AI · alternatives · 2026
24 alternatives to Nous – give GTM agents one context graph across your tools
We built this because deploying more agents did not make our GTM better, it made it worse. Every agent started from scratch each session and took six tool calls for the same prospect across CRM, inbox, and notetaker, often with conflicting results. Since nothing carried over between sessions, agents never became more reliable over time. Nous is a context layer beneath your agents that turns each touchpoint into a structured observation linked to the right person and company. It derives claims, which are beliefs about a fact with their confidence and freshness. The graph runs on your own…
Nous is a context layer for go-to-market teams that consolidates information from multiple tools into a unified graph. It structures observations from CRM, email, and notes into linked records about people and… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 2 we have checked on their own sites, 2 of them free or with a free tier.
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Nexfree tier, from $49/mo▲364Claude Cowork for high-volume GTM workflows
Sep 2026 · nex.ai · its alternatives →
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N71free tier, from $49/mo▲141Give all your AI agents one shared context
Jul 2026 · n71.ai · its alternatives →
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Gro▲373Hey HN, I am Leo Jiang, founder of Gro. I built this to solve the problem of fragmented sales stacks where teams waste hours between lead databases and outreach tools. Gro is a unified AI sales agent that combines a 1b+ contact database with AI propensity scoring and automated outreach across Email and LinkedIn. By putting prospecting and engagement into one workspace, we eliminate manual data hunting so sales reps can focus on closing deals. I would love your thoughts on our approach to automated B2B growth.
Feb 2026 · thegro.ai · its alternatives →
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FullContext▲560Build GTM workflows 20x faster with natural language
2024 · fullcontext.ai · its alternatives →
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Context Gateway▲220We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…
Mar 2026 · github.com · its alternatives →
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Cleanlist AI▲273Natural-language prospecting: find, enrich and sync leads.
Jul 2026 · cleanlist.ai · its alternatives →
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Zams▲271Hey HN, We just launched Zams — a platform to build AI agents that automate repetitive B2B sales tasks like CRM updates, deal follow-ups, lead research, and forecasting. You describe what you want in plain English, and your agent gets it done across tools like Slack, Notion, HubSpot, Salesforce, and more. Zams agents run in two ways: - Command agents – Triggered on-demand from Slack or the web - Ambient agents – Run in the background on a schedule, using context from docs, emails, and dashboards No brittle workflows, no “if-this-then-that” builders. Just intelligent agents that act. We…
2025 · zams.com · its alternatives →
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ThoughtDAG indexes local agent conversations across tools, finds the turns relevant to your work, and turns them into editable context graphs.
Aug 2026 · chenxiachan.github.io · its alternatives →
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I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…
2025 · github.com · its alternatives →
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I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet. It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you. The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard. I…
Apr 2026 · github.com · its alternatives →
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Context Overflow▲109Hey HN, After using a bunch of agents, my friend and I noticed a major issue: an agent could spend a bunch of time solving a tricky task, but when the session ended all that knowledge vanishes. Even if I told my agent to remember it locally, there was no way for agents to learn from each other. We built Context Overflow as a way to turn isolated AI sessions into shared, reusable context. With Context Overflow, agents can: - Search through past solutions when starting a task - Ask questions when they get stuck - Share findings when they solve a non-trivial problem We support a variety of ways…
Mar 2026 · ctxoverflow.dev · its alternatives →
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Leadspicker▲167Deploy LinkedIn GTM workflows & AI bots easily with outreach
2024 · leadspicker.com · its alternatives →
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Hi HN, we’re open-sourcing ktx. It’s an executable context layer that makes agents reliable on your data stack. We built it after going through the experience of building production-grade data agents for dozens of companies. If you’ve also tried building them, or simply tried using Claude Code or Codex on your data warehouse, you’ll know that accuracy is the #1 issue. Agents are great at generating valid SQL, but it’s not always correct SQL. To cite a few examples of “agents gone wrong”: - Stale column + hidden business rule: when preparing a board report, a finance analyst asks Claude Code…
May 2026 · github.com · its alternatives →
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Open Index▲89The structured context layer for AI agents. Build self-learning specialised agents that are context aware. - DrDroidLab/open-index
Aug 2026 · github.com · its alternatives →
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Connect AI agents to governed metadata via MCP
Jan 2026 · dawiso.com · its alternatives →
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Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Feb 2026 · github.com · its alternatives →
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Deep Work Plan▲114Models matter. Context matters more. Give your agent a plan.
Jun 2026 · deepworkplan.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →