v100: Engine for Agentic Research

v100 is my engine for building, running, studying, and evolving autonomous coding agents under real constraints.

It is not a framework in the abstract. It is a concrete Go-based agent runtime featuring a CLI, Bubble Tea TUI, tool safety controls, durable memory, trace replay, benchmarking, evaluation, policy evolution, and long-running execution paths.

I built v100 to close the loop between idea, execution, observation, and iteration.

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🧠 Core Capabilities

v100 operates on a fundamental principle: autonomy requires visibility, not hidden magic. Every agent action is trackable, replayable, and inspectable.

1. Advanced Solvers & Routing

Instead of a rigid prompt loop, v100 implements multiple reasoning strategies (Solvers) that can be swapped or combined:

• react: The classic reasoning loop, enhanced with watchdogs for tool denial and stall recovery.
• plan_execute: A two-phase strategy where the agent previews a plan and executes it, with automatic replanning on failure.
• smartrouter: Cost-performance escalation. It routes "trivial" idempotent tool calls (like reading files) to cheap models (e.g., Gemini Flash or local Ollama) and escalates to frontier models (e.g., MiniMax, Claude Opus) when a dangerous or complex mutation is required.
• rlm: DSPy-style Recursive Language Model pattern with sub-model invocation.
• miniglm: Intelligent provider switching between tool-focused and reasoning-focused models.

2. Autonomous Loops

v100 is designed for long-running, unattended execution:

• Wake Daemon (v100 wake): Runs on a recurring schedule. It can act as a goal_generator (mining TODOs and failures for next steps) or an issue_worker (autonomously picking open GitHub issues, implementing fixes, running local tests, pushing, and closing the issue).
• Research Loop (v100 research): A fully autonomous experiment loop. It proposes code changes, runs remote (Modal) or local experiments, parses a metric, and implements keep/discard (git commit vs. revert) logic automatically.

3. Tooling & Safety

Tools are a first-class part of the runtime. The model interacts with the world through explicitly registered, schema-bound tools (40+ currently available):

• Safety boundaries: Tools are marked Safe or Dangerous. Dangerous tools can require explicit operator confirmation, trigger mandatory "Reflection" turns (Policy.ReflectOnDangerous) to assess confidence, or be blocked entirely.
• Sandboxing: Runs can be executed inside isolated Docker containers with strict Network Tiers to prevent unauthorized data exfiltration.
• Semantic analysis: Includes tools like sem_diff, sem_impact, and sem_blame that understand code entities (functions, classes) instead of just text lines.

4. Memory & External Context

v100 treats memory as runtime infrastructure, not just prompt stuffing:

• Durable Blackboard: A shared workspace for agents to read and write ongoing findings.
• ATProto Integration: Deep Bluesky integration (atproto_index, atproto_recall, atproto_vibe_check, atproto_daily_digest, atproto_graph_explorer). It indexes social feeds and profiles into vector embeddings for semantic RAG, summarizes feed activity, and explores follow graphs for real-time external context.

5. Observability & Trace Replay

If an agent does something surprising, you shouldn't have to guess why.

• Every run emits a structured trace.jsonl.
• v100 replay <run_id> lets you step through an agent's reasoning turn-by-turn after the fact.
• Checkpoints allow you to resume interrupted runs seamlessly.

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🚀 Quick Start

1. Install & Bootstrap

Prebuilt releases are published on GitHub. Alternatively, build from source:

./scripts/build.sh

That rebuilds ./v100 and updates the shell v100 link. The underlying Go command is:

go build -o v100 ./cmd/v100

Bootstrap your configuration and check your environment:

./v100 config init
./v100 doctor

2. Interactive & Unattended Runs

Start a standard interactive run using MiniMax (the preferred provider):

./v100 run --provider minimax --workspace .

Enable Bubble Tea TUI for a better visual experience:

./v100 run --provider minimax --tui --workspace .

Leverage advanced solvers for planning or cost routing:

./v100 run --solver plan_execute --plan --workspace .
./v100 run --solver smartrouter --workspace .

Run fully unattended (the agent executes until completion or budget exhaustion):

./v100 run --continuous --workspace .

3. Inspecting the Engine

Browse recent runs, resume them, or replay their traces:

./v100 runs
./v100 resume <run_id>
./v100 replay <run_id>
./v100 blame <run_id> <file_path>  # See exactly which reasoning turn modified a file

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📂 Project Structure

cmd/v100/          CLI commands
internal/core/     loop, solvers (react, plan, router), budgets, tracing, research, hooks
internal/tools/    40+ tool implementations (fs, git, web, atproto, semantic)
internal/providers/ provider adapters (MiniMax, GLM, Anthropic, Gemini, OpenAI, etc.)
internal/eval/     scoring, benchmarks, experiments, analysis
internal/memory/   durable memory and vector stores
internal/ui/       terminal UI components
docs/              architecture notes
research/          research configs and artifacts

📝 Notes on tone and scope

This is not meant to be a polished general-purpose framework in the abstract. It is my working engine for agentic research. I use it to try ideas quickly, keep the sharp edges visible, and evolve the system in public through actual use.

That means the repo carries a mix of serious runtime infrastructure, rough-edged experimental features, and bespoke tooling. It is built for researchers and power users who want deep control over their autonomous systems.

📄 License

MIT