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https://github.com/razroo/state-trace


https://github.com/razroo/state-trace

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# state-trace



> Graph-native working memory for coding agents: typed memories, causal retrieval, bounded capacity, and compact briefs for small models.



`state-trace` is a bounded working-memory layer for coding and debugging agents that need the right file, failure, and next action under tight token budgets. It is not a replacement for a general-purpose temporal knowledge graph like Graphiti — see [ARCHITECTURE.md](./ARCHITECTURE.md) for the honest comparison.



What it is optimized for:



- artifact-first retrieval for coding agents

- current-vs-stale task state (`engine.current_state()`, `engine.failed_hypotheses()`)

- compact harness-facing briefs for smaller models

- online agent loops and post-hoc trajectory ingestion

- bounded memory with decay, compression, and lifecycle retention

- MCP-mountable, local-first deployment



## Headline: SWE-bench-Verified localization — n=500



The credibility benchmark. Cold-start artifact localization on the full SWE-bench-Verified test split: given only the GitHub issue text and hints (no trajectory), rank the correct patch file at 1 and at 5.



```bash

pip install -e ".[bench]"

python3 examples/swebench_verified_eval.py --limit 500 --backends no_memory bm25 state_trace graphiti

```



| backend | n | Artifact@1 | Artifact@1 CI | Artifact@5 | Artifact@5 CI | AvgLatencyMs |

| --- | ---: | ---: | :---: | ---: | :---: | ---: |

| no_memory | 500 | 0.000 | [0.000, 0.000] | 0.000 | [0.000, 0.000] | 0.01 |

| bm25 | 500 | 0.176 | [0.144, 0.208] | 0.300 | [0.262, 0.338] | 0.19 |

| **state_trace** | 500 | **0.216** | [0.182, 0.252] | **0.322** | [0.284, 0.362] | 27.43 |

| graphiti | 500 | 0.098 | [0.072, 0.126] | 0.216 | [0.182, 0.254] | 5427.39 |



What this says, plainly:



- **state_trace leads on both Artifact@1 and Artifact@5 across every baseline.**

- **vs. Graphiti:** non-overlapping 95% CIs on both metrics (0.216 vs 0.098 on A@1; 0.322 vs 0.216 on A@5). On the same input with the same deterministic embedder/reranker stub, the typed coding-agent ontology plus cold-start lexical fallback localizes the right file and puts it in the top 5 meaningfully more often.

- **vs. BM25:** a real but narrower lead. A@1 0.216 vs 0.176 — 95% CIs just barely overlap (BM25 upper bound 0.208, state_trace lower bound 0.182), so it's a consistent directional win but not a statistical blowout. A@5 0.322 vs 0.300 — CIs overlap substantially, call it a tie with state_trace nosing ahead. The practical takeaway: state_trace's coding-agent ontology matches BM25's simple lexical coverage on cold-start *and* beats it when a trajectory is available (see [BENCHMARKS.md](./BENCHMARKS.md)).

- **Latency:** state_trace retrieves in ~27ms vs BM25's ~0.2ms vs Graphiti's ~5,400ms. For per-action memory lookups in an agent loop, the ~200× delta over Graphiti compounds meaningfully over a long session.



The A@5 ≡ A@1 collapse that appeared in v0.2.0 is fixed in v0.2.1 via a lexical file-path fallback in `retrieve_brief` (pulls candidates from the query + top-scored node `issue_text` metadata when the graph has fewer than 5 file nodes, including paths embedded in GitHub blob URLs).



### Caveats



- Graphiti is run with a deterministic hash-embedder and BM25 + cosine + BFS → RRF reranker (no LLM entity extraction). That's the same simplification `graphiti_head_to_head_eval.py` uses for reproducibility without API keys. A full Graphiti pipeline with GPT-4-class extraction might close some of the gap, at materially higher cost per ingest.

- Cold-start localization from issue text is only one axis. Trajectory-informed retrieval (BENCHMARKS.md) is where state_trace's larger advantage lives.



## What makes the architecture different



Typed coding-agent ontology, not generic Entity/Edge:



- **Nodes:** `task`, `observation`, `decision`, `file`, `goal`, `session`, `command`, `test`, `symbol`, `patch_hunk`, `error_signature`, `episode`

- **Edges:** `patches_file`, `fails_in`, `verified_by`, `rejected_by`, `supersedes`, `contradicts`, `solves`, `derived_from`, `precedes`, `motivates`, and more

- **Intent routing:** the retrieval scorer re-prioritizes edge types per query intent (`locate_file`, `failure_analysis`, `history`, `general`).



Bounded working memory as a first-class constraint:



- `enforce_capacity()` runs decay, compression, and summarization on every step.

- `current_state(session)` answers "what's live right now" directly — cheap for state-trace, expensive for a general-purpose knowledge graph.

- `failed_hypotheses(session)` returns invalidated, superseded, or unrecovered-error nodes — the "don't propose this again" signal.



Local-first, MCP-mountable:



- Hot graph is an in-process `networkx.MultiDiGraph`. Cold storage is WAL SQLite+FTS5.

- `state-trace-mcp` is a stdio MCP server you can mount in Claude Code / Cursor / Codex CLI.



See [ARCHITECTURE.md](./ARCHITECTURE.md) for why these choices matter vs. Graphiti, and [BENCHMARKS.md](./BENCHMARKS.md) for the smaller repo-local benchmarks.



## vs. Graphiti



Graphiti is the stronger general-purpose temporal knowledge graph for AI agents. `state-trace` is narrower: working memory for one coding/debugging session at a time. We're not claiming to replace Graphiti — we're claiming a specific lane where the tradeoffs land differently.



Each row below is a concrete, measured axis, not a vibe.



| Axis | state-trace | Graphiti | Winner for coding agents |

| --- | --- | --- | --- |

| **Artifact@1** on SWE-bench-Verified, n=500 | **0.216** [0.182, 0.252] | 0.098 [0.072, 0.126] | **state-trace** — non-overlapping 95% CIs |

| **Artifact@5** on SWE-bench-Verified, n=500 | **0.322** [0.284, 0.362] | 0.216 [0.182, 0.254] | **state-trace** — non-overlapping 95% CIs |

| **Per-retrieval latency** (same benchmark) | **27 ms** | 5,427 ms | **state-trace** — ~200× faster |

| **Write path per agent step** | Typed insert, zero LLM calls | `add_episode` → LLM entity extraction each step | **state-trace** — cheaper, deterministic, no API key |

| **Default deploy** | Pure Python + local SQLite/JSON; `state-trace-mcp` stdio binary | Neo4j / Kuzu / FalkorDB graph DB + embedder + LLM | **state-trace** — local-first, no external services |

| **Coding-agent ontology** | Typed: `file`, `patch_hunk`, `error_signature`, `test`, `command`, `symbol`, `observation`, `decision`, `task`, `goal`, `session`, `episode` | Generic `EntityNode` / `EntityEdge` / `EpisodicNode` | **state-trace** — retrieval scorer routes on these types |

| **"What's true right now in this session?"** | `engine.current_state(session)` — direct O(graph) query | Inferred from temporal facts via Cypher or LLM | **state-trace** — first-class API |

| **"What have I already tried and rejected?"** | `engine.failed_hypotheses(session)` — direct query returning `invalid_at` + superseded + unrecovered-error nodes | Has to be inferred from `invalid_at` + contradictions | **state-trace** — first-class API |

| **Working-memory capacity bound** | `enforce_capacity` with decay + compression + lifecycle retention. Long-horizon pressure benchmark: Artifact@1 0.771 *while* staying within a 96-unit budget 100% of the time | Unbounded by design; relies on the graph DB to scale | **state-trace** for long debugging sessions that need a memory ceiling |

| **Small-model brief** | `retrieve_brief` produces ~220-token structured brief (`patch_file`, `rerun_command`, `tests_to_rerun`, `failed_attempts`, `recommended_actions`, …) that fits a tight budget | Returns raw nodes/facts; caller compresses | **state-trace** — built for small-model harnesses |

| **MCP-mountable** | `state-trace-mcp` stdio server in the `[mcp]` extra — 11 tools exposed, drop into `~/.claude/settings.json` | No official MCP server; library-first | **state-trace** — plug straight into Claude Code / Cursor / Codex / opencode |

| **Long-lived temporal knowledge across weeks** | Scoped to a session or repo namespace; no cross-namespace fact merging | First-class; bi-temporal validity, contradiction resolution, fact supersession across episodes | **Graphiti** |

| **Multi-tenant SaaS scale** | Single-writer process model; authoritative graph is in-process networkx | Built for it on Neo4j/Kuzu substrate | **Graphiti** |

| **Cross-session learning about users / orgs / policies** | Out of scope | First-class | **Graphiti** |



### When to pick which



Use **state-trace** when:



- Your agent is editing code in a single debugging or refactoring session.

- You talk to an MCP client (Claude Code, Cursor, Codex CLI, opencode) and want working memory without standing up a graph DB.

- Per-action latency matters — you're calling memory on every tool invocation in an agent loop.

- You run on small models where a 220-token structured brief beats a 1,000-token raw dump.

- You need "what file should I patch / what did I already try" to be a direct query, not inferred.



Use **Graphiti** when:



- You need a knowledge graph of facts about the world, users, or an organization that evolves across weeks.

- Multi-tenant, multi-agent shared memory is part of the design.

- You're willing to run Neo4j/Kuzu and pay the LLM-extraction cost per ingest for the ontological payoff.

- Your retrieval patterns are richer than "which file, which test, which failed hypothesis."



They solve adjacent problems. The only reason a comparison is even interesting is that both ship as "memory for AI agents" — the honest answer is they're different products that happen to live on the same shelf.



## Installation



```bash

uv sync                       # or: pip install -e .

pip install -e ".[mcp]"       # stdio MCP server for Claude Code / Cursor / Codex CLI

pip install -e ".[bench]"     # graphiti-core[kuzu] + datasets (for the headline benchmark)

pip install -e ".[llm]"       # OpenAI-backed live benchmarks + LLM ingestion

pip install -e ".[adapters]"  # LangGraph / LlamaIndex adapter shims

pip install -e ".[api]"       # FastAPI app

```



Distribution name: `state-trace`. Python import path: `state_trace`.



## Quickstart



```python

from state_trace import MemoryEngine



engine = MemoryEngine(capacity_limit=24.0, storage_path="memory.json")



task = engine.store(

    "Fix login by tracing the refresh token path",

    {"type": "task", "session": "auth-debug", "goal": "restore login", "file": "auth.ts", "importance": 0.92},

)

engine.store(

    "Login still returns 401 after refresh token exchange",

    {"type": "observation", "session": "auth-debug", "goal": "restore login", "file": "auth.ts",

     "blocks": [task.id], "importance": 0.88},

)

engine.store(

    "Authorization header is dropped before the retry request reaches auth.ts",

    {"type": "decision", "session": "auth-debug", "goal": "restore login",

     "related_to": [task.id], "file": "auth.ts", "importance": 0.91},

)



result = engine.retrieve("Why is login still broken?", {"session": "auth-debug", "goal": "restore login"})

```



## Current state, live hypotheses, failed attempts



The architectural wedge. These APIs return a live view of the session without re-ranking:



```python

state = engine.current_state(session="auth-debug", goal="restore login")

# → {"active_task": ..., "latest_observation": ..., "active_files": [...], ...}



failures = engine.failed_hypotheses(session="auth-debug")

# → [{"id": ..., "reason": ["superseded"], "content": "Login still returns 401 ..."}, ...]

```



`current_state` filters out invalidated and superseded nodes; `failed_hypotheses` surfaces them as "do not propose again" context. A general-purpose temporal graph has to infer this from fact updates; here it's a direct query.



## MCP Server



```bash

pip install -e ".[mcp]"

state-trace-mcp

```



Environment config:



- `STATE_TRACE_STORAGE_PATH` — durable path; `.db`/`.sqlite` uses the SQLite backend. Default: `~/.state-trace/memory.db`.

- `STATE_TRACE_NAMESPACE` — default namespace (e.g. the repo slug).

- `STATE_TRACE_CAPACITY_LIMIT` — working-memory budget (default `256`).



Tools exposed: `store`, `retrieve`, `retrieve_brief`, `record_action`, `record_observation`, `record_test_result`, `ingest_agent_log_file`, `current_state`, `failed_hypotheses`, `list_namespaces`, `graph_snapshot`.



Example Claude Code config (`~/.claude/settings.json`):



```json

{

  "mcpServers": {

    "state-trace": {

      "command": "state-trace-mcp",

      "env": {

        "STATE_TRACE_STORAGE_PATH": "/Users/me/.state-trace/memory.db",

        "STATE_TRACE_NAMESPACE": "repo-x"

      }

    }

  }

}

```



## Online agent loop



```python

engine = MemoryEngine(capacity_limit=256.0)

ctx = {"session": "auth-debug", "goal": "restore login", "repo": "example/auth-service"}



engine.record_action('open "src/auth.ts"', {**ctx, "files": ["src/auth.ts"]})

engine.record_observation(

    "AttributeError: login still fails with a 401 in src/auth.ts",

    {**ctx, "files": ["src/auth.ts"], "status": "error"},

)

engine.record_action('edit "src/auth.ts"', {**ctx, "files": ["src/auth.ts"], "action_kind": "edit"})

engine.record_test_result(

    "pytest tests/test_auth.py::test_refresh_retry",

    "tests/test_auth.py::test_refresh_retry PASSED",

    {**ctx, "files": ["src/auth.ts", "tests/test_auth.py::test_refresh_retry"]},

)



brief = engine.retrieve_brief(

    "Which file should I patch and what test should I rerun?",

    {"session": "auth-debug", "goal": "restore login"},

    mode="small_model",

)

```



The brief fields: `patch_file`, `rerun_command`, `target_files`, `tests_to_rerun`, `current_state`, `failed_attempts`, `recommended_actions`, `evidence`, `symbols`, `patch_hints`, `confidence`, `token_estimate`.



## Trajectory ingestion



```python

engine = MemoryEngine(capacity_limit=256.0)

engine.store_agent_log_file("examples/data/agent_logs/marshmallow__marshmallow-1867.json")

```



Supported inputs: normalized `agent_log` JSON, raw SWE-agent `.traj` files, raw OpenHands event JSON logs.



## Live solve-rate (next credibility step)



`examples/swebench_verified_solve_rate.py` scaffolds end-to-end solve-rate measurement: state-trace brief → LLM patch proposal → SWE-bench-Verified prediction JSONL. It does not run the swebench docker harness; that step is documented in the script's header.



```bash

python3 examples/swebench_verified_solve_rate.py --limit 5 --model gpt-5.1-mini --dry-run

```



## Storage backends



`MemoryEngine(storage_path=...)` picks the backend from the file extension:



- `.db` / `.sqlite` / `.sqlite3` — durable SQLite with WAL journal + FTS5 seed index. Recommended for long-running agent harnesses.

- any other path — JSON blob (simple, single-writer, fine for benchmarks).



See [ARCHITECTURE.md](./ARCHITECTURE.md) for the "why networkx + SQLite, not Neo4j" explainer.



## Namespaces



```python

engine = MemoryEngine(storage_path="memory.db", namespace="payments-api")

engine.retrieve("why is login broken?")  # scoped to payments-api by default

engine.retrieve("...", include_all_namespaces=True)  # opt out

```



Nodes without a namespace remain visible in every view so pre-namespace data is not lost.



## Framework adapters



```python

from state_trace.adapters import StateTraceLangGraphMemory, StateTraceLlamaIndexMemory



lg_memory = StateTraceLangGraphMemory(default_session="coding-session")

li_memory = StateTraceLlamaIndexMemory(session_id="agent-session")

```



Neither adapter imports the host framework; they satisfy the duck-typed memory contract used by each.



## FastAPI



```python

from state_trace.api import app  # POST /store, /retrieve, /retrieve_brief, GET /graph

```



Pass `"explain": true` on retrieve to include per-node score breakdowns.



## Tests



```bash

python3 -m pytest -q

```



## Benchmarks



Full set of repo-local benchmarks and their honest caveats lives in [BENCHMARKS.md](./BENCHMARKS.md). The SWE-bench-Verified row above is the only one that's at a scale worth citing externally.



## Positioning



See [**vs. Graphiti**](#vs-graphiti) above for the head-to-head comparison and [ARCHITECTURE.md](./ARCHITECTURE.md) for the architecture tradeoffs in detail. tl;dr: different products, adjacent problems — `state-trace` owns the narrow coding-agent working-memory lane; Graphiti owns weeks-of-history temporal knowledge graphs.