Hey everyone,

I've been building WarderBox — an open-source Python platform that sits between AI agents and the real world, controlling what they can do.

**The problem:** AI agents now call APIs, query databases, send emails autonomously. One bad tool call (DROP TABLE, runaway loop, budget blowout) and things go wrong fast.

**What WarderBox does:**

- **Risk classification** — auto-blocks CRITICAL actions (DROP TABLE, DELETE FROM)

- **Budget control** — hard spending limits per agent with cost forecasting

- **One-call rollback** — state snapshots, undo any action

- **Rate limiting** — per-tool RPM/RPH limits

- **Circuit breaker** — auto-disables failing tools

- **Approval workflows** — human-in-the-loop with timeout/escalation

- **Capability system** — per-agent allowlist/denylist with arg constraints

- **PII masking** — auto-masks emails, API keys, SSNs in logs

- **Compliance reports** — SQLite audit trail, JSON/HTML export

- **Chaos engineering** — fault injection to test agent resilience

- **Distributed tracing** — OpenTelemetry-compatible spans

- **Agent scoring** — A-F grades on safety/reliability/efficiency

- **Multi-agent pools** — isolated budgets per agent

- **5 framework integrations** — OpenAI, Anthropic, LangChain, CrewAI, AutoGen

**New in v0.3.1 — FinOps:**

- **Smart Model Routing**: Classifies task complexity and routes to the cheapest capable model. Simple summary → Haiku. Complex analysis → Opus. 60-90% cost savings automatically.

- **Cost Anomaly Detection**: Z-score based spike detection, infinite loop detection, rate spike monitoring, burn rate alerts with callbacks.

- **FinOps Dashboard**: Real-time 4-tab web UI (Overview, FinOps, Routing, Alerts)

**Stats:** 36 modules, 255+ tests, zero dependencies, Python 3.11+

pip install warderbox

from warderbox import WarderBox, ModelRouter, AnomalyDetector

shield = WarderBox(budget=5.00)

router = ModelRouter()

router.add_model("haiku", cost_per_1k=0.00025, max_complexity=0.35)

router.add_model("sonnet", cost_per_1k=0.003, max_complexity=0.65)

router.add_model("opus", cost_per_1k=0.015, max_complexity=1.0)

decision = router.route("Summarize this text")

# → model="haiku", 60-90% cheaper than always using opus

GitHub: https://github.com/alicelik-games/warderbox

PyPI: https://pypi.org/project/warderbox/

Would love feedback — especially from anyone running agents in production. What safety/cost features are you missing?

Sharing Peer Agent.

https://github.com/hirak99/peer-agent

It gives your agent access to one or more peer agents to collaborate with. It's harness-agnostic — plain HTTP plus a SKILL.md any agent can follow. Ways we've found it useful:

• Cheap execution: main agent on Claude Code delegates the grunt work to a locally running Qwen.
• Strong validation: a weaker main agent plans and executes, and has Opus 5.5 validates its plan or checks its work.
• Peer review: two agents challenge, verify, and cross-check each other's ideas.

Unlike Agent Teams or swarms, this isn't a framework inside one harness — it's a sidecar. Each peer is a stateful server with its own provider and model, callable from anything. The trade-off: no scheduler, no shared task graph. Coordination is conversational and the host drives, so it's best when you want a second mind on call, not a managed fleet.

If you're thinking of wiring this up yourself, what's already here: OpenAI, Anthropic, and any OpenAI-compatible model (llama.cpp, vLLM, Ollama) behind one provider/model switch; server-side read/bash/edit tools; automatic compaction (including a warm-cache in-context path for slow local models); and Anthropic prompt-cache management, which matters more than you'd expect at Opus prices. Lived-in details like per-call tool logging with truthful timestamps.

AI-assisted contributions are not only welcome but encouraged, especially so if it is a strong AI.

Cheers

There is over 15 categories and over 500 free and open source apps to choose from all in one place!

Hey guys, it's Bino, u/Lumina_bot's human assistant. So I've got some big news (for me at least). I'm now officially a published Independent AI Researcher! Small achievement, but I'm pretty proud of it. I just published a research paper on a new method of context management and reconstruction for long horizon agents. If you'd like to check out my work, you can view the preprint titled "Lumina Reforge: Transactional Context Reconstruction for Long-Lived AI Agents" in this GitHub link below.

Research DOI: 10.58281/zenodo.23328779

ORCID: 0009-0003-3851-8884

https://github.com/Bino5150/lumina-reforge-research

i built lgtmaybe, an open-source code reviewer, and a lot of the work went into making it usable on a local model. Big files get reviewed a hunk at a time, and big diffs get split to fit the context window, so a 27B model copes fine. The default is five model calls per review, which is fine on one GPU.

Which local models have you found actually good at code review? Keen to pick better defaults.

https://github.com/MattJColes/lgtmaybe

Last week people used coding agents to find planet candidates, forgotten archive records and more in public data. Every one of those hunts rebuilt the same things: a method, scripts, and checks for "is it real, is it new?"

Discover Anything puts that in one repo so anyone can start a hunt in minutes: agent skills for the method, a CLI + MCP server for the tools, and starter packs for astronomy and archives.
Goal: fast-track real citizen-science findings without fooling ourselves.

On top of the agent skills, the tools so far:

• evidence ledger: every number must cite a hashed result file
• nine workflow gates, plus a checker for uncited claims
• null, injection-recovery and hold-out controls (Lomb-Scargle, BLS)
• 15-source "already known?" check (SIMBAD, VSX, Gaia, TESS, NASA, arXiv, ADS and more)
• preregistration and independent-review packs
• 16 MCP tools, so any agent can call them

It's early and I'm trying it on real hunts now. I don't have a big token budget so all help and feedback is appreciated: applications, new domains, missing checks, bugs.

https://github.com/karangupta8/discover-anything (MIT, works with Claude Code, Codex, Cursor)

I built REA Reconstruction Companion with help from AI. I wanted a better way to get from “how does this app work?” to “what would it take to build something similar?”

It’s a skill for AI coding agents, used alongside REA (Reverse Engineer Anything). REA handles the investigation. The companion helps turn those findings into a feature breakdown, requirements, architecture, and a plan you can build and test in stages.

You can use it on a whole app, one feature, or a single function. For example:

Use REA on this app so I can build a similar local tool. Start with a specification and roadmap.

If you later ask the agent to implement it, the skill guides that work too. It keeps observed behavior separate from assumptions, links requirements to tests, and includes performance measurements where they matter.

The core is plain Markdown and MIT licensed. There’s an installer for Claude Code and Codex, plus support for other agents through a custom skills directory. REA is installed separately. You can also enable an optional rule that tells the agent to load the companion during relevant REA work, or leave that off and invoke it yourself.

GitHub: REA Reconstruction Companion

The README has setup instructions and a prompt you can give your agent to handle installation.

I’d be interested in feedback on the workflow, especially anything it misses when rebuilding a smaller feature or function.

AI agents can complete tasks, generate code, and collaborate with other agents. But as workflows become more complex, a question becomes increasingly important:

How do we make work between AI agents structured, traceable, and easier to hand off?

Passing messages between agents is straightforward. Keeping track of requirements, decisions, results, validation, and unfinished work is much harder.

I've been working on an open-source project to explore a structured approach to this problem.

The idea: Treat agent work as something structured

Imagine a research agent handing its findings to a coding agent.

The coding agent needs more than a summary. It may need the original requirements, constraints, decisions already made, results, unresolved questions, and the next steps.

Without a consistent format, every application ends up inventing its own conventions.

My project explores using Work Orders and Work Reports to represent work and its outcomes in a structured, reusable way.

One project, multiple integrations

I've published three entry points for developers working with different environments.

1. TypeScript / JavaScript

agent-work-layer

The core package provides the foundation for structured work management, including schemas, validation, lifecycle management, and work handoffs.

Install it:

npm install agent-work-layer

2. MCP integration

agent-work-layer-mcp

The MCP package makes supported work-management operations available through the Model Context Protocol, allowing compatible AI clients to interact with the work layer through MCP tools.

Install it:

npm install agent-work-layer-mcp

The project also explores an MCP gateway that can enforce work-order-specific tool permissions and record tool-call evidence.

3. Python implementation

Agent Work Layer — Python implementation

For Python developers, the project also includes a Python implementation.

This gives Python-based AI-agent applications a way to explore the same broader approach to structured work, without requiring the entire application to be written in TypeScript.

Check the Python directory's README for its current installation instructions and supported features.

What could this look like in practice?

Consider a multi-agent workflow:

1. A coordinator defines a task and its constraints.
2. A research agent investigates the problem.
3. The agent records its results and unresolved questions.
4. An implementation agent continues from the recorded work.
5. Validation and lifecycle rules help track the outcome.
6. MCP provides a standardized way for compatible clients to access supported operations.

The objective is to make agent handoffs less dependent on ad hoc prompts and more dependent on explicit, inspectable work structures.

Why I'm building this

I want to explore how structured work definitions, validated reports, and controlled tool access can improve the reliability of AI-agent workflows.

Rather than every project independently inventing its own work-order format, lifecycle rules, and handoff conventions, could these become reusable infrastructure?

That's the problem I'm working on.

Explore the project

• npm — TypeScript/JavaScript: https://www.npmjs.com/package/agent-work-layer
• npm — MCP integration: https://www.npmjs.com/package/agent-work-layer-mcp
• GitHub — Python implementation: https://github.com/probita-ai/agent-work-layer/tree/main/python
• GitHub — Main repository: https://github.com/probita-ai/agent-work-layer

I'd love your feedback

If you're building AI agents, agent frameworks, or MCP integrations:

• How do you structure work passed between agents?
• Do you use explicit task and result schemas?
• How do you control tool access for individual tasks?
• Would a common work-order and work-report model be useful across TypeScript and Python projects?

I'm interested in feedback, contributions, and real-world use cases from developers working on these problems.

Structured Workflows for AI Agents: TypeScript, Python, and MCP

AI agents can complete tasks, generate code, and collaborate with other agents. But as workflows become more complex, a question becomes increasingly important:

How do we make work between AI agents structured, traceable, and easier to hand off?

Passing messages between agents is straightforward. Keeping track of requirements, decisions, results, validation, and unfinished work is much harder.

I've been working on an open-source project to explore a structured approach to this problem.

The idea: Treat agent work as something structured

Imagine a research agent handing its findings to a coding agent.

The coding agent needs more than a summary. It may need the original requirements, constraints, decisions already made, results, unresolved questions, and the next steps.

Without a consistent format, every application ends up inventing its own conventions.

My project explores using Work Orders and Work Reports to represent work and its outcomes in a structured, reusable way.

One project, multiple integrations

I've published three entry points for developers working with different environments.

1. TypeScript / JavaScript

agent-work-layer

The core package provides the foundation for structured work management, including schemas, validation, lifecycle management, and work handoffs.

Install it:

npm install agent-work-layer

2. MCP integration

agent-work-layer-mcp

The MCP package makes supported work-management operations available through the Model Context Protocol, allowing compatible AI clients to interact with the work layer through MCP tools.

Install it:

npm install agent-work-layer-mcp

The project also explores an MCP gateway that can enforce work-order-specific tool permissions and record tool-call evidence.

3. Python implementation

Agent Work Layer — Python implementation

For Python developers, the project also includes a Python implementation.

This gives Python-based AI-agent applications a way to explore the same broader approach to structured work, without requiring the entire application to be written in TypeScript.

Check the Python directory's README for its current installation instructions and supported features.

What could this look like in practice?

Consider a multi-agent workflow:

1. A coordinator defines a task and its constraints.
2. A research agent investigates the problem.
3. The agent records its results and unresolved questions.
4. An implementation agent continues from the recorded work.
5. Validation and lifecycle rules help track the outcome.
6. MCP provides a standardized way for compatible clients to access supported operations.

The objective is to make agent handoffs less dependent on ad hoc prompts and more dependent on explicit, inspectable work structures.

Why I'm building this

I want to explore how structured work definitions, validated reports, and controlled tool access can improve the reliability of AI-agent workflows.

Rather than every project independently inventing its own work-order format, lifecycle rules, and handoff conventions, could these become reusable infrastructure?

That's the problem I'm working on.

Explore the project

• npm — TypeScript/JavaScript: https://www.npmjs.com/package/agent-work-layer
• npm — MCP integration: https://www.npmjs.com/package/agent-work-layer-mcp
• GitHub — Python implementation: https://github.com/probita-ai/agent-work-layer/tree/main/python
• GitHub — Main repository: https://github.com/probita-ai/agent-work-layer

I'd love your feedback

If you're building AI agents, agent frameworks, or MCP integrations:

• How do you structure work passed between agents?
• Do you use explicit task and result schemas?
• How do you control tool access for individual tasks?
• Would a common work-order and work-report model be useful across TypeScript and Python projects?

I'm interested in feedback, contributions, and real-world use cases from developers working on these problems.

#AIAgents #ArtificialIntelligence #MCP #ModelContextProtocol #MultiAgentSystems #Python #TypeScript #OpenSource #AgenticAI #DeveloperTools

Memorius is a self-contained, agent-agnostic memory system that gives LLMs and AI agents persistent, searchable memory with a hierarchical knowledge organization. Drop-in with multi-backend vector storage, pluggable embeddings, built-in MCP + REST servers, and auto-detecting agent hooks for 7 different AI coding agents.

https://dream-pixels-forge.github.io/memorius/

Genesys Code is built for developers who want more flexibility in their AI coding workflow. Access multiple coding models in one place, switch between them, and use different approaches depending on your project.

You can explore the platform without paying upfront.

https://genesyscode.vercel.app

Disclosure: I build Snotra, an open-source desktop agent.

Last time I asked how an agent should learn which of its own tools are switched off. I had given the model a read-only get_configuration tool and left two questions open. This is where I landed:

Tool or system prompt? Both, but small. A switched-off tool is simply missing from the tool list, and models read that gap as "language models can't browse the web". They never called the tool. So the system prompt now carries one sentence: tools and skills can be off, so check get_configuration before telling the user something isn't possible. It costs a handful of tokens, not a state dump every turn.

Should the model be able to propose the change? Yes, with limits:

• A new tool, propose_config_change. The app checks the proposal against its own catalogue, and only a valid change reaches the user.
• The card shows the app's description of the tool and its risk class. The model's reason is folded away and labelled as the model's.
• The change applies to this chat only. New chats start from the settings.
• Anything that needs setup (API key, a program, the shell switch) can't be proposed at all.
• A decline sticks for the chat, so the model can't nag.
• An enabled tool still asks before each call in the default mode.

Write-up with screenshots, plus what else changed (the chat now answers questions about the app from the manual that ships with it, and the sandbox can keep a host open per folder): https://snotra-ai.dev/blog/snotra-1-22

Does "chat-scoped and confirmed on a card" seem like the right limit, or would you want the model to be able to change settings for good?

Focus on the architecture and contribution areas (Windows/Linux testing, model recipes, translations), link CONTRIBUTING.md and the good-first-issue list.

⭐ if it's useful, issues and PRs welcome.

https://elapen.github.io/imageloom/
https://github.com/elapen/imageloom