Real-time dashboard for Claude Code agent teams
This project (Agents Observe) started as an exploration into building automation harnesses around claude code. I needed a way to see exactly what teams of agents were doing in realtime and to filter and search their output.A few interesting learnings from building and using this:- Claude code hooks are blocking - performance degrades rapidly if you have a lot of plugins that use hooks- Hooks provide a lot more useful info than OTEL data- Claude's jsonl files provide the full picture- Lifecycle management of MCP processes started by plugins is a bit kludgy at bestThe biggest takeaway is how much of a difference it made in claude performance when I switched to background (fire and forget) hooks and removed all other plugins. It's easy to forget how many claude plugins I've installed and how they effect performance.The Agents Observe plugin uses docker to start the API and dashboard service. This is a pattern I'd love to see used more often for security (think Axios hack) reasons. The tricky bit was handling process management across multiple claude instances - the solution was to have the server track active connections then auto shut itself down when not in use. Then the plugin spins it back up when a new session is started.This tool has been incredibly useful for my own daily workflow. Enjoy!
FL score
out of 100
Verdict
high confidence
Competition
7
competitors found, emerging market, funded players
Trend
No signal yet
A real-time observability dashboard specifically for Claude Code agent teams, providing critical debugging and performance insights.
The pain
The gap
Build angle
Strengths
- Extremely specific and well-articulated problem based on firsthand experience.
- High pain severity, leading to strong willingness to pay for a solution that saves time and improves reliability.
- Clear whitespace opportunity for a Claude-specific tool, avoiding direct competition with broad enterprise solutions.
- Strong demand and market growth in the AI agent observability space.
- Creator has already built an internal version, demonstrating feasibility and deep understanding.
Risks
- Building a robust, real-time, multi-instance monitoring solution (especially with Docker and process management) is complex for a solo builder.
- Platform dependency on Claude Code: potential for API changes or Claude releasing their own first-party observability tools.
- The 'Claude Code agent teams' niche, while specific, needs validation on its total addressable market size to ensure long-term viability.
- Monetization strategy needs careful consideration to compete with free/open-source alternatives while providing sufficient value to justify payment.
Next steps
- Create a landing page and collect email sign-ups from Claude Code developers to gauge external interest and validate market size.
- Conduct 5-10 user interviews with other Claude Code users to confirm pain points, desired features, and willingness to pay.
- Focus MVP on the absolute core feature: real-time filtered logging/search from Claude's jsonl output, deferring more complex features like multi-instance management or background hooks until later validation.
- Explore simpler deployment options for users than Docker, or provide clear, easy-to-follow setup guides for the current Docker approach.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 28
- Solution gap
- 23
- Willingness to pay
- 19
- Buildability
- 7
This idea addresses a clear, specific, and severe pain point for Claude Code agent teams, with a strong whitespace opportunity given the lack of tailored solutions. While the demand and willingness to pay are high, the build complexity for a solo builder is a significant concern.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
This idea presents a strong value proposition in a rapidly growing market, but building and defending it could be challenging for a solo builder.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A strong creator-problem fit with a clear niche, but the technical complexity could challenge a solo builder's simplicity goal.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A clear micro-SaaS opportunity with a specific target and value proposition, but needs careful validation of market size and future-proofing against platform changes.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
A highly demanded, specific solution for a painful problem in a growing market, with strong internal validation.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Create a landing page and collect email sign-ups from Claude Code developers to gauge external interest and validate market size.
- Conduct 5-10 user interviews with other Claude Code users to confirm pain points, desired features, and willingness to pay.
- Focus MVP on the absolute core feature: real-time filtered logging/search from Claude's jsonl output, deferring more complex features like multi-instance management or background hooks until later validation.
- Explore simpler deployment options for users than Docker, or provide clear, easy-to-follow setup guides for the current Docker approach.
Who is already there, emerging market
- ClawMetryVisit
A free, open-source observability dashboard specifically designed for OpenClaw AI agents.
Pricing: Free, open-source
A platform for AI agent observability that provides visibility into internal states, decisions, and performance of autonomous AI agents.
Pricing: Custom enterprise pricing
Extends Splunk's monitoring capabilities to AI agents, offering performance, cost, quality, and risk metrics.
Pricing: Contact for pricing (part of Splunk Observability Cloud)
A unified solution for evaluating, monitoring, tracing, and governing AI agents within Azure AI Foundry.
Pricing: Part of Azure AI Foundry, pricing based on Azure consumption.
- LangflowVisit
An open-source visual framework for building and visualizing AI agent workflows, particularly for RAG applications.
Pricing: Free, open-source
- Insight AIVisit
A no-code platform for building AI agents that monitor systems and take autonomous actions.
Pricing: Offers free options, specific pricing not detailed but implies paid tiers.
Offers templates and a visual interface to build agentic workflows for marketing, customer support, and sales.
Pricing: Contact for pricing.
What they charge
Recent news
- ClawMetry: Real-time observability dashboard for OpenClaw AI agents
Product Hunt, March 30 2026
- The best no-code AI agent builder in 2026
Product Hunt, March 27 2026
- Monitor LLM and agent performance with AI Agent Monitoring in Splunk Observability Cloud
Splunk Blog, February 13 2026
- Azure AI Foundry: Top 5 agent observability best practices for reliable AI
Microsoft Azure Blog, August 27 2025
- Mastering AI agent observability: A comprehensive guide
Medium (by Dave Davies), August 28 2025
Market signals
The market for real-time dashboards for AI agent teams is rapidly growing, transitioning from niche to a significant area within the broader AI industry. Recent news and product launches indicate increasing recognition of the need for robust observability and management tools as AI agents become more autonomous and complex. Funding rounds in related AI agent development and orchestration platforms further underscore this growth, with companies like Relevance AI securing seed funding.
What frustrates people
- Complexity and time-consuming manual probing of agent workflows.
- Difficulty in understanding why agents make certain decisions (black box problem).
- Challenges in debugging unexpected outcomes and identifying bottlenecks in agent performance.
- Lack of visibility into agent actions, decisions, and reasoning paths.
- Need for better tools to ensure agents are performing as intended, staying within policy, and operating safely.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
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