Page 50 of 129
Deep research has already evolved from 'help you search' to 'help you research.' But the next step is bigger: self-evolving agents, swarm collaboration, scientific automation. This article covers three directions and an uncomfortable reality: Gartner predicts 40% of agent projects will be cancelled by 2027.
An AI assistant platform's image search needed three rounds of fixes: pushing node_type filtering into the ES query to stop text chunks from hogging top-k slots, making filename matching deterministic instead of relying on the LLM to pass an optional parameter, and switching from Postgres icontains to ES match for CJK-aware partial matching.
When a research agent runs 25, 100, or 2000 turns, what happens? Context suffocation: information piles up, noise increases, attention gets diluted. IterResearch solves this with Markovian state reconstruction; AREX achieves recursive self-improvement with an inner/outer loop. Both answer: how does an agent stay coherent across hundreds of search rounds?
Seven open-source memory frameworks span the spectrum from auto-extracted vectors to human-readable files: Mem0's one-line add(), Graphiti's bi-temporal knowledge graph, Letta's agent-edited system-prompt blocks, LangGraph's namespaced Store, LlamaIndex's priority-based block truncation, Cognee's triple-store pipeline, and Supermemory's temporal vector-graph engine. This post compares their storage, write/forget mechanics, tenant isolation, and benchmark numbers, then offers selection guidance for four common scenarios.
The deep research open-source ecosystem has evolved from 'single frameworks' to 'tool clusters.' This article compares 12+ projects: GPT-Researcher emphasizes multi-agent collaboration, STORM simulates expert conversations, smolagents focuses on state management. Each tool solves different problems.
This is the project's own deep research skill design, fully disclosed. Core choices: only Groundlane MCP for web tools, strict source-quality grading (A/B/C/D), research hands off to post skill for publishing. Not the most powerful, but the best fit for us.
About 10% of conversations on our AI assistant platform randomly lost knowledge base tools — the agent hallucinated answers from training data instead. Root cause: a bare except Exception swallowed Elasticsearch connection failures during retriever initialization, silently skipping tool registration. Fix: retry + surface failures to system prompt + structured metadata tracking.
Previous articles covered the landscape, training from scratch, long-horizon memory, and planning optimization. This one zooms out to see a complete system that threads all these insights together: Tongyi DeepResearch. Its core innovation is Agentic CPT — inserting an agentic mid-training stage between pre-training and fine-tuning, giving the model an inherent agent bias. MoE 30B parameters activating 3B, HLE 32.9 surpassing OpenAI o3.
A user set retrieval top_k to 15, but the monitoring dashboard showed 5 and the streaming UI flashed 5 before jumping to 15. The same top_k value existed at four layers — LLM tool arguments, runtime, trace DB, and streaming payload — each requiring its own override. Three sequential fixes, each revealing the next layer was also wrong.
Parsing a 150-page PDF via Vision API took 29 minutes (one page per request). Batching cut it to 1.5×, adding 5-way concurrency brought it down to 24 seconds. One week after launch, a customer uploaded 23 PDFs at once — 70 parallel Bedrock requests triggered full throttling: 90 pages skipped, 5 files failed, 8 stuck. Fixed with Redis-based cluster-wide slots + backoff retries.
Two NeurIPS 2025 papers answer the same question: how to train a web research agent from scratch? WebThinker chooses 'bolt on web capability to existing reasoning models,' WebDancer chooses 'rebuild everything from data construction to RL training.' Two philosophies, four stages, one core insight: training beats prompting.
Previous articles covered how to train agents. But training requires high-quality data—and deep research training data has been scarce. WebShaper solves this with mathematical formalization: define IS tasks in set theory, then use an agentic Expander to iteratively expand them. S1-DeepResearch goes further: moves training from 'search-centric' to 'real research.'
All deep research agents are 'text-first'—but the real world isn't just text. WebWatcher (NeurIPS 2025) is the first system to integrate visual reasoning into deep research, using OCR, image search, code execution, and other tools to handle charts, screenshots, videos, and other diverse information.
Previous articles covered training from scratch and long-horizon memory. This one goes deeper: how to make the agent's 'planning' itself better? WebWeaver tackles it architecturally (dual-agent iterative outline optimization). DeepPlanner tackles it through training (advantage shaping for planning tokens). Both point to the same conclusion: planning is the ceiling of deep research.
Gemma is Google's open-weights family paired with the closed-source Gemini flagship; Gemma 4 (E2B / E4B / 26B-MoE / 31B) released in April 2026 switched its license from Google Gemma Terms of Use to Apache 2.0 — the single most important change in this article.
Laguna is Poolside's agentic coding model family: XS 2.1 packs 33B-A3B into a 36GB Mac, while S 2.1 brings 118B-A8B with 1M context to 70.2% on Terminal-Bench 2.1 and 40.4% on DeepSWE, both open under OpenMDW-1.1.
Ling separates execution, Ring handles deep reasoning, and Ming covers multimodal tasks; the family prioritizes intelligence per unit of compute, so selection starts with workload, deployment budget, and modality.
Muse Spark is Meta's closed-source agentic model line: version 1.3 combines a 1M-token context, multimodal inputs, and long-horizon tool loops. Standard pricing is $1.25/$4.25 per 1M input/output tokens, while Contributor drops to $0.10/$0.20 in exchange for training rights. It is not the next Llama; it is a separate product line built around models, APIs, and coding agents.
Nex-N2.5 is Nex AGI's open agentic model family: mini scores 82.9 on OSWorld-G at 35B-A3B, Pro tops Claude Opus 5 with 87.4 at 397B-A17B, and Max leads the whole official table on BrowseComp with 92.6 at 1.6T, all open under Apache-2.0.
An agent with a workspace_browse tool said 'file not found' instead of searching. Anthropic, OpenAI, and Google's official guides all point to the same fix: put trigger conditions and workflows in the tool description. A 2025 study found 97.1% of MCP tool descriptions have quality issues.