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Graphiti users overwhelmingly praise the software, as reflected in its consistently high ratings on G2, citing its functionality and user-friendly interface as major strengths. There are no evident complaints in the available reviews, suggesting general satisfaction. The social mentions primarily relate to excitement and updates around related development tools, without specific reference to Graphiti itself. Pricing sentiment cannot be specifically determined from the available data, but the overwhelmingly positive reviews contribute to a strong overall reputation for the tool.
Mentions (30d)
42
9 this week
Avg Rating
4.8
20 reviews
Platforms
3
GitHub Stars
24,254
2,403 forks
Graphiti users overwhelmingly praise the software, as reflected in its consistently high ratings on G2, citing its functionality and user-friendly interface as major strengths. There are no evident complaints in the available reviews, suggesting general satisfaction. The social mentions primarily relate to excitement and updates around related development tools, without specific reference to Graphiti itself. Pricing sentiment cannot be specifically determined from the available data, but the overwhelmingly positive reviews contribute to a strong overall reputation for the tool.
Features
Use Cases
Industry
information technology & services
Employees
6,200
Funding Stage
Other
Total Funding
$7.9B
2,600,000
Twitter followers
417
GitHub followers
11
GitHub repos
24,254
GitHub stars
15
npm packages
We are investigating unauthorized access to GitHub’s internal repositories. While we currently have no evidence of impact to customer information stored outside of GitHub’s internal repositories (such
We are investigating unauthorized access to GitHub’s internal repositories. While we currently have no evidence of impact to customer information stored outside of GitHub’s internal repositories (such as our customers’ enterprises, organizations, and repositories), we are closely
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What do you like best about Alta | AI Revenue Workforce?Alta integrates deeply with our CRM and keeps everything clean. No duplicate records no bad data. The support team also takes feedback seriously. I suggested a feature for account scoring and they rolled it out within a few months. That kind of responsiveness is rare. Review collected by and hosted on G2.com.What do you dislike about Alta | AI Revenue Workforce?The initial sync with our CRM took some time because we have a lot of custom objects. The team helped but it was not instant. Review collected by and hosted on G2.com.
What do you like best about Alta | AI Revenue Workforce?We attend about 10 industry events per year and the follow up was always chaotic. Someone would export a list of scanned badges and then manually email people weeks later. With Alta we upload the list and the agent handles personalized follow up immediately referencing conversations from the event. The LinkedIn integration also connects with people we met. It makes us look professional and responsive. Review collected by and hosted on G2.com.What do you dislike about Alta | AI Revenue Workforce?The event template could be more specific. I had to customize the messaging myself instead of using a pre built option. Review collected by and hosted on G2.com.
What do you like best about Alta | AI Revenue Workforce?I run demand gen at a 30 person company and we cannot afford a big SDR team. Alta acts like an extra team member running outbound 24/7. The personalization is what impressed me most. I tested it by sending myself a test email and it referenced a LinkedIn post I made three months ago about a specific industry trend. That level of detail would take a human hours to find. Review collected by and hosted on G2.com.What do you dislike about Alta | AI Revenue Workforce?The linkedin automation works well but you need to connect your personal profile and I was worried about limits. So far no issues but it is something to monitor. Review collected by and hosted on G2.com.
What do you like best about Alta | AI Revenue Workforce?I really like Alta | AI Revenue Workforce for the text it generates for sales and its ease of use. The close support from the team is also great. The setup is simple, allowing for easy configuration of tone of voice, objectives, and other factors. This helps the AI agent create personalized text for LinkedIn and email on a straightforward canvas screen. The mechanism is more complex, the data sources are versatile, and the product looks better compared to others like Apollo.io. Its integration with HubSpot helps streamline automated flows and synchronization. Review collected by and hosted on G2.com.What do you dislike about Alta | AI Revenue Workforce?The LinkedIn integration is a bit hard to maintain without burning, there's an additional cost for LinkedIn, and it's not stable. Review collected by and hosted on G2.com.
What do you like best about Alta | AI Revenue Workforce?As a UX designer, I’m probably pickier than most about software interfaces. Alta has genuinely impressed me with its clean, intuitive design it’s honestly the best UI I’ve seen after evaluating a few similar AI sales tools. It’s clear a lot of thought went into the user experience, making it easy for our team to adopt without hours of training. Review collected by and hosted on G2.com.What do you dislike about Alta | AI Revenue Workforce?The platform is very powerful, which means there are a lot of settings and configuration options to explore. For a new user who isn't as tech-savvy, the initial setup might feel a bit overwhelming at first glance. Review collected by and hosted on G2.com.
What do you like best about Alta | AI Revenue Workforce?As a product manager, I evaluate tools based on how intuitively they solve real user problems. Alta stands out because it doesn't just automate volume it focuses on relevance. The platform delivers high quality personalization by actually analyzing strong LinkedIn signals and digital footprints, not just firmographic data. This means the outreach it generates feels consultative, not spammy. The user experience is where Alta truly shines it’s clean, logical, and easily the best UI I’ve seen after evaluating several similar AI sales tools. It was also criticalfor us that any new solution fit seamlessly into our existing infrastructure. Alta’s deep integration with our CRM was smooth and required minimal engineering effort, which our team really appreciated. Review collected by and hosted on G2.com.What do you dislike about Alta | AI Revenue Workforce?Nothing to talk in this because they worked closely with us during setup to map our buyer personas and ensure the sequences were calibrated correctly. They genuinely acted as partners in the process. Review collected by and hosted on G2.com.
What do you like best about Alta | AI Revenue Workforce?As a CEO, I'm always looking for tools that remove friction for my team without requiring constant oversight from me. Alta has delivered on that. What impressed me most from the start is the user interface we evaluated a handful of AI sales tools over the past year, and Alta's UI is the most intuitive by far. My team actually wanted to use it, which is half the battle with new software adoption. The quality of personalization the platform generates is also genuinely useful. It picks up on meaningful LinkedIn signals from prospects and weaves that context into outreach in a way that doesn't sound robotic or templated. The integration with our CRM was also surprisingly deep and smooth data flows both ways without hiccups, which saved us from building messy middleware workarounds. Review collected by and hosted on G2.com.What do you dislike about Alta | AI Revenue Workforce?The platform itself performs well, but if I'm being critical, the initial onboarding required more hands-on support from their team than I'd ideally like. They were amazing and responsive throughout the process, which I truly appreciated, but a more robust self service setup with clearer documentation would help companies like mine get up and running even faster. It's not a deal breaker, but it's something to be aware of if you're a smaller team without a dedicated person to manage the implementation. Review collected by and hosted on G2.com.
What do you like best about Alta | AI Revenue Workforce?As someone in HR at a logistics firm, I don't typically evaluate sales software, but I was brought in to assess tools that could improve collaboration between our sales development and operations teams. What stood out to me immediately about Alta was its user interface. After seeing a few similar tools demoed, this was by far the most intuitive and clean design. It didn't feel like we needed a dedicated specialist just to navigate it. The deep integration with our CRM was a major win for us. It meant our sales team didn't have to double enter data, which is a huge pet peeve for them, and I could see how it provided a more unified view of our customer interactions. The team behind the platform was also incredibly responsive during our trial, answering our setup questions clearly without any pressure. Review collected by and hosted on G2.com.What do you dislike about Alta | AI Revenue Workforce?Given that my background isn't in sales, the initial setup did require a bit more coordination between our sales leads and our IT team than I initially anticipated. We had to ensure all our data fields were mapped correctly to get the most out of the personalization features. Review collected by and hosted on G2.com.
What do you like best about Alta | AI Revenue Workforce?The seamless CRM integration is a game-changer. In the past, adopting new sales tools often meant double data entry or manual logging, which led to poor adoption. Alta's deep integration ensures that every email, call, and LinkedIn interaction is automatically tracked in our CRM. This gives us complete visibility into prospect engagement without creating more work for the reps. The AI's ability to use specific firmographic and technographic data to tailor messaging at scale is something we haven't found in other platforms; it moves beyond simple merge tags to genuinely contextual outreach. Review collected by and hosted on G2.com.What do you dislike about Alta | AI Revenue Workforce?For organizations that aren't used to AI-driven processes, there's an initial trust curve. It takes a few weeks to build confidence that the AI is representing your brand appropriately. We started with a "human-in-the-loop" approach, reviewing all outreach, but quickly graduated to full automation as the results proved themselves. Patience during the first few weeks is key. Review collected by and hosted on G2.com.
What do you like best about Alta | AI Revenue Workforce?The biggest win for us has been the shift in our team's focus. Before Alta, our account executives were bogged down with the manual grind of researching accounts and crafting initial outreach. Now, the AI agents handle that entire top-of-funnel process with impressive intelligence. I particularly value how it leverages intent signals—like funding news or LinkedIn job changes—to initiate conversations that are timely and relevant, rather than generic blasts. It feels like we've deployed a tireless research team overnight, which has dramatically improved our team's morale because they get to focus on what they do best: building relationships and closing deals. Review collected by and hosted on G2.com.What do you dislike about Alta | AI Revenue Workforce?To getthe high level of personalization the platform promises, you need to be very disciplined in defining your Ideal Customer Profile (ICP) and building your playbooks. Garbage in, garbage out applies here. However, their customer success team is highly engagedand provides excellent guidance during this setup phase, which mitigates the learning curve. Review collected by and hosted on G2.com.
Opus 4.6/4.7 regression is real and getting worse — 3 weeks of documented failures on a complex project, and a competing AI caught the mistakes Claude missed [long post]
I've been running Claude Pro (Opus 4.7 / Sonnet 4.6) for about 3 weeks on a complex personal AI infrastructure project. I keep structured session logs with timestamps and Birkenbihl-style metacognitive fields after every session. This is not anecdotal — I have receipts. The project for context I'm building a local persistent AI memory stack called GSOC Brain: Qdrant vector DB (~397K vectors across 11 source tags), Neo4j graph (123 nodes / 183 edges), Graphiti 0.29 entity extraction, Ollama with qwen2.5:14b + nomic-embed-text — all running natively on a Windows host. The system is supposed to give Claude cross-chat memory via a custom MCP server. On top of that, I'm operating 18+ custom skill files that define behavior rules for Claude across domains (OSINT/forensics, legal, content, infrastructure). The system prompt explicitly describes the full architecture on every session start. This is not a "chat with Claude" use case. This is sustained agentic work across multiple tools, multiple sessions, strict context requirements, and high-stakes outputs (including legal document drafts). Bug 1: Token overconsumption since update 2.1.88 (late March 2026) Opus 4.7 started burning daily usage limits at a completely different rate after an update around March 31. In one session I hit 94% of my daily limit within approximately 4 messages. The boot sequence — fetching context from Notion MCP, searching past sessions, loading memory — consumed what felt like 10–20x the previous token rate. GitHub issues #42272, #50623, and #52153 document identical patterns from other users. The model appears to over-generate internally even for simple responses. End result: I had to switch to Sonnet 4.6 for most productive work because Opus 4.7 is simply unusable under the daily limit. Bug 2: Claude Code Desktop App completely broken (reported May 14, Conv. 215474208295333) The Desktop App hangs on every single input. Including typing "hello" with no files. Reproducible across: Sonnet 4.6 and Opus 4.7 Multiple fresh sessions With and without u/file references After full reinstall The VS Code extension works fine. Only the Desktop App is broken. Reported May 14. No fix, no acknowledgment. Bug 3: Platform / context confusion — 5 documented errors in a single session, chat aborted On April 29, I had to formally abort an Opus 4.7 session and hand off to Opus 4.6 after documenting 5 consecutive errors. The session log entry literally reads "Opus 4.7 Abbruch (5 Fehler): Zeitrechnung, Platform-Verwechslung, falsche Schlüsse": Miscalculated the current time despite being told the exact time Insisted the Brain stack was running on a Linux VM (BURAN) — the system prompt and memory both explicitly stated C:\gsoc-brain on Windows Drew false inferences from backup file paths rather than the stated architecture Contradicted the stated platform in the same response it had just received Confused WebClaude and Desktop Claude capability boundaries These aren't edge cases. The architecture was in the system prompt, in memory, and in the injected Notion context. Opus 4.7 ignored all of it. Bug 4: Skill files ignored in production I maintain 18+ custom skill files loaded into the system prompt. These include explicit hard rules — e.g., "activate keilerhirsch-knowledge skill for ALL architecture decisions, web search is not optional." In the session that caused the Docker-to-Native migration disaster, I later wrote in my own session log: The model proceeded to recommend outdated tools from training data rather than searching current documentation. It recommended NSSM (last meaningful update 2017) as a Windows service wrapper. NSSM is dead. A competing AI caught this immediately. Bug 5: Another AI caught what Claude missed in a single pass This is the part that stings most. When the Docker-based Brain setup kept failing, I fed the architecture docs into another AI (Manus) for a deep audit. In one pass it identified 5 critical corrections that Claude had never caught across weeks of sessions: NSSM is dead since ~2017 → correct replacement is WinSW or Servy Neo4j 2025.01+ requires Java 21 — Claude had never flagged this, the services kept failing silently Qdrant needs Windows file-handle-limit adjustments to run reliably Orphaned vector risk between Qdrant ↔ Neo4j without a Tentative-Write pattern in the save operation BGE-M3 embeddings (MTEB 63.2, 8192 token context) as a better alternative to nomic-embed-text My own session log the next day reads: Claude was answering from stale training data. The skill that explicitly says "don't do this" was being ignored. Another AI caught it in round one. Bug 6: MCP Server 20-minute Neo4j hang — still unresolved After the native migration, the custom gsoc_mcp_server.py developed a reproducible hang of exactly ~20 minutes between Qdrant connect and Neo4j connect on every startup. Log timestamps from 4 consecutive restarts: 14:59 → 15:20 (21 min) 15:29 → 15:51 (22 min)
View original5/ We will publish a fuller report once the investigation is complete.
5/ We will publish a fuller report once the investigation is complete.
View original3/ We moved quickly to reduce risk. Critical secrets were rotated yesterday and overnight with the highest-impact credentials prioritized first.
3/ We moved quickly to reduce risk. Critical secrets were rotated yesterday and overnight with the highest-impact credentials prioritized first.
View original4/ We continue to analyze logs, validate secret rotation, and monitor for any follow-on activity. We will take additional action as the investigation warrants.
4/ We continue to analyze logs, validate secret rotation, and monitor for any follow-on activity. We will take additional action as the investigation warrants.
View original2/ Our current assessment is that the activity involved exfiltration of GitHub-internal repositories only. The attacker’s current claims of ~3,800 repositories are directionally consistent with our in
2/ Our current assessment is that the activity involved exfiltration of GitHub-internal repositories only. The attacker’s current claims of ~3,800 repositories are directionally consistent with our investigation so far.
View original1/ We are sharing additional details regarding our investigation into unauthorized access to GitHub's internal repositories. Yesterday we detected and contained a compromise of an employee device inv
1/ We are sharing additional details regarding our investigation into unauthorized access to GitHub's internal repositories. Yesterday we detected and contained a compromise of an employee device involving a poisoned VS Code extension. We removed the malicious extension version,
View originalIf any impact is discovered, we will notify customers via established incident response and notification channels.
If any impact is discovered, we will notify customers via established incident response and notification channels.
View originalWe are investigating unauthorized access to GitHub’s internal repositories. While we currently have no evidence of impact to customer information stored outside of GitHub’s internal repositories (such
We are investigating unauthorized access to GitHub’s internal repositories. While we currently have no evidence of impact to customer information stored outside of GitHub’s internal repositories (such as our customers’ enterprises, organizations, and repositories), we are closely
View original📣 @GoogleAI’s Gemini 3.5 Flash is now generally available and rolling out in GitHub Copilot. Early testing shows ➡️ It has strong tool use, fast response times, and high cache efficiency ➡️ It is it
📣 @GoogleAI’s Gemini 3.5 Flash is now generally available and rolling out in GitHub Copilot. Early testing shows ➡️ It has strong tool use, fast response times, and high cache efficiency ➡️ It is it well-suited for fast, iterative agentic coding workflows Try it out in @code.
View originalhttps://t.co/yGiqw0xbji
https://t.co/yGiqw0xbji
View originalStart work on your computer, continue your local session anywhere. 📲 Remote control for GitHub Copilot CLI and @code sessions is now generally available. https://t.co/wwSEBd5lqL https://t.co/Yc5R6tB
Start work on your computer, continue your local session anywhere. 📲 Remote control for GitHub Copilot CLI and @code sessions is now generally available. https://t.co/wwSEBd5lqL https://t.co/Yc5R6tBfBl
View originalYou don't have to level up to contribute to open source. You level up by contributing to open source. Not sure how to get started? Check out our latest GitHub for Beginners episode. https://t.co/Jyze
You don't have to level up to contribute to open source. You level up by contributing to open source. Not sure how to get started? Check out our latest GitHub for Beginners episode. https://t.co/Jyze45KoHo https://t.co/DCqAFACo35
View originalInteractive and non-interactive: these are the two main modes in Copilot CLI. 💻 Our beginner series breaks down the difference, plus how and when to use each one. 💡👇 https://t.co/gZ7GetcgTo
Interactive and non-interactive: these are the two main modes in Copilot CLI. 💻 Our beginner series breaks down the difference, plus how and when to use each one. 💡👇 https://t.co/gZ7GetcgTo
View originalSome open source projects don't just survive. They flat-out refuse to bite the dust. ⚔️ We looked at 10 roguelikes still going strong years (sometimes decades) after launch. Here's what their maintai
Some open source projects don't just survive. They flat-out refuse to bite the dust. ⚔️ We looked at 10 roguelikes still going strong years (sometimes decades) after launch. Here's what their maintainers and communities can teach the rest of open source about longevity. 💡
View originalNeed help picking the right emoji (like we did for this post)? 🤔 @cassidoo made an emoji list generator with Copilot CLI. Learn how she did it and pick up tools and tricks for your next project. 👇
Need help picking the right emoji (like we did for this post)? 🤔 @cassidoo made an emoji list generator with Copilot CLI. Learn how she did it and pick up tools and tricks for your next project. 👇 https://t.co/13xwmu6tE9 https://t.co/pCy8PGfUIE
View originalRepository Audit Available
Deep analysis of getzep/graphiti — architecture, costs, security, dependencies & more
Graphiti uses a per-seat + tiered pricing model. Visit their website for current pricing details.
Graphiti has an average rating of 4.8 out of 5 stars based on 20 reviews from G2, Capterra, and TrustRadius.
Key features include: Build context graphs that evolve with every interaction — tracking what's true now and what was true before., Give agents rich, structured context instead of flat document chunks or raw chat history., Query across time, meaning, and relationships with hybrid retrieval (semantic + keyword + graph traversal)., Python 3.10 or higher, Neo4j 5.26 / FalkorDB 1.1.2 / Kuzu 0.11.2 / Amazon Neptune Database Cluster or Neptune Analytics Graph + Amazon OpenSearch Serverless collection (serves as the full text search backend), OpenAI API key (Graphiti defaults to OpenAI for LLM inference and embedding), Google Gemini, Anthropic, or Groq API key (for alternative LLM providers), Connecting to a Neo4j, Amazon Neptune, FalkorDB, or Kuzu database.
Graphiti is commonly used for: Quick Start.
Graphiti integrates with: Neo4j, FalkorDB, Kuzu, Amazon Neptune, OpenAI, Google Gemini, Anthropic, Groq.
Graphiti has a public GitHub repository with 24,254 stars.
Based on user reviews and social mentions, the most common pain points are: down, critical, breaking.
Based on 122 social mentions analyzed, 5% of sentiment is positive, 95% neutral, and 0% negative.
