aegis-agents

Aegis

A local-first, MCP-native multi-agent platform you control. Aegis ships as a working personal job-tracker + daily briefing for software engineers — but the substrate underneath (agents, tracing, evals, MCP) is built to be lifted into any domain where you want LLM workflows you can debug, eval, and budget.

Built to be read, forked, and bent to your use case. Everything happens on your machine — your data, your API key, your prompts.

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What you can do today

Out of the box — two workflows wired end-to-end:

Inspect anything — every LLM call emits a span. Run aegis api and open http://localhost:8000/trace/<run_id> for a tree of every prompt, response, cost, and latency, or pull the JSON at /trace/<run_id>/json.

Catch regressions — YAML golden cases under evals/<eval_name>/cases/*.yaml run through the real pipeline; the runner writes a row to eval_runs with git SHA, score, and per-case detail.

Budgets that bite — per-run cap (AEGIS_LLM_MAX_COST_USD_PER_RUN) and daily aggregate cap (AEGIS_LLM_MAX_COST_USD_PER_DAY) checked before every LLM call. Tenacity retries handle transient Anthropic / Postgres failures.

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What's actually reusable — the building blocks

Aegis is small (~3k LOC). Pick what you need:

• MCP server scaffold (src/aegis/mcp_server.py) — FastMCP wiring with tools, resources, prompts, and a working Claude Desktop integration. Add a tool by writing one @mcp.tool() function.
• Multi-agent framework (src/aegis/agents/) — AgentBase gives every agent automatic tracing, cost attribution, and uniform AgentResult. Drop in a new agent by subclassing and writing _run.
• LangGraph workflow pattern (src/aegis/workflows/job_analysis/graph.py) — Researcher → Analyst → Critic with conditional refinement. Easy to copy and rewire into a different shape.
• Tracing infra (src/aegis/tracing/) — spans persisted to Postgres (agent_traces table) with a built-in HTML viewer. No Datadog needed.
• Eval harness (scripts/run_eval.py, evals/*/cases/*.yaml) — golden cases as YAML, assertion-based scoring (not exact-match), regression rows in eval_runs. Each case still produces a trace you can open.
• LLM gateway (src/aegis/llm/gateway.py) — Anthropic direct (no LangChain), with cost tracking, per-run + per-day budgets, retry on 5xx / timeouts / rate-limits.
• FastAPI surface (src/aegis/app.py, src/aegis/api/) — REST routers for jobs, briefing, traces, health. Exposed via aegis api. Health check returns 503 when degraded.

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Quick start

Prerequisites

• Docker Desktop — Aegis runs Postgres + Redis locally.
• uv — curl -LsSf https://astral.sh/uv/install.sh | sh
• Anthropic API key — required. Generate at https://console.anthropic.com/settings/keys.
• GitHub personal access token — optional, only for the daily briefing. Generate at https://github.com/settings/tokens. Classic token needs the notifications scope; fine-grained needs Notifications: Read. Without it, briefings still run but skip the GitHub section.

Install

uv tool install aegis-agents
aegis init          # prompts for ANTHROPIC_API_KEY + GITHUB_TOKEN (optional)
aegis up            # boots Postgres + Redis, applies migrations

aegis init writes ~/.config/aegis/{.env,compose.yml,profile.yaml}. Re-run with --force to regenerate. Override the location with AEGIS_HOME=/path.

Edit ~/.config/aegis/profile.yaml to personalise fit scoring (skills, target roles, deal-breakers).

Wire to Claude

Claude Code:

claude mcp add aegis -- aegis serve

Claude Desktop — add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "aegis": {
      "command": "aegis",
      "args": ["serve"]
    }
  }
}

Restart Claude. The aegis tools show up under the connector menu.

Daily ops

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Daily workflow

Morning briefing

In Claude: "Run my daily briefing" — fires run_briefing, returns prioritised markdown with trace_url. There's no background scheduler; briefings only fire when you (or Claude) ask.

Adding a job

1. Copy a job posting (text or URL).
2. In Claude: "Add this job: [paste]" → add_job runs Researcher → Analyst → Critic, loops up to AEGIS_MAX_REFINEMENTS rounds if the critic flags issues, then persists.

Or curl (requires aegis api running):

curl -X POST http://localhost:8000/jobs \
  -H "Content-Type: application/json" \
  -d '{"text": "Senior Backend Engineer at Stripe..."}'

Inspecting a run

• "List my recent Aegis runs" in Claude → list_recent_runs shows newest-first runs with run_id and trace links.
• "Get the trace for <run_id>" → flat span summary.
• With aegis api running, http://localhost:8000/trace/<run_id> → full HTML tree with every prompt and response.

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MCP surface

Tools

Resources: aegis://briefing/latest, aegis://jobs/summary Prompts: daily_briefing_prompt

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Evals — keep prompts honest

The eval harness is maintainer tooling — it ships in the repo, not the package. Clone the repo and run:

uv run python scripts/seed_evals.py             # mirror evals/*/cases/*.yaml → eval_golden_cases
uv run python scripts/run_eval.py job_fit_v1    # run the suite

Each YAML case declares assertions against the analysis (not exact-match output, since LLMs aren't deterministic):

case_id: stripe_senior_backend_apply
eval_name: job_fit_v1
input:
  url_or_text: |
    Senior Backend Engineer — Stripe (Remote, US) ...
expected:
  recommendation: apply
  fit_score: {min: 0.70, max: 1.00}
  must_include_skills: [python, postgresql]
  must_not_have_deal_breakers: true

The runner writes a row to eval_runs per invocation (git SHA, score, pass count, per-case detail). Diff successive runs to track prompt regressions.

When you hit a bad output in agent_traces, the recipe is: copy the JD into a new case file with the correct expected, re-seed, re-run, fix the prompt until it passes.

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Profile configuration

aegis init writes ~/.config/aegis/profile.yaml from the bundled template. Edit it to personalise fit-scoring and briefing output:

skills: [python, golang, distributed-systems]
target_roles: [backend-engineer, ai-engineer]
experience_years: 3
preferred_locations: [singapore, remote]
deal_breakers: [php, wordpress]
summary: |
  Backend engineer with 3 years of experience building distributed
  systems in Python and Go. Interested in AI / agent platforms.

Set AEGIS_PROFILE_PATH=/some/other/path.yaml to point Aegis at a profile outside the default location.

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Tuning

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Project structure

aegis/
├── src/aegis/
│   ├── agents/          # Researcher, Analyst, Critic + auto-tracing base class
│   ├── workflows/
│   │   └── job_analysis/   # LangGraph wiring + service
│   ├── briefing/        # LangGraph daily-briefing nodes + graph + service
│   ├── jobs/            # Job service facade exposed by API and MCP
│   ├── sources/         # GitHub, HN, internal jobs source for the briefing
│   ├── llm/             # Anthropic gateway (cost-tracked, budget-capped, retrying)
│   ├── tracing/         # Span recorder + repository + HTML viewer
│   ├── db/              # asyncpg engine + repositories
│   ├── api/             # FastAPI routers (jobs, briefing, traces, health)
│   ├── app.py           # FastAPI factory
│   └── mcp_server.py    # FastMCP server (tools, resources, prompts)
├── migrations/          # Numbered SQL migrations
├── evals/               # YAML golden cases per eval_name
├── scripts/
│   ├── seed_evals.py    # YAML → eval_golden_cases
│   └── run_eval.py      # Run a suite, write eval_runs
├── tests/
│   ├── unit/            # Mocked LLM, no infra
│   └── integration/     # testcontainers, real Postgres
└── pyproject.toml

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Development

Working on Aegis itself (rather than using it):

git clone https://github.com/nareus/aegis
cd aegis
uv sync                                # install deps + dev tools
aegis init                             # one-time, sets up ~/.config/aegis/
aegis up                               # start Postgres + Redis

uv run python -m aegis.cli serve       # run MCP server from source
uv run pytest                          # full suite, all network mocked
uv run pytest tests/unit/              # fast, no Docker
uv run pytest tests/integration/ -v    # spins up Postgres via testcontainers

uv run python scripts/seed_evals.py    # mirror eval cases to Postgres
uv run python scripts/run_eval.py job_fit_v1

scripts/ and evals/ ship in the repo for regression testing and aren't bundled into the published package.

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What's deliberately not here (yet)

So you know what you're signing up for:

• No background scheduler — briefings run only when triggered.
• No auth on the FastAPI surface — assume loopback / firewalled. Add an API-key middleware before exposing publicly.
• No SSRF allow-list in the researcher fetch — fine for trusted local use; restrict before exposing to untrusted JD URLs.
• No LangGraph checkpointing — a pod restart mid-workflow loses the run.
• No multi-tenancy — single profile, single user, single DB.

These are intentional cuts for a local-first tool.

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License

MIT. Fork it, rewire it, ship it.