition strategies.
• Integrated decomposer routing directly into the execution graph.

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Day 13 (May 12)

Observability & Tracing

• Added Langfuse integration for runtime tracing, analytics, and observability.
• Added LangSmith integration for experiment tracking and execution monitoring.
• Improved debugging and trace visibility across graph execution workflows.

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Updated System Highlights

• Intelligent Router + Decomposer architecture now handles query classification and multi-intent decomposition.
• Custom-trained routing models support adaptive execution flow selection.
• Integrated observability stack with Langfuse + LangSmith for tracing and evaluation.
• Training, validation, and dataset generation pipelines established for continual model improvement.
• System architecture continues evolving toward a scalable, production-ready agent platform.