10+ community deep-research skills represent 10+ philosophies of 'how to do research.' From hyperresearch's persistent vault to jamoeight v2's Co-Scientist 6-agent, from adversarial verification to benchmark alignment. This article puts them all on one table.
A deep research agent produces a report—maybe thousands of words with dozens of citations. How do you score it? Using LLMs as judges is biased, asking humans is too expensive, and benchmarks can't keep up. STC and other recent approaches try to solve this from the 'confidence' angle—but there's no perfect answer yet.
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.
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.
Agent-to-agent communication falls into three patterns: handoff (transfer control), delegate (dispatch and wait for results), and mailbox (real-time peer-to-peer messaging). Implementations vary widely, but MCP and A2A are driving protocol standardization.
Should a sub-agent see the parent's conversation? Fork carries full history but token costs grow exponentially. Fresh saves money but lacks context. Industry consensus: default to Fresh, Fork only when needed, and always pair it with history truncation and result compression.
Parallel + nested agent spawns can burn 200K+ tokens in a single conversation turn. From Anthropic to Microsoft, the industry is converging on tiered responses: compress → downgrade → stop, rather than a binary kill switch.